Computing and Intelligent Systems, Part IV - ICCIC 2011

Communications in Computer and Information Science

234

Yanwen Wu (Ed.)

Computing and Intelligent Systems International Conference, ICCIC 2011 Wuhan, China, September 17-18, 2011 Proceedings, Part IV

13

Volume Editor Yanwen Wu Huazhong Normal University 152 Luoyu Road Wuhan, Hubei, 430079, China E-mail: [email protected]

ISSN 1865-0929 e-ISSN 1865-0937 ISBN 978-3-642-24090-4 e-ISBN 978-3-642-24091-1 DOI 10.1007/978-3-642-24091-1 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: Applied for CR Subject Classification (1998): C.2, H.4, I.2, H.3, D.2, J.1, H.5

© Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

The present book includes extended and revised versions of a set of selected papers from the 2011 International Conference on Computing, Information and Control (ICCIC 2011) held in Wuhan, China, September 17–18, 2011. The ICCIC is the most comprehensive conference focused on the various aspects of advances in computing, information and control providing a chance for academic and industry professionals to discuss recent progress in the area. The goal of this conference is to bring together researchers from academia and industry as well as practitioners to share ideas, problems and solutions relating to the multifaceted aspects of computing, information and control. Being crucial for the development of this subject area, the conference encompasses a large number of related research topics and applications. In order to ensure a high-quality international conference, the reviewing course is carried out by experts from home and abroad with all low-quality papers being rejected. All accepted papers are included in the Springer LNCS CCIS proceedings. Wuhan, the capital of the Hubei province, is a modern metropolis with unlimited possibilities, situated in the heart of China. Wuhan is an energetic city, a commercial center of finance, industry, trade and science, with many international companies located here. Having scientific, technological and educational institutions such as Laser City and the Wuhan University, the city is also an intellectual center. Nothing would have been achieved without the help of the Program Chairs, organization staff, and the members of the Program Committees. Thank you. We are confident that the proceedings provide detailed insight into the new trends in this area. August 2011

Yanwen Wu

Organization

Honorary Chair Weitao Zheng

Wuhan Institute of Physical Education, Key Laboratory of Sports Engineering of General Administration of Sport of China

General Chair Yanwen Wu

Huazhong Normal Universtiy, China

Program Chair Qihai Zhou

Southwestern University of Finance and Economics, China

Program Committee Sinon Pietro Romano

Azerbaijan State Oil Academy, Azerbaijan

International Program Committee Ming-Jyi Jang Tzuu-Hseng S. Li Yanwen Wu Teh-Lu Liao Yi-Pin Kuo Qingtang Liu Wei-Chang Du Jiuming Yang Hui Jiang Zhonghua Wang Jun-Juh Yan Dong Huang JunQi Wu

Far-East University, Taiwan National Cheng Kung University, Taiwan Huazhong Normal University, China National Cheng Kung University, Taiwan Far-East University, Taiwan Huazhong Normal University, China I-Shou University, Taiwan Huazhong Normal University, China WuHan Golden Bridgee-Network Security Technology Co., Ltd., China Huazhong Normal University, China Shu-Te University, Taiwan Huazhong University of Science and Technology, China Huazhong Normal University, China

Table of Contents – Part IV

The Impact of Computer Based Education on Computer Education . . . . Yang Bo, Li Yingfang, Li Junsheng, and Sun Jianhong

1

Factors Affecting the Quality of Graduation Project and Countermeasures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xu Haicheng, Sun Jianhong, Xiao Tianqing, and Fu Jinwei

10

Social Network Analysis of Knowledge Building in Synergistic Learning Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wang Youmei

18

Query Rewriting on Aggregate Queries over Uncertain Database . . . . . . . Dong Xie and Hai Long

25

Ranking Tags and Users for Content-Based Item Recommendation Using Folksonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shimin Shan, Fan Zhang, Xiaofang Wu, Bosong Liu, and Yinghao He

32

Research on Repair Algorithms for Hole and Cracks Errors of STL Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hu Chao, Yang Li, and Zhang Ying-ying

42

“Polytechnic and Literature Are All-Embracing”—Training and Practice of Game Software Talents on Comprehensive Quality . . . . . . . . . Yan Yu, Jianhua Wang, and Guoliang Shi

48

Design of Mobile Learning Scenario Based on Ad Hoc . . . . . . . . . . . . . . . . Zong Hu

54

SET: A Conceptual Framework for Designing Scaffolds in Support of Mathematics Problem Solving in One-to-One Learning Environment . . . . Wang Lina, Chen Ling, and Kang Cui

59

Study on Multi-agent Based Simulation Process of Signaling Game in e-Commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiuju Yin and Kun Zhi

66

Research on Estimation of Nanoparticles Volumes on Rough Surface . . . . Yichen Song and Yu Song

73

Main Factors Affecting the Adoption and Diffusion of Web Service Technology Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Caimei Hu

81

VIII

Table of Contents – Part IV

The Application of Software Maintainability Design in the Intelligent Warehouse Archives System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fei Yang

88

An E-Business Service Platform for Agreement Based Circulation of Agricultural Products of Fruits and Vegetables . . . . . . . . . . . . . . . . . . . . . . Liwei Bao, Luzhuang Wang, Zengjun Ma, Jie Zhang, and Qingchu Lv

93

Two Improved Proxy Multi-signature Schemes Based on the Elliptic Curve Cryptosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengying Li and Qingshui Xue

101

Online Oral Defense System Based on Threshold Proxy Signature . . . . . . Fengying Li and Qingshui Xue

110

Happy Farm an Online Game for Mobile Phone . . . . . . . . . . . . . . . . . . . . . . Quanyin Zhu, Hong Zhou, Yunyang Yan, and Chuanchun Yu

120

Analysis and Intervention on the Influencing Factors of Employee’s Job Insecurity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiong Zou

129

An Analysis on Incentive Mechanism for Agents under Asymmetric Information Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhao Chenguang and Xu Yanli

136

A Study on Managerial Performance Evaluation . . . . . . . . . . . . . . . . . . . . . Zhao Chenguang, Xu Yanli, and Feng Yingjun

144

A Study on Contribution Rate of Management Elements in Economic Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhao Chenguang, Xu Yanli, and Feng Yingjun

151

An Analysis on Real Contagion Mechanism of Financial Crisis . . . . . . . . . Xu Yanli and Jiang Hongmei

159

Teaching and Learning Reform of Visual Foxpro Programming . . . . . . . . . Xiaona Xie and Zhengwei Chang

167

Numerical Simulation of a New Stretch Forming Process: Multi-Roll Stretch Forming Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haohan Zhang, Mingzhe Li, Wenzhi Fu, and Pengxiao Feng

172

Research on the Maturity of the CDIO Capability Evaluation System for Engineering Students . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liang Hong and XingLi Liu

181

The Web Data Extracting and Application for Shop Online Based on Commodities Classified . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jianping Deng, Fengwen Cao, Quanyin Zhu, and Yu Zhang

189

Table of Contents – Part IV

IX

Image Registration Algorithm Using an Improved PSO Algorithm . . . . . . Lin-tao Zheng and Ruo-feng Tong

198

The Research of Network Intrusion Detection Based on Danger Theory and Cloud Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Ruirui, Li Tao, Xiao Xin, and Shi Yuanquan

204

A Network Security Situation Awareness Model Based on Artificial Immunity System and Cloud Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Ruirui, Li Tao, Xiao Xin, and Shi Yuanquan

212

A Research into the Tendency of Green Package Design . . . . . . . . . . . . . . . Zhang Qi, Jiang Xilong, and He Weiqiong

219

Time-Frequency Filtering and Its Application in Chirp Signal Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiumei Li and Guoan Bi

224

Hunting for the “Sweet Spot” by a Seesaw Model . . . . . . . . . . . . . . . . . . . . . Haiyan Li, Jianling Li, Shijun Li, and Zhaotian Liu

233

Multi-objective Optimization Immune Algorithm Using Clustering . . . . . Sun Fang, Chen Yunfang, and Wu Weimin

242

A Novel Hybrid Grey-Time Series Filtering Model of RLG’s Drift Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guo Wei, Jin Xun, Yu Wang, and Xingwu Long

252

The Final Sense Reverse Engineering Electroencephalography . . . . . . . . . . Mohammed Zahid Aslam

260

Eutrophication Assessment in Songbei Wetlands: A Comparative Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Han Bingxue

265

Capital Management of Real Estate Corporations under Tightening of Monetary Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liu qingling and Li Xia

273

Scotopic Visual Image Mining Based on NR-IQAF . . . . . . . . . . . . . . . . . . . Fengbo Tian, Xiafu Lv, Jiaji Cheng, and Zhengxiang Xie

280

Extraction of Visual-Evoked Potentials in Rat Primary Visual Cortex Based on Independent Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . Zhizhong Wang, Hong Wan, Li Shi, and Xiaoke Niu

289

A Novel Feature Extraction Method of Toothprint on Tongue in Traditional Chinese Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dongxue Wang, Hongzhi Zhang, Jianfeng Li, Yanlai Li, and David Zhang

297

X

Table of Contents – Part IV

Stability and Bifurcation of an Epidemic Model with Saturated Treatment Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Gao and Min Zhao

306

Study of Monocular Measuring Technique Based on Homography Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jia-Hui Li and Xing-Zhe Xie

316

An AHP Grey Evaluation Model of the Real Estate Investment Risk . . . Ba Xi, Zhang Yan, and Wu Yunna

325

The Error Analysis of Automated Biochemical Analyzer . . . . . . . . . . . . . . Chen Qinghai, Wu Yihui, Li Haiwen, Hao Peng, and Chen Qinghai

335

LOD-FDTD Simulation to Estimate Shielding Effectiveness of Periodic Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chen Xuhua, Yi Jianzheng, and Duan Zhiqiang

342

Lossless Compression of Microarray Images by Run Length Coding . . . . . A Sreedevi, D.S. Jangamshetti, Himajit Aithal, and A. Anil kumar

351

Wavelet-Based Audio Fingerprinting Algorithm Robust to Linear Speed Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jixin Liu and Tingxian Zhang

360

Design and Implementation for JPEG-LS Algorithm Based on FPGA . . . Yuanyuan Shang, Huizhuo Niu, Sen Ma, Xuefeng Hou, and Chuan Chen

369

A Comparative Study on Fuzzy-Clustering-Based Lip Region Segmentation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shi-Lin Wang, An-Jie Cao, Chun Chen, and Ruo-Yun Wang

376

A Novel Bandpass Sampling Architecture of Multiband RF Signals . . . . . Fachang Guo and Zaichen Zhang

382

Classification of Alzheimer’s Disease Based on Cortical Thickness Using AdaBoost and Combination Feature Selection Method . . . . . . . . . . . . . . . . Zhiwei Hu, Zhifang Pan, Hongtao Lu, and Wenbin Li

392

A Robust Blind Image Watermarking Scheme Based on Template in Lab Color Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . YunJie Qiu, Hongtao Lu, Nan Deng, and Nengbin Cai

402

Digital Circuit Design and Simulation of a New Time-Delay Hyperchaotic System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Xiao-hong and Zhang Zhi-guang

411

Hospital Information System Management and Security Maintenance . . . Xianmin Wei

418

Table of Contents – Part IV

XI

Design and Research of a Multi-user Information Platform Interface in Rural Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xianmin Wei

422

Research on Fluorescence Detection System of Ca2+ . . . . . . . . . . . . . . . . . . Bao Liu, Sixiang Zhang, Wei Zhou, and Yinxia Chang

427

Channel Estimation for OFDM Systems with Total Least-Squares Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tongliang Fan and Dan Wang

433

Detection of Double-Compression in JPEG2000 by Using Markov Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhao Fan, Shilin Wang, Shenghong Li, and Yujin Zhang

441

High Speed Imaging Control System Based on Custom Ethernet Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dawei Xu, Yuanyuan Shang, Xinhua Yang, and Baoyuan Han

450

Design and Implement of Pocket PC Game Based on Brain-Computer Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinghai Yin and Jianfeng Hu

456

Authentication Service for Tactical Ad-Hoc Networks with UAV . . . . . . . Dong Han, Shijun Wang, and Laishun Zhang

464

An Adjustable Entropy Interval Newton Method for Linear Complementarity Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hu Sha and Yanqiang Wu

470

Applied Research of Cooperating Manipulators Assignments Based on Virtual Assembly Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shizong Nan and Lianhe Yang

476

Kusu Cluster Computing Introduction and Deployment of Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liang Zhang and Zhenkai Wan

484

Protein Function Prediction Using Kernal Logistic Regresssion with ROC Curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingwei Liu and Minping Qian

491

A Novel Algorithm of Detecting Martial Arts Shots . . . . . . . . . . . . . . . . . . Zhai Guangyu and Cao Jianwen

503

Method of Bayesian Network Parameter Learning Base on Improved Artificial Fish Swarm Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yan Wang and Liguo Zhang

508

XII

Table of Contents – Part IV

A Research of the Mine Fiber Communication System Based on GA . . . . ZuoMing

514

Research of Resource Management and Task Scheduling Based on the Mine Safety Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xuxiu

521

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

527

The Impact of Computer Based Education on Computer Education* Yang Bo, Li Yingfang, Li Junsheng, and Sun Jianhong Engineering College, Honghe University, Yunnan Mengzi, China, 66100 [email protected]

Abstract. It is well known that the computer technology impacts on modern education deeply, and the first to be affected is the computer education. In this paper, the results about the impact of computer based education (CBE) on computer education will be presented which researched based on a case study. For better results in teaching computer knowledge, the teaching method and curriculum program should be reformed as the computer technology developing. Some detail reference countermeasures will be discussed. Keywords: Computer based education, CBE, teaching method, learning method, Computer education.

1 Introduction The computer technology is developing sharply, in the circumstances, “computer science education research is an emergent area and is still giving rise to a literature [1].” What is the challenge in computer education? Reference [2] considered that the real challenge in computer education is to avoid the temptation to re-invent the wheel. Computers are a revolutionary human invention, so we might think that teaching and learning about computers requires a new kind of education. It is different with the traditional education, computing education should ignore the hundreds of years of education, cognitive science, and learning sciences. Over the past decade the teaching model and learning method has been changed a lot, mostly due to the introduction of information technology, especially in higher education. Computer based education (CBE) is usually used as an assistant education method helping students learning after class. Furthermore, CBE works as a major education method in distance learning and be used to replace conventional classroom teaching. As a result, any educational institutions use the Internet for collaborative learning in a distributed educational process. The students can learn at anytime just facing their computer, no matter where they stay. Computer education stands in the breach to adopt CBE and the scope is far more than other subjects. *

Pecuniary aid of Honghe University Course Construction Fund, ( Computer Course, WLKC0802).

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Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 1–9, 2011. © Springer-Verlag Berlin Heidelberg 2011

《 Fundamentals

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Many excellent methodology for computer education have been proposed by some of top computer education researchers, such as [1] [2] [3] [4] proposed. In this paper, we focused on the research of the impact of CBE on computer education based on a case study. A result of a case study researched on computer department of Engineering College of Honghe University (HU) will be presented. The research based on the current education method of computer department of HU via questionnaires investigation. The research work of this paper is one part of a series of our research works in HU.

2 Background HU is situated in Mengzi, Yunnan province and serves a diverse population of fulland part-time, national and international students. In 2009~2010 academic year, more than ten thousand students enrolled in 12 undergraduate colleges with 33 undergraduate specialties [5]. The same year, the major of computer science and technology as one of them, there are 367 students enrolled in. There are 28 full-time instructors support the routine instruction works. The title of technical post structure is shown as Fig. 1. Professor

associate professor 4%

4%

Lecturer

Assistant

11%

81%

Fig. 1. The title of technical post structure

As a local university, cultivating applied talents for society is the goal of HU and for this HU’s administration is promoting instructional technology as a crucial part of higher education for faculty and students. Response for this, the administrators of computer department felt obliged to reform teaching model and restructure curriculum program as the educational circumstances changing. Because of the CBE is widely adopted in computer education, a strong possibility existed that the student’s learning model is changing, especially the Internet and multimedia technology expand greatly access to knowledge sources. For better results in computer education, the teaching method and curriculum program should be reformed as the student’s learning model is changing [6]. How to reform teaching model and restructure curriculum? Why we investigated the impact of CBE on computer education? Because the impact of CBE on computer education is making the students’ learning method changed. And the changing of students’ learning method is an important factor impacting on computer education.

The Impact of Computer Based Education on Computer Education

3

3 The Questionnaires The about 5 minutes questionnaires consisted of four parts focusing on each student’s general information, perceptions about CBE, the opinions on effect of CBE and the opinions on shortcoming of CBE. The questionnaire construction was formed by close ended questions (multiple choices, scaled questions) and open ended questions. We issued a total of 150 questionnaires, and the number accounted for 41 percent of the total enrolled students. Among them, 142 valid responses were received. A. Student’s General Information The first question: “What is your sex?” The question surveys the general information of students for analysis the relationship between gender and computer education. The results showed as Fig. 2. Male

Female

46% 54%

Fig. 2. Students Gender

B. The Daily Computer Time of Male and Female Students From the Fig.3 we can see that the male students spend more time on computer than female students. Most of female students spend 2 to 4 hours on computer one day, but nearly 40% male students spend 4 to 6 hours on computer a day. Spend more than 6 hours on computer per day, the ratio of male students achieve 13%. Male

Female

100% 82%

80% 60%

44%

40% 20% 0%

18%

6% 0% In 2 hour

38%

2 to 4 hours

4 to 6 hours

13% 0% More than 6 hours

Fig. 3. Computer time daily sktch of the students

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C. The Computer Time Spent on Learning It is well known, many students spend a lot of time on computer is not for study. As reference [7] researching, there are many students addict to the Internet. They spend most of computer time on watching movies, play online game and shopping, etc. On computer-related majors, this problem is particularly acute. As Fig.4 shown, the female students are better than male students, they spend more computer time on study, but the different was not significant.

Male 80% 70% 60% s t n 50% e 40% d u t S 30% 20% 10% 0%

Female

56% 54% 31%

25% 15% Less than 10%

19%

10% to 30% 30% to 50% Percentage of computer time

0% More than 50%

Fig. 4. The percentage of computer time spent on learning

D. Computer Based Learning Approach In this question, we attempt to survey what kinds of computer based learning approach is the students’ favorite. Our surveyed results are shown as Fig.5. Offline courseware was chosen by 72% students, although most of offline courseware was download form Excelsior Course website or commercial education website, at least we understand that the students prefer off-line learning. It is important to consider offering downloadable edition if we publish our courseware online. p

g

pp 72%

80% 60% 40%

33%

19%

20% 0%

33%

Learning Forum

Excelsior Commercial Offline Course education Coursware websites website

Fig. 5. Computer based learning approach

The Impact of Computer Based Education on Computer Education

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Reasons for Using Computer-Assisted Learning

How can we know the CBE is to be accepted by students? For getting answer of this question, we design scaled questions to survey it, the questions and surveyed results as Tab.I shown. F. What Teaching Method is the Students’ Favorite? The purpose of this question is investigation what teaching method is the student favorite? In Fig.6, obviously, most of students like the learning methods which combine traditional teaching method with CBE. However, we find that the traditional classroom teaching method still can not be replaced.

Traditional classroom teaching method Pure autonomic learning Combine traditional teaching method with CBE 12% 23% 65%

Fig. 6. What is the students’ choice on teaching method?

G. What Types of Courses via Computer-Assisted Learning Will Be Better? One question in our questionnaire is: “What types of courses via computer-assisted learning will be better?” The curriculums of Computer Science and Technology major of HU can be divided into four categories: Humanities and Arts course; Basic Theoretic Foundation courses; Software Application Skill Courses (such as Photoshop,

Humanities and Arts course Basic Theoretic Foundation courses Software Application Skill Courses Programming Language and Development Platform courses 88%

100% 80% 60% 40%

51%

42% 12%

20% 0%

Fig. 7. What types of course via computer-assisted learning will be better

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3D Max); Programming Language and Development Platform courses. The surveyed result was shown as Fig.7. From the figure, we can see there are many students like computer-assisted learning except the Basic Theoretic Foundation courses, especially the Software Application Skill courses. And there are many students note that to learn English or other foreign language assisted by computer is very useful.

5 Countermeasure Analysis From previous section description, it is easy to know that the CBE significant effect on computer education. The traditional education model is being subjected to the challenge of CBE in computer education. As Fig. 6 shown, in our surveyed results show that there are 23% of interviewees like pure autonomic learning method through CBE; on the contrary, only 12% of surveyed students still favorite the traditional classroom teaching method. Obviously, CBE is effecting and promoting the change of computer education and education structure. Under the new environment, the teachings model how to respond to such change? Based on previous section description, we promote the following countermeasure: A. The Curriculum Program Reforming We have met a ticklish problem when we discussed how to design the curriculum program for the major of Computer Science and Technology. While students complained about their courses have too many theoretic courses, these courses are not conducive to really work. On the other hand, as undergraduate education, talent training objectives require students must master solid theoretical foundation knowledge. Such like Software Application Skill courses and Programming Language and Development Platform courses are the courses to enhance the students’ practical abilities. But we can not arrange too much in instruction plan because the curriculum limited by credits hours. If we offer courseware for these courses to support students study after-school, the problem will be solved. As Fig. 7 shown, this kinds of course using computer based instruction is acceptable for most of students. B. Assignment and Student’s Grades Assignment provides practice or can expand content that is taught during class time, and allows it to be reinforced or increased for deeper understanding. Assignment helps students become active learners, in charge of their own learning, goals, and outcomes. To the students of universities and colleges, assignment is a necessary way to offer teaches another form of assessment by which they can gage student’s understanding, and as important, student’s misunderstandings. The assignments usually are considered to assess student’s grades. However, many students no longer to complete their assignment independently, they directly search for the answer through Google, Baidu and Yahoo. As the Fig.8 shown, 20% surveyed students always use search engineer to find answer for their assignment and 34% students frequently do in the same way. Therefore, the teachers should consider the type of assignment to prevent occurrence of the similar situations, attempt to find a fair way to assess students’ grades.

The Impact of Computer Based Education on Computer Education

Always

Frequently 0%

Sometime

7

Never 20%

46%

34%

Fig. 8. Do you often use search engineer to get answer for your assignment

C. About Instruction Method Reforming Traditional teaching method of China emphasizes speaking, less interacting with students in the classroom. As previous description, if we want emphasize the instruction of theoretical foundation knowledge in classroom, we must expect students spend more time to study practice course such as software application skills and programming. It is important to let students understand the contact between theory and practical application. Reference [8] proposed a method, the role of participatory teaching methods in the computer science classroom. The list of the methods includes brainstorming, directed dialogues, small discussion groups, role playing, games, debates, panel discussions, and Socratic dialogues. The author has used such methods in Computers and Society classes and to a limited degree in Compiler Design, Computer Architecture and Operating Systems classes and believes that such techniques have a place in the computer science classroom. As a reference method, it may not be suitable for every teacher, but we can modify it to fit our teaching. In short, good teaching method is not unique; it will be different to different students, and different teachers. D. Teacher Team Building The students have strong ability in practice are basic requirement of computer related majors. However, the academic teachers have wealth of theoretical knowledge, but most of them usually lack of practical experience in software development [9]. How to solve the contradictory problem? Reference measures shown as follows: •

•

The teachers training must be intensified. The teacher training plan of general universities and colleges usually send their teachers to some famous universities to study. However, the teachers of computer related majors go to the famous software development companies or computer manufacturers for improve teachers’ practice ability will be better. We can consider hiring some senior engineers as the students’ advisors from the companies and enterprises with cooperation relationship [9].

8

Y. Bo et al. Table 1. Reasons for Using Computer-Assisted Learning Items Able to develop classroom teaching to enhance the level of individual knowledge In-depth study and improve their learning ability Extensive after-school learning, increase learning efficiency The need for completing homework Free choice of learning content, according to their own learning goals They can choose their own study time, decided to learn How long Excellent teaching courseware is more suitable than classroom teaching for computer courses education Personal interests/Study habits

Disagree

Acquiesce

Agree

Strongly agree

0%

16%

49%

35%

7%

16%

53%

23%

7%

23%

42%

28%

14%

21%

56%

9%

2%

12%

51%

35%

7%

19%

49%

26%

26%

23%

30%

21%

9%

26%

40%

26%

6 Conclusions In this paper, we have presented a case study results which showed the impact of CBE on computer education. It is well known that the computer technology impacts on modern education deeply, and the first to be affected is the computer education. For better results in teaching computer knowledge, we have discussed the countermeasures in this paper. Close observation the effect of our reform measures and improvement of deficiencies in a timely manner is the focus of our future work.

References [1] Fincher, S., Petre, M.: Computer science education research, p. 1. Taylor & Francis Group, London (2004) [2] Almstrum, V.L., Hazzon, O., Guzdzial, M., Petre, M.: Challenges to computer science education research. In: Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2005, pp. 191–192. ACM Press, New York (2005) [3] Randolph, J., Julnes, G., Sutinen, E., Lehman, S.: A Methodological Review of Computer Science Education Research. Journal of Information Technology Education, Informing Science Institute, CA 7, 135–162 (2008) [4] Holmboe, C., McIver, L., George, C.: Research Agenda for Computer Science Education. In: 13th Workshop of the Psychology of Programming Interest Group, Bournemouth UK, pp. 207–223 (April 2001) [5] http://iro.uoh.edu.cn/Aboutus.asp (May 2010) [6] Sun, J., Zhu, Y., Fu, J., Xu, H.: The Impact of Computer Based Education on Learning Model (in press) [7] Sun, J.: Solving Strategies Research for the Negative Impact of Computer Technology on Education. In: 2010 Second International Workshop on Education Technology and Computer Science, ETCS 2010, vol. 1, pp. 671–674 (2010)

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[8] Jones, J.S.: Participatory teaching methods in computer science, Technical Symposium on Computer Science Education. In: Proceedings of the Eighteenth SIGCSE Technical Symposium on Computer Science Education, Louis, Missouri, United States, pp. 155–160 (1987) [9] Xu, H., Sun, J., Xiao, T., Fu, J.: Factors Affecting the Quality of Graduation Project and Countermeasures (in press)

Factors Affecting the Quality of Graduation Project and Countermeasures Xu Haicheng, Sun Jianhong, Xiao Tianqing, and Fu Jinwei Engineering College, Honghe University, Yunnan Mengzi, China, 661100 [email protected]

Abstract. The graduation projects (Thesis) and defenses play an important role in guaranteeing the educational quality and implementing the desirable educational goals. In recent years, many universities and colleges have to face a serious problem that the quality of graduation projects is declining. In order to avoid the graduation project and defense become a formality. In this paper, we researched based on a case study to analysis the main cause of graduation project quality going down and then proposed several corresponding countermeasures. Keywords: Graduation project, Graduation thesis, Educational quality, Education.

1 Introduction The graduation projects (Thesis) and defenses play an important role in guaranteeing the educational quality and implementing the desirable educational goals. According to the Department of Higher Education of China (DHEC) document No. 14 of 2004, “Graduation Project (Thesis) is an important part of teaching in order to achieve training objectives. The Graduation Project (Thesis) has an irreplaceable role on training students seeking truth; training students have ability in scientific research and strengthening students’ social awareness to improve their capacity and quality of general practice and so on. Graduation Project (Thesis) is a kind of important expression method combining with education, productive work and social practice, is an important practical aspects on training students’ creative ability, practical ability and pioneering spirit. Meanwhile, Graduation Project (Thesis) is also an important measure to evaluate the quality of teaching; it is a basic requirement to students for graduation and getting a degree. [1]” From the document, it is clearly to know that the highest educational authority of China attaches has strict requirement and great importance to graduation project (Thesis). The graduation projects (Thesis) of computer related majors general have practical value; usually complete a project as the goal. Therefore, we only discuss the situation that takes project as graduation project (Thesis) in this paper. In recent years, we have to face a serious problem that the quality of graduation projects of Computer Science and Technology of Honghe University (HU) is declining. The administrators of Department of Computer felt obliged to survey for Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 10–17, 2011. © Springer-Verlag Berlin Heidelberg 2011

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changing this negative situation. In this paper, we will present the result of our survey and then will discuss the countermeasures.

2 Background HU is situated in Mengzi, Yunnan province, the capital city of Honghe Hani & Yi nationality Autonomous Prefecture. HU serves a diverse population of full- and parttime, national and international students. In 2009~2010 academic year, more than ten thousand students enrolled in 12 undergraduate colleges with 36 undergraduate specialties. Cultivating applied talents for society is the goal of HU and for this HU’s administration is promoting instructional technology as a crucial part of higher education for faculty and students [2]. As a local university, HU focus on cultivating applied talents for local society and promoting development of local economy. Therefore, the students have strong ability in practice are basic requirement of Department of Computer in HU. As a developing university, there are still many problems awaiting solution in HU. Some of them related with our focus topic of this paper. They are: •

• •

Lack of education funding. This is a serious common problem in many universities and colleges of China. Same as other developing universities and colleges, HU invested heavily in construction of infrastructural facilities for prepare for the Undergraduate Education Evaluation in recent years. HU has had to undertake heavy liability for this. The problem of lack of education funding made a great impact on the daily instruction. The structure of faculty is irrational. The ratio of instructors with extensive education experience and research ability is still lower and it need take many years to change it. Educational environment has changed significantly in recent years, but the policy has not changed in time.

In the next section we will analysis these issues how to affect on the quality of graduation projects.

3 Factors Affecting the Quality of Graduation Project A. The Problem of Students’ Attitude 1) They know that they will graduate, regardless how their graduation project going: In order to align with international universities, more and more universities and colleges of China began to use credit system to replace the traditional fixed four-year education system. To adopt academic credit system enables excellent students to finish their study as soon as possible. However, the academic credit system is a new issue at institutions of higher learning in our country. Not only the teachers and students, but also the administrators, their ideology still have not been changed by the academic credit system. Most universities and colleges of China are still “strict entry, easy out." After four years of study, almost every student can graduate, regardless of their academic performance is good or bad. Accordingly, most students do not treat their graduation project seriously.

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2) Positivity affected by the employment pressure: Although the credit system has been implemented in HU, but the proposal of graduation project is to be arranged usually in the seventh semester (the eighth semester usually is the last semester). The graduation project usually is arranged at study plan of the last semester. A few years ago, almost all the students can get a contract of a job before the last semester. Thus the students can hurl themselves into doing graduation project at the last semester if their jobs have been settled down already. Obviously, the qualities of the graduation projects were granted in this situation. Now, the situation has changed. The students have to take part in various examinations of recruitment. Competition for a steady job is very severe. Under the severe employment pressure, the students can not focus on their graduation project, especially at the last semester. In HU, the administrators of department of Computer Science and Technology noticed that the quality of graduation projects is declining year by year. This result was closely related to above two reasons. B. Problems Caused by Rapid Development 1) Problems of admission policies It is well known, the colleges and universities of China do not have the rights of autonomous enrolment. The strict enrolling system defined by DHEC. The universities and colleges were divided into three levels: the key universities; general universities and the third level universities and colleges (include private colleges and universities). After the unified exam once a year, the key universities cream off the highest achievers first. HU as a developing general university is hard to attract excellent students. Especially, while the institutions are expanded enormously after successive years of enrollment expansion, we have to admit that the quality of the new enrolled students has been declining. As Fig.1 shows, the promotion rate of senior school graduates from 27.3% in 1990 rising to 72.7% in 2008 [3]. Meanwhile, the teaching quality has become a problem that the universities hard to guarantee. Promotion Rate of Senior School Graduates ) 100 % ( e 80 t a R 60 n o i 40 t o m 20 o r P 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Rate(%) 27.3 28.7 34.9 43.3 46.7 49.9 51 48.6 46.1 63.8 73.2 78.8 83.5 83.4 82.5 76.3 75.1 70.3 72.7 Year

Fig. 1. Promotion Rate of Senior School Graduates (The data come from Ministry of Education of the People's Republic of China [1]) Note: Promotion rate of senior secondary school graduates is the ratio of total number of new entrants admitted to HEIs (including those admitted into the regular full-time courses run by (TRVUs) to the total number of graduates of regular senior secondary schools of the current year.

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2) Problems of the currently graduation policy Since 1977, the entrance examination system of institutions of higher education recovered, the graduation policy is pursuing “strict entrance, easy out”. Up still now, almost no student can not graduate for graduation project has been not passed defense in HU. The same is true of most universities and colleges of China. The part of graduation project defense is just a formality if no one ever fails it. This kind of policy was Okays to used admission policy, elite education. Now, the situation has been changed, as Fig. 2 shows, the gross enrolment rate of institutions of higher education (IHEs) from 3.5% in 1991 sharply rising to 23.3% in 2008. It means that the higher education in China transformed from elite education to mass education. The graduation policy, “strict entrance, easy out” has been become a factor which conducts the quality of education declined. Many papers have discussed about the issue, such as [4][5][6][7]. Same as these authors’ view, we all think the currently graduation policy should be reformed to adapt the new education environment. Gross Enrolment Rate of IHEs 22- 25.00% 8 1 f o 20.00% eg a 15.00% eh t 10.00% fo oi 5.00% ta R 0.00%

1 99 1

2 99 1

3 99 1

4 99 1

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6 99 1

7 99 1

8 99 1

9 0 99 00 1 2 Years

1 00 2

2 00 2

3 00 2

4 00 2

5 00 2

6 00 2

7 00 2

8 00 2

Fig. 2. (The data come from Ministry of Education of the People's Republic [1])

C. Lack of Qualified Advisors During the rapid development of higher education, the construction of teaching team is difficult to keep pace with. Especially in many general universities and colleges, it is still a difficult problem. The integral structure of the faculty of Department of Computer Science and Technology of HU is shown as Fig.3. The total number of faculty is 28. From Fig.3 we can see that most of them are lecturer, the professor and associate professor only have 3 persons. From academic title view, most teachers only got bachelor degree, as Fig.4 shown. HU as a developing university can not offer good payment to keep the outstanding teachers stay. The problem of qualified advisor is one of factors that affecting the quality of graduation projects. Building the teacher team still need be strengthening at first in developing universities and colleges such as HU.

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Structure of the faculty Professor

Associate professor 1, 4%

4, 14%

Lecturer

Assistant

2, 7%

21, 75%

Fig. 3. The title of technical post structure

Academic title structure PHD.

Master

Bachelors 2, 7% 8, 29%

18, 64% Fig. 4. Academic title structure of faculty

D. Education Funding Shortage Most universities and colleges of China are saddled with huge debt is an indisputable fact. This problem due to rapidly development, for passing “Evaluation of Undergraduate Education Levels” launched by Ministry of Education, many universities and colleges have to make a lot of investment in infrastructure; and another serious problem is that educational funding the government offered was obviously insufficient. In Japan, 1.5% of GDP was used for higher education; in America, the ratio is 1.5%, but China's higher education funding accounts for only 1% of GDP [8]. Funding shortage, the impact on the graduation project mainly in: • •

Education funding shortage conduct poor treatment of advisors of graduation projects, enthusiasm for work is not high. Education funding shortage, most aspects of graduation projects can not be implemented according to plan as required.

4 Countermeasures Analysis How to grantee the quality of education? In previous section, we have discussed about the factors that affecting the quality of graduation projects. In this section, we will promote some countermeasures for enhancing the quality of graduation projects.

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A. The Flexible Scheduling of Graduation Project As previous description, the students can not pay their attention to graduation projects at the last semester under the employment pressure. The best solution for the problem is to implement the credit system to replace the traditional four-year study plan. It is time to gradually change people's viewpoint about university study is four-year plan. In HU, although the credit system had been implemented many years before 2009, actually the implementation was not perfect. Since 2009, the freshman charged on credits, this is a significant sign of real credit system. The credit system no longer has a common "the last semester" concept. The students can make their own study plan to accomplish their studies. The study plan relies heavily on the students. Reference measures shown as follows: • • •

Assign instructor for freshman. We must note that the class advisor is not advisor for study, the class advisor usually take in charge of a whole class. Provide an opportunity for students to apply the proposal and final defense of graduation projects each semester. Combine with professional practice to implement. The graduation projects should be related with actual requirement.

B. Strict Policy Enforcement From the policy of DHEC, we knew that the top level of management of higher education attaches great importance to graduation project. However, the implementation stage of the policy also is a determinant factor of that graduation projects play an important role in guaranteeing the educational quality. The every step of the procedure of education should be under strict controlled for guarantee the educational quality. The reference strategies as shown following: •

• •

Guarantee the quality of proposal defense. The proposal as an important part of graduate project, it specifies what the students will do, how they will do it, and how they will interpret the results. If the committee of graduation project (Thesis) lets a student pass the proposal defense, then must ensure that: the student have done sufficient preliminary reading/research in the area of his choice; he have thought about the issues involved and are able to provide more than a broad description of the topic which he plans to investigate. More effort should be made to control and supervise the procedure of graduation projects. Students should meet regularly with their advisor to discuss the progress of project and address problems in time. The quality of the final defense of graduation projects should be well controlled. Some noticeable practical measures should be taken to proof the defense of graduation project is not just a formality. The projects do not reach the standard required should be postponed to defense.

C. Choose a Topic with Application Value The graduation projects of computer related majors usually have practical value. Certainly, it depends on the chosen of project topic and quality of the projects.

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Consequently, we should ensure that the graduation project topics must come from the requirements of actual work. The projects topics can be update a used system or develop a new project for work requirement, but not a hypothetical application projects. D. Teacher Team Building The advisors of graduation projects are usually acted by experienced teachers. However, although the academic teachers have wealth of theoretical knowledge, but most of them usually lack of practical experience in software development. Because of this, many good teachers can not play as a good advisor of graduation projects. For solve the problem, the teachers training must be intensified; and another way, we can consider hiring some senior engineers as the students’ advisors from the companies and enterprises with co-operation relationship. The second method has two advantages: • •

The students may have an opportunity to participate in the actual project development to complete their graduation project. Some students’ job may settle down for his outstanding performance during the working of graduation projects.

5 Conclusions and Future Works In recent years, we noticed that the quality of graduation projects of computer major of HU is declining. Address the serious problem; we have discussed the reasons for causing this problem, and proposed several corresponding countermeasures. However, there are still many difficulties to solve the problem. The most typical problem is pressed for education funding. It is also a common problem of many developing universities and colleges of China. Because of lack of funds, many good measures can not effectively implement. It is also an issue worth of research topic in the future.

References [1] Ministry of Education P.R.C, With regard to the strengthening of common institutions of higher learning graduation project(thesis). Department of Higher Education (14) 2004 [2] Sun, J., Zhu, Y., Xu, H.: The Impact of Computer Based Education on Learning Model (in press) [3] Ministry of Education of the People’s Republic (June 2010), http://www.moe.edu.cn [4] Shihua, D.: From ‘Strict Entry‘ to ‘Strict Out‘: new ideas to get rid of stubborn of examoriented education. Research on Education Development, 27–30 (June 2005) (in Chinese) [5] Wang, Z.W.: After Enlarging Enrollment,Colleges and Universities Need’Easier Entrance and Stricter Exam Marking Schemes. Journal of Luoyang Teachers College 19(3), 77–79 (2000) (in Chinese)

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[6] Wen, M.: Expand Enrollment and Easy Entry, Strict Out. pp. 61–63. Beijing Education. Higher Education (July 2003) (in Chinese) [7] Zheng, D., Gong, B., Pan, X., Fei, C., Jiang, Y., et al.: Stric Entry, Strict our, Imperative Way. Researches in Higher Education of Engineering 5, 32–34 (2002) [8] Wang, J.-h.: Competitive and Non-competitive–Analytical Framework of Funding for Higher Education Provided by Government. Journal of China University of Geosciences (Social Sciences Edition) 10(1), 13–19 (2010) (in Chinese)

Social Network Analysis of Knowledge Building in Synergistic Learning Environment Wang Youmei Dept. of Educational Technology, Wenzhou University, Wenzhou city, China [email protected]

Abstract. Knowledge building is a meaning making process with social negotiation and dialog. In the new environment adapting to the group learning context supported by synergistic learning toolkits, this study explores the community structure social relation and participation character in classroom knowledge building by Social network analysis. This results show that there come into being a social relation network with knowledge convergence under the learner knowledge building led by teachers, which make views and ideas of learners assembling to support knowledge building, at the same time this paper also describes the participant character in learning community. This study can provide a new way to learning technology innovation in different culture context.

，

Keywords: Knowledge building, Synergistic learning, Social Network Analysis.

1 Introduction Knowledge building is actually a term with different research contexts. While the constructivists highlight the construction from the angle of the subjects of knowledge processing, Scardamalia and Bereiter(2003) focused on the procedure of knowledge processing, they defined the knowledge building as “the continuous generation and development of valuable knowledge in the community”[1]. Social constructivism believes that knowledge is a constructive process that interacts with community members, and it can’t exist without the social cultural context where the individuals live. During the process of knowledge building the learner and team members have the common learning target and key points, share the learning achievements via synergistic learning. Therefore, the community structure and relationship formed by the learner during the process of knowledge building is a critical research perspective of enhancing the quality of knowledge building. Based on the new learning environment built by the synergistic learning toolkit which adapts to the collective learning context, this paper used the method of social network analysis, namely sociogram and social network matrix analysis to explore the community relationship and participative features formed by the learners during the interaction. These participative features and community interactive structure to a large extent determine the quality of synergistic learning and the effects of collective knowledge building. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 18–24, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 Research Review Since the anthropologist Barnes(1954) first used the concept of “social network” to analyze the social structure of a Norwegian fishing village, the social network has been regarded as one of the simplest, clearest, and most persuasive research perspective for analyzing social structure [2]. “Social network” is defined as a community of cooperative or competitive entities, among which there is certain linage. In the cooperative community, each participant is called an actor, shown as a node in the graph. The relationships between the participants are shown as the connection lines between the nodes. Social network analysis (SNA) is a useful tool for studying the relationship between people, which focuses on the social system level, and pays attention to the functions of the entire interaction sphere and social context. The theoretical perspectives that use the method of social network analysis to study problems mainly center on the relationship between actors (network topology) but not the features of actors, and stress the mutual influence and dependence between actors which leads to the emergence of collective behaviors. The social network features of actors can be further observed and understood via social network information. Such graph analysis of social relations is widely used in social science, information science, economics, political science, computer networks, and other disciplines. In recent years, SNA is also applied in network synergistic learning, especially in knowledge building and interaction analysis of learning community. For example, Garton, Haythornawaite and Wellman recommended studying the online network, in particular, the network of learners, with the method of social network analysis. The studies have proved the application of social network analysis in explicit learning context. Martinez and his colleagues compared the centricity of inter-community and intra-community information communication. Haythornthwaite analyzed the power distribution features of several relationship networks, and she pointed out that the time changes and media communication channels depend on the distribution of network power. Cho, Stefanone and Gay found that the participant who owned more power in the network information communication also had greater influence. Reffay and Chanier concentrated on the evolution of cohesion in the learning community. De Laat combined the social network analysis and content analysis to prove the centralized interactive pattern in the learning process, suggested that the knowledge building process mainly focused on the information sharing and comparison phases (namely the first phase). The domestic scholar Chen Xiangdong(2006) systematically introduced the application of social network analysis in online learning study. Li Jiahou et al. (2007) deemed the social network analysis as a new method for the educational communication research in the network age. Wang Lu(2006, 2007) studied the quality of knowledge building in the interactive asymmetric network synergistic learning via the social network analysis, with the focus on the content analysis of interactive learning communication, and explored the research from the angle of individual case at the same time. The researches mentioned above mainly aim at the non-structural network learning environment, and there are few researches adopting social network analysis for studying the knowledge building problem in the well structured informationalized class teaching environment, especially for the class knowledge building in the specific cultural context.

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3 Technological Environment For the sake of exploring the knowledge building in the specific cultural context, this study is based on the technological system of synergistic learning. Based on the investigation of the current learning technological system, Professor Zhu Zhiting and his team pointed out that the current learning technological system framework represented a discrete way of thinking, the educators and the learners acted in a divisive pedagogic framework, it was a concept of teaching in isolation, difficult to meet the demand of the society. It is mainly manifested in the interaction layer—the learners and the educators lack deep interaction; in the communication structure layer—information aggregation mechanism is missing; in the information processing layer—collective thinking is absent; in the knowledge building layer – there is no division, cooperation, integration tool; in the practice layer – the information, knowledge, behaviors, emotions, and values have no efficient linkage. Such isolation severely influences the efficacy of teaching. Therefore, Professor Zhu Zhiting proposed the new synergistic learning framework and meta-model. The synergistic learning technological system is a practice-oriented innovative model [3]. The synergistic learning technological system is a well-structured collective teachingoriented learning environment. In order to support the synergistic learning, the team technologically developed synergistic learning tools, including 2 sub-tools – synergistic marking and synergistic building [4]. This study adopted the synergistic building tools to analyze the social network of knowledge building, the tools realize the collective knowledge building and the schematic presentation of collective memory. Understanding multiple solutions to a problem is really helpful to inspire the students’ thinking, in particular, the peers’ solution can more easily arouse the interest of other students in the class. For example, when the students get their corrected exercise books from the teacher, they want to know how the others do where they made mistakes. In addition to providing reference answer, the teacher should also display the correct solutions made by other students. Though the teacher can adopt various display or discussion forms in the class, once the class is over, these short-lived collective memories will disappear. If we can gather the individual memories to form collective memory and save it, it can remind the students of the previous class discussions during their review for the final and prevent them from making the same mistakes repeatedly. The synergistic building tool can be used for gathering, processing and saving collective memory. In a learning environment supported by the synergistic learning tools, the interactive structure and participative features of community will not only influence the learning atmosphere, but also determine the quality of synergistic learning and the effects of collective knowledge building to a large extent. This paper analyzed the relationship between the learners and the relationship between the learner and other people during the network synergistic learning process via social network analysis, and further analyzed and reflected the effects of the relationship between members on synergistic cooperation.

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Fig. 1. Teacher-Side of Synergistic Building Tool

4 Design of Research A. Objective and content of research This study adopted the method of experiment, with the synergistic learning tools as the technological context, to explore the functions and effects of the visualized synergistic learning knowledge building tools on supporting the knowledge building. By taking part in the synergistic class learning process, and using the method of social network analysis, this study evaluated the effects of synergistic building tools, analyzed the main path of knowledge building and processing, provided a reference for realizing deep knowledge building in class. B. Design and implementation of research The experiment was carried out with the class teaching of the undergraduate course in a university as the experiment scenario. We conducted network teaching once a week, and used synergistic technological tools to support class teaching and students’ learning. The purpose of data collection wasn’t told before the experiment to ensure the authenticity and reliability of data and reduce the interference. The data was collected in two classes randomly selected, the teacher controlled the process, and used the synergistic building tools for discussion. The questionnaire, questions and topic were all designed before the class. The topics for discussion include open questions such as the procedural process evaluation and course portfolio.

5 Analysis of Results C. Features of the sociogram of the learning community While doing the SNA, if there are not many nodes, sociogram can be used for representing the relationships between the actors, in which the nodes represent the

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actors, and the connection lines represent the linkages between the nodes. On the contrast, if there are many nodes, matrix is generally used for representing social network. The subject of this study was a 38-people class, thus we adopted the graphic method (sociogram) to analyze the relationships between the learners and the community structure of this class. We used the circles No.1-No.38 to represent the 38 community members, and connection lines with arrows to represent the relationships between them. The arrow of connection line points to the sender of the content which was replied, and the double-headed arrow represents the mutual reply. Based on the collected material, the s sociogram is as follows:

Fig. 2. Sociogram of Members Participating in the Synergistic Learning

From the above sociogram we can directly see that the features of the learning community in the synergistic building environment: using visualized knowledge building tools to conduct class teaching; the teacher plays the dominant role in the entire learning process, and controls the entire teaching process. Most of the learners thought about and replied to the questions proposed by the teacher, and during the discussion, the teacher controlled the synergistic building tools, shared the representative answers and questions with all the learners, and proposed them in the form of question. At the same time, some learners replied to the questions proposed by others. In the figure, No.6 and No.26 learners received the replies from others. There were also several learners that didn’t participate in such class learning. From the collected materials we can also find that most of the learners just replied to the problems proposed by the teacher or student, only a few students raised questions or complement. No.6 and No.26 learners put forward questions about the problems needed to be solved, which aroused the thinking of other learners, such learners are relatively more active in thinking. D. Social network analysis of subject–oriented community In the following, we respectively analyzed the social network formed by the two discussion topics (procedural evaluation, course portfolio), drew the corresponding sociogram and matrix for comparison, as shown in Figure 3:

Social Network Analysis of Knowledge Building in Synergistic Learning Environment

Fig. 3. Matrix Formed by the Discussion of Procedural Evaluation

Fig. 4. Sociogram of Members Participating in the Learning of Course Portfolio

Fig. 5. Matrix Formed by the Discussion of Course Portfolio

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From the figure above we can see that most of the learners participated in the discussion of procedural evaluation, and No.4, No.6, No.21 and No.26 learners showed higher degree of participation than others; and some learners had low degree of participation in the discussion of course portfolio, 28.9% learners didn’t participate in the discussion, and the participating learners had lower connectivity degree than they did in the former topic discussion. There may be three reasons for this phenomenon: first, the learners’ positivity reduced as time went by; second, the learners originally knew more about procedural evaluation than course portfolio, thus they had more ideas about the procedural evaluation; third, the main content of the two topics are different, for procedural evaluation, the learners mainly answer “why”, “which methods”, “differences”, and for course portfolio, the learners mainly answer “what it is”, “what it contains”.

6 Conclusions and Discussion Synergistic learning is a new learning framework, which reorganizes the current learning technological system based on the sociality of the cognitive subject, dynamic theory of cognitive process, ecological theory of knowledge building, to support the class teaching and learning activities in the technological context. This study studied the effects of the visualized synergistic learning tools on the knowledge building process, analyzed the knowledge building procedures and the relationship between learners formed accordingly. The study found that using these tools for class teaching formed a social network of participants different from the common network discussion platform, which can more easily and rapidly gather the thoughts of learners, mainly via the teacher. However, there are some problems at the same time: there are only a few direct connections between learners; the learners don’t have strong sense of participating in the synergistic knowledge building. And the factors like the experimental subject, the topics discussed in the experiment, and the time of experiment have certain instability. Since this study aims at specific experimental subject, its validity and pertinence need to be further improved.

References [1] Scardamalia, M., Bereiter, C.: Knowledge Building. In: Encyclopedia of Education, 2nd edn. Macmillan Reference, New York (2003) [2] Reuven, A., Zippy, E., Gilad, R., Aviva, G.: Network analysis of knowledge construction in asynchronous learning networks (2003) [3] Zhu, Z.: Synergistic Learning: A New Learning Paradigm in Connected Age. In: Keynotes on Advanced Seminar of 1st Global ET Summit Conference, Shanghai, China, July 30 (2006) [4] Wang, Y.: Usability Test on Synergistic Learning Technology System. Research on e-education (03), 62–66 (2009)

Query Rewriting on Aggregate Queries over Uncertain Database Dong Xie and Hai Long Department of Computer Science and Technology, Hunan University of Humanities and Science and Technology, Loudi, Hunan Province, China [email protected]

Abstract. Although integrity constraints effectively maintain certainty of data, and some situation may be satisfied with integrity constraints. This paper proposes uncertain databases and candidate databases. To uncertainty of tuple values in uncertain databases and their aggregation queries with different results for every candidate database, the work adjusts query semantics to employ query writing for computing the maximum and the minimum of aggregation attributes based on certain range semantics, the method can execute effectively SQL query in databases. The experiments show that the overloads of rewritten queries are longer than original queries, but the overloads should be accepted. Keywords: Relational database, Uncertain data, Aggregation query, Query rewriting.

1 Introduction Integrity constraints (ICs) effectively maintain data consistency and validity, it enables the data to conform to the rules of entities in the real-world. The current commercial DBMS supports ICs, but they focus on a series of ICs to maintain every database is certain. However, an entity that is in the real-world frequently corresponds to inconsistent data in the database. A database may become uncertain with respect to a given set of integrity constraints while data from different sources are being integrated. In the situation, it can be difficult or undesirable to repair the database in order to restore consistency. The process may be too expensive, and useful data may be lost. One strategy for managing uncertain databases is data cleaning [1], which identifies and corrects data errors. However, these techniques are semi-automatic and infeasible for some applications such as a user wants to adopt different cleaning strategies or need retain all uncertain data. The trend toward autonomous computing is making the need to manage uncertain data more acute. There are an increasing number of applications whose data must be used with a set of independently designed constraints. As a result, a static approach with respect to a fixed set of constraints is enforced by data cleaning may not be appropriate. Current database technologies don’t support query results without certain data based on certain databases. If databases violate ICs, conflict tuples don’t Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 25–31, 2011. © Springer-Verlag Berlin Heidelberg 2011

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effectively denote the uncertain semantics of data. An alternative approach is to employ the techniques of certain query answering to resolve inconsistencies at query time over uncertain databases [2]. In uncertain data situation, certain queries are the problem of retrieving “certain” answers over uncertain databases with respect to a set of integrity constraints and query conditions. A relevant decision problem is aggregation queries under the range semantics. Aggregation is common in data warehousing applications where uncertainties are likely to occur, and keeping uncertain information may be useful. In the presence of aggregation operators, the notion of certain queries needs to be slightly adjusted. An aggregation query returns a different answer in every possible world. Therefore, it has no certain answer. Under the range semantics, a minimum and maximum value of a numerical attribute of a relation may be computed. The paper employs an appropriate method, which may return certain results with respect to user queries and integrity constraints. We propose relevant concepts such as uncertain database and candidate database. Since the certainty of tuple values and aggregation queries may return different results in uncertain databases, query semantics are adjusted as certain range semantics for computing maximums and minimums of aggregation queries by query rewriting, it rewrites an original query to a SQL query which can return certain results effectively. The experiments show that the execution times of rewritten queries are longer than original queries, but the overloads should be accepted.

2 Preliminary Example 1. Table 1 denotes balances of customers as guest(guestkey,balance), guestkey attribute is primary key. Relational tuples violate the primary key constraint. Given a query Q1 returns customers whose balances are greater than 900. Table 1. Guest guestkey

balance

t1

g1

1900

t2

g1

500

t3

g2

2600

t4

g3

2100

t5

g3

2600

Q1 obtains {g1,g2,g3,g3}, but they are uncertain. g1 may be less than 900 such as t2, but t1 who is satisfied with Q1 contains g1; g3 appears twice in results. However, since guestkey is the primary key, the results don’t have repeated values. As a result, {g2,g3} are certain results for Q1.The first reason that g2 is a single tuple which is satisfied with Q1, the second reason that even if g3 appears twice but the two tuples are satisfied with Q1. so we may rewrite Q1 to obtain certain results.

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27

Candidate data are subsets of uncertain data, they contain certain tuples with respect to integrity constraints. The concept keeps all tuples and do not enforce to delete or ignore uncertain tuples. Candidate data show that data may be cleaned for users to understand data are uncertain. Definition 1. Give I is an instance in database D. To a series of integrity constraints∑ of D, if I is satisfied with∑, this denotes as I╞∑. If ∀ I╞∑, D is a certain database, otherwise D is an uncertain database. Definition 2. Candidate database(CDB). Give a series of integrity constraints∑ and an instance I in database D. If instance I’╞∑, I’ is a candidate database about I with respect to ∑, moreover, there are not I * ╞∑ and (I I’) (I’ I) ⊃ (I I *) (I * I). To a query Q, if every CDB D’ Rep(D,IC) and D’╞Q(t’), tuple t’=(t1 ,…,tn) are certain results. That is, x 1,…,x n are assigned as t1 ,…,tn respectively, if n 0, Q is a Boolean query. To every CDB D’ Rep(D,IC), if D’╞Q(t’), the query is true, otherwise is false.

－ ∪ －

∪ －

∈

－

＝

∈

Example 2. Table 1 has four candidate databases as following: D1 ={t1,t3,t4},D 2={t1,t3,t5},D 3 ={t2,t3,t4},D 4 ={t2,t3,t5}. Every candidate database is an uncertain database, they are approximate with the uncertain database in table 1 as far possible as. If I is a candidate database with a series of integrity constraints∑ in database D, there are not I ⊂ I*, I*╞∑ and I* ⊆ I. If these conditions exist, there are tuples t ∈ I* and t ∉ I; since I*╞∑, so tuple t’ who is conflict with tuple t in I*, that is ,t’ ∉ I and t’ ∉ I*, this obtains (I,I’) ⊃ (I,I *), I is not a candidate database according to definition 3.

△

△

3 Aggregation Query Since tuple values in uncertain databases are certain, aggregation queries return different results for every candidate database, but don’t return certain results. Therefore, the work employs the range semantics to compute a minimum and maximum value of a numerical attribute in a relation. Definition 3. Interval. Give a series of integrity constraints∑ and an instance I in database D. If an aggregation query q returns values range from a to b over every candidate database, which is denoted as v ∈ [a,b],and the certain results of q over I are in the interval [a,b], it is denoted as I╞q ∑ ∈ [a,b], a is the lower bound, b is the upper bound. If there are not subsets that exist certain results, [a,b] is the optimal certain results, a is the maximum lower bound, b is the minimum upper bound.

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An aggregation query q denotes as the following: SELECT G,agg 1(e1) e1,…, aggn(en ) en FROM R [WHERE W] GROUP BY G Where, G is a series of aggregation attributes, agg 1(e1),…, aggn (e n) are aggregation expressions. q G is a query without aggregation and grouping in an aggregation query: SELECT G FROM R [WHERE W] Definition 4. Query range. Set D is a database,q is an aggregation query, give a series of integrity constraints∑, qG is a query without aggregation and grouping in an aggregation query q. To every candidate database I, if t is certain results of qG over D and every aggregation value v range from glb (the maximum lower bound) to lub(the minimum upper bound), (t,glb,lub is certain results of q over D.

）

Example 3. Give a relation R(K1,K2,K3). Its candidate databases are the following: I1={t1,t3,t5}, I2={t1,t3,t6}, I3={t1,t4,t5},I4={t1,t4,t6},I5={t2,t3,t5}, I6={t2,t3,t6},I7={t2,t4,t5},I8={t2,t4,t6}. Table 2. Table R K1

K2

K3

K4

t1

c1

n1

h

1000

t2

c1

n2

h

100

t3

c2

n2

h

2000

t4

c2

n2

l

200

t5

c3

n2

l

3000

t6

c3

n1

l

NULL

Give a query for summing K4.The amounts of attribute values {6000, 3000, 4200, 1200, 5100, 2100, 3300, 300} are not uncertain results, which are expressed as [300, 6000]. Suppose a query for summing K3 whose values=’h’. t1, t2 and t3 are tuples which are satisfied with the original query. The query range contains values of n1 and n2, it need obtain boundary values of every K3 value for summing K3. The interval values of candidate databases are {(n1,1000),(n2,2000)},(n1,1000), (n2,2000)},{(n1,1000),(n2,0)},{(n1,1000),(n2,0)},{(n1,0),(n2,2100)},{(n1,0),(n 2,2100)},{(n1,0),(n2,100)} {(n1,0),(n2,100)} respectively. As a result, the query range is {(n1, 0, 1000), (n2, 0, 2100)}. In every candidate database, attribute values range of K3 from 0(the maximum lower bound) to 1000 (the minimum upper bound) while K2=’n1’, the minimum upper bound is in I1,I2,I3

和

Query Rewriting on Aggregate Queries over Uncertain Database

29

and I4, the maximum lower bound is in other candidate databases; attribute values range of K3 from 0(the maximum lower bound) to 2100 (the minimum upper bound) while K2=’n2’, the minimum upper bound is in I5 and I6, the maximum lower bound is in other candidate databases. We compute the maximum lower bound and the minimum upper bound by employing query rewriting. Give a series of integrity constraints∑ and an SQL query q in relational database, the method rewrites original query q to an new other SQL query for obtaining certain results. The next step we should propose a rewriting algorithm for original queries. Algorithm 1. RewriteAgg(q,∑) Input: aggregation query q; a series of integrity constraints∑ Output: a rewritten query Q for q BEGIN Cand=projection sets of R ∪ subsets of maxima and minima of aggregation DISTINCT attributes and PRIMARY KEY; min_cand1= KEY projection sets of cand without repeated KEY ; filter_cand1=the key value of min_cand1 is the key value of R with negative conditional selection predicate or selection predicate value is null; min_cand2=min_cand1

－filter_cand1；

min_cand2= projection KEY subsets of min_cand2; min_cand3= projection subsets of G and the minumum of aggregation attributes after join between cand and min_cand2; max_cand= the summation maximum of aggregation attributes subsets in cand by grouping G; end_cand= maxima and minima of max_cand left join min_cand3 by G Q=cand+ min_cand1+ min_cand2+ min_cand3+ max_cand+ end_cand

；

END

4 Experimental Analysis The experimental setting is the following: OS(Windows XP),CPU(AMD Athlon(tm) 64 X2 2.01GHz), MEMORY(1GB),RDBMS(SQL Server 2005). Query writing achieves by JAVA, experiments employ the first query and the sixth one of TPC-H[7] for generating different size uncertain data and consider the references as follow: (1) Data size(s). Set s=0.1GB,0.5GB,1GB and 2GB.1GB data have 8,000,000 tuples approximately. (2) In uncertain databases, n tuples who violate the key constrain share a common key value. For example, if n=2, every key value appear twice.

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(3) The percent of uncertainty (p). if p=50%, 1GB data have 4,000,000 illegal tuples. Every relation has a same p value. Set p=0,1,5,10,20 and 50. Experiments should consider high p value to test the method. The number of projection attributes and aggregation attributes in queries. Table 4 shows that rewritten queries are longer than original queries in execution time. Since Q1 has more aggregation and projection attributes, its execution time is longer than Q6. The overload is affected by the result sizes of result original queries without grouping. We remove all groupings of Q1 to obtain result sets, which are more numbers than other queries. Algorithm 1 computes middle result sets to produce the candidate set filter_cand1, and this set produces other middle sets. Though overload of rewritten queries are longer than original queries, this may be accepted. Table 5 gives overloads of the rewritten query of Q6 with different data sizes(100MB,500MB,1GB,1.5GB and 2GB) while n=2. In order to compare certainty, we keep the number of violated tuples as a constant. To 400,000 violated tuples, 2.5,3.3,5,10 and 50 of p is correspond to 2GB,1.5GB,1GB,500MB and 100MB respectively. The change of overloads is linear with the change of the database size. Table 3. Features of queries the number of projection attributes

the number of aggregation attributes

Q1

10

8

Q6

1

1

Table 4. Execution time(s=1GB,p=5%,n=2) original query

rewritten query

Q1

35

512

Q6

32

59

Table 5. the rewritten query of Q6 with different data sizes original query

rewritten query

s=0.1

2

2

s=0.5

16

30

s=1

32

59

s=1.5

44

87

s=2

56

120

Query Rewriting on Aggregate Queries over Uncertain Database

31

5 Conclusion This paper employs an appropriate method to return certain result in uncertain databases. Query semantics are adjusted as certain range semantics for computing boundary values of aggregation queries by query rewriting, it rewrites an original query to a new SQL query which can return certain results effectively. The experiments show that the overloads of rewritten queries are longer than original queries, but the overloads may be accepted. The further work should consider aggregation queries with join among multi-relations in uncertain databases.

References [1] Dasu, T., Johnson, T.: Exploratory Data Mining and Data Cleaning. John Wiley, New York (2003) [2] Motro, A., Smets, P.: Uncertainty Management in Information Systems. From Needs to Solutions. Kluwer Academic Publishers, Boston (1997) [3] Xie, D., Yang, L.M., Pu, B.X., et al.: Aggregation query rewritings based on clusters in inconsistent databases. Journal of Chinese Computer Systems 29(6), 1104–1108 (2008) [4] Decan, A., Wijsen, J.: On First-order query rewriting for incomplete database histories. In: Proc. of the Int’l Conf.on Logic in Databases, pp. 72–83 (2008) [5] Soliman, M.A., Chang, K.C., Ilyas, I.F.: Top-k Query Processing in Uncertain Databases. In: Proceedings of the IEEE International Conference on Data Engineering, pp. 896–905 (2007) [6] Ruzzi, M.: Efficient Data Integration under Integrity Constraints: a Practical Approach: [PhD Thesis]. Roma: University of Rome La Sapienza (2006) [7] Transaction Processing Performance Council. TPC BENCHMARK H (Decision support) standard specification (2010), http://www.tpc.org/tpch

Ranking Tags and Users for Content-Based Item Recommendation Using Folksonomy Shimin Shan1, Fan Zhang1, Xiaofang Wu1, Bosong Liu1, and Yinghao He2 1

2

School of Software School of Management, City Institute 1,2 Dalian University of Technology 1,2 Dalian, Liaoning Province, China [email protected]

Abstract. Selecting tags for describing the given item is the key to develop practical content-based item recommender systems using folksonomy. A novel strategy is proposed in the paper. With the strategy, tags are selected on the basis of users’ behavior pattern analysis. According to the strategy, an algorithm was implemented to rank users with representativeness of the tagging behaviors. Results of the statistical experiments show that the proposed strategy and algorithm can rank tagging users and can be used to discover tagging solutions which are widely accepted by the majority of all the users. Therefore, the proposed strategy and algorithm can be utilized to refine tags for describing items. Keywords: folksonomy, content-based recommendation, tagging behavior.

1 Introduction Collaborative tagging systems allow internet users to classify and manage online resources with custom annotations. Tagging is not new, but the aggregation of personalized tags all over the web exhibit interesting features. For example, the distribution of tags in collaborative tagging systems has been shown to converge to a stable power law distribution over time without any central controlled vocabulary being used to constrain the individual tagging actions[1]. According to its convergent features and simplicity, collaborative tagging has become one of the most useful ways of categorizing or indexing content in case of there is no “librarian” to classify items or there are too many items to classify by a single authority[2]. In contrast with taxonomy, the unstructured vocabulary and the network formed by inter-related users, resources (items) and tags is commonly referred to as folksonomy[3]. As the production of proactive tagging behaviors performed by users, tags can be regarded as meaningful identifiers for classifying item collection. On the basis of the assumption, many researchers agree that the tags can be used to improve the personalized information retrieval and recommendation. Therefore, personalized recommendation using folksonomy has been becoming an important issue in the fields of E-Commerce. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 32–41, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Corresponding to the two basic classical strategies of personalized recommendation, content-based filtering and collaborative filtering recommendation, tags have been utilized to facilitate recommendation in two ways. On the one hand, tags are employed as the meaningful metadata to describe the content of items and are used as the universal descriptors across multiple domains for content-based recommendation. On the other hand, tags are treated as the semantic indicators of the users’ preference and the foundation for measuring the users’ similarity towards the collaborative filtering recommendation. Although some works have been done, there is still a long way to get the practical personalized recommendation system based on folksonomy. Among all the obstacles, the thorniest one is the tag’s semantic ambiguity problem. The problem is the very key to developing various tag-related applications. Specially, for the content-based item recommendation, the semantic ambiguity makes it hard to judge whether a tag is suitable for describing the given item. The origin of the semantic ambiguity of tags has many facets. Besides the essential multiple meanings of the polysemy, the users’ different perceptions towards the same meaning of a term is also the key source of the semantic ambiguity. It is not difficult to understand that, in tagging applications, user’s perception towards resources is supported by his/hers background knowledge and is shown by user’s tagging behavior patterns. Corresponding to the diversity of perception, various users may assign the same tag to multifarious items or assign irrelevant tags to the given item according to their personalized understandings. Considering perception, tag-based item model is different from the other models based on natural attributes. The core problem and basis of content-based item recommendation is how to construct feature space in which items’ similarity can be computed with the common standard. From this perspective, as far as tag-based model is concerned, whether a tag is proper for identifying a given item do not depends on whether the tag describe the natural essence of the item, but on whether the tag-item pair is concordant with the common sense hold by the majority of all the tagging users. Therefore, a strategy is proposed in this paper for discovering representative tagitem pairs. In the strategy, tag-item pair is referred as tagging behavior and tagging users are employed as the mediator for locating representative tagging behaviors .Thus, candidate tags can be refined and the tag-based space used for finding similar items can be constructed more efficiently. Furthermore, an algorithm is introduced to reach the goal of the strategy. The rest of the paper is organized as follows: Section 2 provides a brief review of the content-based filtering recommendation using folksonomy. The proposed algorithm is presented in Section 3, and statistical experiments are conducted in Section 4. Finally, Section 5 gives conclusion remarks and future works.

2 Related Works The increasing volume of information on the web has motivated recommender systems to be widely used to support users to get information they need[4]. According to the strategy used for collecting and evaluating ratings, recommender system can be classified into content-based recommender system, collaborative filtering recommender system and hybrid recommender system[5].

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Comparatively speaking, content-based recommendation has a longer history. With this strategy, recommender systems analyze the features of the user’s preferred item collection to estimate his preference. Thus, those items whose features are similar with the user’s preference model are recommended to the user. In contrast with collaborative filtering recommendation, the basic content-based filtering has a disadvantage that its recommendation could only be made within the specialized domain, because the item similarity computation needs consistent attribute space. Folksonomy, also known as collaborative tagging, social classification, social indexing and social tagging, is formed by all the web users in a way of contributing tags. And it may be one of the right ways in which the bottleneck of the content-based recommendation mentioned above can be handled. In folksonomy, items’ descriptions are made on the basis of users’ perception in the form of tagging. Thus, resources of diverse domain can be compared in the same conceptual space. Therefore, folksonomy has been proposed to support content-based recommendation and several works have been reported. In [6], users and items are modeled with tag vectors and the TF*IDF measure is used to get the relevance of each tag with similar meaning. Furthermore, hierarchical agglomerative clustering algorithm was utilized for fighting ambiguity of tags and discovering tag clusters to infer resource relevance. Similarly, Marco de Gemmis et al. agreed that folksonomies can be valuable sources of user preference information and proposed a strategy that enables contentbased recommender to infer user interests by both the “raw” item attributes and tags [7]. Recently, a probabilistic framework attempting to generalize the personalization of social media systems was introduced by Jun Wang et al[8]. In that paper, 12 basic tasks that qualify for personalized in tagging systems were identified and three of them including collaborative item search were formally studied and modeled. It is noteworthy that all the works above are depend on the underlying assumption that all the tags in the tag-based model are of the same value no matter how special the contributors are. However, the assumption can’t be applied in every situation. For instance, some people are used to annotate items with custom tags for personalized classifying and item indexing. Obviously, such tags are too special to be used as the stable item descriptors widely accepted by others. Therefore, to eliminate the negative impact, the candidate tags for constructing item model and item feature space should be evaluated according to the corresponding user’s tagging behavior patterns. Unfortunately, few works has been done to solve the problem. Hence, a strategy and the corresponding algorithm are proposed in this paper for refining the candidate tags.

3 Proposed Strategy and Algorithm A. Strategy Description The proposed strategy is very simple: the tags used by representative tagging users should be considered with high priority for constructing item’s tag-based model. The representative tagging users are those whose tag usage solutions, that is tag-item pairs referred as tagging behaviors, are concordant with the common sense hold by the majority of all the tagging users. To apply the strategy, the definitions and the algorithm below is introduced. Unlike the ranking algorithm proposed in [9], a bipartite graph is introduced to model folksonomy.

Ranking Tags and Users for Content-Based Item Recommendation Using Folksonomy

35

B. Definitions Definition 1. Tagging Behavior (TB): tag usage solution of assigning a certain term to a given item (item). Tagging behavior is used to model the folksonomy as a bipartite graph in this paper for the sake of keeping the relation between tags and items. For instance, as shown in Fig. 1, tag T1, T2 and T3 assigned to the item I1 correspond to three tagging behaviors. Likewise, the relations between three items (I1,I2 and I3) and Tag T1 can also be viewed as three TBs. Definition 2. Tagging Behavior-Tagging User model (B-U model): A graph G(V,E) is bipartite with two vertex classes X and Y if V = X U Y with X I Y = φ and each edge in E has one endpoint in X and one endpoint in Y. In the paper, folksonomy is denoted by bipartite graph G(X,Y,E) where X represents the set of tagging users and Y represents the set of tagging behaviors, and E represents the set of relations between tagging users and their tagging behaviors. Definition 3. Exemplary Coefficient (EC): The representative degree of the tagging behavior or tagging users. The goal of the strategy proposed is to select tags by ranking the representativeness of tagging solutions and tagging users’ behavior pattern. Thus, those tagging solutions accepted by the majority can be used to support selecting proper tags to describe items with common sense.

Fig. 1. Definition of tagging behavior

Therefore, Exemplary Coefficient is introduced. The higher the EC is, the more representative a tagging behavior (user) is. In other words, a tagging behavior with high EC is the tagging behavior widely used and accepted by majority of users. Similarly, a tagging user with high EC is the user whose tagging behaviors are widely agreed by others. According to the proposed strategy, two heuristic rules are introduced. 1) Rule 1: representative users tend to perform representative tagging behaviors 2) Rule 2: representative tagging behaviors tend to be performed by representative users. C. Algorithm Description Given two vectors X and Y where X(u) stands for the EC of the tagging user u and Y(ti) stands for the tagging behavior ti respectively, X(u) and Y(ti) are updated with operations below considering the two heuristic rules.

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Operation I: X

(u )

=

∑Y(

ti )

(1)

ti (u )

Operation O: Y

(ti )

=

∑X()

(2)

u

u ( ti )

In operation I, ti(u) stands for all the tagging behavior performed by user u and EC of the given user u (X(u)) can be obtained by sum up all the EC of tagging behaviors performed by u. In the same way, EC of the given tagging behavior ti (Y(ti)) can be got by sum up all the EC of tagging users as shown in operation O where u(ti) stands for all the users performing the ti tagging behavior. Similarly to PageRank[10] and HITS[11], the algorithm initializes all the elements in the bipartite model with the same EC. As show below, the whole procedure of the algorithm is the iterations of updating elements’ EC according to the two heuristic rules based on the network structure. Algorithm: Calculate the Exemplary Coefficient of Tagging Users and Tagging Behaviors Input: B-U Model, k as number of iteration Output: Xk and Yk which are the results of X and Y by performing k iterations Steps: 1. Initialize X and Y as X0 and Y0 respectively. All the elements in X0 and Y0 are set to 1. 2. for j=1:k a. Get X 'j by using the operation I and Yi-1. That is, '( u ) '(ti ) X j = ∑ Y j −1 ti ( u )

b. Get

Y j'

by using the operation O and

X 'j . That is, Y

'(ti ) j

= ∑Xj

'(u )

u ( ti )

c. Get

X j by normalizing the X 'j

d. Get

Yj

by normalizing the

Y j'

3. End for 4. Return Xk and Yk

Taking the model shown in Fig. 2 as example, the effectiveness of the algorithm can be illustrated intuitively. It is not hard to get the following observations by examining the sample model:

1) U2 should be more representative than U1 even though they both perform three tagging behaviors and two of the behaviors are the same (TI2 and TI3). The reason is that TI4 performed by U2 is more popular than TI1 performed by U1 2) U4 performs the most tagging behaviors in the sample (5 times). Plus, TI4, TI7 and TI8 performed by U4 are also performed by many other users. Therefore, it is reasonable to draw the conclusion that U4 is the most representative user. 3) Although performed more tagging behaviors, U5 should not be more representative than U3. The reason is that the tagging behaviors of U3 are also adopted by U2 and U4, while U4 is the most representative user. On the other hand, TI9 and TI10 are only used by U5 without acceptance of the other users. 4) TI2 should be more representative than TI1 for the reason that TI2 is performed by more users.

Ranking Tags and Users for Content-Based Item Recommendation Using Folksonomy

37

Fig. 2. The Sample model

Fig. 3. Result of the ranking algorithm based on tagging behavior

5) In spite of being used by more users, TI3 should not be more representative than TI6 because that TI6 is used by more representative users. 6) Although be both performed once, TI5 should be more representative than TI9 because the tagging user of the former tagging behavior (U4) is more representative than the tagging user of the latter (U5). Result of the proposed algorithm is shown in Fig. 3. As shown in the result, all the former observations are supported. For example, EC of the U2 is higher than U1 which means that U2 is more representative than U1. Additionally, U4 has the most EC corresponding to its leading representative position. From the tagging behavior perspective, on the other hand, the EC of TI2 is higher than the EC of TI1 and the EC of TI5 is higher than the one of TI9.

4 Evaluation The whole experimental procedure was separated into representative user discovering phase and correlation verifying phase. In the former phase, tagging users are sorted with EC in each month and certain portion of all the users with highest EC was regarded as the representative users according to the given ratio. In the latter phase, the representative users’ TBs and the correlation mentioned above were evaluated.

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Del.icio.us datasets provided by Robert Wetzker et al. [12] is utilized in the experiments. Considering the data volume, earlier data (collected from Sep., 2003 to July, 2004) was used which includes 130213 tags, 426546 resources and 31701 users. The datasets are divided into training dataset (from Sep., 2003 to May, 2004) and testing dataset (including June, 2004 and July, 2004). And, the training dataset is used to find the representative users according to the users’ representative rank and the given selection ratio. The correlation between the ratio of selecting representative tagging users and their TBs’ average EC was firstly evaluated. The result is shown in Fig. 4 and the detail is listed in Tab.1. As shown in the result, the two variables are strongly negative correlated. In other words, average EC of TB’s is increasing while the ratio of selecting representative users is decreasing. Take the result in June, 2004 as an example, the average EC of TBs is 0.00537590288510 while the ratio is 2/1000. When the ratio was 1/1000, the average EC turned to 0.00881971282008. Moreover, It is clear that the representative users’ average EC is higher than the average EC of all the users. Similarly, as shown in Fig. 5 and Tab. 2, negative correlation was also discovered between the percentage of representative TB against personal TBs and the ratio of selecting representative tagging users. And, the representative TBs were selected with threshold determined by the extent of the EC change. Fig. 6 shows an intuitive explanation to the threshold selection. Table 1. Results of Representative users’ Average EC Representative user selecting ratio 1/1000 2/1000 3/1000 4/1000 5/1000 Average EC of all the users

Testing Data 2004-06 2004-07 0.00881971282008 0.00938925536752 0.00537590288510 0.00527648477112 0.00388170401006 0.00414521581582 0.00339073812395 0.00372271970412 0.00307910272506 0.00347574131248 0.00036985257740 0.0003292316979

Table 2. Results of Representative users’ representative TB’s Percentage

Representative user selecting ratio 1/1000 2/1000 3/1000 4/1000 5/1000

Testing Data 2004-06

2004-07

0.421144148843 0.257989663537 0.187054721843 0.163966942148 0.148992322456

0.597924187725 0.337507353559 0.266003032500 0.239701704545 0.224081745374

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All the results approved that the EC is suitable for measuring the representativeness of the tagging users and TBs. Furthermore, representative users with high EC are tend to perform representative TBs. The higher EC a tagging user has, the more portion of his TBs are representative. Therefore, a conclusion can be safely drawn that the proposed strategy and the algorithm are effective.

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5 Discussion and Conclusion In the paper, a strategy is proposed for discovering valuable tags by means of ranking tagging users and the corresponding algorithm was implemented. The experimental results confirmed the hypotheses of the strategy and the effectiveness of the algorithm. More experiments using other datasets need to do for fully evaluating the method. Meanwhile, several problems including the ETB threshold selection strategy for discovering the exemplary tagging behaviors and exemplary tagging users will be studied in the future. Acknowledgment. The work was partly supported by the Fundamental Research Funds for the Central Universities and National Nature Science Foundation of China (Grant No. 70972058 and 60873180).

References [1] Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. ACM, New York (2007) [2] Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. Journal of Information Science 32(2), 198–208 (2006) [3] Marlow, C., Naaman, M., Boyd, D., Davis, M.: HT 2006, tagging paper, taxonomy, Flickr, academic article, to read. ACM, New York (2006) [4] Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 734–749 (2005) [5] Dattolo, A., Ferrara, F., Tasso, C.: The role of tags for recommendation: A survey. In: 2010 3rd Conference on Human System Interactions, HSI (2010)

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[6] Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized Recommendation in Social Tagging Systems Using Hierarchical Clustering. In: Recsys 2008: Proceedings of the 2008 Acm Conference on Recommender Systems, pp. 259–266. Assoc. Computing Machinery, New York (2008) [7] de Gemmis, M., Lops, P., Semeraro, G., Basile, P.: Integrating Tags in a Semantic Content-based Recommender. In: Recsys 2008: Proceedings of the 2008 Acm Conference on Recommender Systems, pp. 163–170. Assoc. Computing Machinery, New York (2008) [8] Wang, J., Clements, M., Yang, J., de Vries, A.P., Reinders, M.J.T.: Personalization of tagging systems. Information Processing & Management 46(1), 58–70 (2010) [9] Hotho, A., Jschke, R., Schmitz, C., Stumme, G.: Folkrank: A ranking algorithm for folksonomies. In: Proc. FGIR 2006 (2006) [10] Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web (1998) [11] Kleinberg, J.: Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46(5), 604–632 (1999) [12] Analyzing social bookmarking systems: A del.icio.us cookbook. Robert Wetzker, Carsten Zimmermann, and Christian Bauckhage. In: Mining Social Data (MSoDa) Workshop Proceedings, ECAI 2008 pp. 26–30 (July 2008)

Research on Repair Algorithms for Hole and Cracks Errors of STL Models Hu Chao, Yang Li, and Zhang Ying-ying School of Mathematics & Physics, Changzhou University, Changzhou, JiangSu Province, China [email protected]

Abstract. In the process of transformation of STL model, there can be many data errors such as hole, crack, missing position, reverse normal vector, redundancy and so on,attribute to the existence of data option error and the possibility of missing of triangle.This can cause abnormality in the following Rapid Prototyping and have effects on the application of STL models. This paper get the relation of points, edges and surfaces by creating topology for STL model, and introduce graph data structure that representing the edges which in only one triangle. The points list of a hole can be got by using Breadth-first traversal of graph. And then get the holes which are caused by crack based on the characters of crack. So, design the repair algorithms based on different characters of errors. Hole errors can be repaired by adding triangles which is generated by the points have been recorded in hole check algorithm based on the principle of minimum angle, cracks can be repaired by alter the coordinate of point. Using VC++ and OpenGL develop the error check system , and the check and repair algorithms are verified valid. Keywords: STL models, Rapid Prototyping, error check, error repair, hole.

1 Introduction STL file is the commonest file format which is used in the data conversion between CAD model and Rapid Prototyping system.The U.S.3D System Inc. established the format of STL file in 1987,it describe the surface of a three-dimensional solid mode discretely and approximately with small triangle as the basic unit.And the amount of triangular patches greatly affects the degree of contour approximation,we can get a high approximation degree when the amount is large,but this can contribute to data overload or data round-off errors; the approximation degree is low when the amount is small. Overall, the common deficiency exists in the research of existing error repairing for STL models: there isn’t a whole description for an error, this lead to the possibility of missing check or some other kind errors; they didn’t consider the different characteristics of every kind error when they repairing errors, also they didn’t make out reasonable strategies and steps; most of them only study one or several errors not all, also the repairing algorithms the advanced are not good enough to check errors and they are lack of systematic and practical[1~3].Most foreign software have complicated Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 42–47, 2011. © Springer-Verlag Berlin Heidelberg 2011

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functions and it is complex to manager them, also they are very expensive to use. This paper put forward a method to get faster processing speed and simpler management by classifying the errors which exist in STL data files. Then, we can improve efficiency greatly and provide technical support for following rapid prototyping.

2 Description of STL Model Errors STL file is the actual standers of data conversion between three-dimensional data model and Rapid Prototyping system. In CAD modeling, one file is often converted from one CAD software to another. It is conventional for the lost of information in the conversion process contribute to the different solid surface domain in different CAD software. So, there are various kinds of errors in the model generated by STL data file. The common errors are as follows: hole and crack, reverse normal vector, overlap, and so on[4~6]. We describe the hole and crack errors and analyze their characteristics in this paper. A.

Reversal Normal Vector

When we say a triangle normal vector is reversal we mean that the direction of turning of the triangle is wrong or the triangle is recorded against the required orientation rules of STL file, namely, one triangle normal vector fall short of right hang rule or the normal vector doesn’t point outside of the solid model. Often it is the confusion of the order of three vertexes recorded in STL file generated the disagreement between recorded and calculated normal vector which we say reversal normal vector. We can see the normal vectors of the whole model or all triangles in a surface of the model are reversal when this error happens. We can calculate normal vector by the three vertexes of a triangle and their order, and then compare it with the recorded normal vector. In fact, we identify the existence of reversal normal vector based on the result of dot product of the two vectors. If the result is a positive value then we say there isn’t error, or else we say there is reversal normal vector. B.

Hole and Crack

Hole is the commonest error in STL file, it is generated by the lost of triangle, especially the model composed of by a number of large curvature surface. When we do surface triangulation on these models, the small triangles maybe lost, once there is any triangle lost, we get hole error. Especially to those complex models, if the triangle is very small or the number is very large, it is easy for losing small triangles, and this can lead to the existence of hole error. The existence of rounding error can lead to crack error. There are many common grounds between hole and crack. The existence of crack can also lead to the existence of hole, and the only difference is that there are two edges in crack-hole with the distance between them larger than the given value. The hole error is shown In Fig.1. We can get the following characteristics of hole as following by analyzing the causes of holes: the vertexes in a hole can be a ring by ordering end to end point. One edge can exist in two faces in normal situation, but there can be exist edges who only in one face when hole error exist. So a hole is composed by several edges only in one face. Similarly, every edge of a crack is can be in only one face.

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Fig. 1. Hole error

3 Error Check and Repair Algorithms for STL Model A.

Topology Creating

We get the STL model through triangulating the surface of CAD solid model, but there isn’t any topology information in any triangle. We must storage the vertexes of every triangle redundantly, but this can do nothing to the relation of faces. Topology can affect the assign of error check and repair algorithm. We generate three classes Point, Edge and Face to create topology for STL model. The Point class includes Point ID number, ID number of edges which include the current point, coordinates of points, ID number of faces which include the current point; Edge class includes Edge ID, Face ID, and two point IDs; Face class includes three coordinates of points, normal vector and three edge IDs. And then what we should do is adding the related data to appropriate variable to get the topology we need. First, we record the information of every triangle read from STL file to face class, and then add other data to relevant variables. Also, we do something to the repeated points and edges. If one point or edge has existed we didn’t storage them. B.

Error check algorithms for STL model

We design some algorithms to check model error based on the error characteristic to check error. Now, we introduce several algorithms to check different errors. 1) The check for reversal normal vector: We compare the actual normal vector calculated with the three vertexes of a triangle with the one read from STL file. We can decide whether it is reversal normal vector error. 2) The check for hole and crack: The crack is also a special hole in that a crack is a slit hole, and they all derived from single edges. We check whether the traverse of graph is a ring to identify this error for hole or crack in model doesn’t have certain shape. We can take it through the following steps.

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Step1: Create an undirected graph with all single edges in edge array. Step2: Get the points list and parent points list of a hole by traversing the undirected graph created in step1 using Breadth-first traversal of graph. a) Design an array to record the visit signs of all points and init them to be false. b) Select a point which is signed false in the mark array, and add it to queue. Go to end if we can’t find this point. c) Traverse adjacent point of all unvisited points and add them to queue. d) Set the first node of queue to be current point and delete it from queue if the queue isn’t empty, and go to c). e) Go to end if the queue is empty. Step3: Find all the points of a hole based on the points list got from Step2 and the weight of graph. a) Design a hole points array. b) Select a unvisited edge of the graph and set its visit mark by altering its weight, and then add the two points of the edge to hole point array. Go to h) if we can’t find this edge. c) Fix a vertex and determine whether the parent node of the other point is not the start node and is the same node with the parent node of the fixed point. d) Add the parent point of unfixed point to hole point array and make it the unfixed point. e) Add the unfixed point to hole point array if their parent nodes are same, go to g). f) Add the parent node of unfixed node to hole point array and make the unfixed point fixed and the fixed point unfixed if the fixed point isn’t start point. Go to c). g) Record the hole information and go to b). h) End. Step4: Traverse all the holes to check where there are holes with two points whose distance is smaller than the given value. If the hole exists, then we add the hole to crack hole array, or else add it to a temporary hole array. Step5: Add all the hole information in temporary hole array to hole array.

4 The Designation of Error Repair Algorithms for STL Model A.

The Repair Algorithm for Hole Error

The hole is resulted from triangle missing. So, we must replenish the missing triangles to repair hole error for STL model. We redistribute hole points to construct triangles based on that a triangle is composed by three points[7~9]. But there are many methods to find three points to make a triangle.We use the method based on the principle of minimum angle to construct triangles, we choose one point whose left point and right point composed the minimum angle. And we can calculate normal vector with the selected three points and add this triangle to model. So, the triangles we need to repair hole have one less. Also, we should set the middle point visit mark to be true for it can’t be in other triangles. Do as above-mentioned until there are only two points whose visit mark is

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Start No

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Fig. 2. Flow chart for hole repair algorithm

false, and then set their visit mark true. So, we have all the missed triangles added and repaired hole error. The flow chart for hole repair algorithm is shown in Fig.2. B.

The repair algorithm for crack error

We also should set the addition information of crack points, and it is similar to that of hole repair algorithm. The only difference between them is that we should alter some point coordinates and some information else of the relevant triangle. We make the left point and right point one in crack error repair algorithm.

5 Conclusion We set research priorities on the check and repair algorithm of hole and crack error in STL file, and we also analyze the characteristics of triangle normal and three vertex coordinates in STL file. We got the relation of points, edges and surfaces by creating topology for STL model. We developed the browser for STL file and error check and repair system by using OpenGL, and the check and repair algorithms are verified valid[10~11]. Certainly, it need more research to be perfect to repair error in STL model rapidly and accurately. Acknowledgment. This work has been supported by Mathematics & Physics Subjects Foundation of Jiangsu Polytecnic University Nos.ZMF09020021.

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References [1] He, Q., Zhang, S.-s., Bai, X.-l.: Fast repairing errors in STL files. Application Research of Computers 26(5), 1983–1984 (2009) [2] Li, J.-f., Zhong, Y.-x., Li, D.-s.: Research on errors identifying and repairing of STL file. Machinery Design & Manufacture (2), 40–42 (2002) [3] Yang, J., Han, S.H., Park, S.H.: A Method for verification of CAD model errors. J. Eng. Des. 16(3), 337–352 (2005) [4] Zhou, H.-m., Cheng, X.-w., Liu, F., Li, D.-q.: Research on Repair Algorithms for STL Files. Journal of Computer Aided Design & Computer Graphics 17(4), 761–767 (2005) [5] Tao, J.: Geometric Errors on Polygonal Models: A Survey. J. Comput. Sci. & Technol. 24(1), 19–29 (2009) [6] Tang, J., Zhou, L.-s., Zhou, R.-r., et al.: An Algorithm for Mending STL File. Mechanical Science and Technology 17(4), 677–679 (2000) [7] Zhao, J.-b., Liu, W.-j., Wang, Y.-c.: Research on Algorithm for Diagnosis and Modification of STL File Errors. Computer Applications 23(2), 32–36 (2003) [8] Zhang, J.-l., Zhang, J.-f.: Research on the Automatic Repair Algorithms for STL Files. Mechanical Engineering & Automation (1), 20–22 (2008) [9] Bischoff, S., Kobbelt, L.: Structure Preserving CAD Model Repair. Comput. Graph. Forum 24(3), 527–536 (2005) [10] Zheng, Z.-g., Xia, W., Yuan, M.-h., et al.: Research of second development based on UG and MFC. Computer Engineering and Design 28(23), 5787–5791 (2007) [11] Gong, F.-m., Li, H.-s., Yang, Q., et al.: A STL File Browser Based on OpenGL. Computer Engineering and Applications 6, 116–117 (2002)

“Polytechnic and Literature Are All-Embracing”— Training and Practice of Game Software Talents on Comprehensive Quality* Yan Yu, Jianhua Wang, and Guoliang Shi Institute of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China [email protected]

Abstract. Most countries immensely support the development of game industry in the world so far, the quantity demand of game software talents are increasing day by day. Game software is adjoin typical by science and art, is a complex subject by interactive multi-subjects. Harbin Normal University confirms the guide thought "All-embraced on Polytechnic & literature" & trains the goal of comprehensive applicable talent, establishes multi-steps implementation to ensure the overall development of students, enhance the quality of education highly. Keywords: game software, comprehensive applicable talent, teaching reform.

1 Introduction Today's higher education development appears many of interactive subjects, especially the appearance of computer technology & continuously development, derive many comprehensive interactive subjects, eg. One of them is game software subject. It is the composite of computer technology, art, scheme script music, building etc. Along with the rise of digital media technology & computer game industry, there are many institutes of higher learning which have set the reference subjects of digital media, Animation computer game software at present, & it appears many training institutions[1] in society. Most countries are running support digital media & computer game industry in the world; let this industry get a unprecedented development. Each university & training institutions mostly adopt the model of cooperation between school & enterprise when they set the reference talents training plans, this way on a level enhance the school quality of game software subject highly. However if we want to train the applicable talents of real applicable society, the school model of our university & training plans still exist some questions.

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This work was supported by grants from Intellectual Education and Information Engineering in Heilongjiang Key Laboratories (081203) and Computer Applications Technology in Heilongjiang Key Subjects and Natural Science Foundation of Heilongjiang Province of China(F200935).

Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 48–53, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 Existence Problems during Training Talents in Our University Talent training model is a case which set by schools to train the knowledge structure ability structure, diathesis structure of students, it is concentrated reflection the education thought & education thought of schools. Our university exist some problems for many years during the talents training, these problems block the steps of our university talents training, and influence the training quality of talents, and thus it's hard to see "MASTER" for many years. The late famous scientist Tsien Hsue-shen had asked premier Wen Jiabao : " why our schools can't train excellent talents[2]." composite the thoughts of old gentleman Qian in our opinions, our university exist following problems on talents training model. Firstly, "Too many branches, talents are hard to stand out among [2]". The set of subject & major is too minutely & narrow, emphasize excessively training "special talent" who focus on one area, subjects, specialized field are divided more & more minutely thus ignore the overall development of a talent. Mostly of the university exist this problem on course setting minutely specialized field much too specialized course naturally train students to be "A frog in a well" Students are hardly to get the closely major or which major knowledge he or she likes at classroom this limit the development of students immensely it locked the views of students on a level, so can't train high quality talent which can be understood. Secondly, ignore the training[3] on comprehensive quality badly. Many years model of exam-oriented education separate the normal orbit of our education. Comprehensive quality can't break away from the reality which only a slogan in our each grade education. Even in the higher education, it also exists to seek a one-sided view of specialized achievements, while ignore the situation of comprehensive quality badly Except above, as well as our exams system exist problems. Examinations of Many university only pay attention to the external examination performance however ignore the imagery thinking training of students. Such problems have to be faced & solved by our university.

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3 The Guide thought of Synthesis Talent Training Model Harbin Normal University set a Game Software major in 2006, & established Software College in 2008; meanwhile innovation trial plot of game software talent was approved at the same year. Game Software Major is a comprehensive interactive subject. From the preparing of Game Software Subject, college leader & college teachers are thought model & case for comprehensive Game Software talent training. Finally we find the answer in the education thought of Tsien Hsue-shen. The old gentleman Qian thought that:" who study polytechnic, should learn some literature & art special to learn the model of thinking of literature & art. Scientists should have some artistic culture, can learn the imagery thinking of literary artists, can immensely stride an association [2]." All-embraced on science, engineering course & liberal art is the only way to train the outstanding talent in the heart of old gentleman Qian

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4 The Practice of Harbin Normal University to Train Comprehensive Game Software Talent It has guide though of science, software institution of Harbin Normal University set a serial of operable steps insure that students can get overall comprehensive diathesis training. Several years experiences on running a school to prove that the serial reform measures got a good effect.

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A. Curriculum Provision "All flowers are in Boom"

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The curriculum provision of our Game Software major try it best to do "large & allinclusive" on a basis of "specialized & perfection". Except for the correlative core course of Game Software major, college set abundant Major Elective Courses& Optional Elective Courses which are offered to students to choose freely. These Elective Courses involve Literature, Art, Music, Design, Sports, building, History, philosophy, mathematics physics engineering etc. Students can choose one of course according to interesting, characters, & combining the condition of themselves. This measurement offers a widely development space to students, it really achieves individuation teaching, classify training. In order to leave enough credit hours for Elective Courses, our college made a aggressive reform when set teaching plans, to press the credit hours [4]of general courses possible as, thus lighten the studying pressure for students. Ex. Computer introduction theory, Computer English etc. courses, can be applicable adopted the method of Concentration Teaching or Seminars Teaching, exam manner can be taken the way of big project write small thesis & small report to check the performance, thus lighten the study load of students immensely. In order to enhance the efficiency of classroom teaching, & enhance the studying interesting of students, college reforms teaching & checking methods [5] actively. Most of courses are adopted the instruction theory of Constructivism as a guide line,& taken students as the center to drive teaching methods it increases the credit hours of experiment practice, meanwhile transfer the studying interesting of students. During the course exam stage, most of courses are added the proportion of experiment exam, to examine the master of students on the course in big project, course design forms.

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Optional Elective Courses Major Elective Courses Basis Courses Core Courses Fig. 1. Set frame of Game Software Major Course

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In order to increase the efficiency of experiment teaching, our college practice the classification management system[6] of Open Lab. college divides the Test Room into three grades, Class A public test room, Class B Major Lab & Class C Specialization Lab, divides the test topic into Class A topic, Class B topic & Class C topic, students accord with themselves' Elective Courses & Commitment Courses design or project topic, to achieve admittance of different laboratory dynamically, this measure arouse the studying & creative enthusiasm of students hugely. Several years’ experiences have proved that the capability of immensely students is endless, interest & hobby is widely, prospect & future are untapped. It appears quite accomplished students on each project direction of science, engineering, literature & art. These years, students of Game Software major all achieve a proud achievement in all multi-term match forms of education at all levels. B. Encourage to Establish Student Clubs, Support Students to Develop Overall In order to advance the completely development of students on “science, engineering, literature & art.”, college encourages students to establish Association & Organizations, & gives supporting, meanwhile reward them who make a excellent achievement. Under the introduction of college policies, immensely students spurt unparalleled creative enthusiasm, set up College Literature Club, Drama Sketch Club, Dance Club, Program Development Group, Robot Interest Group, Web Development Group, Music Club, Sports Club, Press Corps, Psychology Collegiums, Volunteer Association etc. one by one. Once one student offers a complete club establishment plan, it can be established formally after approved by college leaders students use the spare time put in to clubs activities which established by themselves, this arouses the creative enthusiasm of students hugely. From another view, each hobby of student as well as the source of inspiration of Game Software creation. They can draw nutrition, advance with each other between the major study of students & the hobby of clubs.

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C. Hold Competitions Trimly, Set up Achievability & Confidence In order to check the study achievement of students, push the studying passion of overall development, college organizes multi-competitions & gives spirit & suitable material reward to winner each year. Ex. College holds Contest of Maths Models, Dance & Music Competition, Sketch Competition, Programmer Competition, Speech & Debate, All Sports Competition, Literature Competition, Students' Manufacture Competition, Painting & Calligraphy Competition, Computer Works Competition etc. on every year. Students get studying & practices in clubs, many students achieve works of themselves. Through matches college set a stage to exhibit themselves for students, it did make a actively action to arouse the studying interest & creation passion of students in a maximum limited. Meanwhile, to show the mien of our college students at the front of the whole school train the proud & confidence of students. So far each one of our college attends a club at least, health life & study way advance physical health development of students, in case students to have many bad habits & customs & hobby, let them progress in collective.

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D. Through the Exhibition of Student's Works to Improve the Teaching & Studying to Grow Together In order to let schools & society to understand students, let students go to school, go to society & really achieve the overall development of “Polytechnic & literature”, our college set the exhibition rules[7] of students' works specially, & continuously perfect it during process of implement step by step. So far, students' works exhibition is held once each term, it fully develop the hobby specialty of students, & fully exhibit the originality & wisdom of students. Through the student works exhibition, offer a stage for immensely students to show themselves fully, each attended student of exhibition try their best to achieve works, this has become one of the most power on teaching & studying of our institution.

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5 Continuous Improve the Overall Development of Students in Cooperation between School and Enterprise

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At the beginning of our Game Software major it has confirmed the training model[8] of combining with enterprises training, carry out the 3+1.Students contact with enterprises from different levels, different sides all the time during the four years of undergraduate course. Through enterprise expert lecture, engage enterprise teachers to teach, trained together by teachers of enterprise & school, enterprise internship till to the internship of enterprises. In order to make students to fit the needs of enterprises meanwhile reach the goal of overall development of “Polytechnic & literature”. Our college has abundant forms, involves widely practice training project. During students' practice at enterprises, except to accomplish jobs, accord with the character of students & wishes to attend each area of enterprise operation.Ex. Students can attend the enterprise decision, enterprise management, product designation, enterprise culture, staff literature activity, market research, after service, draft of letters, benefit analysis, survey of staff's status etc.areas. Enterprises need all kinds of talents, our students have different natural condition & hobby specialty can find their applicable position in enterprise. To find the students' potential as possible, this training way was welcomed by students, satisfied by enterprises, & get approval of society.

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6 Conclusion Game Software Major is a comprehensive subject which is interactive by multisubjects. Computer game works involve all elements of human society train comprehensive talents of "All-embracing Polytechnic & literature", it is our responsibility which is gifted by period. Only talent who has a strong comprehensive ability, is learned & accomplished can fit the needs of future society then to be the "MASTER” of industry. Scientist Qian Xue Sen didn't achieve his wish completely, under the thought guide of "All-embraced on Polytechnic & literature", Harbin Normal University teachers of Game Software Major think actively, probe to try hard practice one way to train the comprehensive quality & ability of students.

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Accord with practice efficiency of recently years experiences, brilliance achievement. However how to set teaching plan in science reasonably how to deal with relation between Major study & interest etc. Problems, it has to be solved in practicing.

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Reference [1] Li, W.: Debate between General Talent & Speciality Talent——Undergraduate Education Talent location of digital media technology. Computer Education (6), 97–99 (2009) [2] Fu, G.: Revelation of “Tsien Hsue-shen ’s asking”. People’s Education (12), 11–12 (2009) [3] Liu, Y.: Initial probe of comprehensive talent training. Economic Engineering (11), 120– 121 (2002) [4] Yu, Y., Liu, Y., Wang, J.: Strengthen the Building of the Provincial Colleges of Software Applied Software Talents Training. Computer Education (4), 24–26 (2010) [5] Yu, Y., Wang, J.: Practice and Exploration of Training Talent Model of Game Software Majors. Computer Education (7), 104–107 (2010) [6] Yu, Y., Zhang, J., Zhou, G., Sun, H.: Research and Practice on Opening-laboratory with Application-oriented Talents Training. Computer Education (18), 25–27 (2010) [7] Zhang, J., Yu, Y.: Student works exhibition improve the teaching reform practice & study of Game Software Major. Computer Education (19), 56–58 (2010) [8] Yu, Y., Wang, J.: Study on College-Enterprise Cooperation in Practical Teaching of Computer Majors. Computer Education (15), 47–49 (2009)

Design of Mobile Learning Scenario Based on Ad Hoc Zong Hu College of Education, Ningbo University, No.818 Fenghua Road, Ningbo, Zhejiang 315211, China [email protected]

Abstract. Mobile learning is the combination product of mobile technology and e-Learning technology, it plays a very important role in building a learning society. Mobile Learning can not do without the guidance of instructional design, This paper build mobile learning network architecture based on Ad Hoc, analysis and design according to the characteristics of the feature, scenario, activity of mobile learning. Keywords: Mobile Learning, Scenario, Ad Hoc.

1 Introduction Mobile learning[1] is the combination product of mobile technology and e-Learning[2] technology. On the one hand, portable PC, smart phones and other mobile devices should be seen as existing expansion technology of e-Learning, on the other hand, such as mobile learning will be personalized, multi-media, Ambient Intelligence, tactile interaction, mobile devices and other new technology merge into the field of education and training, making it show different features from the usual fixed-based cable network and desktop computer features of e-Learning.

2 Background A. Mobile Ad Hoc Network[3] MANET (Mobile Ad Hoc Network) is multi-hop temporary autonomous system composed by a group of wireless-enabled mobile terminals. In the network, each mobile terminal act as both hosts and routers. Mobile ad hoc networks have no infrastructure and its flexibility networking, so it has been widely applied in the field of temporary communication of the military rescue teams and disaster relief areas such as temporary communications. In recent years, with the further development of wireless network technology, the scope of application research of mobile self-organizing networks have gradually expanded, how to build business applications based on mobile self-organizing network, provide end to end service with quality assurance and effective communication, unified application development platform for different mobile ad hoc network nodes to has become one of the focus. Mobile Internet extend the traditional Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 54–58, 2011. © Springer-Verlag Berlin Heidelberg 2011

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network coverage and make the mobile self-organizing network fit for not only battlefield command and disaster rescue, you can also apply to enterprise networks, office networks, campuses and civilian telecommunication networks. Mobile ad hoc networks will provide more convenient services with a certain quality. People can connect to the network to see or download the appropriate information using mobile communication terminals (such as mobile phone, PDA, smart phone, etc.) this technology will make anytime, anywhere learning to become possible. At the same time the mobile communication terminal can interact with the other users through mobile communication terminals, makes interactive learning of content-based also can occur at any time in any environment, and can integrate into the actual work and life scenario. B. Mobile learning Mobile learning applications have been widely used, involving primary, secondary schools, universities, vocational training, distance education. 1)Primary and secondary fields - to improve communication efficiency and outdoor learning effect between teachers and students. 2)University fields - to conduct experimental research to improve the efficiency of interaction, and promote collaborative knowledge building, mobile campus building. 3) Vocational training - to solve user problems. 4) Distance Education - to provide supplementary learning content and learning support information. 5) Education of the whole of society - to provide various types of learning resources and support services.

3 Mobile Learning Features Mobile learning act as an extension of traditional e-Learning, while they share common feature in some areas, such as multimedia features, interactive, Learnercentered. And mobile learning have some unique feature, such as providing anytime, anywhere learning environment, Learning activities need context[4], providing justin-time[5] learning content, giving the learner strong ownership sense and facing more challenges in the design and application of mobile learning.

4 Network Architecture of Mobile Learning Based on Ad Hoc Mobile self-organizing Internet is a new network of wireless mobile self-organizing networks acting as access network, mobile self-organizing Internet have two major components: mobile ad hoc network node and mobile ad hoc network gateway. In this network, nodes can form multi-hop wireless mobile ad hoc networks, each mobile ad hoc networks composed by mobile ad hoc network interconnected nodes and belonging to the same mobile ad hoc network gateway can form a mobile selforganizing network domain. Multiple mobile ad hoc network domains can

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Fig. 1. Network architecture of mobile learning application based on Ad Hoc

Interconnect through mobile Internet gateway deployed in the edge of Internet domain, creating an Internet-scale wireless mobile ad hoc access network. MANET gateways deployed in the edge of Internet, and form a flexible overlay network. Its topology as shown in Fig. 1. The difference from other mobile self-organizing network is that this paper propose application research of mobile learning, make mobile self-organizing networks access to the Internet and become a part of the Internet. But the mobile ad hoc networks is not only as a part of the Internet exists, but to build a complete transmission based on Internet, with Internet-scale independent network. Mobile ad hoc networks can be used for a separate interaction with the Internet (content independent). This allows mobile ad hoc networks to build large urban areas for learning network. the user in the region can access the region MANET gateway to query the appropriate learning information, and go to learning interaction between the cross-regional mobile self-organizing network gateway .

5 Scenario Design Mobile technology application can render a dynamic, interactive, personalized learning content, can provide powerful learning management and support, can make the learning activities and work, life and other organic fusion, can implement flexibility teaching activities and collaborative activities based on scenario[6]. And the design of mobile learning should first be demand-driven design, learnercenter design, teaching staff or technology development people design mobile learning products or activities in accordance with their subjective wishes, finally these products are very difficult for the learners or very poor effect. In order to avoid such things, we should analysis the user's mobile learning needs, and verify and modify

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based on the users evaluation during the all stages of development. MOBILearn project leader, Sharples think that the user is an important source of information design, but also the participants of the design. The design process of mobile learning think through the ideas and methods of instructional design, but also played the advantages of mobile technology, emphasis on mobile learning experience of learners. A. generic issues Mobile environment generic issues, including user information, mobility[7], mobile interfaces, media types, communication support. In the mobile environment, users must take into account the differences in user profile information, such as students and teachers are all apply SMS in the mobile learning, but the requirements of SMS application are different. Mobility is the most important features in the mobile environment, can be characterized as user mobility, device mobility and service mobility. Under the mobile environment, the user interface has many limitations to be overcome, thus in the mobile environment mobile interface design can also display a certain difference, general, a typical feature of the mobile learning is nuggets, and can show different media types. Mobile communication technology will significantly promote and strengthen. Communication support should also serve as the general feature of the mobile environment to analyze. B. mobile learning context issues Mobile learning scenario analysis includes six elements, identity / role, learner characteristics, activities, collaboration, time and space, tools. C. learning experience and learning objectives Learning experience include well-organized content, learning outcomes/learning feedback, target learning content presentation/representation, individual and group learning activities, social interaction and so on, learning objectives include individual learning goals and group learning goals. D. scenario creation Mobile learning scenarios include the background, users, goals, events/activities and other factors, according to the different learning process can be divided into knowledge transfer and knowledge building. Simple knowledge transfer can be expressed as the classroom instant scenarios feedback, podcasting and field exploration, knowledge building can be implemented through collaborative[] problem-solving. E. Activity design Mobile learning needs learners can be active participated in the learning process, and independent learning abilities, skills and knowledge have been improved and have further motivation to learn. To design of effective mobile learning, in addition to fully consider the user's needs, but also follow the guide of teaching Design theory, create effective strategy to optimize mobile learning program, but the process is complex and requires some

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creativity. Designers need to design appropriate learning resources and appropriate scenarios to provide a suitable strategy for mobile learners. Learning activities go on between the learners and the learning environment, there are interactive activities with intended purpose, a complete learning activities, including learning objectives, learning topics, activities, processes, activities, rules, forms of organization, information resources and learning tools.

6 Conclusion With the development of the 3G wireless network communication technology, mobile learning will become a new learning mode, play a great role in the education filed and gradually become a hot spot in the future. This paper propose to use Ad Hoc to build mobile learning application network, and on this environment analysis and design according to the characteristics of the feature, scenario, activity of mobile learning, at finally learners can participate in learning at anytime, in anywhere. Acknowledgment. This study was supported by Scientific Research Fund of Zhejiang Provincial Education Department(Y200804422).

References [1] Keegan, D.: The future of learning: From E-learning to M-learning[DB/OL], http://learning.ericsson.net/mlearning2/project_one/thebook/ chapter1.html [2] An overview of e_Learning g Activity. 2003 EDUCAUSE. EDUCAUSE center for Applied Research (2003) [3] IETF MANET Working Group (May 2000), http://www.ietf.org/html.Charters/MANET-eharter.html [4] Uden, L.: Activity theory for designing mobile learning. Int. J. Mobile Learning and Organisation 1(1), 81–103 (2007) [5] Spool, J.M.: Web site usability: a designer’s guide. Academic Press, USA (1997) [6] Kuku lska-Hulme, A., Traxler, J. (eds.): Mobile Learning: A Handbook for Educators and Trainers. Routledge, London (2005) [7] Sinha Roy, N.L., Scheepers, H., Kendall, E., Saliba, A.: A Comprehensive Model Incorporating Mobile Context to Design for Mobile Use [2007-7-5], http://www.chi-sa.org.za/CHISA2006/Presentations/Roy.ppt

SET: A Conceptual Framework for Designing Scaffolds in Support of Mathematics Problem Solving in One-to-One Learning Environment Wang Lina, Chen Ling, and Kang Cui School of Education Technology, Beijing Normal University, Beijing, China [email protected]

Abstract. By reviewing and analyzing the researches on mathematics problem solving, this paper designs SET, a conceptual framework for designing scaffolds effectively in one-to-one learning environment. It aims at supporting pupils on solving mathematics problems successfully in the guidance of SET. Keywords: scaffold, problem solving, one-to-one learing environment.

1 Introduction Problem solving has an important role in the study of mathematics. A primary goal of mathematics learning and teaching is to develop students’ abilities to solve complex and realistic mathematics problems, such as the descriptions listed in NCTM and Mathematics Curriculum Standards for students in China. Many mathematics teachers have such puzzles, “Why are some students able to successfully compute answers for arithmetic problems, although they are unable to solve word problems that require using the same basic arithmetic computations?” It’s very popular in the process of mathematics learning and makes teacher confused very much. Exactly, problem solving is an activity, which provides students with opportunities to construct and experience the power of mathematics. Generally, it’s not easy for the students, especially for the pupils, to solve the mathematics problems. In the process of problem solving, pupils would encounter different kinds of difficulties, such as incorrect and improper problems understanding, problem representation and knowledge applying ( Anand & Ross, 1987; Montague & Bos, 1990). But, how can we do in front of this? How can we help pupils become successful problems solvers? One of the most significant barriers to successful problem solving stems from the lack of skilled facilitators to support learners (Hmelo-Silver, 2004). As the development of information technology, one-to-one learning environment has become popular in schools, and brings new opportunities for improving the teaching and learning. One-to-one learning environment means that, every student in a class has a learning device to participate in learning activities, such as laptop, mobile phone and hand-on device (Chan, Roschelle, His, Kinshuk, & Brown, 2006). Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 59–65, 2011. © Springer-Verlag Berlin Heidelberg 2011

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One-to-one learning environment makes new ways of learning (such as embed learning, exploration learning, engaged learning, experience learning, excitement learning, effective learning and empowerment learning) come true (Yu S. Q. & Dong, 2009). These are mainly resulted by: • • • • • •

availability of knowledge and support (anytime, anywhere) multimedia learning resources and all kinds of software (e.g., Graphing Calculator) flexible learning support can fit for personal needs timely feedback to the process of learning cognitive conservation of what have done and what have got, so give the chance for teachers to guide and practice more effectively collaborative and authentic nature of the learning environment

In this study, we attempt to answer the questions aimed to formulate some guidelines for designing effective scaffolds of primary mathematical problem solving, taking full use of one-to-one learning environment.

2 Advantages of Scaffods in One-to-One Learing Environment for Mathematics Problem Solving It is nearly 30 years since Wood, Bruner and Ross introduced the idea of ‘scaffold’ to represent the way of students’ learning that can be supported. The notion of scaffold comes from the socio-constructivist’ model of learning (Vygotsky, 1978; Wertsch, Mcnamee, McLare & Budwig, 1980) in which learning is believed to occur in the context of social interactions in which a more knowledgeable person guides a learner’s emerging understanding. Scaffold can be defined as tools, strategies, or guides that support students in gaining higher levels of understanding that would be beyond their reach without this type of guidance (Jackson et al., 1996; Saye & Brush, 2002). The theory of scaffold should successfully predict for any given learner and any given task what forms of support provided by what agents and designed artifacts would suffice for enabling that learner to perform at a desirable level of proficiency on that task, which is known to be unachievable without the scaffold. Therefore, it’s important to provide effective scaffold when the learner has difficulties. Nevertheless, researches in this area, while limited, reveal some important findings regarding the content, the forms and the functions in different areas. A study of sixth graders indicated that achievement was enhanced for students in a modeling condition when compared to students in no such conditions (Pedersen & Liu, 2003). Many researches have suggested that scaffolds may enhance inquiry and performance, especially when students are required to access and use them (Simons & Klein, 2007). Scaffolds can appear in multiple forms, which can be classified as either soft or hard scaffolds (Saye, Brush, 2002). Soft scaffolds are dynamic and refer to the domain of teacher actions in support of learners’ efforts at the moment of when a learner has a specific need (Berk & Winsler, 1995, Roehler & Cantlon, 1997, Saye & Brush, 2002). Hard scaffolds are static supports that can be developed in advance based on anticipated or typical learner difficulties associated with a task (Saye & Brush, 2002). There two kinds of scaffolds have been found to support learners in a variety of ways.

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One-to one learning environment make it possible for the students to get soft scaffolds and hard scaffolds in the process of problem solving, to take advantages of both of the kinds. However, in the area of mathematics problem solving of pupils, what are the effective scaffold? How can scaffolds support pupils solve the mathematics problem successfully? And how can we make the most use of scaffolds? Therefore, the purpose of this paper is to present an alternative approach addressing different aspects of scaffolds during the process in the mathematics problem solving. Instead of focusing on the exact form of the scaffolds, it brings forward a framework, that is SET, suggesting many dimensions need to be considered and to be combined to influence how to design effective scaffolds for pupils in the process of mathematics problem solving, in the one-to-one learning environment.

3 Design Parameters in the Framework for Designing Scaffolds of Problem Solving There are a number of ways to design scaffolds that influence the process and outcomes of learning, which differ in their target learners, learning content and the strategies they employed. There are some specific reasons to design the SET framework, consisted of (1) the process of the pupils’ mathematics problem solving, (2) the difficulties pupils facing with, (3) the goal of the activity and (4) the learning environment (see Figure 1).

Fig. 1. Design parameters of SET

A. The Process of the Pupils’ Mathematics Problem Solving There are many different descriptions of the process of problem solving. Polya (1957) described the stages of problem solving as (1) understanding the problem, (2) making a plan, (3) carrying out the plan, (4) looking back. Mayer (1987) also divided it into four stages, respectively as (1) transferring, (2) integrating, (3) planning and (4) executing. Besides, there also exist many other descriptions, such as five stages and six stages. In this research, we divide it into three stages as (1) awareness, (2) solving and (3) generalizing, respectively pointing to the before, the middle and the after stages of the problem solving. The middle stage is the most important, which can be subdivided into transfer, integrate, plan and execute.

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B. The Difficulties Pupils Facing With There exist many differences in diverse areas, of students from different ages. In this study, we concentrate on pupils to solve mathematics problems. Although it is true that different students have different kinds of difficulties, there also have some commonplaces. Relevant researches reveal that pupil have difficulties in the process of mathematics problem solving, such as incorrect and improper problems understanding, problem representation and knowledge applying. C. The Goal of the Activity Some findings reveal that pupil can solve the mathematics problem correctly. However, they cannot realize how they solve it and cannot transfer this successful experience into another. So, to solve the problem successfully is not the goal of the activity, understanding of the problems, and let the pupil know how to solve it, .are the more important keys. Thinking development as the core of the mathematics learning still cannot be neglected and minored in the process. D. The Learning Environment Learning environment has an important role in designing effective scaffolds. It decides the form, the kind, the number and flexibility, which are the most basic characters of scaffolds. In traditional learning environment, scaffolds could mainly depend on the guidance of teachers from what they talks in the class, and something written on the blackboard or on the papers. The function of such kinds of scaffolds is limited, mainly belonged to hard scaffolds. In our study, one-to-one learning environment is the key. It can bring some new chance for designing effective scaffolds, especially the soft scaffolds. Kinds of software and resources, as well as the method it uses should be the factors to be reviewed for improving pupils’ problem solving.

4 Review of Content of Set Considering the four parameters comprehensively, in this study, we put forward the framework SET - presenting an alternative approach addressing different aspects of scaffolds during the process in mathematics problem solving. The SET framework includes SUS, ECS and TUS (see Figure 2).

Fig. 2. The SET framework

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A. SUS Situation Understanding Scaffolds SUS is design to help pupil understand the situation or context of the mathematics problems. In case of the lack of knowledge and experience, pupils cannot understand the situation, which results in their disability of handling the word problem, even if they have mastered the relevant skills. Some types of problems, such as engineering, medicine, weather and transportation, are unfamiliar for pupils in their daily lives. In the other sides, pupils from different districts have different knowledge and experience background, which made it much more complicated. One-to-one learning environment can provide multimedia resources. Therefore, problems could be presented in the form of word, picture, flash, video and combination of two or more kinds of Medias. In that case, pupils could understand the situation more easily, facing with the way they need and the way they like. Furthermore, it can motivate pupils to solve the problem, too. In general, SUS need be considered from two dimensions. Firstly, it should be concerned the appropriate representation of mathematics problem in the kinds of multimedia. But it doesn’t mean that the problem is represented in a most proper form only, the reality is that, it needs to be designed in different forms as many as possible, in order to meet the needs of pupils from diverse levels and diverse backgrounds. In such a way, any pupil could find the one which is fit for him or her. Secondly, the flexibility and personality of the representations’ presentation are also worth paying attention to. It means that all of the representations are not presented overly, at the same time. Pupil should have the choice to select the one, most fit for his or her ability, or select the one which give a great challenge. So, the adaptabilities of presentation, such as how to represent, how to present and how to make the choice, are all the elements h the designers need to consider in this phase (See Table 1). Table 1. The Design Dimensions of SUS Mathematics Problems

The Way of Representation

SUS The Adaptability of Presentation

B. ECS (Extracting and Constructing Scaffolds) ECS is designed to help pupil extract correct knowledge for solving the problem, and construct their personal understanding of the problems, which stems from the opinions of constructivism (Von Glasersfeld, 1989; Steffe, Driver, Cobb, Spiro etc.). During the process of solving problems, pupils need to analysis the internal conditions of the mathematics problems. They need to translate their awareness of the problem into what they understand and what they can think about. At the same time, they need to integrate all the implicit and explicit information of problems, in order to make a reasonable plan to solve it, and then execute successfully. For example, 12 students are going to go boating. Actually, one boat only permits five students on it. So, the question is how many boats they should have for the 12 students?

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Obviously, this problem can be solved by division. The equation is 12÷5, and get the result of 2.4 accordingly. But, is 2.4 the right answer? So in the process of translate, integrate, plan and execute, we may realize that there cannot exists 2.4 boats. Therefore, the right answer should be 3 boats, which is calculated by mathematical knowledge, but also inferred from the basic natural knowledge. It is worth paying attention to that the four sub-processes are not linear, they are reorganized getting along with the problems solving once and again. During this process, different scaffolds can be designed to support pupil, including hint scaffold, distributed scaffold and connective scaffold. Hint scaffold means giving pupils some hints and clues of solving problems. It mainly based on the theory of cognition and meta-cognition. Such as, have you encountered such a kind of problem? Can the problem be solved by addition, subtraction or other methods? This scaffold of this kind can be presented in question, in table and picture etc. Distributed scaffold, based on the theory of distributed cognition, means supporting pupils’ problem solving at different places, in different forms. Distributed cognitive theory is a framework (not a method) that involves the coordination between individuals, artifacts and the environment (Edwin Hutchins, 1980). It emphasizes not only the interaction among the group of a social group, but also concern on the coordination of internal and external (material or environment) structure. The external artifacts play an important role in the view of distributed cognition. Therefore, distributed scaffold should be considered in the SET framework. For example, calculator can help pupils computing, therefore, they can concentrate on the method they think about; some cognitive tools, such as Geometry sketchpad, offer a hands-on learning experience that leads to a deeper understanding. They can boost mathematical understanding through dynamic interactive and visualization, and allow pupils to build and investigate their own constructions of the mathematical concepts and solving problems. Connective scaffold, based on the theory of Connectivism, is designed to help pupils connect relevant knowledge and sources in order to solve the problem, and make them more clear what other problems they can solve in the similar method. Oneto-one learning environment make it possible to present flexibly, and make the ideas of conncetivism come true. Connective scaffold can be designed in the form of words description, index introduction and mind mapping etc. C. TRS (Transferring and Reflecting Scaffolds) TRS is designed to help pupils understand how to solve the mathematics problems clearly and why they can do it. Thus, they can transfer into other problems of the same kinds, not only solving the one. In order to construct the knowledge effectively by solving problems, pupils need to supervise, retrospect the process of problems solving, and generalize some method, make more broader and flexible connection with the conception, the facts etc. In such a way, pupils can improve their meta-cognition by reflecting, and they can develop good senses of problems, and improve their abilities of solving problems accordingly. Generally, three elements need to be considered in this phase, respectively (1) retrospect and reflect, (2) generalize and summarize, (3) connect and transfer. Actually,

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not all the pupils would realize these and execute these sub-processes. So, we can design TRS to help them. For example, in the one-to-one learning environment, it can present a hint of asking pupils to reflect what they are doing, what they have done and what they have learned; BBS can be provided for them to write down how they solved the problems and what they get, etc. It can present more relevant knowledge and problems to challenge what they learn; and it can also permit the pupils to communication with each other and construct collaboratively.

5 Conclusion This paper has illustrated the SET framework, which describes an alternative approach addressing different aspects of scaffolds during the process of mathematics problem solving. It clarified the pedagogical functions that SET can support on pupils’ mathematics problems solving, including in the sub-processes of aware, solving and generalization. Nevertheless, it’s not enough to give the descriptions of the framework alone. What is the mark of successful SET and what kinds of scaffolds are effective? It needs more studies to exemplify the SET, and needs more studies to measure the effectiveness of scaffolds in multiple ways, such as, self-report measures of goal setting, task strategies, self-monitoring, self evaluation, time planning and management, help seeking, usefulness for each web-based tool, shifts in students’ conceptual understanding and declarative knowledge and the frequency of self-regulatory processes used learning (Azevedo et al., 2005).

References [1] Chan, T.W., Roschelle, J., His, S., et al.: One-to-one technology-enhanced learning: An opportunity for global research collaboration. Research and Practice in Technology Enhanced Learning, pp.3–29 (January 2006) [2] Jackson, S.L., Stratford, S.J., Krajcik, J., Soloway, E.: Making dynamic modeling accessible to pre-college science students. Interactive Learning Environments 4, 233–257 (1996) [3] Jonassen, D.H.: Using cognitive tools to represent problems. Journal of Research on Technology in Education 35(3), 362–382 (2003) [4] Mayer, R.E., Hegarty, M.: The process of understanding mathematical problems. The Nature of Mathematical Thinking, 29–53 (1996) [5] Pedersen, S., Liu, M.: The effects of modeling expert coginitive strategies during problembased learning. Journal of Educational Computing Research, 353–380 (2003) [6] Polya, G.: How to solve it. A new aspect of mathematical mehtod. Princeton University Press, Princeton (1957) [7] Saye, J.W., Brush, T.: Scaffolding critical reasoning about history and social issues in multimedia-supported learning environments. Educational Technology Research and Development 50(3), 77–96 (2002) [8] Shengquan, Y.: New insight into e-learning: Transformation of online education paradigm. Journal of Distance Education, 3–15 (March 2009) [9] Zhang, J., Sun, Y.: Constructing knowledge by problem solving –analysis of internal conditions. Educational Theory and Practice 21, 43–45 (2001)

Study on Multi-agent Based Simulation Process of Signaling Game in e-Commerce Qiuju Yin and Kun Zhi School of Management and Economics, Beijing Institute of Technology, Beijing, China [email protected]

Abstract. Although customer purchase behaviors are usually affected by the intrinsic qualities of the product, so are the influence of its social network and business behaviors. By applying signaling game theory and multi-agent modeling and simulation method, the paper focuses on the customer behaviors and the transaction process with consideration of the learning from customer’s social network and the impact of the business behavior. First, the process of signals transferred between business and customer is analyzed. Then, an anticipated product quality model based on its experience and its social network’s experience is given where quality uncertainty exists. Moreover, the utilities of both business and customer are established. Finally, a simulation process of signaling game between business and customer based on multi-agent is designed and a prototype of simulation is build, in order to offer a decision support for the transaction under asymmetric information. Keywords: multi-agent, signaling game, social network, customer learning, simulation.

1 Introduction Most researches on customer behaviors focus on its psychology character and take the view from business. However, the customer behaviors depend not only on the intrinsic qualities of the product, but also on the influence of its social network and business behaviors. So the former works have a difficulty to explain the cause of the behavior of customer. Moreover, most works are carried through economics or data mining methodology, the paper try to model and simulate the behaviors of customer and business during the transaction by applying complex adaptive system theory. In the paper, business, target customer and its social network customers are regarded as agents with decision capability. The paper will discuss how the customer judges the quality of the product with regarding the purchase experience of its own and its social network’s under the uncertainty of product quality. Meanwhile, signaling game theory is applied to analyze the signals transferred between business and customer in order to analyze the asymmetric information transferred between business and customer. The Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 66–72, 2011. © Springer-Verlag Berlin Heidelberg 2011

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utility functions of business and customer are put forward to evaluate the impact of signals and types (refer to product quality for business, and true purchase power for customer) on business and customer during the transaction. For validating the result of repeated transactions with asymmetric information and influence of customer’s social networks, a multi-agent simulation process of the transaction is designed.

2 Related Literature Reviews With the wide spread of Internet and e-Commerce, social network analysis becomes more and more popular in on-line network environment. Izquierdo used an agent-based model to illustrate how quality uncertainty by itself could lead to market failure, and showed that the spread of information through social networks could greatly mitigate this market failure[6]; Haibo Hu emphasized on the empirical studies on online friendship networks, online communities and online social media[1]; Pedro Domingos viewed market as a social network and modeled it as a Markov random field[3]. The idea of Muti-agent modeling and simulation is also applied in the analysis of customer behavior widely. Tao Zhang modeled the behavior of customer during the decision, pointing out that the intrinsic character of customer was the foundation under competitive environment through a large number of homogenous customers by multi-agent simulation[7]; Wenjuan Zhu researched the reverse selection problem due to asymmetric information, taking innovation level of vendor as signal to analyze the optimal decision of subcontractor [8]. The author is also engaging the research on the customer behavior under the asymmetric information. In the former works, the author obtained the behavior of business and customer under the optimal resource allocation by applying genetic algorithm, and Bayesian equilibrium by applying signaling game. Moreover, the author put forward the idea that business platform for signaling game facing to transaction decision support [2,4-5].

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Process of Signaling Game between Business and Customer

A. The Choose of Signals It is hypothesized that business transacts only one kind of product with customer, but with the uncertainty of quality of each product. Neither business nor customer knows about the true type of the other, but can infer them from the activity or information they have observed about the other. Meanwhile, both of the business and customer know that the other will infer its type from the activity, so they will use the activity and information diplomatically, so called signals. Here we choose two signals, each from both sides, to analyze the process of transaction: advertisement input (AI) for business and product searching input (PSI) for customer. Both of the two signals can be perceived by the signal receiver and can be

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taken as a base when it takes the next activity. At the same time, even same signals adopted by different types of signal sender also can bring different utilities. Assuming the product price keep constant during each transaction. Business transfers product’s quality through AI. Customer will buy when its anticipation about product quality is greater than the product price, otherwise, it will give up. So business try to increase its AI to attract customer, however, a high AI will decrease its utility. Customer transfers its true purchase power through PSI. High purchase power will increase the business utility in the future. When the business observed the PSI exceed a threshold value, it will give a more promotion for this transaction. So the customer tries to get the promotion by showing a high PSI, however, a high PSI will decrease its utility. B. Analysis on the Process of Signaling Game Basing on Learning Experience of Customer’s Social Networks The process of signaling game between business and customer, is a process that how business and customer send some tactic signals to the other during the transaction. The process can be divided into two parts, one is the process that customer will adjust its anticipation about the product quality basing on its own previous purchase, and the other process means customer will adjust its anticipation basing on its own previous purchase and its social networks purchase experience. In each transaction, the product quality of business is represented by λt , of which, t means the transaction time. The true purchase power of customer is represented by θ t , which reflect the upgrade of next purchase quantity. AI of business is represented by A , and PSI is represented by S . For each customer, the initial anticipation of product quality is 1. With the transaction’s going on, customer will adjust its anticipation of product quality basing on its last purchase experience and its social networks purchase experience[6]. ξ sef means the customer’s sensitiveness toward its own purchase experience, while ξ soc means the customer’s sensitiveness toward its social networks’ purchase experience. According to above, the anticipation of customer about the product quality in this transaction is as follows:

) ) ) ) q t +1 = qt + ξ sel ( q t − q t ) + ξ soc ( qt − qt ) )

Of which, qt +1 is the anticipation about product quality during the time, q t is the perceived product quality during the

t

(1)

t +1

transaction

)

transaction time, q t1 is the

anticipation about the product quality for the t transaction time, while q t is the average of the perceived product quality of customer’s social network for the t transaction time.

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C. Signaling Game Utility Functions of Business and Customer In the paper, business and customer are regarded as Agents with decision capability, who will adjust its next activity decision basing on the other’s activity. The decision will be taken according to the utility function. Here, we assume the utility function of business is product price plus the potential income brought by true purchase power, and minus API, while the utility function of customer is usage benefit minus product price, and minus PSI. If anticipated product quality is higher than product price, the transaction happens, and the utility of business is as follows, otherwise, 0 (AI is ignored in this case). ) If q t +1 ≥ p then

U t ( B, λt ,θ t ) = p + ϖ (θ t ) − A − S (λ t ) Otherwise U t ( B , λt , θ t ) = 0

)

Of which, qt +1 is the anticipation about product quality during the

(2)

t +1

transaction

ϖ (θ ) is the potential income that customer with θ true purchase power for business in the future, A means advertisement input. In the time, p means the product price,

reality, the influence of advertisement input should vary toward each transaction with the time going on, but in order to emphasis the learning process of business and customer, the influence is neglected here. If transaction happens, the utility function of customer is as follows, otherwise, 0 (PSI is ignored). ) If q t +1 ≥ p then

U t (C , λt , θ t ) = V (λt ) − p − W (θ t ) Otherwise U t (C , λt , θ t ) = 0

(3)

Of which, V (λt ) means the usage benefit when the product quality is λt , p is the product price, while W (θ t ) means PSI with a

θ t product searching input.

4 Agent-Based Simulation Process of Business-Customer Signaling Game Agent-based modeling and simulation (ABMS) method is applied to analyze the signaling game between business and customer. A.

Agent Design

In the paper, there are three kinds of agents: business agent, target customer agent and social network customer agent. Transactions happen between business and target customer, while social network customer’s purchase experience will influence the induction of target customer toward the product quality.

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Fig. 1. Simulation Process of agent-based signaling game between business and customer

B.

Simulation Process of Agent-Based Signaling Game

Netlogo platform is used to emulate the process, and the simulation process is as follows: (1) Agent generation: generate 10 business agents, 50 target customer agents, and some social network customer agents (the quantity is random). (2) Set the transaction times.

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(3) Initiate agents attribution: initiate the Types of each agent, the initial utility of business is 1, the initial anticipation of product quality is 1, the product price is 1, and initiate the random linkage among target customer and its social network customers. (4) Business transact with target customer randomly. (5) Business Agent choose its AI level, and target customer Agent choose its PSI level. (6) Target Agent judge the transaction situation, that is, anticipate the product quality basing on the perceived AI of business, its own purchase experience, and its social network’s purchase experience. If the anticipation is higher than the product price, then purchase the product, otherwise, give up the current transaction. (7) Calculate the utilities of each business and customer basing on formula (2) and (3). (8) If transaction happens, then modify the anticipation about product quality. (9) Return to (5) for a new transaction, till the transaction times. The detail process chart is as Figure 1. The simulation process above erects the activity rules of business and customer during the transaction. C. Simulation According to the design of the simulation process, we develop a prototype of agent-based signaling game between business and customer, as follows: In the prototype, the business agent, the target business and the utilities of business and customer can be shown with the transaction going on.

Fig. 2. Simulation prototype of agent-based signaling game between business and customer

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Conclusions

Comparing with the former researches, the paper analyzes the transaction process between business and customer with its social network, and applies signaling game theory to discern the signals transferred which will affect the utilities of business and customer. In order to obtain the utilities after repeated transactions of different TYPES of business and customer, a simulation process of agent-based business-customer signaling game is put forward. The paper is a part of the study on business-customer signaling game, the following work will focus on the realization of simulation, the validation of utility functions, and the analysis of simulation result, in order to obtain the equilibrium signals after multiple transactions, and finally can offer a decision support method to judge the true TYPE of business and customer according to their signals. Acknowledgment. Thank for the support of National Nature Science Foundation of China grants 70802008.

References [1] Hu, H., Wang, K.: Analysis of online social networks based on complex network theory. Complex Systems and Complexity Science (2), 1214 (2008) [2] Yang, H., Yin, Q.: A Study of Multi-Stage Signaling Game Modeling in Customer Relationship Management (CRM). Transactions of Beijing Institute of Technology (2), 185–188 (2008) [3] Domingos, P., Richardson, M.: Mining the Network Value of Customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, USA, pp. 57–66 (2001) [4] Yin, Q., Gan, R.: Design of business platform for signaling game facing to transaction decision support. Computer Engineering and Applications 43(10), 241–243 (2007) [5] Yin, Q., Yan, Z.: Comparative study of business–customer signaling game and its genetic algorithm. Computer Integrated Manufacturing Systems (6), 841–846 (2005) [6] Izquierdo, S.S., Izquierdo, L.R.: The impact of quality uncertainty without asymmetric information on market efficiency. Journal of Business Research 60, 858–867 (2007) [7] Zhang, T., Zhang, D.: Agent-based simulation of consumer purchase decision-making and the decoy effect. Journal of Business Research, 912–922 (August 2007) [8] Zhu, W., Zhang, C.:The model of signaling games for the to evaluate its vender’s abilities. In; 6th International Conference on Service Systems and Service Management, pp. 314–317 (2009)

Research on Estimation of Nanoparticles Volumes on Rough Surface Yichen Song1 and Yu Song2 1

Department of Psychology, Beijing Normal University Beijing, China 2 School of Control and Computer Engineering, North China Electric Power University Baoding, Hebei Province, China [email protected]

Abstract. By using the data of nanoparticles in the area of 2μm×2μm from Atomic Force Microscope (AFM), computer automatic identification method of nanoparticles, which considers the complex situation, including normal, rupture and peak cluster situation, has been researched. The total volume of nanoparticles can be gained by traveling throughout the matrix to get the base and height of every nanoparticle. Then we give a method to inspect the accuracy of the result, which differs from the true value by 1.82E-20 m3 and is acceptable. This paper also evaluates the advantages and disadvantages of the method and discusses the possible improvement ways. In the research we have used the following tools: MATLAB, EXCEL, Windows brushes and SPSS. Keywords: nanoparticles, volume estimation, rough surface, accuracy inspection.

1 Introduction Nano materials[1] exhibit excellent properties and nowadays more and more researchers are focusing on the test analysis and related theory about nano materials[2,3]. As nanoparticles are at the transitional region between the microscopic world to the macroscopic world, people could only observe them by electron microscopy at high magnification. With the improvement of both theories and methods, however, recently more and more special qualities of nanoparticles have been revealed thanking to the appearance of Scanning Tunneling Microscope (STM) [4], AFM[4] and so forth, paving the road for the nano science and technology. Among them AFM is one of the most important tools to characterize the nanoparticles[4,5]. It has the high resolution at atomic scale and can offer 3D images of real time and real space, so that much qualitative and quantitative information about nanoparticles physical properties can be gained. Fig. 1 is an AFM image of a part of macromolecule material surface[6], according to which the surface is not smooth but has rise and fall in nanoscale. The protruding parts shown in the figure are the nanoparticles formed on the macromolecule material surface. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 73–80, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 Problem Statement and Analysis The peak-like things in Fig. 1 should be departed into two parts. The parts that are equal to or higher than 5nm are nanoparticles, while the ones that lower than 5nm are the crystalline structure of macromolecule material itself, which do not participate in calculation. Because the obvious deboss parts are formed from the macromolecule material surface rupture, the base of a certain particle should be the material surface, not the bottom of the rupture yet.

Fig. 1. 3D AFM image of nanoparticles on macromolecule material surface

A. Research Hypothesis 1) There is no effect on the crystalline structure of the material when nanoparticles attach on it. 2) Data from AFM is gained by measuring particles from the same plane. 3) The part where nanoparticles contact with macromolecule material is a plane. 4) 512×512 discrete points are uniformly distributed on the 2μm×2μm surface of the material, for it is the upper limit of the accuracy of AFM. 5) Every point represents a parallellepipedum whose base is a square. All the squares have equal areas, which is (2×10(-6))2÷5122m2. B. Analysis of Base Surface This research uses AFM image of the nanoparticles on the macromolecule material surface. From the 3D image we could see the crystalline structure and nanoparticle structure, whose height is in the interval [-50, 50] nm. Thus we can assume that the [0, 50] nm is the protruding part of the macromolecule material while the [-50, 0] nm is the debossing part. Based on the hypothesis we definite the rupture situation as follows: if the height of a certain point is in the interval [-50, 0] nm, then it is a rupture.

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3 Model and Analysis A. Definition of Terms 1) Protuberance: in a single line of the matrix, if the tend of a certain piece of height data at first monotonously increases then monotonously decreases, the piece of data represents a protuberance. 2) Nanoparticle: among all the protuberance, if a certain protuberance has at least one point that equal to or higher than 5nm, it is a nanoparticle. 3) Peak cluster: a series of continuous nanoparticles shown in Fig. 2 is a peak cluster. Their base is the common base of these continuous nanoparticles, for the nadir during the series data is higher than 5nm. The continuous protuberances shown in Fig. 3 are not a peak cluster, for the nadir during the series is lower than 5nm. 4) Fracture phenomena: the parts that are lower than 5nm are rupture, which cannot be calculated when we consider about the base. According to the definitions above, we obtain the solution method to calculate the different kinds of nanoparticle base lines: 5) A single particle: a single particle can have one and only one protuberance, on which at least one point is higher than 5nm, and the start point and end point of the protuberance are both between 0nm and 5nm, which means that the particle has neither peak clusters nor ruptures. Then the line between the start and end point is the base of the particle. 6) Rupture: if a particle stands beside a rupture, step over the rupture and find the nearest minimum, whose height is between 0nm and 5nm, of the particle. Then the line between the minimums on two sides of the particle is the base. 7) Start and end point of every line in the matrix: considering the complex situation, we definite the point as follows: if the start or end point of a certain line is the key point to help finding the base of a particle, then the height of the point is 0, no matter what the real height is.

Fig. 2. Peak cluster

Fig. 3. Non peak cluster

To sum up, all the methods to find the different kinds of base can be describe as a single one: as for any nanoparticle, travel from the point that the increasing side of the particle intersects with the 5nm high horizon line to the left, until meeting the first minimum whose height is between 0nm and 5nm, which is the left endpoint; travel

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from the point that the decreasing side of the particle intersects with the 5nm high horizon line to the right, until meeting the first minimum whose height is between 0nm and 5nm, which is the right endpoint. The line between the two endpoints is the base of the particle. B. Description of Algorithm Divide the data of 2μm×2μm area in to 512 sub-matrix, each of which is 1×512. Scan each line respectively to find the base of every particle, according to the principles above. Then we gain the height of every point. By doing integral calculation twice, which is helpful to improve the accuracy of the result, the total volume of the nanoparticles is obtained. First integral calculation process will use the original data directly, which means to scan the lines and sum the results of the lines up; second process will use the transposed matrix and do the same calculation. The last result of total volume is the average of results from the two processes. Specific algorithm is as follows: 1) Finding Bases: Set the value in line 1 and line 512 into 0, and get the new matrix C. Scan each line respectively. Part of the code is as follows: for i=1:512 for j=1:512 ……(the algorithm of finding bases) end end In order to show the find-base algorithm in detail, data of line i is given as an example. Data in this line are from 512 points, thus scan the data from j=0 to j=512. When a point C(I, j) 5 is met, start to find the start point (BS, C(i, BS)) and the end point (BE, C(i, BE)), which are used to find the base of a particle. The principles of finding points are as follows: From point j, scan the data in interval [0, j] and [j, 512] respectively. BS is sure to be in [0, j] while BE is in [j, 512].

≥

∈ ∈

Finding BS: set up a simulation pointer bs (initial value is j, bs [0, j]) to help scanning. It will find the first minimum C(i,bs), and make sure that C(i,bs) [0,5]. Namely when C(i,bs) C(i,bs-1) and (i,bs) C(i,bs+1) and C(i,bs) [0,5], we get the start point which is used to find the base surface. When the condition above is true, make BS=bs, otherwise bs=bs-1 and continue to scan.

≤

＜

∈

∈

Finding BE: set up a simulation pointer be (initial value is j, be [j,512]) to help scanning. It will find the first minimum C(i,be), and make sure that C(i,be) [0,5]. Namely when C(i,be) C(i,be-1) and (i,be) C(i,be+1) and C(i,be) [0,5], we get the end point which is used to find the base surface. When the condition above is true, make BE=be, otherwise be=be+1 and continue to scan. When BE is found, make j=BE, which is the start point of the scan process of next particle, in case of redundant scanning.

＜

≤

∈

∈

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2) Finding Height Set up a line y=h+m(i-BS) between the start point (BS, C(i, BS)) and end point (BE, C(i, BE)) found above, and make t=BS. t will travel through the interval [BS, BE] to judge. When C(i, t) y, get the corresponding height ah, then add it to the total height H. At this time, record the height of the nanoparticle, E(i, t) = C(i, t) - y.

≥

3) Volume Calculation When i have scanned over the interval [1, 512], we get the final total height H. Then the total volume of the nanoparticle is obtained: V=H*S*10-9. We use matrix E to create a 3D fit curve surface image of nano structure, illustrating that the method of this paper is correct to recognize the nano structure. When calculating the volume, we use the original data first, and get V1=2.1602×10(-19)m3. Then we use Excel to make the matrix transposed and get 3 V2=2.2877×10(-19)m . The final result is the average of the two volume above, V3 = (V1+V2) ÷ 2 = 2.22395×10(-19)m3. C. Accuracy Analysis In order to inspect the accuracy of the calculating model, we set up another model. This model is based on one of our original hypothesis that when finding the base, we assume that if an edge point is a key point to find the base line of a certain particle, then make it 0. Obviously, the 0 point can only be appeared in the first column and the last column. We put forward the hypothesis to simplify the calculating process, however it doesn’t reflect the real situation. This hypothesis offers a source of error when apply the model into practical problem. The boundary value of the model is simplified into a 8×8 random numerical table (see Tab.I). If we divide the table equally into four table, every small table would have two columns whose value is 0. Considering the boundary-value-is-zero hypothesis offers the major source of error, the proportion of numbers whose value is 0 will be a good index to judge the accuracy of the model. The formula of calculating the proportion of boundary points is: Table 1. An example of numerical table 0

37.91058

-17.6023

0

0

-4.83169

33.56998

0

0

-39.9271

-33.0154

0

0

-6.03968

19.92514

0

0

-7.93513

-14.6633

0

0

-34.1218

38.52206

0

0

3.590639

-12.0744

0

0

33.36693

-20.6414

0

0

-19.9891

-6.11478

0

0

-38.9826

34.31824

0

0

45.83357

25.75695

0

0

-49.0712

15.20839

0

0

29.47905

9.31957

0

0

3.757001

-13.9422

0

0

-19.6366

-45.3587

0

0

-39.5698

6.141017

0

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2a 2 = (1) a2 a This is a decreasing function in (0, ∞ ), illustrating that the more points a matrix

ηz =

has, the more accurate the result is. The proportion of boundary points of 9 matrix from 2×2 to

29 × 29 is shown in Tab.2. Table 2. proportion of boundary points in different matrixes Number of lines Proportion Number of lines Proportion

2 1 64 0.03125

4 0.5 128 0.015625

8 16 32 0.25 0.125 0.0625 256 512 … 0.0078125 0.00390625 …

In order to study the error, we use quartered method to divide the original matrix into 4 small matrixes continuously. Then calculate the nanoparticle volume by our model and add all the volume of small matrix together. At last we get a series of volumes of the 512×512 matrix under different division. Obviously, every division increases (512/2num-1)×512 boundary points, namely the more we divide the matrix, the greater the error. The original 512×512 matrix equals to the matrix that divide the original one into one part, and assume that the particle volume of it is the real value. All the result under different division will be compared with the real value. Create a curve by using the difference of results and real value, so that we can predict the result when no division is occurred. Compare the result and value by using statistical principle, and get the accuracy and confidence level. D.

Model Operation and Result

1) Write a program that can divide the given matrix into (512/2num)2 small matrixes and add the volumes together. Scan the top left corner of every small matrix in order to locate them. Repeat twice, including line repeat and column repeat, to achieve this goal. The distance between two repeats is 2num. Use our model to calculate those 2num×2num matrixes and add them up, so that we get the total volume Vnum . This process is done by MATLAB[7]. 2) After getting the total volume of the original matrix, we can get the volume of small matrix. The formula is as follows:

v num =

Vnum ⎛ 512 ⎞ ⎜ num ⎟ ⎝2 ⎠

2

(2)

This is the average value of nanoparticle volume of each small matrix, which can be considered to be the calculated value of small matrix.

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3) The total volume of 512×512 matrix is:

v' =

512 ⎛ 512 ⎞ ⎜ num ⎟ ⎝2 ⎠

2

=

2 2 num 512

(3)

4) Calculate the difference d between the two volumes above, and the ratio

w

of d and calculated value. All the calculated results in the process above are shown in Tab.3. 5) Using n as the horizontal axis and w as vertical axis, we make a scatterplot chart in SPSS[8] and create a regression curve. The function of the curve is as follows:

w = 0.0843n 0.4389

(4)

We test the results to see that coefficient (t=34.694, p<0.01) and exponent (t=17.191, p<0.01) are both significant. Regression effect test shows that the regression equation is significant (F=1204, p<0.01). Table 3. All the calculated results in the process

n Vnum vnum ′ vnum d

w

4

16

64

256

1024

4096

16384

1.88 E-19 4.7E -20 5.4E -20 6.97 E-21 0.14 806 5

1.66E19 1.03E20 1.35E20 3.15E21

1.41E19 2.2E21 3.38E21 1.17E21

1.13E -19 4.4E22 8.44E -22 4.04E -22

7.75 E-20 7.57 E-23 2.11 E-22 1.35 E-22

4.22 E-20 1.03 E-23 5.27 E-23 4.24 E-23

1.56E20 9.5E25 1.32E23 1.22E23

0.3045 47

0.5310 79

0.916 77

1.78 8542

4.12 3449

12.875 01

6) The function above is based on the hypothesis that the result is accurate. However, as we can see from Table 2, there is still difference on proportion of 0.00390625. Thus we can approximately predict the fluctuation range of the accuracy of the result of a 512×512 matrix. At this time n =1, through the function we get w =0.0843. This is the proportion of difference in calculated value. Do the inverse operation of above process:

d = w ×V

(5)

Get d =1.82E-20, which can be considered the difference between calculated result and predicted real value in this paper. According to Table 3, the smaller the matrix, the greater the difference, but they are all positive numbers, namely calculated value through the model is always a little smaller than the real value. In our calculation, the calculated result is 1.82E-20m3 less, which is caused by the proportion of 0.00390625 points in the whole matrix.

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4 Conclusions This paper offers a model to calculate the volume of nanoparticles on macromolecule material surface, especially considering the uniqueness of each single particle. Finding correct base line leads to an accurate result. Also, this model well adapts to practical situation. By using computer program, we can get results rapidly by changing matrixes to suitable size. The accuracy test model is based on the error analysis of volume calculation model. High pertinence helps to increase the accuracy of the model. However, as there is another hypothesis in the paper that every point represents a square column, the accuracy still has space to improve, because in fact not all the point stands a square column and on the surface there shouldn’t be scattered points yet. This model has the same idea with integral calculus, but the latter also has limitation. In addition, the space among particles is not taken into account, which can also be considered in the future. In future study, we will develop corrector formula according to the accuracy model to optimize the calculation model.

References [1] Yu, J.-S., Yuan, Z.-L., Xie, G.-Z., Jiang, Y.-D.: Preparation, Properties, and Applications of Low-Dimensional Molecular Organic Nanomaterials. Journal of Electronic Science and Technology 8(1), 3–9 (2010) [2] Ting, A., Feng-Qi, Z., Ping-Fei, Z.: Progresses in the Preparation Study of Energetic Nanomaterials. Nanoscience & Nanotechnology 6, 60–67 (2009) [3] Zhou, Z.: First-principles studies and design of one-dimensional nanomaterials. In: 10th Chinese National Conference on Computers and Applied Chemistry, Hang Zhou, pp. 284– 285 (2009) [4] Xian, W.: Application on Surface of Nanometer Material about Scanning Microscope and Electron Diffraction Microscope. China Science And Technology Information (1), 52– 53,56 (2010) [5] Chun-ling, W., Bang-yan, Y.: A. Novel Technique to Manufacture Nanostructured Materials by Machining. Science Technology and Engineering (19) (2009) [6] Xie, J.-M., Zhu, Y., Wang, X.-N., Liu, Z.-L., Sun, X., Lu, Z.-H.: The analysis of DNAbinding protein’ s AFM image. Computers and Applied Chemistry (3), 181–185 (2003) [7] Wu, W.-g. (ed.): MATLAB & Excel Engineering Calculation. Tsinghua University Press, Beijing (2009) [8] Zhu, D., Zhu, X.-H.: Psychological Statistics and SPSS Application. East China Normal University Press, Shanghai (2009)

Main Factors Affecting the Adoption and Diffusion of Web Service Technology Standards Caimei Hu College of Economic and Management, Heilongjiang Institute of Science and Technology, Harbin, Heilongjiang Province, China [email protected]

Abstract. After the PC and the Internet technology, Web Service technology is touted as the third IT technology revolution. Although the application range of Web Service is expanding in the areas of e-business and e-government. But the adoption of Web Service standard is still very slow. This paper introduces the Web Service technology standard system, based on the innovation diffusion analysis framework of Technology - Organization - Environment (TOE) analyses the main factors affecting the adoption and diffusion of Web Service technology standards. Keywords: Web Service, TOE Analyses, Technology Standard, Technology Adoption, Technology Diffusion.

1 Introduction Web Service technology is considered to be the third IT technology revolution after PC and the Internet technology, and expected to play a pivotal role in information systems (IS) development and integration [1] [2]. But there is no strict definition for Web Service till now. There are several definitions to Web Service by different organizations. The world standard organization World Wide Web Consortium (W3C) defined Web Service as software application program identify via URL, the interface and binding of it can be defined, described and found by XML document which make the message deliver method based on Internet protocol can interact with other application programs directly. Microsoft argues that Web Service is logical unit which supports data and service to other applications and the applications can access Web Service via standard web protocol and data formats. IBM considers Web Service as modularized application program; it can be published and allocated, and called from any position of Web. It can execute any function from simple request to complicated business process. With the development of Internet and the mature of electronic commerce, more enterprises adopt the electronic commerce application based on Web. But the methods which they deal with the relationships between purchaser and suppliers are different. Currently, the main problem of electronic commerce is how to connect these Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 81–87, 2011. © Springer-Verlag Berlin Heidelberg 2011

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applications through lower cost and link the business application systems of enterprise in a wide range. Web Service can solve this problem, but unfortunately it has not been used widespread yet. A survey developed in 2005 showed that 68% of respondents stated that their deployment of Web services are on hold until the various standards bodies reach some agreement on how Web services will actually work. Just 5% of them stated that their enterprise has large-scale Web services-based SOA deployment sites. Most firms are still in the early stages of adoption and have only a few (<5), if any, Web services in production [3] [4]. So it is important for the development of electronic commerce and electronic government to analyze the factors affecting the diffusion of Web Service technology standards.

2 Web Service Technology Standards System Web Services platform is a set of standard, it defines how the application programs realize the interoperability on Web. People can write on Web Service with any language and platform which they like, as long as they can be accessed through Web Services standard. Web Services platform mainly use three technologies as follow: A. XML and XSD Extensible Markup Language (XML) is a basic format used to express data on Web Service platform; its major advantage is independence on any platform and firm. Independence is more important than technology superiority. Because software firm will not choose technology that developed by its competitors. Although XML solved the problem of data expressing, it did not define a set of standard data types. W3C established a set of standard XML Schema XSD to solve this problem. It defines a set of standard data types, and gives out a language to extend these data types.

（ ）

B. Simple Object Access Protocol (SOAP) SOAP provides a standard RPC method to call the Web Service, and defines the format of soap messages and how to use the soap via the http protocol. C. Web services Definition Language (WSDL) WSDL is a language based on XML which used to describe the function, parameters and return values of the web. Because it is based on XML, WSDL can be read by both machine and human.

3 Theoretical Basis The main theoretical foundation of researches on standard diffusion is the innovation diffusion theory. The traditional innovation diffusion theory mainly studies the impact of individuals’ communication in a large scale social system on the diffusion of technology form the viewpoint of communication. And consider that the diffusion and

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adoption rate of technology are influenced by relative advantage, compatibility, complexity, experimental and visibility. Tornatzky and Fleischer (2005) extended the traditional innovation diffusion model based on the criticize to it, they put forward a more comprehensive research frame, which is the T OE(technology, organization, environment) model used to the analysis of technology innovation diffusion [5]. Kwon, Zmud (1987) and Premkumar (1997) analyzed the factors that affect organizations to adopt EDI technology from 5 aspects which include innovation, environment, organization, task and organization characteristics, and the result showed that the influence of innovation, environment and the organization is great [6] [7]. DePietro etal. (1990) also studied the impact of innovation, environment and organization on the adoption of innovative technology [8]. Therefore, this paper analyses the factors that affect the diffusion of Web Service technology standards under the frame of technology, environmental organizations.

4 Analysis on the Factors Affecting the Adoption and Diffusion of Web Service Technology Standards A. Influences of Technology Standards a) Advantages of Web Service technology standards From the point of technology, Web Service realizes the integration of application programs which enables the business process within the company more automatic. A company can exposure its key business application to particular suppliers and customers through Web Service. Because Web Service runs on the internet which can realize anywhere, the running cost is relatively low. The greatest advantage to realize B2B integration through Web Service is that it can achieve interoperability conveniently. Just change the business logical into Web Service, any specified partners can call the business logic, and no matter what system platforms and development languages they use. It has greatly reduced the time and cost of B2B integration and enables many medium and small enterprises to realize B2B integration. From the point of economic, Web Service represents the important development of technology, enterprises have obtained huge benefits from the internet technology, and Web Service can expand it. Gartner Group (2002) pointed out that Web Services is the new way people think about how to obtain and provide business services, it brought huge benefits including cut costs, shorten system installation time, improve enterprise agility and flexibility. Web Services can be conducive to enhance the efficiency, decision quality and speed of enterprises. By now, only the web services can integrate the data spread in various systems and Information Island, and enables the manager access to these data in real time and also enables partners access to related information and services directly which in order to optimize its value chain process. Web services technology makes it easy to the integration between people, processes and information, which shorten the business process period and enhance the response speed. At the same time, it can provide important data to more users in real time and improve the flexibility and agility of enterprises.

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b) The complexity of Web Service technology standards Complexity is the relative difficulty refers to understanding and using a technology, which negativly related to the adoption level. Moore and Benbasat (1991) proved that complexity is an important factor in deciding whether to use of personal computer to the members of an organization. Dedrick and West (2003) proved that complexity is the consistent standard to the adoption of Linux operating system through a series interviews to the information system managers. Although Web Service technology standards are very complex, many organizations figure that they will adopt Web Service technology in time if it's needed. c) Knowledge barriers in the adoption of Web Service technology standards Web Service technology is a knowledge intensive technology. Generally speaking, knowledge intensive technology is complex, and the transfer of it between organizations is more difficult than traditional technology. For instance, an enterprise can not copy the application of a technology in other organizations simply in the adoption of a data warehouse technology, but it must be able to know how to integrate data from functional departments in the organization and take them into the decision making process, and it also must familiar with the information flow and its unique organization structure. All of these are the knowledge barriers that faced in the process of knowledge intensive technology adoption. These barriers can affect or delay the technology adoption process in enterprises. The knowledge barriers in the adoption process of Web Service technology standards can be divided into three levels. Firstly, knowledge barriers refer to technology. These barriers include knowledge about software and hardware infrastructure, technical characteristics, technology security, technology standards, unique business and so on. Secondly, knowledge barriers refer to project. These barriers include knowledge resources, project leader, and functional department participant which are needed in program development and application. For example, who lead the website construction project? Who is responsible to public the data to the website? What kind of outsourcing strategy should be selected? The answer to these questions should be based on the considering of its own organizational structure and culture. Thirdly, knowledge barriers refer to application. These barriers include the particular business goals of Web Service technology, the value of each technical feature to organization, the potential to integrate with existing IT projects, and the impact of technology on the current organizational structure and systems. The key point is ascertain the service focus of Web Service technology and its contribution to the organization operation [9]. d) Web Service Technology standards are still immature Some Web Service technology standards are still not mature. “Payload” vertical standard is one of the major challenges faced by the adoption of Web Service. Although Web Service technology is not depending on the payload based on XML, the lack of industrial standard of payload restraint the function of Web Service. Because it can not clarify which format fit to its potential partners. The security standards of Web Service are also not mature. Security problem is the first obstacle to the application of Web Service. Many organizations argue that security is not only a technical problem, but also a business problem. The early

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adopters of Web Service technology consider that the lack of mature security standards has negative impact on the compatibility. Global XML Web Services Architecture (GXA) platform includes many new standards in Web Service interactive area. The main objective of GXA is to define the grammar and semantics to the new family of specified Web Service agreements. These new agreements try to deliver the basic functions of SOAP and XML to the next generation of mutual adaptability. Although many big companies including Microsoft and IBM have done a lot of work to create new Web Service standards in the past few years, there's a long way in the future. In addition, current Web Service standards have not standardized the protection of message yet. In other word, there is no guarantee that information is not intercepted and tampered by third-party. B. Organizational Factors Some researches find that organization is the most prominent factor to the adoption of technology [10] [11]. The philosophy, information technology level, information technology infrastructure and financial support of organization have important impact to the adoption of Web Service technology. a) Technology capability of organization Whether adopt the technology is not only depend on the wishes but also depend on the capacity of organization. The adopter of technology should learn the necessary knowledge to obtain the capacity related to the technology combining with its own business characteristics. Enterprises should master appropriate programming language (eg. Java or C ) and related standards (mainly WSDL and SOAP) in the adoption process of Web Service. But these are not the main obstacles.

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b) Organization philosophy Beside technology capabilities, the change of philosophy is also an important challenge. Generally, the staffs who master Web Service technology are not willing to share related knowledge with others. This is not conducive to the improvement of the whole team. So organizations should strengthen internal knowledge management through construct learning organization. Knowledge management enables the knowledge of employee evolves to the knowledge of organization and team. For instance, encourage employee to share their good experience and skills through diary, execute job rotation and internal technology communications. c) Organization scale Organization scale also is an important factor impact on the adoption of Web Service technology standards. Usually, large enterprises have advantage compare with small ones in the adoption of new technology. Large enterprises command considerable funds, talents and R&D capacity, so they can realize scale economy quickly after the adoption of new technology. But the bureaucracy of large enterprise is more complex, it needs more time in decision-making. The bureaucracy of large enterprise is not conducive to adopt new technology. However, small enterprise is effective and more conducive to adopt new technology with the change of market.

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C. Environmental factor Environment is the dominant factor in the adoption of technology for enterprises [12]. Its main manifests as following: a) Industry concentration The adoption of Web Service technology is easier in the high concentration industries. For example, most enterprise has business relations with a few large enterprises in the car and financial sector which have high concentration. Under this condition, the adoption of new technology largely depends on the large enterprises. The adoption of Web Service technology is relative difficult in the low concentration industry. In these industries customers and suppliers are very small organizations whose IT resources are very limited. Therefore, although Web Service standard can bring huge benefits, it is difficult to persuade those small enterprises to adopt it. b) Stakeholder Here, stakeholders are individuals or organizations that influence the adoption of Web Service technology, which include customers, suppliers, IT suppliers and information technology supporting (IS) department of enterprises. Many B2B integration projects of small and medium enterprises are implemented by the strong demand of their main suppliers or customers. Because of the network externalities, the diffusion speed of a technology is positive correlated with the number of adopter. For example, Dollar Rent A Car, INC. attracted more customers and suppliers through establishing alliance based on standards, and gain competitive advantage by seamless integration. The IT suppliers usually are the early adopter of technology standards. IT companies such as Microsoft, IBM and Oracle participate in the standardization of Web Service technology, and adopt these standards in their software products and tools. IS department of organization is the main department influence the adoption of Web Service technology standards. Because of the steeper potential learning curve, IS department is usually refused to accept the new standards. But other departments might want to try these standards to obtain the necessary technology. c) Industry technical inertia Rogers (1983) divided the adopters of technology innovations into 5 types: innovators, early adopters, early majority, late majority, laggards. Many organizations are unwilling to abandon the technology that industry has used consistently, and will not be too radical. They would rather to be the late majority. Industry technical inertia is an important obstacle of technology adoption.

5 Conclusions According to a report of Internet Data Center (IDC), the world investment to the development of Web Services software was $11 billion in 2008, and this number was just $1.1 billion in 2003. IT department of many enterprises list Web Service technology into strategy agenda, in order to save costs and solve the integration problems. Many organizations also plan to invest on the Web Service technologies that meet future demand. The development of Web services technology is entering an

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important stage that higher level Web services technologies will be formed and adopted. There are many factors affect the adoption and diffusion of Web Service technology standards. Beside the further mature and complete of Web Service technology standards, the influence of organization and environment also could not be neglected.

References [1] Brown, M.: The Integration Report. EAI Journal, 29–30 (May 2003) [2] InfoWorld, InfoWorld Research Brief on Web Services Applications, InfoWorld (2002) [3] Adam, C.: From Web Services to SOA and Everything in Between: The Journey Begins (June 2005), http://www.webservices.org/index.php/ws/content/view/full/63 404 [4] Ciganek, A.P., Haines, M.N., William (Dave).: HasemanHorizontal and Vertical Factors Influencing the Adoption of Web Services. In: Proceedings of the 39th Hawaii International Conference on System Sciences, pp. 1–10 (2006) [5] Tomatzky, L.G., Fleischer, M.: The Processes of Technology Innovation, pp. 117–148. Lexington Books, Lexington Massachusetts (1990) [6] Kwon, T.H., Zmud, R.W.: Unifying the Fragmented Models of Information Systems Implementation: Critical Issues in Information Systems Research, pp. 252–257. John Wiley & Sons, Inc., New York (1987) [7] Premkumar, G., Ramamurthy, K., Crum, M.R.: Determinants of EDI Adoption in the Transportation Industry. European Journal of Information Systems 6(21), 107–121 (1997) [8] DePietro, R., Wiarda, E., Fleischer, M.: The Context for Change: Organization, Technology, and Environment, The Process of Technological Innovation, pp. 151–175. Lexington Books, Lexington Massachusetts (1990) [9] Nambisan, S., Wang, Y.-M.: Communications of The ACM 42(1), 97–101 (1999) [10] Iacovou, C.L., Benbasat, I., Dexter, A.S.: Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology. MIS Quarterly 19(4), 465–485 (1995) [11] Zhu, K., Kraemer, K.L., Xu, S., Dedrick, J.: Information Technology Payoff in EBusiness Environments: An International Perspective on Value Creation of E-Business in the Financial Services Industry. Journal of Management Information Systems 21(4), 17– 54 (2004) [12] Teo, H.H., Wei, K.K., Benbasat, I.: Predicting Intention to Adopt Interorganizational Linkages: An Institutional Perspective. MIS Quarterly 27(1), 19–49 (2003)

The Application of Software Maintainability Design in the Intelligent Warehouse Archives System Fei Yang Faculty of engineering and information, Zhuhai city polytechnic, Guangdong, China [email protected]

Abstract. Old archives in many places have been unable to meet the needs of the times, so have started building new archives. Therefore, it is necessary to develop a new intelligent warehouse archives system. Through detailed analysis and design, we designed an intelligent warehouse archives system. The intelligent warehouse archives system includes monitoring system and management system. Monitoring system mainly achieves archives of fire, security, warehouse temperature and humidity and so on, one-stop monitoring and management. The management system's function mainly has: archives resource management, monitoring management, administration. In order to improve software maintainability, throughout the software development process we considered software's maintainability each step, and enhanced software's maintainability as far as possible. This paper describes the system at the same time to describe the details of improve software maintainability by taking appropriate measures. Keywords: intelligent warehouse archives system, software maintainability, mode.

1 Introduction With the development of society, it is necessary to manage archives around the important traditional photographs, newspapers, magazines, books, important documents, files, related cultural relics and other information, but also manage important digital photos, images, audio, video and other new form of archives. And the services provided to the outside world more and more, both to give the relevant staff provide archives conveniently, but also some relevant exhibitions, publicity, meetings and training. For these, old archives in many places have been unable to meet the needs of the times, so have started building new archives. Therefore, it is necessary to develop a new intelligent warehouse archives system. Through detailed analysis and design, we designed an intelligent warehouse archives system. First, we introduce this system. System is mainly divided into two parts, one is the monitoring system based on the C/S mode, develops with VC, part is the management system based on B/S mode, develops with JAVA, use the SSH framework. Monitoring system mainly achieve security systems, fire protection systems, temperature and Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 88–92, 2011. © Springer-Verlag Berlin Heidelberg 2011

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humidity monitoring system, smart shelves system, notification system, and so on onestop monitoring, management and realization of the linkage between the various systems. For example, when a fire protection system detects events, in addition to control of the high pressure water mist of spray, at the same time notify system for sound and light alarm, telephone, SMS alerts, broadcast dispersal sound recording, notify the smart shelves into the ventilation system, and notify the entrance security control system to open the door. The B/S management system's function mainly has: archives resource management, monitoring management, administration. Archives resource management including electronic file, digital file gain, processing, approval, archiving, storage, borrowing, and query for use and so on; The monitoring management is mainly communicates with the monitoring system and examines archives each place safe condition, the fire condition and so on, may also carry on some simple control operation, may carry on the statistical analysis to the historical data and so on; The administration is only to the conference, the training, the checking attendance and so on carries on the management at present. We have developed some systems before, because the maintainability is too bad, cause customers often complain and the maintenance personnel are very painful, the maintain cost is very high. For example an OA system which does for some national capital committee, because the system requirement analysis does is not very well, therefore caused the user to set the new request or change the original request in afterward unceasingly. Simultaneously because system architecture design is not very reasonable, have problems frequently in the systems operation process. Moreover because the turnover lost the some codes and the documents, although for this project's maintenance, has arranged three maintenance personnel specially, but regarding the question solution speed and the effect, the user is very unsatisfied, the maintenance personnel are also very painful. In view of this, simultaneously we also want to do this time warehouse archives system a model project, so that will be able to continue more similar projects later, therefore we must do as far as possible this project maintainability. In order to improve software maintainability, throughout the software development process we considered software's maintainability each step, and enhanced software's maintainability as far as possible.

2 Development Plan and Development Platform In the Feasibility analysis stage, after we have determined the system development feasibility, when select development plan, also pays great attention to technology advancement, system portability, open, and so on. We chose to use C / S and B / S mixed mode to achieve the corresponding functions. Because the system must do with many hardware devices, moreover consider from the security angle that the monitoring aspect uses the C/S mode to realize is quite appropriate. Considered the archives related the service handle personnel are quite scattered, if uses C/S realizes completely, to the later distributed, the maintenance, the upgrade can have quite many troubles, therefore regarding the management, the service handle aspect to realize with the B/S mode is quite appropriate. Had determined realizes the system by C/S and the B/S mixed mode, we consider are carry on the C/S mode monitoring system's develop with what platform , and B/S mode management system's Developing platform.

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We finally chose VC as monitoring system development platform. Consider the main system must drive some hardware devices, and the system requires relatively high stability and efficiency. To the interface, we decide to use BCG, it has the interface library to help beautify the interface. As regards the operation of the database, although not so convenient such as VB, DELPHI, but we can use the corresponding controls to be simplified, such as, modular, stand-alone database operation and so on. The B/S structure's management system’s developing platform aspect, is quite at present popular, has the quite good advanced platform, has based on the COM+ .NET platform and based on the EJB J2EE platform. They have the respective characteristic. The .NET platform may use VISUAL the STUDIO integration environment to carry on the development, the contact interface is quite friendly, is the multi-language platform, may support C++, VB, C# and so on many kinds of languages, but only supports the WINDOWS operating system. J2EE is relatively quite complex, is the sole language platform, can only use JAVA to develop, but J2EE support for multiple operating systems. Finally, we chose the J2EE platform to develop. Because J2EE is one of the advanced B/S platforms quite popular at present , moreover supports WINDOWS, LINUX, UNIX and so on many kinds of operating systems ,at the same time supports DB2, ORACLE, SQL SERVER and so on many kinds of database management systems through some free driver packages.

3 Requirement Analysis Stage In requirement analysis stage, to reduce the following perfective maintenance, the corrective maintenance work, we use many kinds of means to unearth the user‘s potential requirements as far as possible. The initial period we use UML multi-level use-case diagram, some of the images, the WORD documents to demonstrate to the user function of the system. For some complex deal is with the timing diagram, activity diagram, etc. demonstrate, and then allow users to ask questions also asked whether they have new requirements, we do modify. After exchanges many times have obtained the user quite comprehensive requirement, starts to let the developers make the UI prototypes, let the user use, and ask their requests , after revised many times, we obtained one comprehensively, detailed requirement. In this process, in order to obtain more requirements from the use, we also have been to several the similar systems’ company to observe and study, and carries on the investigation to their user, simultaneously have also consulted some related experts.

4 Design Stage In the design stage, we let the system more modularization, a loose coupling as far as possible, enables the system to have a better extendibility and maintainability and so on. First, we enable the system support many kinds of database management system, the default uses ORACLE, in practical application, if the user need to use other database management system, so long as through revise the configuration files to be possible to realize. Second, Interface for different users may have different requirements, such as interface requires to display different fields, as well as the

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length of some fields require to alter in different situation, we design a template editing function that lets user set the appropriate input interface and display interface as their own business needs. Third, the system has used MVC the pattern SSH frame, enables the view, the pattern, the control and so on to separate, this caused the system to realize the loose coupling, realize changes three any, will not affect other. Moreover this kind of pattern can realize a model correspond to multiple views. For example, regarding the C/S system, we have designed the RS485 connection module, the CAN connection module, the database operation module especially and so on. Regarding the B/S system, we have designed the list component, the tree component, the file template edition component and so on.

5 Codes In the code aspect, we have stipulated a set of programming standard, moreover requested everybody certainly to defer to the standard to write the program. Described each function before the function , return value meaning, and significance of each parameter, for representatives of special significance and need to be explained(e.g., parameter dictates represents file condition, 0 expressions have not filed away; 1 expressions has filed away; 2 expressions already destruction). There have comments every few lines, the variable name to norms and so on.

6 Test and Version Control In the test aspect, software testers all-the-way tracking, start from the requirement analysis, have testers to carry on the track test, moreover used testing tools such as Test Director, Web Stress, QuickTestPro and so on to be auxiliary carry on the test, with the aim of discovering more mistakes in the system, and made the revision, facilitated the later maintenance work. In the version control aspect, our documentation, source code, all other project-related information for the strict management. First regarding the documents, we request the related personnel, must write the corresponding documents, and these documents must be examine and verify, passes to CVS. Regarding the source program, we request the developers to pass to the newest program on CVS every day before getting off work, the second day take out the program from CVS to start work. In the test aspect, there are test case, test example, test script and results of test and other documents required to be uploaded to the CVS, and complete the change management. Moreover regarding the program and the documents on CVS, record once in a while, by against as a result of computer reasons such as hardware damage or virus and so on, cause the data loss.

7 Concluding Remark After many aspects endeavor, our system complete on time, also has the quite high maintainability. At present the system has already operated for nearly 5 months, the users reflected the situation is very well, they also very satisfied to the system. The system operation time is not very long, with the lapse of time, perhaps in maintainable

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aspect, also will expose many problems. We will continue study each kind of related new technology unceasingly, better improve our software development process in the later development, with the aim of developing quality better software including maintainability.

References [1] Zhengren, J., Yan, R., Tao, L.: Practical Software Engineering, 2nd edn. Tsinghua University Press, Beijing (2001) [2] He, P. (ed.): Software testing tutorial. Mechanical Industry Press, Beijing (2006) [3] Bosch, J.: Design & Use of Software Architectures. Addison-Wesley, Reading (2000) [4] Wang, S., Xuan, S.S.: Database management system, 4th edn. Tsinghua University Press, Beijing (2006) [5] SEI Software Technology Review, Maintainability Index Technique for Measuring Program Maintainability (2002), http://www.sei.cmu.edu/

An E-Business Service Platform for Agreement Based Circulation of Agricultural Products of Fruits and Vegetables Liwei Bao1, Luzhuang Wang1, Zengjun Ma2, Jie Zhang2, and Qingchu Lv2 1

Zhejiang University City College Hangzhou, P.R. China 2 China Agriculture Wholesale Market Association Beijing, P.R. China [email protected]

Abstract. According to the analysis of the processes of circulation of agricultural products of fruits and vegetables, and IT application requirements of the market entities participating in the circulation processes, the novel pattern of agreement based circulation of agricultural products of fruits and vegetables has been discussed. Getting the guidance from the agreement based circulation pattern, an E-business service platform constructed on application service modules and mechanism of users choose some of the application service modules and define them to manage their business processes has been proposed. With the mechanism, the application service modules chosen and defined by user are integrated as an application service package and applied as management information system of business processes of circulation on the Ebusiness service platform. The architecture and function composing of the Ebusiness service platform has been presented. The E-business service platform for agreement based circulation of agricultural products of fruits and vegetables has been designed and implemented. Keywords: E-business, service platform, agreement based circulation, fruits, vegetables.

1 Introduction There exist very great difference between the circulation of agricultural products and industrial products, especially the fruits and vegetables. The agricultural production has apparently seasonal characteristic. The expected income of agricultural planting is not only influenced by weather, but is affected by the condition of market supply and demand, the situation of circulation channels also, during the picking period. The agricultural products of fruits and vegetables are very easy to rot and difficult to store. These properties of the fruits and vegetables require that to make the bargain rapidly, and to dispatch the products into the consumption areas as soon as possible to reduce loss. The process of circulation of agricultural products consists of a chain of transactions and logistics activities. In traditional pattern of circulation of agricultural products, the flow-of-exchange which often followed by many unnecessary logistics Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 93–100, 2011. © Springer-Verlag Berlin Heidelberg 2011

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activities took fruits and vegetables into consumption areas overtime. How to solve the problems? Solutions by applying E-business had been discussed in many papers, such as in [1-7]. However, these schemes hardly took the IT abilities of the market entities, business processes of the circulation of agricultural products of fruits and vegetables into consideration, only assumed that if the E-business would be applied in the field of circulation of agricultural products, considerable benefit would be obtained. In this paper, a novel pattern of circulation of agricultural products, which named “agreement based circulation” of fruits and vegetables, is analyzed to further disclose the intrinsic characteristics of requirement of the E-business applied to circulation of agricultural products of fruits and vegetables. With the requirement analysis of the business processes of the agreement based circulation of agricultural products of fruits and vegetables, an E-business service platform has been designed and implemented for the market entities of the circulation of agricultural products of fruits and vegetables.

2 Agreement Based Circulation of Agricultural Products of Fruits and Vegetables In China, the processes of circulation of agricultural products of fruits and vegetables are rather complex. The market entities participating in the processes mostly include: (1)home gardeners; (2)rural cooperatives, the cooperative patterns can be pattern of cooperatively planting and cooperatively sale or pattern of individually planting and cooperatively sale; (3)agricultural production enterprises; (4) individual brokers; (5)partnership brokers; (6)broker companies; (7)agricultural products acquisition processing enterprises; (8)agricultural products wholesale markets; (9)supermarket chains; (10)large retail enterprises and smaller retailers; (11)logistics service providers, include transportation service providers and storage service providers; (12)other relevant service providers; (13)entities in consumption field include household consumers, organizational purchasers such as refectories, food and beverage industries, etc. Among the above market entities, the home gardeners, rural cooperatives including two cooperative patterns and agricultural production enterprises can be classified as cropper planters, their target customers could be individual brokers, partnership brokers, broker companies, agricultural products acquisition processing enterprises or supermarket chains, sometimes could be large retail enterprises and smaller retailers or entities in consumption field. Agricultural products wholesale markets are the marketplace organizer, site and environment service providers for block trades of agricultural products. In general, supermarket chains, large retail enterprises and smaller retailers contact the consumption field directly, and they organize the supply source of agricultural products from home gardeners, rural cooperatives, agricultural production enterprises, individual brokers, partnership brokers, broker companies, agricultural products acquisition processing enterprises, agricultural products wholesale markets and/or some combinations of these channels. The logistics service providers which include transportation service providers and storage service providers, and other relevant service providers are also very important parts of the processes of circulation of agricultural products, which cannot be ignored. The business activities of the market entities have formed the processes of circulation of agricultural products shown as in Fig.1.

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Fig. 1. Schematic diagram of the processes of circulation of agricultural products of fruits and vegetables

In the circulation system of agricultural products of fruits and vegetables shown as in Fig.1, the fruits and vegetables are transferred from cropper planters to consumption areas through several circulation links according to agreements. From this point of view, agreements are the most important facts determining the efficiency and costs of the chain of transactions. For the convenience of the discussion, the individual brokers, partnership brokers and broker companies are sorted as brokers from their functions in the circulation processes. The concept of agreement based circulation of agricultural products of fruits and vegetables means that the market entities of cropper planters, brokers, agricultural products acquisition processing enterprises, supermarket chains, retail enterprises, agricultural products wholesale markets, logistics service providers, that participating in the circulation processes, establish a kind of cooperating business relationship with long-term stability, sustainable development, reciprocity and mutual benefit, basing on prior arrangements. The prior arrangement could be verbal-agreement, contract or any other business cooperation convention. As a new pattern of circulation of agricultural products which founded on fiduciary duty and collaboration, it makes each links of the circulation of fruits and vegetables dovetailed effectively. Under the agricultural products circulation pattern, fruits and vegetables can be transferred from cropper planters to consumption areas as soon as possible [8-10]. The applications of the agreement based circulation of agricultural products of fruits and vegetables to retail enterprises and agricultural products wholesale markets had been studied in [11, 12]. The new pattern of agreement based circulation of agricultural products of fruits and vegetables changes the traditional trading mode of direct trade, and focuses on the collaboration of circulation business processes. It is obviously that running the agreement based circulation of agricultural products of fruits and vegetables needs the support of an information integration system to exchange business information and collaborate in the circulation processes. The

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information integration system should integrate the management information systems of business processes or ERP systems of the market entities participating in the circulation. Therefore, the information integration system could be designed as two layers architecture, the lower layer could be constituted by the management information systems of business processes or ERP systems of the market entities, and the upper layer could be composed of business data integrator and application interfaces. However, most of the market entities have not built their management information systems of business processes because of high cost of the information system building and maintenance and/or insufficiency in IT application ability. For this reason, the usual methods of system integration and conventional E-commerce system [13, 14] could be hardly successfully applied to the agreement based circulation of agricultural products of fruits and vegetables. It is necessary to develop an E-business service platform with the functions of information management of business processes of circulation of fruits and vegetables, data exchange among business partners, system interface between the service platform and the management information systems of business processes or ERP systems of the market entities if they have built them, to help the most market entities who have not built their information systems apply the application services supplied by the service platform to construct their business management systems with very cheap cost. Through the Ebusiness service platform, no matter whether the market entities participating in the agreement based circulation have built their information systems, they can manage their business processes and interchange the business data with their business partners. The management of the circulation of agricultural products would be extended from the internal business processes of organization of the market entities to the whole processes of circulation of fruits and vegetables.

3 E-Business Service Platform for Agreement Based Circulation of Agricultural Products of Fruits and Vegetables According to the above analysis of the IT application requirements of the agreement based circulation, the E-business service platform for agreement based circulation of agricultural products of fruits and vegetables can be designed as a Web based application service system. The service objects of the platform can be summarized as seven classes of market entities participating in the circulation of agricultural products of fruits and vegetables: (1)cropper planters; (2)brokers; (3)agricultural products acquisition processing enterprises; (4)supermarket chains; (5)retail enterprises; (6)agricultural products wholesale markets; (7)logistics service providers. To the different class of market entities, their information system application requirements are diverse. For instance, to cropper planters, the business information they need to manage includes: (1)information of agricultural means of production, such as seed, pesticides, fertilizer, mulching film, etc; (2) information of suppliers of agricultural means of production; (3)information of planting plots; (4)information of field management in planting process; (5)sale information of agricultural products; (6)information of acquirers of vegetables and fruits; (7)information of cost; (8)information of agreements with agricultural products acquirers and suppliers of agricultural means of production. Brokers can be considered as the results of

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socialization specialization division of labour from agricultural planting or retail of agricultural products as intermediaries or agents. They are familiar with the situation of planting and picking of agricultural products in some areas, or they are acquainted with the consumption needs in some regional markets. They connect rural areas with cities, and connect production with wholesale or retail of farm produce. The business information they need to manage includes: (1) purchase information of agricultural products; (2) information of cropper planters; (3) information of transportation and storage; (4) information of cost; (5) information of agreements with agricultural products acquirers and suppliers. Almost all of the cropper planters have not built their management information systems of the business processes. Similarly, few of the brokers and logistics service providers use information system to manage their business processes. But to supermarket chains, large retail enterprises and agricultural products acquisition processing enterprises, almost all of them apply ERP systems to manage their business processes. Yet, from the point of the whole circulation of fruits and vegetables, these ERP systems become “information isolated islands” in the system of management of agreement based circulation of agricultural products of vegetables and fruits. Not only would they need to dovetail their internal business processes management with agreement based circulation management to reduce the circulation time, but they would like to obtain the information of planting plots and field management in planting processes for the traceability and food quality safety control also.

Fig. 2. Schematic diagram of the architecture of E-business service platform for agreement based agricultural products of fruits and vegetables

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By summarizing the above IT application requirements of seven classes of market entities in the agreement based circulation, the E-business service platform for agreement based agricultural products of fruits and vegetables can be designed as the architecture of three parts: (1)application services for business processes management of agreement based circulation described as applications service hall; (2)P2P data interchange service for business management of agreement based circulation; (3)service platform management consists of users management and system maintenance; which shown in Fig.2. The E-business service platform substantially is a multi-tenant service system. The module of users-management actually is that of tenants-management. Every user can include several user members, and various user members can be given various operation rights by the user or the tenant. As in Fig.2, the E-business service platform supplies a set of application service modules to users which is presented as application service hall. A user can enters the application service hall to select some of the service modules and define them according to the business management requirement as an application service package.

Fig. 3. Schematic diagram of the service modules of E-business service platform for agreement based agricultural products of fruits and vegetables

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An application service package actually is a management information system of business processes in the agreement based circulation. If a user has built its ERP system, the E-business service platform should supply the service of data exchange between the application service package and the ERP system through the module of system interface management of application service. The E-business service platform supplies users with the service of business data interchange to their business partners through the service module of P2P data interchange service for business management of agreement based circulation. Under the architecture of the E-business service platform, the set of the service modules which supplied in the application service hall had been summarized as 14 function modules shown in Fig.3. The 14 service modules loaded on the E-business service platform are: (1)supply management; (2)plot management; (3) planting management; (4)cost management; (5)customer relationship management; (6)sales management; (7)agreement management; (8)production management; (9)storage service management; (10)transportation service management; (11) vehicle management; (12)interface of application service management; (13)business data exchange management; (14) distribution management. Users can select some of the service modules to construct their management information system according to their business processes management requirements. For example, brokers can select the service modules of supply management, sales management, agreement management, cost management, storage service management, transportation service management, CRM, and select business data exchange management module to interchange the business information of agreements, plot and planting management etc with business partners such as cropper planters, agricultural products acquisition processing enterprises, supermarket chains, retail enterprises and agricultural products wholesale markets.

4 Conclusion With help of the application service package supplied by the E-business service platform, the seven classes of market entities participating in the agreement based circulation of agricultural products of fruits and vegetables can realize the business processes information management and E-business baaed process collaboration to shorten the circulation time and obtain more benefit. Acknowledgment. The paper is supported by the National Key Technology R&D Program 2008BADA0B08 and 2006BAD30B08, the construct program of the key laboratory in Hangzhou.

References [1] Cheng, X., Cui, J., Lin, J.: The Application of E-business in China’s Trade of Agricultural Products. Technology and Innovation Management(in Chinese) 30(4), 465–467, 474 (2009) [2] Guan, H., Chen, J., Qian, Y.: Research on the Trade Mode and Development of Agricultural Product in the Light of E-Commerce. China Business and Market (in Chinese) (1), 45–47 (2010)

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[3] Sheng, G.: On Construction of Collaborative E-commerce in Virtual Wholesale Market of Farm Produce. Commercial Research (in Chinese) 395(3), 189–193 (2010) [4] Wang, J., Lv, X., Ma, X., Song, Z.: Construction of Electronic Commerce Plat of Agricultural Products in Tianjin. Tianjin Agricultural Sciences (in Chinese) 15(1), 83–85 (2009) [5] Zhang, R., Weng, K., Luo, X.: The Research on E-commerce Development of Agricultural Products in China. Enterprise Science And Technology & Development (in Chinese) 248(2), 14–17 (2009) [6] Zhu, Z., Liu, J.: Study on Electronic Commerce Plat of Distinctive Agricultural Products in Hebei Province. Journal of Hebei Agricultural Sciences (in Chinese) 12(11), 140–141 (2008) [7] Wu, H., Wan, J.: Considerations about Developing E-commerce of Chinese Agricultural Products. China Market (in Chinese) 32(8), 72–73 (2008) [8] Zhang, H., An, Y.: Agri-food Chain of Trust: Based on System Stream Framework. China Business and Market (in Chinese) 2, 19–22 (2010) [9] Liu, J., Shen, D.: Study on the Pattern of Designated Retail Sales of Agreement Based Circulation of Agricultural Products of Fruits and Vegetables. Commercial Times (in Chinese) 5, 32–33 (2010) [10] Zhang, R., Liu, Y.: Study on the Pattern of Extending Service of Wholesale Market of Agreement Based Circulation of Agricultural Products of Fruits and Vegetables. China Business (in Chinese) 1, 82–84 (2010) [11] Wang, Y., Sun, M., Wang, Y., Wang, F.: Constructing a New Pattern of Agreement Based Circulation of Agricultural Products. Agricultural Economy (in Chinese) 1, 80–83 (2010) [12] Zhang, H., An, Y.: Outlook of Development Tendency of Circulation Pattern of Connecting Agriculture with Supermarkets. Agricultural Outlook (in Chinese) 1, 39–42 (2010) [13] Awad, E.M.: Electronic Commerce: From Vision to Fulfillment, 3rd edn. Prentice Hall Press, Englewood Cliffs (2006) [14] Schneider, G.P.: Electronic Commerce, Seventh Annual Edition. China Machine Press (2006)

Two Improved Proxy Multi-signature Schemes Based on the Elliptic Curve Cryptosystem Fengying Li1 and Qingshui Xue2 1

Dept. of Education Information Technology, East China Normal University, 200062, Shanghai, China 2 Technical School, Shanghai Jiaotong University, 201101, Shanghai, China [email protected]

Abstract. In a proxy signature scheme, one original signer delegates a proxy signer to sign messages on behalf of the original signer. In a proxy multisignature scheme, n original signers cooperate to delegate their signing power to one proxy signer. In 2003, Chen, Chung and Huang proposed one proxyprotected proxy multi-signature scheme (CCH1 scheme) based on the elliptic curve cryptosystem. Park et al. pointed out that CCH1 scheme is insecure, however, they didn’t provide a modified scheme or new schemes. To resist the forgery attack from the original signer proposed by Park et al., based on CCH1 scheme, one improved scheme is proposed. In 2004, Chen, Chung and Huang proposed another proxy multi-signature scheme (CCH2 scheme) also based on the elliptic curve cryptosystem. By observation, Park et al. showed that CCH2 scheme can’t resist the conspiracy attack from all original signers. As to CCH2 scheme, Park et al. neither provided an improved version or new version. Based on CCH2 scheme, a modified scheme is brought forward. Keywords: Cryptography, digital signature, proxy signature, proxy multisignature, elliptic curve cryptosystem.

1 Introduction The proxy signature scheme [1], a variation of ordinary digital signature schemes, enables a proxy signer to sign messages on behalf of the original signer. Proxy signature schemes are very useful in many applications such as electronics transaction and mobile agent environment. Mambo et al. [1] provided three levels of delegation in proxy signature: full delegation, partial delegation and delegation by warrant. In full delegation, the original signer gives its private key to the proxy signer. In partial delegation, the original signer produces a proxy signature key from its private key and gives it to the proxy signer. The proxy signer uses the proxy key to sign. As far as delegation by warrant is concerned, warrant is a certificate composed of a message part and a public signature key. The proxy signer gets the warrant from the original signer and uses the Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 101–109, 2011. © Springer-Verlag Berlin Heidelberg 2011

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corresponding private key to sign. Since the conception of the proxy signature was brought forward, a lot of proxy signature schemes have been proposed [2-14]. Later, the threshold proxy signature schemes were proposed [2, 6-8]. In a (t , n) threshold proxy signature, the original signer can authorize a proxy group with n proxy members. Only the cooperation of t or more proxy members is allowed to generate the proxy signature. The multi-proxy signature scheme was first proposed in [15]. The multi-proxy signature scheme is a special case of the threshold proxy signature scheme. The kind of proxy signature scheme allows an original signer to authorize a group of proxy members can generate the multi-signature on behalf of the original signer. In 2000, Yi et al. first proposed the proxy multi-signature schemes [16]. In a proxy multi-signature scheme, an original signer group can authorize a proxy signer on behalf of the original signer group. Afterwards, some proxy multi-proxy schemes were proposed [17-18]. In 2003, Chen, Chung and Huang [13] proposed one proxy-protected proxy multisignature scheme (CCH1 scheme) based on the elliptic curve cryptosystem. In 2006, Park et al. [12] pointed out that CCH1 scheme is insecure, that is to say, CCH1 scheme can’t resist the forgery attack from a malicious original signer. However, they didn’t provide a modified scheme or new schemes To resist the forgery attack from a malicious original signer proposed by Park et al., in the paper, based on CCH1 scheme, one improved scheme is proposed. In 2004, Chen, Chung and Huang [14] proposed another proxy multi-signature scheme (CCH2 scheme) based on the elliptic curve cryptosystem as well. By security analysis, Park et al. [12] showed that CCH2 scheme can’t resist the conspiracy attack from all original signers. As far as CCH2 scheme is concerned, Park et al. neither provided an improved or new version. Based on CCH2 scheme, a modified scheme is brought forward by the authors in the paper. In the paper, we will organize the rest as follows. In section 2, we will detail the CCH1 scheme and its security analysis. In section 3, an improved CCH1 scheme will be stated. CCH2 scheme and its security analysis will be described in section 4. In section 5, we will bring forward a modified version of CCH2 scheme. Finally, the conclusion will be given in section 6.

2 The CCH1 Scheme and Its Security Both CCH1 scheme and CCH2 scheme are based on conventional PKI setting, so two schemes consists of three stages-proxy signing key generation, proxy signature generation and proxy signature verification. Basically, they have the same system initialization that all original signers choose the common elliptic curve domain parameters. Let q be a power of prime p and E be an ordinary elliptic curve over Fq and let h be a hash function. Suppose that the order of E must be divisible by a large prime r. In the case, there is a base point P = ( x P , y P ) which produces the largest

cyclic subgroup of E ( Fq ) .

For each original signer Ai (i = 1,2,..., n) , he or she secretly chooses a random integer d i ∈ Z r* as its own private key, and calculates the corresponding public key

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Qi = d i P = ( xQi , yQi ) . The proxy signer also chooses a random integer d B ∈ Z r* , and

then calculates QB = d B P = ( xQB , yQB ) . Each public key Qi (i = 1,..., n) and QB must be certified by CA. mw is a warrant consisting of the identities and public keys of the original signers and the proxy signer, the delegation duration, messages types to sign, etc. In 2003, Chen, Chung and Huang proposed two proxy multi-signature schemes: one is proxy-unprotected and the other is proxy-protected. We will only review the proxy-protected scheme, denoted by the CCH1 scheme. It is stated as follows. A. Proxy Signing Key Generation

In order to delegate the signing capability to the proxy signer B , the following steps will be carried out. Step 1. Each Ai (i = 1,2,.., n) chooses an integer k i ∈ Z r* at random, and calculates

Ri = ki P = ( x Ri , y Ri )

(1)

and

si = d i xQi h(mw , Ri ) − ki mod r.

(2)

Step 2. Each Ai sends σ i = (mw , Ri , si ) to the proxy signer in a secure way. Step 3. Upon receiving σ i from Ai (i = 1,2,.., n) , the proxy signer B calculates

(

)

U i = xQi h(mw , Ri ) Qi − si P = ( xU i , yUi )

(3)

and checks xU i = xRi mod r. Step 4. If all σ i ' s are valid, B calculates the proxy signing key as follows: n

d = d B xQB +

∑ s mod r.

(4)

i

i =1

B. Proxy Signature Generation and Verification When the proxy signer B signs a message m on behalf of the original signers { A1 ,..., An } , B executes one ECDLP-based ordinary signing algorithm with the proxy signing key d. Suppose that the resulting signature is Sig d (m) , then the proxy signature affixed to m for the original signers is (m, mw , R1 ,..., Rn , Sig d (m)) . When the proxy signature is verified, the verifier first calculates the proxy public value Q corresponding to the proxy signing key d as

∑ ((x n

Q = xQB QB +

i =1

Qi h( mw , Ri )

)Qi − Ri )(= dP).

（5）

With the value, the verifier confirms the validity of Sig d (m) by validating the verification congruence of the designated signature scheme.

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Park et al. pointed out that CCH1 scheme can’t resist the proxy signing key forgery attack from a malicious original signer. The attack is briefly stated as follows. Without loss of generalization, suppose that A1 is a malicious original signer. First, A1 chooses random integers k1 ,..., k n ∈ Z r* , and then calculates n

(

)

R1 = xQB QB + k1 P + ∑ xQi h(mw , Ri ) Qi i =2

(6)

= ( x R1 , y R1 ) where Ri = ki P = ( x Ri , y Ri ) for 2 ≤ i ≤ n . The forged proxy signing key d is given by n

d = d1 xQ1 h( mw , R1 ) −

∑ k (mod r ) .

(7)

i

i =1

From Eq. (1), the proxy public value Q calculated by any verifier satisfies the following equality:

∑ (x Q h(mw , Ri ))Qi − ∑ Ri n

Q = xQB QB +

n

i =1

i

i =1

⎞ ⎛ (8) = x Q1 h(mw , R1 ) Q1 −⎜ k i ⎟ P ⎟ ⎜ ⎝ i =1 ⎠ n ⎞ ⎛ ki ⎟ P = ⎜ d1 x Q1 h(mw , R1 ) − ⎟ ⎜ i =1 ⎠ ⎝ Therefore, any verifier can verify the validity of the proxy multi-signatures produced by using d. As a result, the malicious original signer A1 can produce a proxy signing key d without the participation of the original signers A2 ,..., A n and the proxy signer B . Park et al. pointed out the CCH1 scheme is insecure, whereas, they didn’t provide a modified scheme. In the consequent section, based on CCH1 scheme, an improved version is proposed by us.

(

)

n

∑

∑

3 The Improved CCH1 Scheme A. Proxy Signing Key Generation In order to delegate the signing capability to the proxy signer B , the following steps will be carried out: Step 1. Each Ai (i = 1,2,.., n) chooses an integer k i ∈ Z r* at random, and calculates Ri = ki P = ( x Ri , y Ri )

(9)

and si = d i xQi h(mw , Ri ) − k i xRi mod r.

(10)

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Step 2. Each Ai sends σ i = ( mw , Ri , si ) to the proxy signer in a secure way. Step 3. Upon receiving σ i from Ai (i = 1,2,.., n) , the proxy signer B checks whether

(

)

si P = xQi h(mw , Ri ) Qi − x Ri R i

(11)

holds. Step 4. If all σ i ' s are valid, B calculates the proxy signing key as follows: n

d = d B xQB +

∑ s mod r.

(12)

i

i =1

B. Proxy Signature Generation and Verification When the proxy signer B signs a message m on behalf of the original signers { A1 ,..., An } , B executes one ECDLP-based ordinary signing algorithm with the proxy signing key d. Suppose that the resulting signature is Sig d (m) , then the proxy signature affixed to m for the original signers is (m, mw , R1 ,..., Rn , Sig d (m)) . When the proxy signature is verified, the verifier first calculates the proxy public value Q corresponding to the proxy signing key d as

∑ ((x n

Q = xQB QB +

Qi h( mw , Ri )

i =1

)Qi − xR Ri )(= dP). i

(13)

With the value, the verifier confirms the validity of Sig d (m) by validating the verification congruence of the designated signature scheme.

4 The CCH2 Scheme and Its Security A.

Proxy Signing Key Generation

Step 1. Each Ai (i = 1,2,..., n) chooses a random integer k i ∈ Z r* , and calculates Ri = ki P = ( x Ri , y Ri ) . Step 2. If x Ri = 0 , then return Step 1; otherwise, Ai broadcasts Ri to the other original signers. Step 3. On receiving R j (1 ≤ j ≤ n, j ≠ i ) , Ai calculates n

R=

∑R

i

= ( xR , y R ) ,

(14)

i =1

si = d i h(mw , xQi , xQB , x R ) − ki mod r and sends σ i = (mw , si ) to the proxy signer via a public channel.

(15)

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Step 4. On receiving σ i from Ai for 1 ≤ i ≤ n , the proxy signer B calculates R i ' = h(mw , xQi , xQB , x R )Qi − si P = ( x Ri ' , y Ri ' )

(16)

and checks x Ri ' = xRi mod r. Step 5. If all σ i ' s are valid, then B calculates n

d = dB +

∑ s mod r

(17)

i

i =1

as the proxy signing key. B. Proxy Signature Generation and Verification The proxy multi-signature affixed to the m is in the form of (m, mw , R, Sig d (m)) , where Sig d (m) is the signature generated by a designated scheme using the proxy signing key d. When the verifier verifies the signature, he or she calculates the proxy public value Q corresponding to the proxy signature key d as n

Q = QB +

∑ h( m i =1

w , xQi , xQB , x R )Qi

− R.

(18)

With the value, the verifier can confirm the validity of Sig d (m) by validating the verification equality of the designated signature scheme. Park et al. [12] pointed out that CCH2 scheme was not proxy-protected, that is to say, without agreement of the proxy signer B , by conspiracy of all original signers A1 ,..., An , valid proxy multi-signatures can be forged. It is detailed as follows. The original signer Ai chooses an integer k i ∈ Z r* at random, and then calculates Ri = k i P for i = 1,2,..., n . Furthermore, A1 adds QB to R1 . Next, calculates ⎛ Ri = QB + ⎜ ⎜ i =1 ⎝ n

R=

∑

⎞

n

∑ k ⎟⎟⎠ P.

(19)

i

i =1

The forged proxy signing key d produced by the original signers A1 ,..., An is gotten as follows:

∑ (d h(m n

d=

i

i =1

w , xQi , xQB , x R )

)

− ki mod r.

(20)

When proxy signatures are verified, any verifier can compute the proxy public value Q , which satisfies the equation (21).

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n

Q = QB + ∑ h(mw , xQi , xQB , x R )Qi − R i =1

⎛ ⎞ ⎛ n ⎞ = QB + ⎜ ∑ d i h( mw , xQi , xQB , x R ) ⎟ P − ⎜ ∑ k i ⎟ P −Q B ⎝ i =1 ⎠ ⎝ i =1 ⎠ n

⎛ n ⎞ ⎛ n ⎞ = ⎜ ∑ d i h(mw , xQi , xQB , x R ) ⎟ P − ⎜ ∑ k i ⎟ P ⎝ i =1 ⎠ ⎝ i =1 ⎠

(

)

⎛ n ⎞ = ⎜ ∑ d i h(m w , xQi , xQB , x R ) − k i ⎟ P ⎝ i =1 ⎠ = dP.

(21)

This indicates that any verifier can be convinced that any proxy multi-signatures signed by using the forged proxy signing key d are produced by agreement of A1 ,..., An and B . Similarly, Park et al. pointed out the CCH2 scheme is insecure, but, they provide a modified scheme neither. In the following section, based on CCH2 scheme, an improved scheme is proposed by us.

5 The Improved CCH2 Scheme A . Proxy Signing Key Generation Step 1. Each Ai (i = 1,2,..., n) chooses a random integer k i ∈ Z r* , and calculates Ri = ki P = ( x Ri , y Ri ) . Step 2. If x Ri = 0 , then return Step 1; otherwise, Ai broadcasts Ri to the other original signers. Step 3. On receiving R j (1 ≤ j ≤ n, j ≠ i ) , Ai calculates n

R=

∑R

i

= ( xR , y R ) ,

(22)

i =1

si = d i h(mw , xQi , xQB , x R ) − ki x R mod r

(23)

and sends σ i = ( mw , R, si ) to the proxy signer via a public channel. Step 4. On receiving σ i from Ai for 1 ≤ i ≤ n , the proxy signer B checks whether si P = h(mw , xQi , xQB , xR )Qi − x R Ri holds. If it holds, σ i is valid; otherwise, the scheme fails.

(24)

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Step 5. If all σ i ' s are valid, then B calculates n

d = dB +

∑ s mod r

(25)

i

i =1

as the proxy signing key. B. Proxy Signature Generation and Verification The proxy multi-signature affixed to the m is in the form of (m, mw , R, Sig d (m)) , where Sig d (m) is the signature generated by a designated scheme using the proxy signing key d. When the verifier verifies the signature, he or she calculates the proxy public value Q corresponding to the proxy signature key d as n

Q = QB +

∑ h( m i =1

w , xQi , xQB , x R )Qi

− xR R.

(26)

With the value, the verifier can confirm the validity of Sig d (m) by validating the verification equality of the designated signature scheme.

6 Conclusions In the paper, we have reviewed CCH1 scheme and CCH2 scheme which all based on the elliptic curve cryptosystem. Park et al. pointed that both schemes are insecure, however, they didn’t propose improved ones. In order to resist the forgery attack proposed by Park et al. and make both schemes own the security property of proxyprotection, based on CCH1 and CCH2 scheme, two improved proxy multi-signature schemes have been given, respectively. Acknowledgment. We thank the reviewers for their valuable comments that helped us improve the quality and presentation of our work.

References [1] Mambo, M., Usuda, K., Okamoto, E.: Proxy Signature for Delegating Signing Operation. In: Proceedings of the 3rd ACM Conference on Computer and Communications Security, New Dehli, India, pp. 48–57. ACM Press, New York (1996) [2] Li, J.G., Cao, Z.F.: Improvement of a Threshold Proxy Signature Scheme. Journal of Computer Research and Development 39(11), 515–518 (2002) (in Chinese) [3] Li, J.G., Cao, Z.F., Zhang, Y.C.: Improvement of M-U-O and K-P-W Proxy Signature Schemes. Journal of Harbin Institute of Technology (New Series) 9(2), 145–148 (2002) [4] Li, J.G., Cao, Z.F., Zhang, Y.C.: Nonrepudiable Proxy Multi-signature Scheme. Journal of Computer Science and Technology 18(3), 399–402 (2003) [5] Li, J.G., Cao, Z.F., Zhang, Y.C., Li, J.Z.: Cryptographic Analysis and Modification of Proxy Multi-signature Scheme. High Technology Letters 13(4), 1–5 (2003) (in Chinese)

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[6] Hsu, C.L., Wu, T.S., Wu, T.C.: New Nonrepudiable Threshold Proxy Signature Scheme with Known Signers. The Journal of Systems and Software 58, 119–124 (2001) [7] Hwang, M.S., Lin, I.C., Lu Eric, J.L.: A Secure Nonrepudiable Threshold Proxy Signature Scheme with Known Signers. International Journal of Informatica 11(2), 1–8 (2000) [8] Hwang, S.J., Chen, C.C.: Cryptanalysis of Nonrepudiable Threshold Proxy Signature Scheme with Known Signers. INFORMATICA 14(2), 205–212 (2003) [9] Li, L.H., Tzeng, S.F., Hwang, M.S.: Generalization of proxy signature-based on discrete logarithms. Computers & Security 22(3), 245–255 (2003) [10] Hsu, C.L., Wu, T.S.: Efficient proxy signature schemes using self-certified public keys. Applied Mathematics and Computation, Corrected Proof, Available online 9, July 2003 (in Press) [11] Hwang, M.S., Tzeng, S.F., Tsai, C.S.: Generalization of proxy signature based on elliptic curves. Computer Standards & Interfaces 26(2), 73–84 (2004) [12] Park, J.H., Kang, B.G., Park, S.W.: Cryptanalysis of some group-oriented proxy signature schemes. In: Song, J.-S., Kwon, T., Yung, M. (eds.) WISA 2005. LNCS, vol. 3786, pp. 10–24. Springer, Heidelberg (2006) [13] Chen, T.S., Chung, Y.F., Huang, G.S.: Efficient proxy multi-signature schemes based on the elliptic curve cryptosystem. Comput. Secur. 22(6), 527–534 (2003) [14] Chen, T.S., Chung, Y.F., Huang, G.S.: A traceable proxy multi-signature scheme based on the elliptic curve cryptosystem. Appl. Math. Comput. 159(1), 137–145 (2004) [15] Hwang, S.J., Shi, C.H.: A Simple Multi-Proxy Signature Scheme. In: Proceeding of the Tenth National Conference on Information Security, Taiwan (2000) [16] Yi, L., Bai, G., Xiao, G.: Proxy multi-signature schemes: A new type of proxy signature scheme. Electronics Letters 6, 527–528 (2000) [17] Hwang, S.J., Chen, C.C.: A New Proxy Multi-Signature Scheme. In: International Workshop on Cryptology and Network Security, Taiwan (2001) [18] Sun, H.M.: On Proxy (Multi-) Signature Schemes. In: International Computer Symposium, Taiwan (2000)

Online Oral Defense System Based on Threshold Proxy Signature Fengying Li1 and Qingshui Xue2 1

Dept. of Education Information Technology, East China Normal University, 200062, Shanghai, China 2 Technical School Shanghai Jiaotong University, 201101, Shanghai, China [email protected]

Abstract. Nowadays, proxy signature is one of research hotspots in the field of information technology. However, most researches focus on the theory and its analysis, there is few application researches, in particular, applications based on threshold proxy signature are less than others. In the real life, there are many drawbacks in the local oral defense system and more and more unfairness has been shown. Based on the threshold proxy signature scheme from the bilinearpairing, online oral defense system is proposed. The system consists of main sever, oral defense experts, oral defense students, oral defense secretary and CA (Certificate Authority). It mainly characterizes easily confirming oral defense experts authorized by main server and the oral defense experts can’t deny their own oral defense suggestions submitted. Threshold is a practical processing way in the system. As long as the number of oral defense experts agreeing reaches the number requested, i.e., the threshold value t, related students pass the oral defense procedure. The research opens a new windows for the application of proxy signature technology in remote education and it is very important for us to popularize online oral defense system and enhance the security of modern remote education including online oral defense system. Keywords: Online oral defense system, proxy signature, threshold proxy signature.

1 Introduction Information is one of the useful resources in history. With information technology rapidly developing, it is changing people’s living, working and studying. Utilizing advanced information technology Efficiently makes people better acquire useful information. Although open Internet network which now is the main information media and platform, brings people huge advantages, it also brings tremendous menaces. For example, in the beginning of the Gulf War, the military army headed by the united states launched the electronic war named by White Snow. 24 hours before the flight war, the spy and alertness power of Iraq’s military power are paralysed by the united army. Sometimes, for one day, there would be nearly 150 thousand dummy Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 110–119, 2011. © Springer-Verlag Berlin Heidelberg 2011

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and wild information flooding into Iraq’s information receivers. The flood of information made Iraq’s information system paralysed, command control disordered and aerial system unuseful. The problem of information security has been key and strategic one related with social safety, cultural safety, economical safety, military safety and even national safety. The aim of protecting information to be legally used is to solve the problem. Cryptography technology is the key one of information security. Not only can it provide the secret property of information, but also it can provide the security properties of authentication, integrity, undeniablity and so forth. The secret property can be acquired by encryption technology and the latter three properties can be gotten by digital signature technology. The research and application of digital signature in the field of education are widely eyed by domestic and oversea scholars and they have been generally applied to. Authorization and transferring of digital signature are currently needed to be urgently solved. Proxy signature [2] is a kind of most efficient and potential technology. As a kind of novel digital signature, proxy signature has a bright future of applications in remote education, mobile learning, virtual learning and so on. But, there is few application of proxy signature in remote education and other fields[4-7]. In the paper, we will apply it to design an online oral defense system based on threshold proxy signature [8]. The new application model will be constructed and implemented. In the paper, we will organize the content as follows. In section 2, we will detail the background of online oral defense system. We will state the proxy signature and threshold proxy signature in section 3. In section 4, some knowledge on bilinear pairing will be described. The model of online oral defense system will be detailed in section 5. In section 6, the implement of online oral defense system will be stated. In section 7, we will summarize the advantages of online oral defense system. Finally, the conclusion is given.

2 The Background of Online Oral Defense System Last several years, the quantity of Chinese master students has reached very high level in the world, however, the quality of education can’t make the same progress. In order to improve the education quality of master students, all levels of education departments adopt all kinds of policies related to strictly control the quantity of diplomas. For instance, there are some systems including blind reviewers and papers selected for survey. These systems for sure can improve the nature and fairness of paper reviewing to some extent and make masters write better dissertations, as enhances the quality of masters’ dissertations in some aspects. However, the nature is not so simple. The systems can’t put an end to falsification at all. For example, not all of dissertations will be blindly reviewed and there exist other fraudulent practices. Therefore, oral defense is one very important activity to masters’ education. Oral defense should be solemn, sacred and fair. However, there exists something bad such as only for the course during the oral defense. Due to too many masters who will graduate, there often are many students for oral defense for one time. Thus, as far

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as each student is concerned, he or she has no much time for oral defense and the procedure is only one procedure. The experts for oral defense are generally invited by the tutor. For the sake of face, generally speaking, they will not baffle the students for oral defense. Naturally, some fraudulent practices will inevitably take place. Moreover, the personal relationship between tutors and students also leads to some unfairness for the judgment of dissertations. In addition, inviting experts from other cities will bring low efficiency and some waste of manpower, material resources and money. In order to solve the problems, we will propose the online oral defense system. Then, what’s the online oral defense system? Oral defense system is one virtual oral defense meeting by the technology of network communication. In the system, experts for oral defense and students for oral defense may be in different geographical places. From the network, they will be able to stay in the same place. By various kinds of ways of communication such as graph and voice, they can talk with each other, share information and cooperate to work. The application system can finish the actual oral defense. The system will bring great conveniences for experts and students for their oral defense in real and intuitional communication, and it is vital to overcome the drawbacks of oral defense on the spot.

3 Proxy Signature and Threshold Proxy Signature In 1996, Mambo, Usuda and Okamoto[2] proposed the conception of proxy signature. In the system, a signer named by the original signer can delegate another signer, named by the proxy signer, to produce valid signature in behalf of the original signer and the signature produced is called proxy signature. Proxy signature needs some security properties-unforgeability, verifiability, undeniability, distinguishability, proxy signer’s deviation, identifiability, prevention of misuse and so forth[2]. It seems to us that efficient proxy signature, in addition, should have the security properties of withdrawal of proxy signing power, simplicity of schemes, high efficiency of implementation and so on. According to different standards classified, proxy signature can be divided into different classes [2,4-8]. Threshold proxy signature is one of its varieties. In threshold proxy signature, t or more proxy signers of n proxy signers will sign messages in behalf of the original signer. The kind of proxy signature possesses the advantages as follows: (a) If attackers want to produce proxy signature, they have to get t sub-proxy private keys. Generally speaking, it is very tough; (b) Even if one or some proxy signers are unwilling to cooperate or show, leak or tamper sub-proxy keys, it will not influence the comeback of the proxy signature key; (c) Authority is distributed and avoided to misuse. Some key decisions may need some members of directorate to cooperate to sign. Due to these good security properties, threshold proxy signature gets much attention. In the system, the knowledge of bilinear pairings is used. In the following, bilinear pairings will simply be stated.

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4 Bilinear Pairing

G1 and G2 be a additive group and a multiple group with the same prime order q respectively. P is the generation element of G1 . Suppose that the discrete logarithm problem on G1 and G2 is difficult. Bilinear pairing e : G1 × G1 → G2 meets the Let

three properties as follows [1,3]. 1) Bilinearity. For each P, P '∈ G1 and each a, b ∈ Z , e( aP, bP' ) = e( P, P ' ) holds. 2) Nondegeneration. For ∀P '∈ G1 , if e( aP, bP ' ) = 1 , then P = O . 3) Calculability. For each

ab

P, P'∈ G1 , there exists an efficient algorithm to

compute e( aP, bP ' ) . We can construct bilinear pairings from Weil pairings or Tate pairings modified on supersingular ellipse curves. Based on this kind of group G1 , difficult cryptographic problems are defined as follows.

P, P'∈ G1 , find an integer n such

1) Discrete Logarithm Problem (DLP): Given that P = nP ' . 2) Computational

Diffie-hellman

Problem

(CDH):

Given

( P, aP, bP) ∈ G (a, b ∈ Z ) , calculate abP . * q

3 1

3)

Decisive

Diffie-hellman

Problem

(DDP):

Given

( P, aP, bP, cP) ∈ G (a, b, c ∈ Z ) , judge if c = ab(mod q ) holds or not. 4 1

* q

4) Gap Diffie-hellman Problem (GDH): a kind of problem that CDH problem is difficult while DDH problem is easy. A group that CDH problem is difficult while DDH problem is easy is called Gap Diffie-hellman group.

5 The Model of Online Oral Defense System The system is composed of main server, oral defense experts, oral defense students, oral defense secretary and certificate authority (CA). Main server as the original signer, is charge of system initialization and delegates its signing power to oral defense experts, and/or collects and verifies oral defense experts’ suggestions. That’s to say, main sever will pass its signing power to all of oral defense experts on behalf of school oral defense committee. If the number of experts agreeing exceeds the number specified (threshold value), main sever will finish one student’s oral defense. Certificate authority (CA) in the system releases and authenticates certificates of the public key of each participator. Oral experts will check students’ oral defense. In the implementation, oral defense students are able to visually communicate with oral

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expert5 expert1

expert2

CA

Internet

server

expert 3

secretary

expert4

student

Fig. 1. The model of online oral defense system

defense experts by network. During the course, oral defense students introduce their own research results, then oral defense experts ask some questions related online and oral defense students answer these questions online. After each student finishes answering the questions asked, according to students’ answer, oral defense experts give them score online or pressed corresponding button which shows PASS or NOPASS. By doing so, oral defense experts sign their suggestions, then pass them to main server or oral defense secretary. Main servers or oral defense secretary can verify oral defense experts’ identities and the message signed. Meanwhile, oral defense students’ identities need also be identified during the implementation. Threshold is one efficient way in the system. If only the number of experts agreeing reaches specified one, i.e., threshold value t, students will pass the oral defense. If schools, i.e., main servers, want to know the suggestions of oral defense experts, they will be able to verify them.

6 Implementation of Online Oral Defense System In the system, main sever as the school and the original signer owns private key

xo ∈ Z q* and corresponding public key YO = xo P , which is issued and verified by CA. Each of oral defense experts as proxy signers,

Pi (i = 1,2,..., n) has private key

xi ∈ Z q* and corresponding public key Yi = xi P verified by CA. mw is the

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delegation information which main server passes to oral defense experts. It includes the identities of main server and oral defense experts, threshold value t, n, the valid period and so on. ASID (Actual Signers’ ID) denotes actual signers, i.e., identities of oral defense experts agreeing. ASID can be gotten by related buttons in front of oral

G0 and G1 have the same prime order of q . P is one generation element of group G0 . e : G0 × G0 → G1 is a secure bilinear pairing. In defense experts. Group

addition,

H 1 : {0,1}* × G0 → Z q* and H 2 : {0,1}* → G0 \ {1} are two secure hash

functions. The system consists of three phases as follows. A. Oral Defense Experts Acquire Proxy Share The phase is the initial one of the system. Main sever will select n experts in expert databases at random as the oral defense experts for the time and will delegate signing power to the n oral defense experts on behalf of main servers or schools. The phase is detailed as follows. Step 1. Main sever, i.e., original signer, chooses a random integer computes

U = rP

， h = H ( m ,U ) , Q = H 1

w

2

( mw )

r ∈ Z q* and

， V = (r + hx )Q ， o

σ = (U ,V ) and s = n (hr + xo ) .Then, main sever passes message (mw , σ , s ) −1

to

each of oral defense experts, i.e., proxy signers by Internet, Extranet, Intranet and so forth. Step 2. Each of oral defense experts Pi , i.e., proxy signers, verifies (m w ,σ , s ) by equation (1) and (2).

e( P,V ) = e(U + hYO , H 2 ( mw ))

(1)

nsP = hU + YO

(2)

If both equation (1) and (2) hold, each oral defense expert accepts message

(mw ,σ , s) and calculates si = s + xi + ki defense expert

， k ∈Z i

chosen at random by oral

Pi .

Step 3. Each of oral defense experts polynomial

* q

Pi selects one (t − 1) -degree

f i ( x) = si + ai ,1 x + ai , 2 x 2 + ... + ai ,t −1 x t −1 ,here ai ,0 = si

， thus,

f i (0) = si . Then Pi computes and broadcasts ai, j P ( j = 1,2,..., t − 1) and k i P to other oral defense experts. Furthermore, exert

Pi calculates and safely passes

f i ( j ) ( j = 1,2,..., n; j ≠ i ) to other (n − 1) oral defense experts.

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Step 4. Once expert

Pi receives f j (i ) ( j = 1,2,..., n; j ≠ i )

， P verifies i

f j (i )

by equation (3). t −1

f j (i ) P = ∑ i k ⋅ a j ,k P

(3)

k =0

here,

a j ,0 P = n −1hU + n −1Yo + Y j + k j P .

If equation (3) holds, expert meanwhile, computes Let as xi ' =

Pi computes xi ' = ∑k =1 f k (i) as secret proxy share, n

Yi '= xi ' P as public proxy share.

f ( x) = ∑k =1 f k ( x ) n

，

，

then

we

can

rewrite

xi ' = ∑k =1 f k (i) n

f (i) and Yi '= xi ' P as Yi '= f (i ) P respectively.

B. Oral Defense Experts Threshold Proxy Signature Generation Without Leaking Secrets Let

m be the message to be signed. The message is the suggestions of oral defense P1 , P2 ,...., Pt is t experts,

experts agreeing. Without loss of generalization, suppose

i.e., proxy signers, to cooperate to sign,. The phase will work as follows. Step 1. Each expert

Pi (i = 1,2,..., t ) calculates wi = ∏ j =1, j ≠i t

j and j −i

σ i = ( xi ' wi + xi ) H 2 (m) . Thus, expert Pi ’s proxy signature on message m is partial proxy signature σ i . Pi passes σ i to main server or oral defense secretary. Step 2. After receiving σ i from expert Pi , main server or oral defense secretary will check its validation by equation (4).

e( P, σ i ) = e( wiY 'i +Yi , H 2 ( m)) If all of

σ i (i = 1,2,..., t )

(4)

from all of oral defense experts are valid, main server or

oral defense secretary computes σ ' =

∑

t i =1

σi .

secretary gets the threshold proxy signature

Finally, main server or oral defense

(m, mw , ASID,U , σ ' , k1 P,..., k n P) on

message m. C. Oral Defense Experts Threshold Proxy Signature Verification After receiving the threshold proxy signature

(m, mw , ASID,U , σ ' , k1 P,..., k n P)

on m, any verifier can judge its validation and identify the oral defense experts agreeing, i.e., proxy signers. The phase is stated as follows.

Online Oral Defense System Based on Threshold Proxy Signature

Step 1. From warrant

117

mw and ASID, any verifier can identify main server, i.e.,

original signer, and oral defense experts agreeing. Thus, their public keys can be gotten from CA. ASID includes the identities of oral defense experts agreeing. Step 2. Any verifier including main sever or oral defense secretary will check the validation of (m, mw , ASID,U ,σ ' , k1 P,..., k n P ) by equation (5). n

e( P, σ ' ) = e( H1 ( mw , U )U + YO + ∑Yi + i =1

t

n

∑ Y + ∑ k P, H i

i =1

i

2

(5)

( m))

i =1

If equation (5) holds, the threshold proxy signature (m, mw , ASID,U ,σ ' , k1 P,..., k n P ) from oral defense experts is valid. Threshold proxy signature (m, mw , ASID,U ,σ ' , k1 P,..., k n P ) can be proved by equation (6).

7 Advantages of Online Oral Defense System 1) Schools can select oral defense experts at random in all kind of fields from experts library. By proxy signing delegation, oral defense experts can pass their advices to schools. Schools can verify and confirm the oral defense advices from oral defense experts. Meanwhile, oral defense experts can’t deny their suggestions related with oral defense students. 2) Rights can be fairly distributed. Information can be uniformly provided in time. Virtual communication by face to face helps mutual understanding and individuation, quickens decision-making, simplifies negotiation, lessens confusion and misapprehending, improves persons’ responsibility and weakens some sublime experts’ advices. According to the threshold value, t out of n experts agreeing, imply students related pass the oral defense. In the real world, it should be fair. 3) Save manpower, material resource and money. Due to convenience of oral defense system, the traveling time and cost are reduced. It’s possible for oral defense experts to rapidly gather in short time. Thus, time, resources and money are saved and working efficiency is greatly increased. 4) Remote control and checking can efficiently decrease fraudulent practices. Direct and virtual communication with oral defense experts make credit standing to be constructed and are efficiently supervised. The system makes decision-making and sealed transaction more rapid and convenient. 5) Better quality of oral defense system. In the case of not adding cost, more participation and more transparent to the course of oral defense. Under strict control and monitoring, decisions are made. It is an unprecedented challenge to both students and experts. Therefore, the quality of online oral defense system is high.

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t

e( P, σ ' ) = e( P, ∑ σ i ) = e( P, ∑ ( xi ' wi + xi ) H 2 (m)) i =1

i =1

t

t

= e( P, (∑ ( xi ' wi ) + ∑ xi ) H 2 (m)) i =1

i =1

t

= e( P, ( f (0) + ∑ xi ) H 2 (m)) i =1

n

t

= e( P, (∑ f i (0) + ∑ xi ) H 2 (m)) i =1

i =1

n

t

= e( P , ( ∑ s i + ∑ x i ) H 2 ( m)) i =1

(6)

i =1

n

n

t

i =1

i =1

i =1

= e( P, (ns + ∑ xi + ∑ k i + ∑ xi ) H 2 (m)) n

t

n

= e ( P , ( H 1 ( m w , U ) r + x o + ∑ x i + ∑ xi + ∑ k i ) H 2 ( m ) ) i =1

i =1

i =1

n

t

n

i =1

i =1

i =1

n

t

n

i =1

i =1

i =1

= e(( H 1 (mw ,U )r + xo + ∑ xi + ∑ xi + ∑ k i ) P, H 2 (m)) = e( H 1 (mw ,U )U + YO + ∑ Yi + ∑ Yi + ∑ k i P, H 2 (m))

8 Conclusions Online oral defense system makes oral defense experts and students who are in different places in the same virtual oral defense meeting. After opening the website or system client, the system can be logged in. Thus, PPT can be showed at the same time; multimedia can be played; communication will take place by Peer to Peer; multiple persons can discuss; files will be transferred; remote control and cooperation are made; poll will be voted; in addition, suggestion can be made by proxy signers and meeting can be recorded and replayed, without the limit of time and places. Therefore, the efficiency can be increased on the double. The oral defense system will efficiently accelerate the application of IT network by school users. While we know many advantages of the system, we predict there exist some disadvantages in the online oral defense system. For instance, by remote transferring, the acting of mouths may mismatch the voice and the picture may be delayed. The question focuses on bandwidth and transferring. However, the paper will not discuss the kind of problem. As far as threshold proxy signature is concerned, in the system, there are some open questions which are urgent to solve. For example, how to realize anonymous voting, how to realize modifiable voting, how to withdraw the proxy signing power, and so on. These problems will be further researched in the future in the system.

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Acknowledgment. We thank the reviewers for their valuable comments that helped us improve the quality and presentation of our work.

References [1] Cha, J.C., Cheon, J.H.: An identity-based signature from gap diffie-hellman groups. In: Desmedt, Y.G. (ed.) PKC 2003. LNCS, vol. 2567, pp. 18–30. Springer, Heidelberg (2002) [2] Mambo, M., Usuda, K., Okamoto, E.: Proxy Signature for Delegating Signing Operation. In: Proceedings of the 3rd ACM Conference on Computer and Communications Security, New Dehli, India, pp. 48–57. ACM Press, New York (1996) [3] Boneh, D., Lynn, B., Shacham, H.: Short Signature from the Well Pairing. In: Advances in Cryptology-Asiacrypt 2001, Springer, Heidelberg (2001) [4] Wang, C.: Study on the Applications of Undeniable Signature and Proxy Signature. XIDIAN University, Xi’an (2003) (in Chinese) [5] Yi, L.: Study on Proxy Signature Schemes and Their Applications. Xi’an XIDIAN University, Xi’an (2000) [6] Zhang, J., Wang, H.: New proxy blind signature scheme and its application in electronic cash. Application Research of Computers 26(1), 347–358 (2009) [7] Wu, M., Wang, R.: A study on the Application of proxy blind signature in electronic commerce based on mobile agent technology. Journal of Nanjing University of Posts and Telecommunications 25(5), 84–94 (2005) [8] Xue, Q.S.: Design, Cryptanalysis and Implementation of Novel Proxy Signature Protocols. Shanghai Jiaotong University (2005)

Happy Farm an Online Game for Mobile Phone Quanyin Zhu, Hong Zhou, Yunyang Yan, and Chuanchun Yu Faculty of Computer Engineering, Huaiyin Institute of Technology, Huaian, Jiangsu Province, China [email protected]

Abstract. This paper illustrates the game Structure, the file hierarchies, the whole game classes designed and methods to develop the mobile game Happy Farm which is widely welcomed in China recently. Game API in the Mobile Information Device Profile MIDP 2.0 is used to build the game engine; And components designs and implementation steps of the game are introduced in detail. Various techniques, such as object pool, multi-threaded, socket connection, Maps etc., are applied in Happy Farm's development. Experiment demonstrates its performance and proves that this case is meaningful and useful to develop other online mobile games. In addition, some propositions for further research are also suggested.

（

）

Keywords: Happy Farm, mobile phone game, game structure, MIDP.

1 Introduction Internet on mobile terminals is developing at an astonishing speed, which indicates the mobile service in China possesses a tremendous market value in the future [1]. Online mobile games have been progressed very quickly recently, and some researchers focused on transforming the internet game to the online mobile game [2,3,4]. Mobile games of graphics class which are suitable for different people of all ages are widespread now. An Activity Theory (AT) model using modern digital technology, AT components and example are given [5]. A method for customization of all aspects of game (contents, graphics, game style, rules, victory conditions) with a wide possibility is researched [6]. Some interesting and useful ideas are experimented such as rating the descriptiveness of images in a system as well as some methods to improve position [7], estimating and detecting various hand gestures and postures of a user with a two-axis accelerometer [8], and a plausible physically based model for animating and rendering fire and smoke on a mobile platform [9]. Some applications of mobile phone games for the future experience are researched. For example, applying the human ability to control a video game on a mobile device, electroencephalographic (EEG) Mu rhythms are used which depends on the signals obtained using a specially designed electrode cap and equipment, and be sent through a Bluetooth connection to a PC which processes it in a real time [10], using an input data from slide on-off and microphone interface on the mobile phone which is based on pop-up function of wipi platform and uses traditional materials [11]. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 120–128, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Online games of mobile phone are welcomed by everyone because it can be played every time and everywhere. The game Happy Farm which is widely welcomed in China recently can be played by children and senior citizen. However, it only can be played on internet. The case study is a main approach which are very useful to develop the mobile phone games and was reported on some papers [2,3,12,13]. This paper focused on an interested game Happy Farm which is widely played. The online mobile games’ client architecture, depended on the jQuery framework, and server architecture, depended on the Struts and Hibernate framework, are introduced. Communications between the client and the server apply the middleware platform which is developed on the Socket API.

2 Game Class and Frameworks A. Class Design The process of the game development is based on the idea of object-oriented, so that the function of each module should be as independently as possible. There are a total of 8 classes designed for the game depend on Java game class: 1) GameMIDlet GameMIDlet Class inherits from javax.microedition.midlet. MIDlet, it should implements three means at least that are startApp(), pauseApp() and destroyApp().The MIDlet will be on the start which is transferred from startApp() through Java Application Manager (JAM), that means the game can be start. The MIDlet will be paused when JAM implements pauseApp(), and make it to wind up by using the destroyApp(). GameMIDlet is a kind of entry class for game development. It can build a new object employed hereafter for each game class. Some new methods commonly used to facilitate other classes for called. For example, public Image loadImage(String path), by referring to the method, and others can easily import images. 2) GameMenuCanvas GameMenuCanvas Class inherits from javax. microedition.lcdui.Canva, and it can achieve the interface of java.lang.Runnable this is mainly responsible for the handling of the menu interface and buttons. It affirms that a variable in which GameStatus used to determine the state of the game, when the game is set up state, the display settings screen; and when the game is to help state, display help screen. 3) GameRegister GameRegister Class carry out the interface of javax. microedition.lcdui. CommandListener and javax.microedition. lcdui.Runnable, they login and registration for user interface processing by getting user input relevant information to the user's login or register.

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4) Connection Connection Class accomplishes the communication between the server and the client. The server has two for each client connection object to complete the data to send and receive. Each client will be automatically activated after start-up connection and establish the conversation with the server. 5) GameRMS GameRMS Class which encapsulates the method to complete the relevant function will be implement when each user logs need to be storage and retrieval information in the user's mobile phone. 6) MainMenu MainCanvas Class inherits from javax.microedition.lcdui. Canvas. It is responsible for the main game interface display. 7) ThreadCrop ThreadCrop Class realize the interface of javax. microedition.lcdui.Runnable. It is primarily responsible for the growth of vegetables. 8) TransformStr TransformStr class encapsulates methods to achieve a number of uncertainties related to the length of characters in vertical display which does this by passing a string and brush; you can return a string with a vertical transparent image. The relationship between the whole projects is shown in Figure 1. GameMIDlet GameRegister

MainMenu

GameRMS

Connection GameMenuCanvas

TransformStr

ThreadCrop

Fig. 1. Class Relationship Diagram

B. File Hierarchies The File Hierarchies of the game show in Figure 2. a) code for the source code belongs to package which process all the classes in this package under it. b) iamge is the picture of resources which is only for .png format. It will be used throughout the game, including the images stored resources, and named according to their purpose.

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Fig. 2. File Hierarchies Diagram

C. Game Frameworks The framework of the game is divided into three kinds of states: Wait for the status, operation status (running state is divided into multiple sub-states), the end of the state, they run the game in the whole world. The game structure is based on the state machine to run the game a variety of different forms of run-time is divided into a one state, at any time there will be a state to be executed. Taking into account the game state nested problems, all of the state of each area separated from the main thread in the game constantly judgments in order to achieve the transition between states. The game structure is shown in Figure 3.

Fig. 3. Game structure

3 Game Designs A. Game Startup The final form of the game is .Jar format. it is reality a zip compressed format. It is the only difference is that with the zip which a META-INF directory, including a MANIFEST.MF file for the description of the project, meet the format standards of the content can be the automatic identification, and running the program can also be used Midlet.getAppProperty ( "xxxx") method to get the file in the property value.

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The content of document MANIFEST.MF is as follows： Manifest-Version: 1.0 MIDlet-Vendor: HYIT MIDlet-Version: 1.0.0 MicroEdition-Configuration: CLDC-1.1 MIDlet-Name: QQ Happy Farm（Mobile version） MicroEdition-Profile: MIDP-2.0 MIDlet-1: GameMIDlet,/images/icon.png,src.code.GameMIDlet B. Game Main Menu 1) Logo interface Logo interface is used to display the game copyright. It is encapsulated in the GameMenuCanvas class. The Logo screen can be drawn by calling GameMenuCanvas of drawLogo (Graphics g) method when the game is in Logo state. The rolling screen background is achieved by calling GameMenu- Canvas the drawRect (Graphics g) methods which should to start a thread, and constantly changing draw a small square of the X and Y coordinates to achieve the scrolling effect.

(a) Logo Interface

(b) Main Menu (c) Setting Menu

Fig. 4. Game Logo Interface, Main Menu and Setting Menu

2) Main Menu GameMIDlet class is the game entrance class which should be set the current display of GameMenuCanvas class first, public void startApp() { display.setCurrent(menuCanvas); } showMenu() is used to initialize the menu, loadImage (String) is used for image loading, setColor (Graphics, int) is used to set the brush color, setColor(Graphics,int), thread run() is used for the network connection to the network, drawMenu Back(Graphics) used to draw the menu background, in the drawMenu(Graphics) and

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drawRect(Graphics) call drawMenu Back(Graphics) method to draw the whole menu, drawSeting (Graphics) drawing settings information, drawHelp(Graphics) drawing help information, paint(Graphics) by calling drawMenu(Graphics) methods to drawing the menu which is a logical hub for the entire game etc., thread run() is mainly used to redraw the screen and changing the background of the coordinates of the sun and the clouds to achieve dynamic effects, and . keyPressed (int) is the key approach throughout the game. The game logo interface, main menu and setting menu are show in figure 4. 3) Implementation Logic of the Main Menu The game starts from GameMIDlet class. GameMIDlet class as the entrance of the game class, it should to set the current display category as GameMenuCanvas class at first. Eight static constants (GAME_MENU, GAME_START, GAME_REGISTER, GAME_SETING, GAME_HELP, GAME_EXIT, GAME_LOGO, and GAME_LOGIN) are defined used to identify in which the state of the menu is in MenuCanvas class. In the initial state of the game GameMenuCanvas status Logo, when the players from the Logo screen press any key to enter the menu interface, through the keyPressed (int) method call initMenu () method, set the state of the game for the menu state (GAME_MENU), by detecting the left and right key of the mobile phone to display the game menu options (for example, start the game, user registration, etc.), MenuIndex value plus one for right-click, and minus one for left-click. The state of games will be determined and the relevant picture will be drawn depending on the value of MenuIndex when the user presses the OK button. C. Game Implementations 1) The Methods of the Main Menu Class The game main menu is processed by MainMenu class. The methods are used in the class, as shown in Figure 5.

Fig. 5. The methods used in the class of Main Menu

MainMenu (Display, GameMIDlet) is a kind of constructor which is used to pass the entrance classes and brushes come in convenient for later on, and some variables are also initialized in the constructor. initMainMenu (Graphics) is used to initialize the main

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interface, through which the method call paint (Graphics) method to draw the game main menu, and drawGameBack (Graphics) method draw the game's background using paint (Graphics) method. drawShop (Graphics) method is used to painting shop. drawStorage (Graphics) is used to the painting warehouse. drawAddone (Graphics) for the painting stores to buy goods +1 dynamic effects. drawMyseed (Grahpics) method is used to painting whom purchased the seeds. keyPressed (int) method is used to key processing. 2) Running Game Five static constants (LAND, MESSAGE, MENU, TOOL, and FRIEND) are defined in the MainMenu class which is used to identify the selected state. When the players enter the game from the login window of the main interface, the initial state (SelectStatus) is land (LAND) which can also be selected by press ‘3’ key. Press ‘1’ key for information widow, press ‘9’ key for menus widow, press ‘7’ key for tools widow, and press ‘0’ key for friends widow. The land and friends widows are show as Figure 6.

(a) Land

(b) Friends Widow

Fig. 6. Land and Friends Widows

In the menu bar, players can buy in a store seeds and the start is 400 gold coins. Once purchased, you can buy in the tool bar to select the seed planted in the land. The vegetables can be a single removal or remove all by the player after mature. Players can use the tool bar, shovel to eradicate leaves when removal is completed. In the warehouse will be shown the fruits after removal is completed, you can sell the fruits of your own. Further more, player's experience and gold has also increased. Warehouse, planting and harvest are show as Figure 7.

(a) Warehouse

(b) Planting

(c) Harvest Fig. 7. Warehouse, Planting and Harvest

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D. Conclusions and Future Works Along with the progress of 3G and 4G technology, the market for mobile phone games will have a huge prospect. Mobile phone games are welcoming for numerous mobile ISP. The currently popular game for mobile phone Happy Farm has been attempted in this article. JAVA-based games can be implemented on all intelligent mobile phones, and own a better portability. The ubiquitous mobile phone games offer players a great convenience to enrich their spare time. This paper describes the farm game’s development platform based on MIDP2.0. The development of the game Happy Farm for mobile phones has been described; the game framework, game classes design and methods are given as well. Due to the restrictions on the mobile processor performance, many technical issues such as the map algorithms, game performance and network performance need to be optimized and researched.

References [1] Li, J., Alexandra, P., Sanxing, C., Yajing, C.: Trends on Interactive Platforms for Social Media through Web2.0. In: International Conference on MASS 2009, September 20-22, pp. 1–2 (2009) [2] Zhu, Q., Zhao, L., Cao, S., Shen, J., Zhang, S.: A BnB Mobile Game Online Based on J2ME and J2EE. In: 7th ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2009, December 2-4, pp. 19–24 (2009) [3] Zhu, Q.-Y., Zhang, H., Sun, W.-J.: Research of key technologies of mobile network games based on J2ME and J2EE. Computer Engineering and Design 29(20), 5218–5221 (2008) [4] Xu, C.-w.: A Software Framework for Online Mobile Games. In: International Conference on Computer Science and Software Engineering, CSSE 2008, vol. 1412, pp. 558–561 (2008) [5] Sedano, C.I., Botha, A., Pawlowski, J.M.: A conceptual framework for ubiquitous mobile environments. In: Portable Information Devices, and the 2008 7th IEEE Conference on Polymers and Adhesives in Microelectronics and Photonics, 2nd IEEE International Interdisciplinary Conference, August 17-20, pp. 1–6 (2008) [6] Mininel, S., Vatta, F., Gaion, S., Ukovich, W., Fanti, M.P.: A customizable game engine for mobile game-based learning. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, October 11-14, pp. 2445–2450 (2009) [7] Hu, X., Stalnacke, M., Minde, T.B., Carlsson, R., Larsson, S.: A Mobile Game to Collect and Improve Position of Images. In: Third International Conference on Next Generation Mobile Applications, Services and Technologies, NGMAST 2009, September 15-18, pp. 70–73 (2009); DOI: 10.1109/NGMAST.2009.64 [8] Baek, J., Yun, B.-J.: A sequence-action recognition applying state machine for user interface. IEEE Transactions on Consumer Electronics 54(2), 719–726 (2008) [9] Park, D., Woo, S., Jo, M., Lee, D.: An Interactive Fire Animation on a Mobile Environment. In: International Conference on Multimedia and Ubiquitous Engineering, MUE 2008, April 4-26, pp. 170–175 (2008) [10] Pour, P.A., Gulrez, T., AlZoubi, O., Gargiulo, G., Calvo, R.A.: Brain-computer interface: Next generation thought controlled distributed video game development platform. In: IEEE Symposium on Computational Intelligence and Games, CIG 2009, December 15-18, pp. 251–257 (2008)

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[11] Kim, M., Park, S.-R., Song, S.-K.: Mobile interface for physical interactive games applied. In: IEEE 13th International Symposium on Consumer Electronics, ISCE 2009, May 5-28, pp. 1024–1028 (2009) [12] Nascimento, L.M., de Almeida1, E.S., de Lemos Meira, S.R.: A Case Study in Software Product Lines - The Case of the Mobile Game Domain. In: 34th Euromicro Conference Software Engineering and Advanced Applications (SEAA), pp. 43–50 (2008) [13] Cho, H., Yang, J.-S.: Architecture Patterns for Mobile Games Product Lines. In: ICACT 2008, pp. 118–122 (February 2008)

Analysis and Intervention on the Influencing Factors of Employee’s Job Insecurity Qiong Zou School of management, Wuhan University of Science and Technology, Wuhan, Hubei Province, China [email protected]

Abstract. The job insecurity reflects the perception and worry of employees when their work or the important characteristics of their work are threatened. In order to reduce staff stress and improve employee’s work performance, this paper includes society, organization and individual factors influencing on job insecurity. And then, the article provides five countermeasures to relieve employee’s job insecurity. Keywords: Job insecurity, Organizational communication, Job control, Job involvement, Employee assistance program.

1 Introduction With the fierce global competition, the tide of enterprise reorganization and simplifying are continuing, the employee reduction are increasingly frequent, statistics show that Fortune 500 have been or are reducing employee or retrenching, the current companies don’t consider employee reduction as the countermeasures of technology bubble, but consider it as the important changes of enterprise management employee system. The scope of employee’s job insecurity is very wide, and it is no longer confined to a particular class. To different extent, the job insecurity exists in enterprise manager, white-collar or blue-collar classes. The employee’s job insecurity brings the negative influences from many aspects, and it is both long-term and short-term, both affects the individual physiology and mental health of employees and the commitment, work attitude and behavior intention of employees for organization, which ultimately affects the performance of employees and enterprises. Therefore, the academic circles and enterprises should pay high attention to employee’s job insecurity, which will become one of the things that enterprises focus on persistently in new period.

2 The Origin and Definition of Job Insecurity The concept of job insecurity can be traced back to the early research of industrial and organization psychologists, their main concern is the positive role that job insecurity plays in the organization. In the period of the great depression, there has seen some Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 129–135, 2011. © Springer-Verlag Berlin Heidelberg 2011

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early research results about job security. Between 1940 and 1960, with the beginning of the human relations school after the study of Hawthorne, it stimulated the interest of the new study on the job satisfaction and motivation of the employees. In the following 20 years, job security is considered as one of the external hygiene factors, or one aspects of job satisfaction, or a job characteristic. Intuitively speaking, both the general public and the specialized researchers all agree that the job insecurity reflects an uncertainty whether the work can survive or not. However, in the initiative research of job insecurity, Greenhalgh and Rosenblatt proposed a more complex definition: “Job insecurity is in a threatened working circumstance, the powerlessness for the maintenance of the hoped continuity.” This definition is cited and discussed most by the subsequent scholars, but simultaneously it receives many questions. Generally speaking, the current theory circle doesn’t reach the agreement on the definition of the job insecurity, this situation largely has been limited the progress of theory and empirical study.

3 The Influencing Factors of Job Insecurity A. Social factors (1) Global competition Under the impact of world economic integration, the technological change and innovation speed accelerate, and the international market competition is stormy. The “inverse price” of raw materials price and industrial products factory price makes the cost burden of enterprises heavier and heavier; the poor market development makes the sales growth and capacity expansion mismatch; the decreasing production and sales rate makes the circulating fund work slowly; the supply exceeds demand of the products makes the price depress continually; the energy consumption and labor cost show the increasing trend; the lack of strategic management and the diversification decentralize the financial and material resources. When facing these pressures, many enterprises deal with competition through reform, while the reform always increases the job insecurity of employees. (2) Economic policy and economic structure The rapid change of economic policy and economic structure are also one of the factors which lead to the employee’s job insecurity. The change of economic policy and economic structure can always lead to the change of enterprise strategy and production mode, and there exists the personnel adjustment during the change of enterprise strategy and production mode, resulting in the appearance of the insecurity of workers’ work. (3) Personnel policy The personnel policies are more and more flexible in human resources market, the signing of the short-term contracts and the terminating of the contract are more convenient than 20 years ago, the appearance of this situation is the important factor which leads to the job insecurity of employees.

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B. Organizational factors (1) Organizational change The organizational change leads to the loss of work, and it is one of the most significant reasons which produce the pressure and job insecurity. According to Lee and Bobko, the expected organizational change and job insecurity are positively correlated. The organizational change is shown through downsizing, restructuring and merger, and it has been found that it is related with stress and tension. Greenhalgh and Rosenblatt also note the changes, the merger, employee reduction, restructuring and new technology and physical danger is the source of threat, which will lead to job insecurity. Meanwhile, Schweiger, Roskies and Guefin respectively through horizontal and vertical research have also confirmed the merger, employee reduction and annexation are related with the high level of job insecurity. (2) Organizational condition The organizational condition is the management situation of organization, including organizational communication, confidence of management, objective management state, etc. These factors will affect the perception of employees’ subjective threat, and then further affect their job insecurity. In the experimental study on the two factories, Schweiger and Denisi find that in the merger process, the communication with the employees can help to deal with the uncertainty and avoid the related negative results. The failure of communication will lead to the result that the employees feel uncertain about their future, and it is just this uncertainty not the change itself, which is stressful for employees. It is an important task to manage the job insecurity to communicate with employees as soon as possible according to the expected change results. Lack of confidence of management also is the influencing factor of job insecurity. According to the three years’ longitudinal study on paper mills, banks and social health and medical care department, Kinnunen, Mauno and Natti believe that the objective management state of organization is the source of job insecurity, and the more negative the economic situation of the organization, the higher the possibility of the job insecurity. (3) Organization performance and the fairness of the procedures Whether the organization has a good performance or not will affect the judgments of employee for organization survival period, and further affect the judgment of its work sustainability, and ultimately affect the employee’s job insecurity; if the organization internal program can not be fair and justice, it will increase the employee’s powerlessness, and further increase the employee’s job insecurity during organizational change. So far, there has been few researches on the relationship between organizational justice and job insecurity, but Greenhalgh and Rosenblatt believe that if the organization lacks the strong systems, the employees can’t participant in the decision and have no right to appeal, it will increase the powerlessness when employees face working threats.

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C. Personal factors (1) Job factors The job control, job involvement, role ambiguity and role conflict all can affect employee’s job insecurity. Mauno and Kinnunen, according to the study on the 387 full-time dual-earner couples in Finland, have found that low job control is the most significant prediction index of job insecurity. Probst and his Colleague point out that those employees participant in the work more actively found the stronger negative influence of job insecurity, and they also point out that the job involvement plays an important role in job insecurity. Those employees who are worried about job insecurity will show high job involvement, which are related with the worry of the job sustainability. Role ambiguity and role conflict all can threaten the individual’s personal control, only the extreme role ambiguity and role conflict will produce higher job insecurity and impede the employee’s normal work. The study of Ashford, Lee and Bobko's has shown that the role conflict is not related with job insecurity, while role ambiguity and job insecurity are positively correlated. However, the study of Pasewark and Strawser on accountants has shown that the role conflict and job insecurity are positively correlated significantly. (2) Personality Few studies focus on the role of personality in the study of job insecurity. However, Roskies and Guefin suggest that the job insecurity is the pressure source of chronic occupation. Because the job insecurity exists long time, the personality becomes particularly more important. Mauno and Kirmunen put forward that the personality can better predict the emotional part of the losing jobs (such as fear). Other studies also have been found that the external control points, self-esteem, negative emotions and low consistency are related to job insecurity. (3) Employees’ background The researchers concern about the influences of employees’ background for job insecurity, such as demographic characteristic factors (such as age and gender), job factors (such as the experience of casual worker and the nature of employment relationship), time and organization factors (such as the nature of their organization), and form different opinions. Some scholars discuss the relationship between employees’ background and job insecurity, such as age, sex and education, etc. some scholars concern about the gender influence on the result of the job insecurity, and also some scholars concern about the buffer function of job insecurity.

4 The Management Strategy of Mitigating Employee’s Job Insecurity Job insecurity, a negative implication for the organization, its destructive power is enormous, and how to control job insecurity effectively is an urgent problem that needs to solve. Now that social factors and personal factors are beyond our control, the management and intervention of organizational factors should be the main point of mitigating employee’s job insecurity.

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A. The enterprise should build a scientific and strategic human resource planning Building the human resource planning from the perspective of strategy, forecasting the scientific economic trends and the development prospects of this industry to prevent the employee’s job insecurity caused by employee surplus and greatly employee reduction, and also should treat employee reduction and rightsizing of enterprise reorganization in a scientific way. The study of Casscio shows that it can’t achieve the expected results through employee reduction and rightsizing to make profit, but will lead to a series of negative effects. B. The enterprise should build transparent employee participating platform to increase internal communication and enhance sense of trust Because job insecurity is the employees’ cognition based on reality or assumption, to build transparent employee participating platform, to let employee participant in various decision-making of enterprises, and to clear the long-term and short-term planning of enterprises, can effectively avoid to increase employee’s job insecurity because of the impact of the wrong message, and also can enhance the sense of trust between employee and organization. Even if it is imperative to retrench or reduce employee, the participation of employees can effectively prevent the “survivor syndrome” because of employee reduction, and it can also keep the centripetal force of employee to organization and retain the organization of excellent employee. C. The enterprise should improve organizational performance The high performance of organization shows the competitive advantage of organization in the same industry, and also indicates the stable development of the organization in a certain period. It can guarantee the income of employees and the sustainability of the future work, and then to improve organizational performance can reduce the external job insecurity of employees. D. The enterprises should train employee better The enterprises should support the active action of employees, provide the corresponding training and active feedback and reinforcement, thus can enhance the employee’s work competition, in order to avoid increasing the employee’s job insecurity because of lacking work ability when the job characteristics change. And the training of employees can also increase the sense of belonging and satisfaction of employees for enterprises. On the one hand, the managers should be trained. It can help the managers to learn some psychological theories and tutorial techniques, understand the employee’s psychological state and job expectations and fears to prevent and identify the appearance of psychological problems of employees. And through timely counseling and interviews, the managers can eliminate fear and stress of employees to reduce their job insecurity. On the other hand, the enterprises should organize the special training or group counseling for employees to maintain positive mood, work-life balance, self-growth, career planning, organizational communication and so on to improve the ability of self-management and selfplanning of employees.

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E. Develop employee’s assistance program The above is only the countermeasures for employee’s insecurity from different perspectives, while the management technique of employee assistance program (EAP) which is popular in the developed countries makes the work stress management become an effective measure to stabilize the employee’s heart, to make staff not to be surprised at anything sudden and to make them to come together. It is a systematic, long-term welfare and support programs set by the enterprises for employees. Through the organization diagnosis and suggestion, professional guidance, training and consulting for employee and their immediate family provided by professionals, the enterprises aim to help to solve the various psychological and behavioral problems of employees and family members, and to improve the employee’s job performance in enterprises. The employee’s insecurity is the interaction result between personal and environmental factors, and EAP is a good solution whether the reason belongs to which one. The meaning of implementing the EAP is to provide “spiritual welfare” for employees, to eliminate the stress and insecurity, and then to raise employee morale, improve the organizational climate, raise organizational culture, reduce the management cost of organization, in order to help the enterprises to cope with the changes and crises better, such as business reorganization, merger and employee reduction, etc. and to ensure the healthy growth of our organization and employees.

5 Conclusions Since 20 years’ research, the field of job insecurity has accumulated a considerable amount of research results, although many scholars still have some different ideas about the defining of the concept of job insecurity, “the reflection of job insecurity is the uncertainty whether it can survive or not for work” is accepted unanimously. The researchers make a more detailed study on the influencing factors from the society, organization and individual. In view of job insecurity brings so many negative consequences for enterprises and individuals, the managers should from organizational factors build scientific, strategic human resource planning, increase internal communication, improve organizational performance, train employees better and develop employee assistance program to mitigate the employee’s job insecurity. It is noteworthy that the employee’s job insecurity is a “double-edged sword”, because it will bring negative consequences and is also a pressure mechanism, and the moderate job insecurity can optimize the working attitude and behavior of employees, and then to improve organizational performance. Meanwhile, the enterprises should also effectively control the employee’s job insecurity, and try to exclude the core employee outside the scope of job insecurity.

References [1] Chen, H.: The Source and Result of Job Insecurity: The Empirical Study on Zhejiang IT Enterprises Employees, pp. 27–39 (2006) [2] Cheng, J., Wang, Q., Yuan, Y.: Analysis on the key Influencing Factors of Job Insecurity—from the Perspective of the Current Management of Private Enterprises. Management & Fortune 2, 58–59 (2009)

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[3] Cheng, Y.: The research progress and assumption of the contract nurses’ job insecurity. Journal of Nursing Administration 9, 29–31 (2009) [4] Feng, D., Lu, C., Xiao, A.: The relationship between Job insecurity and well-being, performance: the role of self-efficacy. Acta Psychologica Sinica 40, 448–455 (2008) [5] Hu, S.: Job Insecurity and Its Influence Mechanism for the Organization of Outcome Variables, pp. 4–8 (2008) [6] Hu, S., Zuo, B.: Job Insecurity and Its Influence on Job Pressure, Job Satisfaction and Performance. Chinese Journal of Clinical Psychology 15, 142–145 (2007) [7] Li, T.: The Causes and Countermeasures of the Production of Enterprises Employee’s Insecurity. Chinese Human Resources Development 2, 30–33 (2006) [8] Li, Z., Li, Y.: The Influencing Factors and Countermeasures of Job Insecurity. Modern Management Science 2, 62–63 (2008) [9] Liu, Y., Li, Y.: The Coping Strategies of Employee’s Job Insecurity. Human Resources Development 4, 90–91 (2009) [10] Xie, Y., Xiao, A., Ren, X., Shi, K.: The Influence of Procedural Justice on Job Satisfaction and Organizational Commitment: the Intermediary Role of Job Insecurity. Chinese Journal of Clinical Psychology 15, 138–141 (2007) [11] Zhu, Y.: Study on the Relationship between Employee’s Job Insecurity and Job Satisfaction. Chinese business 9, 292 (2009) [12] Burgard, S.A., Brand, J.E., James, S.: House Perceived job insecurity and worker health in the United States. Social Science & Medicine, 777–785 (2009) [13] Linz, S.J., Semykina, A.: How do workers fare during transition? Perceptions of Job Insecurity Among Russianworkers (1995-2004); Labour Economics, 442–458 (2008)

An Analysis on Incentive Mechanism for Agents under Asymmetric Information Condition Zhao Chenguang and Xu Yanli School of Management, Harbin Normal University, Harbin, 150025, P.R. China [email protected]

Abstract. Agents’ speculation behaviors are series problems which need to be solved. Through mathematical models and game theory, this paper analyzes optimal incentive contracts between principals and agents under asymmetric information condition in order to solve the problems in establishing incentive mechanism for agents. The relationship between incentive and risk is discussed and the best equilibrium between them is given. Supervision mechanism is introduced and analyzed in the design of incentive mechanism for agents. It expands the theoretical frame for incentive mechanism. The results are as follows: Supervision mechanism is related and complementary with incentive mechanism. Both can encourage or guide the agents to work hard towards common goals. Therefore, the function of the incentive mechanism and the supervision mechanism should be considered simultaneously in the design of incentive contracts for agents. It helps to prevent agents from speculating. Introduction of supervision mechanism into incentive mechanism not only results in theoretical innovation, but also has great application value in practice. Keywords: asymmetric information, incentive mechanism, mechanism, game theory, mathematical models.

monitoring

1 Introduction Incentive mechanism is an important problem in management science. There are mainly two aspects about incentive theory research. One is the incentive theory research in management science. The other is the incentive theory research in economics. Early theories of motivation in management science include Maslow’s hierarchy of needs theory, McGregor’s theory X and theory Y, Herzberg’s motivationhygiene theory. Contemporary theories of motivation in management science include three-needs theory, goal-setting theory, reinforcement theory, equity theory, expectancy theory[1,2]. The outstanding weakness of the incentive theory research in management science is that it has not broken through in quantitative analyses[3-5]. Incentive theory researches in economics make great progress. Their main analytic framework are established based on principal-agent relationship. Wilson, Spence and Zeckhauser, Ross present state-space formulation[6,7]. Mirrlees, Holmstrom present parameterized distribution formulation[8-10]. The third model is general distribution formulation[11]. These methods are used widespread. However, the shortage of the Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 136–143, 2011. © Springer-Verlag Berlin Heidelberg 2011

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incentive theory research in economics is that it is concerned too narrow[12,13]. The important characteristic of this paper is that supervision mechanism is analyzed in the incentive theory research. It will expand the theory frame for incentive mechanism.

2 Basic Framework of Incentive Mechanism Based on PrincipalAgent Relationship

，

，

Diligence level or effort degree for an agent is represented by e e ∈ A and A is action collection adopted by the agent. θ represents the external nature state, whose

g (θ ) . Production π is highly related with the diligent degree e for the agent, and also be affected by the nature state θ . Function π can be described as π = π (e, θ ) . The salaries S for the agent paid by probability density function is represented by

the principal are related with the diligent result

π

for the agent. That is,

S = S (π ) = S (π (e, θ )) . The utility function for the principal is a function of production π and the salaries S for the agent paid by the principal. That is, v( S ,e, θ )= π (e, θ ) S ( π (e, θ )). According to the state-space formulation[14-19],

－

the expectation utility function for the principal can be described as:

E (v) = ∫ v( S , e, θ ) g (θ ) dθ The utility function for the agent is a function of effort degree e and the salaries S π for the agent paid by the principal. That is expressed as u( S ,e, θ ). The expectation utility function for the agent can be described as:

( )

E (u ) = ∫ u ( S , e, θ ) g (θ ) dθ The principal try to maximize their expectation utility function for themselves through choosing the salary function S π and the diligence level e for the agent. The problem that the principal is faced with can be described as:

( )

max E (v) = max ∫ v(S , e, θ )g (θ )dθ e , s (π )

e , s (π )

But the maximum expectation utility function for the principal is restricted by two constraints for the agent. The first constraint is the participation constraint for the agent. That can be also called the individual rationality constraint (IR). That is to say, the expectation utility that an agent gets from accepting the contract can not be less than the largest expectation utility that an agent gets when not accepting the contract. The largest expectation utility that an agent gets when not accepting the contract, which can be called as the reservation utility of an agent, represented by u , is decided by other market opportunity an agent is faced with. The participation constraint (IR) of an agent can be described as: (IR)

E (u ) = ∫ u ( S , e, θ ) g (θ )dθ ≥ u

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The second constraint is the incentive compatibility constraint for an agent. Because the principal can not observe the diligence degree for an agent directly, an agent always chooses the diligence level that makes his own expectation utility function maximum under any incentive contract. Thus any results that the principal hopes can be realized only through the behavior of the expectation utility maximum of an agent. ∗ In other words, if e is the action of an agent whom the principal hopes and e is any action that an agent can choose, an agent would choose action

e∗ only when the

∗

expectation utility that an agent gets from choosing e is no less than the expectation utility that an agent gets from choosing e . The incentive compatibility constraint (IC) can be described as follows:

（IC）

Eu ( S , e* , θ ) ≥ Eu ( S , e, θ ) , ∀e ∈ A

In a word, the basic framework of the incentive contract between a principal and an agent can be described as follows:

max E (v) = max ∫ v(S , e, θ )g (θ )dθ e , s (π )

s.t.

( IC )

( IR)

e , s (π )

E (u ) = ∫ u ( S , e, θ ) g (θ )dθ ≥ u

Eu ( S , e* , θ ) ≥ Eu ( S , e, θ ),

∀e ∈ A

The nature of principal-agent theory is to maximize the expected utility function for the principal through the principal’s selecting the remuneration function s π paid to the agent and the agent’s action e . At the same time, the expected utility function for the principal must be subjected to the two constraints for the agent. One is the participation constraint that is also called the individual rationality constraint (IR) and the other is incentive compatibility constraint (IC).

( )

3 Design of Incentive Contracts for Agents Asymmetric information means that an agent has his own private information at some aspects, which only an agent understand himself and the principal do not understand or can not understand because of the information cost too high etc.. Because of the information asymmetry, the principal can not measure the diligence degree of an agent accurately. Therefore, the participation constraint and the incentive compatibility constraint for an agent function at the same time. Although the principal can not observe accurately whether the agent endeavor or not, the principal can observe some supervision signals which are related with effort degree of the agent. Therefore, the principal can design the remuneration contract for the agent according to production and supervision signals observed. A. Model Assumptions

：

Hypotheses are presented as follows in order to research conveniently

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Hypothesis 1: Suppose that the enterprise production function can be described as

π = ke + ε . ε is a random quantity, representing the undetermined factors, such as 2 market, etc.. ε ～N(0, δ ε ). The abilities of the agents is described by k . Eπ = E (ke + ε ) = ke Var (π ) = Var ( ke + ε )= δ ε . 2

Hypothesis 2: Suppose that the monitoring signal is just positive related with the effort degree of agents. The monitoring signal can be described as S m = he + σ . σ is a random quantity, representing the accurate degree of the monitoring signal. σ

～

N(0, δσ ). The related degree between monitoring signal and effort degree is 2

described by

h.

E ( Sm ) = E ( he + σ ) = he Var ( Sm ) = Var ( he + σ )= δ σ

2

Hypothesis 3: Suppose that the production and the monitoring signal are independent mutually, therefore, Cov (π , S m ) = 0 . Hypothesis 4: Suppose that the principals are risk neutral and the agents are risk averse. Then, the absolute risk-averse index of the principals is rp=0, and the absolute risk-averse index of the agents is ra > 0 . The risk cost that the agents undertake is

1 1 raVar ( S ) . The effort cost that the agents undertake is C(e)= be2 . b is effort 2 2 cost index. Hypothesis 5: Suppose that the incentive contract designed by the principal to an agent is S (π ) = α + β ⋅ π + γ ⋅ S m . S (π ) is the remuneration income for an agent.

α is the fixed income for an agent. β is the sharing coefficient of production by the agent. γ is the remuneration proportion paid to the agent according to monitoring signal. B. Model Establishment and Model Solution On the basis of the hypotheses mentioned above, the expectation utility function of the principals can be described as:

Ev( S , e, θ ) = E[π − S (π )] = E[π − (α + βπ + γS m )]

－ β )E( π )- γE ( S = - α +(1－ β ) ke － γhe = - α +(1

m

)

The expectation utility function of the agent can be described as:

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1 Eu ( S , e, θ ) = E[ S (π ) − C (e) − raVar ( S )] 2

=

1 1 E[(α + βπ + γS m ) − be 2 − raVar (α + βπ + γS m )] 2 2 =α

1 + β ⋅ E (π ) − be 2 + γ ⋅ E ( S m ) 2

1 ra [ β 2Var (π ) + γ 2Var ( S m ) + 2 βγCov(π , S m )] 2 1 2 1 = α + β ke − be + γhe ra β 2Var (π ) 2 2 1 2 raγ Var ( S m ) 2 1 2 1 1 2 2 2 = α + β ke − be + γhe ra β 2δ ε raγ δσ 2 2 2 -

－

－

－

－

The incentive compatibility constraint of the agent can be described as:

max e

max

Eu ( S , e, θ ) =

e

［ α + βke − 12 be + γhe － 12 r β δ － 2

2

a

1 2 2 raγ δσ 2

］

2

ε

(1)

e is: β k − be + γh = 0

The first order of

e=

kβ + hγ b

(2)

Then, the incentive contract designed by the principals to the agents is: max E (v ) = max [ −α + (1 − β )ke − γhe] e , s (π )

e ,α , β ,γ

s.t. (IR)

α + βke − be 2 + γhe － ra β 2δ ε 2

－ 12 r γ δ 2

a

1 2

2

σ

1 2

≥u (IC)

e=

u is the reservation utility of an agent.

kβ + hγ b

(3)

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The diligence degree of an agent can not be observed directly by the principals under asymmetric information condition. The equation of the participation constraint of an agent is valid. That is:

α + β ke − be2 + γhe － ra β 2δ ε 2 － raγ 2δ σ 2 = u 1 2

1 1 2 2 1 2 1 1 α = u − βke + be - γhe + ra β 2δ ε 2 + raγ 2δ σ 2 2 2 2

(4)

The maximum expectation utility function of the principals is:

max [−α + (1 − β ) ke − γhe] =

e ,α , β ,γ

max[k ( β ,γ

The first order of

kβ + hγ 1 kβ + hγ 2 1 1 2 2 ) − b( ) − ra β 2δ ε − raγ 2δσ − u ] b 2 b 2 2

β

and

(5)

γ

respectively is:

β∗ =

h 1− γ k 1 + rab(

δε k

(6)

)

2

k (1 − β ) γ∗= h δ 1 + rab( σ ) 2 h

(7)

Then, the solution can be deduced as follows:

β∗ ＝ γ∗＝

δσ 2 δσ

2

h rb 2 2 2 + ( ) 2 δ ε + ( a 2 )δ ε δ σ k k

δε 2 h 2 k 2 rb 2 2 ( )δ ε + ( )δ σ + ( a )δ ε δσ k h kh

e∗ =

k (k 2δσ + h 2δ ε ) 2 2 2 2 b(k 2δσ + h 2δ ε + rabδσ δ ε ) 2

(8)

(9)

2

(10)

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β∗

is the optimal sharing coefficient of production designed by the principals to

the agents.

γ∗

is the optimal remuneration proportion paid to the agents according to

monitoring signal.

e∗ is the optimal effort degree of the agents.

4 Analysis and Discussion According to the model and the solution, some conclusions can be drawn up as follows: Conclusion 1: The incentive mechanism and the supervision mechanism can encourage or guide the agents to work hard. Only if β >0 and γ >0 the increase of β and γ can raise effort e . This

，

illustrates that the sharing coefficient of production β and the supervision signal γ can encourage the agents to work hard. Conclusion 2: Supervision mechanism is related and complementary with incentive mechanism. Both can encourage or guide the agents to work hard towards common goals. If supervision signal γ increases, then the sharing coefficient of production β decreases inevitably. On the contrary, if supervision signal γ decreases, then the sharing coefficient of production β increases inevitably. Conclusion 3: The function of the incentive mechanism and the supervision mechanism should be considered simultaneously in the design of the incentive contract. Conclusion 4: The diligence degree of the agents has nothing to do with the fixed remuneration paid to the agents. The fixed remuneration system does not have the function of encouragement to the agents.

5 Conclusion Supervision mechanism is related and complementary with incentive mechanism. Both can encourage or guide the agents to work hard towards common goals. Therefore, the function of the incentive mechanism and the supervision mechanism should be considered simultaneously in the design of incentive contracts for agents. It helps to prevent agents from speculating. Introduction of supervision mechanism into incentive mechanism not only results in theoretical innovation, but also has great application value in practice. Acknowledgment. It is a pleasure to acknowledge the support of the Young Academic Backbone Funding Schemes of Harbin Normal University for this project.

References [1] Robbins, S.P., Coulter, M.: Management, pp. 424–455. Prentice Hall, Peking (2002) [2] Stiglitz, J.E.: Incentives, risk, and information: notes towards a theory of hierarchy. The Bell Journal of Economic 6(2), 552–579 (1975)

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[3] Xu, Y.-l.: A design of incentive contracts of independent directors under asymmetric information condition. Journal of Harbin Institute of Technology 37(12), 1711–1713 (2005) [4] Xu, Y.-l.: Design of incentive contracts of independent directors. Chinese Journal of Management Science 14(Special Issue), 193–196 (2006) [5] Xu, Y.-l.: Research on incentive mechanism of executives based on ability and relative performance evaluation. Chinese Journal of Management Science 16(Special Issue), 41– 44 (2008) [6] Spence, M., Zeckhauser, R.: Insurance, information, and individual action. The American economic review 61(2), 380–391 (1971) [7] Ross, S.: The economic theory of agency: the principal’s problem. American Economic Review 63, 134–139 (1973) [8] Mirrlees, J.A.: The optimal structure of incentives and authority within an organization. The Bell Journal of Economics 7, 105–132 (1976) [9] Mirrlees, J.A.: Note on welfare economics: information and uncertainty. Essays on Economic Bahavior under Uncertainty, 35–47 (1974) [10] Holmstrom, B.: Moral hazard and observability. Bell Journal of Economics 10, 74–91 (1979) [11] Zhang, W.-y.: Game theory and information economics, pp. 235–262. Shanghai People Press (2006) (in Chinese) [12] Xu, Y.-l.: Design and expansion of incentive mechanism framework based on monitoring mechanism. Journal of Harbin Institute of Technology 38(10), 1626–1629 (2006) [13] Xu, Y.-l.: Expansion of the incentive mechanism theory frame. Chinese Journal of Management Science 15(Special Issue), 153–156 (2007) [14] Spence, M., Zeckhauser, R.: Insurance, information, and individual action. The American Economic Review 61(2), 380–391 (1971) [15] Ross, S.: The economic theory of agency: the principal’s problem. American Economic Review 63, 134–139 (1973) [16] Mirrlees, J.A.: The optimal structure of incentives and authority within an organization. The Bell Journal of Economics 7, 105–132 (1976) [17] Mirrlees, J.A.: Note on welfare economics: information and uncertainty. Essays on Economic Bahavior under Uncertainty, pp. 35–47 (1974) [18] Holmstrom, B.: Moral hazard and observability. Bell Journal of Economics 10, 74–91 (1979) [19] Laffont, J.J., Martimort, D.: The theory of incentives: the principal-agent model, pp. 157– 160. Princeton University Press, Princeton (2002)

A Study on Managerial Performance Evaluation Zhao Chenguang, Xu Yanli, and Feng Yingjun School of Management, Harbin Normal University, Harbin, 150025, P.R. China [email protected]

Abstract. Managerial performance evaluation is an important problem in management science. In order to evaluate managerial performance fairly, a newly named analytical method is presented here. This method, two stage relative efficiency model, evaluates managerial performance by eliminating the influence of existing conditions. An example analysis is given. Empirical results show that managers in firms with relatively superior existing conditions still need to work hard to maintain their competitiveness and achieve better than before performance. On the other hand, managers in those firms with relatively inferior existing conditions can also achieve better than before performance and fair evaluation if they conscientiously make great efforts. The method presented here can avoid penalizing good managers who manage within an unfavorable existing condition as well as avoid rewarding poor managers who manage in a favorable existing condition. In a word, the newly named analytical method is an equitable and objective measuring method for efficiency. Keywords: two stage relative efficiency method, data envelopment analysis (DEA), managerial performance, management possibility set.

1 Introduction Measurement of managerial performance is an important problem in management science. Charnes, Cooper and Rhodes[1] present a method named data envelopment analysis (DEA) which is an efficiency assessment technique. Banker and Morey[2,3], Golany and Roll[4] developed one-stage method. However, the single-stage approach runs into difficulty when accounting for environmental factors. Fried et al. [5], Bhattacharyya et al. [6] put forward to two-stage approaches to measure performance. The major disadvantage of two-stage approaches is that they can not account for measurement error. Fried et al.[7,8] present a three-stage approach and a four-stage approach to measure performance. However, these approaches can not account for statistics noise when measuring performance. Avkiran and Rowlands[9] put forward to a three-stage model improving the one presented by Fried et al.[10]. However, they do not provide empirical evidences. Few of the above methods have considered the external environment and the strength of an organization. These factors make up an organization’s “objectively basic condition”. They affect the managerial performance. The subjectively efficient efforts of the managers have not been revealed. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 144–150, 2011. © Springer-Verlag Berlin Heidelberg 2011

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The influence of objectively basic condition should be eliminated if a fair evaluation of managerial performance in an organization would be obtained. A new method to measure managerial performance is presented in this paper.

2 Methodology for Two Stage Relative Efficiency Analysis A two-stage relative efficiency method is proposed. The method is called a two-stage relative efficiency method. The procedure is as follows. A performance measurement system of decision-making units is established in the first stage, and the efficacy coefficient method (ECM) to measure the performance of an organization is used. The data envelopment analysis (DEA) is employed to measure managerial performance in the second stage. The method can be described in detail in the following. Two performance scores are needed in order to analyze two-stage relative efficiency. The performance score in the previous year which is a measure of its intrinsic strength for the next-year operations is defined as the reference index (RI), and the performance score in current year is defined as the current index (CI). The set of (RI, CI) is defined as an index state. In the first stage, performance measurement techniques, such as analytic hierarchical process (AHP) or efficacy coefficient method (ECM) are used to produce the performance score for every organization in each period. In the second stage, the reference indices are considered as inputs and the current indices are considered as outputs. Then DEA method (BCC model) is employed and the managerial efficiency of the organizations is calculated.

3 Model Establishment and Theoretical Analysis A. Definition and Description of Algorithm Managerial efficiency can be defined as a behavior attribute that reflects the efficiency produced purely by management activities while the influence of the objectively basic condition being eliminated. Figure1 illustrates the following definitions. Y

Y

YA

•A

YB

•B

YC

g (X )

•C

0

X

Fig. 1. Management possibility set

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Definition 1: A set of points are defined as management possibility set T if they can be expressed as follows: N

T={(X, Y):

∑λ X s=0

s

N

s

≤ X , ∑λsYs ≥ Y , s=0

N

∑λ s =0

s

= 1, λs ≥ 0 ,

s = 1,2,L, N }

where ( X 0 , Y0 ) = (0,0) . Management possibility set T must meet the following postulates: , and (1) Convexity: If ( X 1 , Y1 ) ∈ T ( X 2 , Y2 ) ∈ T 0 ≤ λ ≤ 1 , i.e. T is convex set. (λX + (1 − λ ) X , λY + (1 − λ )Y ) ∈ T , for 1

2

1

,

(1)

then

2

(2) Inefficiency: (a) If ( X , Y ) ∈ T , and λ ≥ 1 , then (λX , Y ) ∈ T ; (b) If ( X , Y ) ∈ T , and 0 ≤ λ ≤ 1 , then ( X , λY ) ∈ T .

(3) Minimum extrapolation: Management possibility set T is the intersection set of all T that satisfy postulate (1 ) and (2), and subject to the condition that each of the observed vectors ( X j , Y j ) ∈ T , j = 1, 2,…,n. Definition 2: The upper boundary of management possibility set T is defined as management frontier expressed by Y = g (X ) , which represents the maximum current index that can be obtained from any given reference index. The management frontier provides a method to measure the managerial efficiency. In Figure 1, the maximum current managerial level for firm C with the reference management level X C is now g ( X C ) . Therefore the managerial efficiency score for firm C is given by YC g ( X C ) . Definition 3: The upper boundary OA of management possibility set T in Figure 1 is called the efficient management frontier surface. A decision-making unit is deemed managerial-efficient if it is on the efficient management frontier surface. It is deemed managerial-inefficient if it is under the efficient management frontier surface. In Figure 1, unit A is managerial-efficient, while unit B is managerial-inefficient. The managerial efficiency of a decision-making unit can be measured on the basis of the management possibility set and the efficient management frontier surface. For unit B(x, YB ) in Figure 1, its projection on the efficient management frontier surface is A(x, Y A ). (x, Y A ) indicates that the optimal management efficient state should be reached at the given input x, i.e., to become efficient, unit B(x, YB ) must increase its output from YB to Y A to reach the point A(x, Y A )while x remaining invariable. Let reference index be the horizontal axis (X) and current index the vertical axis (Y). All the index state of an organization can be depicted in Figure 2. The projection of a decision-making unit can be obtained by DEA model. Therefore, the upper boundary OCA of management possibility set can be obtained. The management possibility set T can be divided into two subsets. One is the boundary set (OCA). The other is the interior set.

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Definition 4: Managerial efficiency of unit B(x, YB ) can be defined as θ, where θ=

YB Y A ×100%. Supposing U 1 ( x1 , y1 ) and U 2 ( x 2 , y 2 ) denote a unit respectively, and their managerial efficiency are θ 1 and θ 2 . If θ 1 > θ 2 , it can be concluded that the managerial efficiency of unit U 1 ( x1 , y1 ) is superior to that of unit U 2 ( x 2 , y 2 ) . Managerial efficiency can eliminate the influence of objectively basic conditions and reflect the contributions made by a manager through his subjective effort.

Y YA

•A •

•B

•C

0

X

Fig. 2. Management frontier and interior set

B. Model Establishment Based on DEA The possibly maximum output of the organization j can be solved in management 0

possibility set T via DEA model in the condition that the inputs be given. The model with single input and output is as follows.

max Z ⎧ n ⎪ ∑ λ j x j ≤ x j0 ⎪ j =0 ⎪ n ⎪ ∑ λ j y j ≥ Zy j0 s.t . ⎨ j = 0 ⎪ n ⎪ ∑ λ j = 1, ∀λ j ≥ 0, ⎪ j =0 ⎪ ⎩ j = 0,1, 2 L n

（2）

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Suppose Z* is the optimal value of linear program (2), then Z*y

j0

is the possibly

maximum output in the condition that the input x j 0 is given. Managerial efficiency of a unit is on the efficient frontier surface of management possibility set if Z* equals 1. Managerial efficiency of a unit which is below the efficient frontier surface is inefficient if Z* is bigger than or equal to 1. The managerial efficiency θj of the organization j can be calculated as follows if the optimal value of linear program (2) for the organization j is Z ∗ j .

θ j = 1 / Z ∗ j × 100%

(3)

4 Empirical Study Two stage relative efficiency method is used to measure managerial performance of forestry companies in China. Having collected the necessary information for all sample companies under evaluation, this paper calculates the performance score for each company by means of efficacy coefficient approach (ECA). The performance scores in financial situation, asset quality, debt-paying ability and development ability for each company are shown TABLE I. From TABLE 1, we can find that the top 5 performers in 2007 are Company 5 (82.1), Company 9 (73.6), Company 12 (72.0), Company 22 (67.2) and Company 23 (65.7). However, we can not judge the performance of managers based on the performance score calculate by ECM because such performance score reflect the strength of units in 2007. It contains the influence of difference in objectively basic conditions. To give a fair evaluation of managers and reflect their real subjective efficiency, the difference of objectively basic conditions should be considered and eliminated. In this case, we have computed the performance score of units in 2006 and 2007. Let the performance score in 2006 as reference indexes(x) and the performance score in 2007 as current indexes(y), we can construct the managerial possibility set as follows: 38

38

s =0

s =0

T={(X, Y): ∑ λs X s ≤ X , ∑ λs Ys ≥ Y ,

38

∑λ

s

= 1, λ s ≥ 0 ,

s = 0,1,2,L,38 }

(4)

s =0

where ( X 0 , Y0 ) = (0,0) . We can compute the managerial efficiency of sample companies in 2007 by solving the linear program model (2). The results are also shown in TABLE 1. From TABLE 1, we can find that the rank by ECM score is quite different from that by managerial efficiency. As shown in TABLE 1, Company 5, Company 11 and Company 3 are relatively managerial efficient (with ME = 1.000) in 2007, other 27 companies are relatively managerial inefficient. The performance score for Company 5, Company 11 and Company 3 were 79.4, 45.5 and 33.0 in 2006 respectively. By their managers’ hard working, these three companies’ strengths reached 82.1, 58.2 and 34.1 respectively in

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2007. This indicates that the three companies’ managers have devoted greater subjective efforts than those inefficient companies. Companies with better objectively basic conditions still realize better performance (with higher managerial efficiency). For example, Company 5 (79.4), Company 9 (77.1) and Company 12 (69.4). Their managerial efficiencies in 2007 are 1.000, 0.915 and 0.959 respectively. On the other hand, those companies with inferior objectively basic conditions can greatly increase their strength, thus their managerial efficiency is higher, Company 12 (ME=1.000) is a good example. Performance scores of company 11 45.5 in 2006, and its performance scores increase to 58.2 in 2007. This means that the strength of company 11 has been increased. Table 1. ECA Score and Managerial Efficiency (ME) S/N

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

2006 ECA Score 46.00 69.20 59.40 97.20 57.50 54.90 56.50 53.50 52.50 59.10 61.50 80.10 81.90 47.60 54.00 67.20 91.10 58.00 86.50 50.00 84.70 86.60 62.90 70.90 55.10 68.50 75.60 58.80 59.00 71.70 91.10 60.8 55.50 49.40 28.90 85.70 67.80 87.70

2007 Rank 37 14 21 1 26 30 27 32 33 22 19 11 9 36 31 17 2 25 6 34 8 5 18 12 29 15 12 24 23 13 2 20 28 35 38 7 16 4

ECA Score 60.97 75.40 55.46 60.78 71.90 68.54 75.08 53.34 70.34 62.68 72.36 77.88 73.38 53.31 72.97 74.17 55.42 58.22 62.37 53.44 70.99 82.37 28.09 74.44 70.28 85.97 46.10 64.31 65.92 70.67 92.63 78.03 49.64 53.99 43.74 94.62 55.08 68.17

Rank 25 7 28 26 14 19 8 33 17 23 13 6 11 34 12 10 29 27 24 32 15 4 38 9 18 3 36 22 21 16 2 5 35 31 37 1 30 20

2006-2007 ME Rank 0.958 0.873 0.713 0.642 0.945 0.929 0.998 0.737 0.988 0.808 0.908 0.848 0.792 0.814 1.000 0.875 0.586 0.760 0.659 0.782 0.754 0.871 0.347 0.854 0.950 1.000 0.515 0.832 0.851 0.807 0.979 0.987 0.668 0.799 1.000 1.000 0.645 0.720

9 15 31 35 11 12 5 29 6 22 13 19 25 21 1 14 36 27 33 26 28 16 38 17 10 1 37 20 18 23 8 7 32 24 1 1 34 30

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The results show that this method can eliminate the influence of the objectively basic condition and reflect the real managerial performance of managers.

5 Conclusion Objectively basic condition including variable external environment and organizational strength affects the performance of an organization. Two-stage relative efficiency method is put forward to evaluate managerial performance by eliminating the influence of objectively basic condition. The empirical results show that firms with better objectively basic conditions have to work hard to maintain their competitiveness and achieve better performance. On the other hand, managers in those firms with inferior objectively basic condition can also achieve better managerial performance if they make efforts subjectively and they can also acquire fair evaluation. The method presented can avoid penalizing good managers who operate within an unfavorable objectively basic condition and can avoid rewarding poor managers who operate in a favorable objectively basic condition. In a word, the two-stage relative efficiency method is an equitable and objective efficiency measure method. Acknowledgment. It is a pleasure to acknowledge the support of the Young Academic Backbone Funding Schemes of Harbin Normal University for this project.

References [1] Charnes, A., Cooper, W.W., Rhodes, E.: Measuring the efficiency of decision making units. European Journal of Operational Research 2(6), 429–444 (1978) [2] Banker, R.D., Morey, R.: Efficiency analysis for exogenously fixed inputs and outputs. Operations Research 34(4), 513–521 (1986) [3] Banker, R.D., Morey, R.: The use of categorical variables in data envelopment analysis. Management Science 32(12), 1613–1627 (1986) [4] Golany, B., Roll, Y.: Some extensions of techniques to handle non-discretionary factors in data envelopment analysis. The Journal of Productivity Analysis 4(4), 132–419 (1993) [5] Fried, H.O., Lovell, C.A.K., Eeckaut, P.V.: Evaluating the performance of US credit unions. Journal of Banking and Finance 17(2), 251–265 (1993) [6] Bhattacharyya, A., Lovel, C.A.K., Sahay, P.: The impact of liberalization on the productive efficiency of Indian commercial banks. European Journal of Operational Research 98(2), 332–345 (1997) [7] Fried, H.O., Schemidt, P., Yaisawarng, S.: Incorporating the operating environment into a nonparametric measure of technical efficiency. Journal of Productivity Analysis 12(3), 249–267 (1999) [8] Fried, H.O., Lovell, C.A.K., Schemidt, P.: Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 17(1/2), 157–174 (2002) [9] Avkiran, N.K., Rowlands, T.: How to better identify the true managerial performance: State of the art using DEA. Omega 36(2), 317–324 (2008) [10] Fried, H.O., Lovell, C.A.K., Schemidt, P.: Accounting for environmental effects and statistical noise in data envelopment analysis. Journal of Productivity Analysis 17(1/2), 157–174 (2002)

A Study on Contribution Rate of Management Elements in Economic Growth Zhao Chenguang, Xu Yanli, and Feng Yingjun School of Management, Harbin Normal University, Harbin, 150025, P.R.China [email protected]

Abstract. The contradiction between the rapid development of science and technology and the lag of management level is becoming more and more prominent. Chinese government has clearly advanced that management elements should participate in distribution. In this paper, the contribution rate of management elements in economic growth is studied by a method of quantitative measuring and calculating. Management is regarded as an efficiency-promoting organizational behavior. A concept of equal efficiency curved surface production function is proposed and growth rate equation is established. Based on the equation and according to the dynamic changes of efficiency, the quantitative measuring method in management that affecting economic growth is proposed and empirical tests are carried out. The contribution rate of quantitative calculation management element in economic growth has provided theoretical basis for participation of management factor in distribution. Keywords: equal efficiency curved surface production function, growth rate equation, management contribution rate.

1 Introduction The famous economist Solow advanced the theory of scientific and technological progress in the role of economic growth in 1957[1]. However, the effects of science and technology on modernization must also be guaranteed by scientific management. The higher the science and technology lever is, the higher management level would be required, and the contradiction between high science and technology development and low management level will be more and more prominent. If such contradiction cannot be solved in time, it would become a main barrier for social development. And the only way to clear the barrier is to improve management level. During the rapid economic development process, management elements in the role of economic growth is becoming more and more prominent, and further researches need to be carried out. The Sixteenth Report of CPC National Congress proposed the principle of establishment of labor, capital, technology and management and other production factors to participate in the distribution in accordance with the principle of contribution. The Government’s Work Report also mentioned that to improve the national science and technology evaluation system and incentive system, to establish Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 151–158, 2011. © Springer-Verlag Berlin Heidelberg 2011

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policies of technology and management participating in the distribution, and to award outstanding scientific and technical personnel and management staff. Therefore, it is quite meaningful to study the contribution rate of management elements in economic growth by the method of quantitative measuring and calculation.

2 Concept of Equal Efficiency Curved Surface Production Function The contribution rate of management elements of quantitative measurement in growth rate is very important to arouse people’s attention to the role of management and the innovation of management. What need to be considered first is that in what aspect the role of management should be understood and by what means could the measuring method be determined. The concept of management is with distinct features of times. Management science, as a product of human civilization, has been developed with the development of science. People have different understandings to the role of management in different times. According to study of Harold Koontz that by the year of 1980, there had been totally 11 schools of management, and based on their own theoretical systems, each school had its own understanding to the concept of management[2-4]. However, people do not attach enough importance to understand the role of management from a dynamic and development angle. Nowadays, the international competition is extraordinary fierce. To regard management as an organizational behavior to promote efficiency would help to adjust companies to such severe environments. To understand the role of management from such a dynamic and development angle will be helpful to the realization of management innovation. At the same time, the specific measuring method could also be established to measure the role of management elements in economic growth according to the dynamic changes of efficiency. Equal efficiency curved surface production function can be used as an important tool to measure the contribution rate of management elements in economic growth. The concept of efficiency curved surface production function mentioned in this paper is derived from frontier production function. The concept of equal efficiency curved surface production function is to be established in the following text. According to American economist P.A. Samuelson, the frontier production function is defined as a technical relationship that is used to indicate the possible maximum yield by a combination of a specific number of inputs (production elements) [5]. If x1 , x2 ,L, xn indicate production elements, and Y indicates yield, then the general form of frontier production function can be described as

Y = F ( x1 , x2 ,L, xn , ; t )

(1)

of which t is the time variable. Capital K and labor L are generally regarded as the most important production elements, and then the (1) can be written as Y= F(K,L; t)

(2)

In 1957, the famous American economist R.M. Solow proposed the famous “reminder method” also called “Solow method of remainder”[6-8].

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Since the frontier production function has the attribute of maximum yield, it requires the evaluation unit to achieve the maximum yield by a combination of production elements under a complete competitive circumstance, which means that each evaluation unit should be under a most cost-effective state to conduct production activities. In researching economic issues in micro-field, it is not realistic to require each evaluation unit under an idealistic condition. For example, it is impossible that production activities of different areas in China can satisfy the same frontier production function. To better describe the production activity state of evaluation unit, a concept of equal efficiency curved surface production function for evaluation unit is proposed. Take the form of frontier production function of formula (2), suppose an evaluation unit’s production elements are K 0 and L0 at the time of t 0 , and the practical yield is

Y0 , then

η = Y0 / F ( K 0 , L0 ; t 0 ) is the efficiency of this evaluation unit at the time of

t 0 . Generally it is called

Y = ηF ( K , L; t ) which is the equal efficiency curved surface production function of this evaluation unit at the time of t 0 . It is not difficult to learn that equal efficiency curved surface production function is used to describe the zero growth track of an evaluation unit in a broader sense of science and technology progress. Cobb-Douglass production function is the earliest and most influential frontier production function. This function is suitable for researches in some economic problems in China[9-12]. Its general form is:

Y = AK α Lβ , ( α > 0, β > 0 )

(3)

Of which Y indicates yield, K indicates capital input, and L indicates labor input. A optimization method can be adopted to estimate Cobb-Douglass production function according to a group of input and yield values of K i , Li , Yi ( i=1,2,…N ). Therefore, it is not difficult to enlist the equal efficiency curved surface production function corresponding to frontier production function(3) of each evaluation unit at the time of t 0 :

Y = ηAK α Lβ , ( α > 0, β > 0 )

(4)

3 Establishment of Growth Rate Equation Equal efficiency curved surface production function can not only be used to measure the contribution rate of science and technology progress in economic growth, but also to describe the role of management elements in economic growth. The effect of management elements in economic growth can be described by the changes of efficiency at different times in the equal efficiency curved surface production function. By the derivation of the two ends of formula (4) and divide by Y, it can be derived that

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1 dY 1 dη 1 dA 1 dK 1 dL = + +α +β Y dt η dt A dt K dt L dt Make it dt = Δt = 1 , replace differential with increment, it can be derived that

ΔY Δη ΔA ΔK ΔL = + +α +β Y η A K L

(5)

And formula (5) is the growth rate equation of this evaluation unit. Its left end is the relative increase of yield, and the first item of right end is relative efficiency increase, the second item is the relative scientific and technological increase (management elements are not included). Δη η + ΔA A describes the role of scientific and technological progress in a broader sense that including management elements. The third and fourth items are the products of output elasticity and relative increase rate of capital, labor input. The meaning of formula (5) is that the output growth of evaluation unit is brought by the increase of capital and labor input, the scientific and technological progress and the efficiency growth. Of which the increase of efficiency is a result of management effort. Relative data in formula (5) ΔY Y ΔK K ΔL L can be

（

）

,

,

,

derived from historical data analysis, and α β can be derived from a frontier production function that established by a optimization method.

4 Role of Quantitative Calculation Management Elements in Economic Growth Through the growth rate equation of evaluation unit, it is not difficult to derive the measuring method for the contribution rate of management in economic growth. As in reality the input element contribution, as well as scientific and technological contribution and management element contribution may have negative growth, therefore, the proportion of management contribution in algebra sum of different production elements contributions can not be simply listed as the contribution rate of management in growth rate. To establish the concept of contribution rate of management in economic growth, different situations should be considered according to different contributions of production elements. Suppose the contribution rate of management in economic growth is e, then there are four situations for the calculation method of e: (1) Management contribution and the sum of other production elements contribution are a positive growth. Under such condition, the contribution rate of management in economic growth is the proportion between management contribution and the sum of all production elements contributions:

e=

Δη

η

ΔY Y

(2) Management contribution is negative growth, and the sum of other production elements contribution is positive growth. The role of management in such case should be considered in two aspects:

A Study on Contribution Rate of Management Elements in Economic Growth

①∣ Δη η ∣ > ΔA A + α ΔK

155

K + β ΔL L . The economy is in negative

growth, which is all caused by management, then the contribution rate of management in economic growth should be —100% that is

，

②∣

e = —100%

∣

Δη η ≤ ΔA A + α ΔK K + β ΔL L , management contribution offset or partially offset the sum of other production elements contribution. It is not difficult to see that the contribution rate of management in economic growth should be between -100% and 0, and the contribution rate can be determined by the offset proportion between management contribution and the sum of the other production elements contribution, that is

e=

Δη

(

η

ΔA ΔK ΔL +α +β ) A K L

(3) Management contribution is positive growth, the sum of other production elements contribution is negative growth. In such case, the role of management should also be considered in two aspects. Δη η > ΔA A + α ΔK K + β ΔL L , in this case, the economy is in positive growth, it is a result of the positive growth of management contribution. Therefore, the contribution rate of management should be 100%, that is

①

∣

∣

② Δη η ≤ ∣ ΔA A + α ΔK

e = 100% K + β ΔL L

∣,

in this case, management contribution offset or partially offset the sum of other production elements contribution. It is not difficult to see that the contribution rate of management in economic growth should be between 0 and 100%, and the contribution rate can be determined by the offset proportion between management contribution and the sum of other production elements contribution, that is

e=

Δη

ΔA ΔK ΔL +α +β A K L

η

(4) Management contribution and the sum of other production elements contribution are both in negative growth. In such case, the contribution rate of management is the proportion between management contribution range and relative economic growth, and its symbol is the same with that of management contribution, that is

e=

Δη

η

Suppose management contribution

ΔY Y

a = Δη η , and the sum of other element

，

contribution is b = ΔY Y − Δη η and formula (5), the contribution rate of management of the above four different situations in economic growth can be calculated as by the following formula:

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Δη

ΔY , when a ≥ 0 , b ≥ 0 or a ≤ 0 , b ≤ 0 η Y Δη ΔY Δη e= − ,when a < 0 , b > 0 , a + b ≥ 0 or a > 0 , b < 0 , η Y η a+b ≤ 0 e = 100% when b ≤ 0 a + b > 0 e = —100% when b ≥ 0 a + b < 0

e=

， ，

Due to different situations of production element contributions, the management contribution rate cannot be calculated by a uniform formula. The concept of calculating management contribution rate is established according to different cases of production elements contributions. Such a concept can break the limitation of the present definition of management contribution rate, and the positive growth of all production elements contribution is just a special case. When other production element remain unchanged and economic growth is positive, the economic growth is absolutely brought by management contribution growth. Also, when other production element remains unchanged and economic growth is negative, it is absolutely caused by the negative growth of management contribution. The new definition of management contribution rate proposed in this paper is totally matched to such facts.

5 Empirical Tests With the above calculating method, the contribution rate of management in economic growth was calculated among industry sectors of Chinese provinces, cities and autonomous regions in the year 2000 (see Table 1). Relative data is obtained from industry basic data of Chinese provinces, cities and autonomous regions of the year 1999 and 2000 of China Industrial Economy Statistical Almanac 2001. Firstly of all, use the sectional data of 1999 and 2000 and the optimization method to estimate the equal efficiency curved surface production function of each unit of the whole system, and then calculate the effective surface of the production function of the year 1999 and 2000 as below:

Y1999 = η1999 1.1542 K 0.7012 L0.3140 Y2000 = η 2000 1.2869 K 0.6887 L0.3301 Then use the method introduced in this paper and obtain data in Table 1 (Taiwan province is not included). The role of production elements in economic growth may have a hysteretic nature. The input of the year may not functionate at the same year. That’s why management contribution rates of some provinces, cities and autonomous regions fluctuate or even give negative values. Therefore, it would be more reasonable to adopt an average value of a cycle as the contribution rate of an evaluation unit. From an overall point of view, the contribution rate of 12.2% of the national management in economic growth is relatively reasonable.

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Table 1. Contribution rate of management elements in industry economic growth of china Relative Increase of Output

ΔY Y Beijing Tianjing Hebei Shanxi Neimeng Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Xizang Shanxi Gansu Qinghai Ningxia Xinjiang China

0.28 0.15 0.14 0.11 0.17 0.25 0.23 0.33 0.14 0.17 0.27 0.08 0.18 0.09 0.20 0.12 0.08 0.15 0.18 0.10 0.08 0.12 0.10 0.14 0.08 0.10 0.14 0.26 0.22 0.21 0.35 0.18

Managenent Contribution

Production Element Contribution

0.12 0.03 0.00 -0.03 0.07 0.10 0.13 0.08 0.00 -0.01 0.04 -0.07 -0.04 0.01 0.01 0.00 -0.03 0.01 0.00 -0.04 0.05 0.01 0.02 -0.06 -0.06 -0.28 0.01 0.06 -0.01 0.13 0.32 0.02

0.04 0.01 0.03 0.03 -0.01 0.02 -0.02 0.12 0.03 0.07 0.10 0.05 0.11 -0.03 0.06 0.01 0.00 0.03 0.06 0.04 -0.06 0.00 -0.03 0.10 0.03 0.38 0.03 0.08 0.15 -0.02 -0.06 0.04

Δη η

Managenent Contribution Rate (%) e αΔK K+βΔL L 41.45 22.84 -2.25 -20.38 42.90 40.73 56.08 23.95 -0.67 -7.01 12.87 -46.11 -16.14 14.05 5.57 -3.25 -23.58 4.03 -1.74 -30.42 64.97 12.30 17.13 -27.59 -43.65 -80.73 4.12 22.25 -5.00 63.30 92.34 12.20

6 Conclusion The role of management in economic growth is more and more obvious. Management is an organizational behavior to promote economic efficiency. The propose of equal efficiency curved surface production function, the establishment of growth rate equation, and the quantitative calculation method of contribution rate of management elements in economic growth have provided theoretical basis for management elements participating in distribution. Acknowledgment. It is a pleasure to acknowledge the support of the Young Academic Backbone Funding Schemes of Harbin Normal University for this project.

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References [1] Solow, R.M.: Technical Change and the Aggregate Production Function. The Review of Economics and Statistics 139 (1957) [2] Cheng, S.-w.: Nowdays and Future of Management Science. Journal of Management Sciences in China 1, 8–14 (1998) [3] Robbins, S.P., Coulter, M.: Management, pp. 424–455. Prentice Hall, Peking (2002) [4] Robbins, S.P.: Management, pp. 25–43. People’s College Publishing, Peking (1997) [5] Samuelson, P.A.: Foundations of Economics Analysis. Harvard University Press, Cambridge (1948) [6] Leighton Thomas, R.: Introductory Econometrics:Theory and Applications. Longman Inc., New York (1985) [7] Fomby, T.B., Carter Hill, R., Johnson, S.R.: Advanced Econometric Methods. SpringerVerlag New York Inc., Secaucus (1984) [8] Henri, T.: Introduction to Econometrics. Prentice-Hall Inc., Englewood Cliffs (1978) [9] Li, Z.-n.: Econometrics: Method and Application, pp. 181–203. Tsing Hua University Press (1997) [10] Li, C.-f.: Econometrics, pp. 264–284. Shanghai Finance & Economy University Press (1998) [11] Li, Y.-n., Zhang, Z.: Econometrics, pp. 85–109. Higher Education Press (2007) [12] Liu, J.-h.: Modern Econometrics, pp. 89–98. Nanjing University Press (2000)

An Analysis on Real Contagion Mechanism of Financial Crisis Xu Yanli and Jiang Hongmei School of Management, Harbin Normal University, Harbin, 150025, P.R. China [email protected]

Abstract. Recently the financial crisis triggered by the U.S. subprime mortgage crisis, has been conducted from credit market to capital market, from financial market to real economy, and has caused great damage to the global economy. In order to explore and block the route for crisis’s further spread in real economy, this paper applies systematic method to analyze the real contagion mechanism of financial crisis, and works out such influencing factors as competitive effect, income effect, effect of cheap imports and competitive devaluation effect, and concludes that real contagion is possible to be controlled. Keywords: financial crisis, contagion effect, real contagion mechanism.

1 Introduction Study on financial crisis has always been hot among circles of international economy and finance. At present, three types of models have been developed. First-generation crisis model points out that the causes of crisis lie in the deterioration of macroeconomic fundamental variables or the disharmony between expanded monetary policy and fixed exchange rate; second-generation model points out that government's economic policy and public policy lead to the multi-dimensional equilibrium points in economy, while self-enabling factors transfer economy from a good balance to a poor one, and crisis is self-enabling; new-generation financial crisis model, jumps out of traditional macroeconomic analysis such as monetary policy, exchange rate system, fiscal policy and public policy, etc., but focuses on the role of financial intermediation, believing that financial intermediation is the root of financial crisis under the background of financial globalization [1, 2]. The financial crisis triggered by the U.S. subprime mortgage crisis has been conducted from financial market to real economy [3, 4] and caused great damage to the latter. Studies are mostly concentrated on financial contagion, but less on real contagion. A clear analysis on real contagion mechanism is helpful to block crisis’s further spread in real economy.

2 Analysis on Real Contagion Mechanism The so-called crisis contagion refers to the possibility of a country’s crisis leading to the crisis of another country, and it stresses that the reason of a certain country’s crisis Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 159–166, 2011. © Springer-Verlag Berlin Heidelberg 2011

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is the already occurred crisis of another country --- if crisis did not happen in another country, it would not happen in this certain country[5]. The contagion mechanism of financial crises can be divided into real contagion and financial contagion. The current financial crisis triggered by the U.S. subprime mortgage crisis has entered the stage of real contagion, so analyzing the real contagion mechanism of financial crisis will help us to take corresponding countermeasures. Real contagion means that a country’s crisis has worsened the balance of international payments and operation of economic base of another (or several other) country in close trade relation with it. For easy analysis, we assumed that crisis has entered the RSi (Real Sector) of i country ( i =1, 2, 3… N ). According to the form of trade between the two countries, real contagion can also be divided into direct and indirect contagions. Direct contagion mechanism mainly includes competitive effect, income effect, effect of cheap imports and industry linkage effect; indirect contagion mechanism mainly refers to competitive devaluation effect. A. Competitive effect Assuming a crisis breaks out in i country, its exchange rate will depreciate, commodity export competitiveness will be strengthened, and export to trade partner j country will increase; meanwhile, after the crisis takes place in i country, its economy will enter recession, domestic business confidence will decline, business investment will decrease, and import from j country will also decrease, which will lead to the increase of trade deficit and decrease of foreign exchange reserves of j country, and thus damage its economic base. It has direct impact on the sales and output of j country, and to make matters worse, if the competitiveness of j country declines to a certain serious degree, international investors may expect its exchange rate depreciation and then attack its currency; meanwhile, the domestic currency depreciation caused by the financial crisis in i country will result in the decline of competitiveness of its trade partner j country, which will then cause the rise of the country’s unemployment (especially in export sector). If the government intends to alleviate domestic unemployment pressure through expanded monetary policy and fiscal policy, it is possible to induce speculative impact. B. Income effect In order to clearly explain the income effect of A country’s financial crisis contagion on B country, we use a simple model to illustrate. The model construction is as follows: all variables except interest rates in the model are expressed in logarithmic form, and the two countries’ exchange rates are pegged to the fixed exchange rate of C country. Assume that initially the variables of A and B countries are of same value, and the interest rate parity and purchasing power parity are established. Variables not explained in the following equation are coefficients.

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The money demand equation of two countries:

mi − pi = φ yi − γ ri

i =A, B

(1)

In (1), m represents the amount of nominal money, p represents price level, y represents the real income level, and r represents nominal interest rate. Assuming that A, B and C countries respectively produce a different product, whose price is determined according to the producing country’s wage level, then the price level p should be the weighted average worked out according to the proportion of each product to the producing country’s consumption basket. The price level is expressed as follows:

pi = α wi + ε ( w j + si − s j ) + (1 − α − ε )(si + p ∗ ) i , j =A,B and i ≠ j (2) In (2),

w represents wage level, p∗ represents the product price of C country,

here p is assumed to be constant. α and ε represent the proportions of products to the consumption baskets of A and B countries respectively. Each country's aggregate demand for goods depends on the relative prices of goods: ∗

yi = β ( si − s j + w j − wi )

i , j =A,B and i ≠ j

(3)

In (3), β represents the degree of substitution between two goods. The bigger β is, the higher the substitution degree is. According to (1), the impact of A country's financial crisis on B country’s money demand actually lies in two aspects: price level and real income. According to (2), after A country's currency depreciates, the falling of commodity price ( sB − s A + wA ) will lead to the decline in B country’s price level. Also according to (3), if A country’s commodity price drops, its product competitiveness will increase, and substitution with B country’s goods will result in the decline in B country’s real income. The decline of price level and real income leads to the decline of money demand of B country’s residents. Under fixed exchange rate, extra money supply is transferred to the purchase of government’s reserves through government’s foreign exchange window, which speeds up the consumption of B country’s reserves and accelerates the collapse of its fixed exchange rate system. Parameter β represents the degree of substitution between two countries’ goods. According to (3), the bigger

β

is, i.e. the higher the substitution between the goods

of A and B countries is, the greater the decline of B country’s real income

yB caused

by A country’s same-level currency depreciation is. Our previous discussion shows that, the greater yB declines, the greater the deduction of B country’s money demand is, and the faster the consumption of B country’s reserves is. Therefore, the bigger β is, the stronger the conduction effect on B country is. Parameter ε represents the

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proportion of foreign goods to the domestic consumption basket. According to (2), the bigger ε is, the greater the decline of B country’s price level caused by A country’s same-level currency depreciation is. It will lead to the greater decrease of B country’s money demand and the faster consumption of its reserves. So the bigger ε is, the stronger the conduction effect on B country is. C. Effect of cheap imports When financial crisis breaks out in i country, its exchange rate will depreciate, its price level will decline compared with that of j country, which will lead to the

j country’s consumer price index, so that the home currency demand of j country’s residents will decrease. Therefore, j country’s residents will exchange for decline of

more foreign currencies, which will result in the reduction of its central bank's foreign exchange reserves and thus induce currency crisis. However, this effect is not entirely negative. When i country's export price is relatively low, it will improve j country's trade condition, for j country can get cheaper imports, which will potentially enable its higher consumption at a certain level of nominal income. So in terms of this impact mechanism, the impact of i country's financial crisis on j country is mixed. D. Effect of competitive devaluation In order to better analyze the competitive effect in indirect contagion mechanism, we take into account a simplified global economic system, assuming that there are only three countries, respectively represented by A, B and C, and each of them specializes in producing a certain goods, and only domestic input is used in the production process. A and B countries represent two "neighboring" countries in the system, C country represents the "central" country. Suppose that neighboring countries only trade with the central country, but there is no trade between themselves, and meanwhile, assume that at the beginning, both A and B countries unilaterally peg their exchange rates to the currency of C country and achieve equilibrium. Intertemporal factors do not play much role in analyzing contagion effect, so here we will concentrate on the simplest static analysis. The effect function of each country is as follows:

U A = log C A − f (YA ) U B = log C B − f (YB ) U C = log C C − f (YC ) A

In (4), C ,

B

（4）

C

C and C are consumption indexes and are defined as follows: 1

1

C A ≡ (C AA ) 2 (CCA ) 2

，C

1

B

1

≡ (CBB ) 2 (CCB ) 2 1

1

C C ≡ (C AC + CBC ) 2 (CCC ) 2

（5）

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Superscripts in (5) represent countries of consumers, and subscripts represent C

countries of producers, for example, C A means residents of C country consume goods of A country. Every neighboring country consumes its domestic products as well as products of the central country. Similarly, the central country consumes its domestic products as well as products of the neighboring countries. However, the two

C AC and C AC consumed by the central country are C C totally substitutable. The assumption that C A and C A are totally substitutable is neighboring countries’ products

easily broadened. Qualitative analysis shows that the substitution between products of neighboring countries is greater than the substitution between products of either neighboring country and the central country. In all countries, Y represents the output, f represents the single function about labor efforts varying with higher level of economic activity. Resource constraints are as follows:

YA = C AA + C AC ,

YB = CBB + CBC

YC = CCA + CCB + CCC Here we define

(6)

M as the stock of each country's money supply. In the central C

country, the monetary authority exogenously determines the money supply M . In contrast, the neighboring countries’ monetary authorities peg their currencies to the currency of the central country, and determine their nominal exchange rates against A

B

the central country to be E and E respectively, and adjust their money supplies endogenously. Money is used to finance economic activity and thus the equilibrium conditions of the three countries’ money markets are as follows:

M A = PAAC AA + PCACCA M B = PBB CBB + PCB CCB M C = PAC C AC + PBC CBC + PCC CCC

(7)

In (7), P represents price of commodities. When there is no capital accumulation, the trade balance relationship is as follows:

PCACCA = E A PAC C AC PCB CCB = E B PBC CBC

(8)

In A country, the most optimal economic entities will choose their consumption levels to be consistent with the following condition:

PAAC AA = PCACCA

(9)

If the utility-based consumer price index is defined as follows: 1

1

P A = 2( PAA ) 2 ( PCA ) 2

(10)

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M A = P AC A

(11)

So, the trade balance relationships in B and C countries are similar. In order to describe people's consumption patterns in C country, we firstly define

CPC ≡ C AC + CBC to be the central country’s consumption of the neighboring C C countries’ (hereinafter represented by P ) goods. If PA < PB , the central country C C C C only consumes A country’s goods, i.e. CP = C A ; if PA > PB , the central C C C C country only consumes B country’s goods, i.e. CP = CB . If PA = PB , the central country imports goods from both A and B countries (assuming the central country C

imports same quantity of goods from the neighboring countries). If we use PP to represent the comprehensive price of goods imported from neighboring countries, we can get the follows:

PPC = min{PAC , PBC }

(12)

According to the above definition, we can get the central country’s equilibrium condition as follows, which is similar to those of neighboring countries:

MC =PPCCPC +PCCCCC =2PPCCPC =2PCCCCC =PCCC

(13)

1 C 2 P

(14)

1 C 2 C

P = 2( P ) ( P ) C

We will start analyzing from the equilibrium state, that is, the commodity prices of two neighboring countries are equal to the commodity price of the central country:

PAA / E A = PBB / E B . Assume that domestic enterprises could not adjust the prices of their products in domestic market, i.e. prices are sticky, and

PAA , PBB and PCC are all determined in

advance. However, this is in line with law of one price: the arbitragers in international market will make the prices expressed in different countries’ currencies but converted from the same currency reach uniform in value, through low buying but high selling A

(e.g. PC

= E A PCC ).

So under the above condition, we can analyze the economic impact of a neighboring country’s currency devaluation on another neighboring country and the central country. If A country's currency devaluates compared with the currency of the central country, E will rise, PA > PB , CB will drop to zero, and C A will rise too. Meanwhile, the central country’s consumption of neighboring countries’ goods increases due to the devaluation of A country’s currency. In fact, A

C

C

C

C

CPC = C AC = M C / 2 PPC = M C E A / 2 PAA . So when E A rises, C AC rises too. Since the central country’s money supply remains unchanged, the central country’s consumption

CCC of its goods also remains unchanged.

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Assuming that B country tries to maintain its pegged exchange rate against the central country, its consumption of the central country’s goods will decrease rapidly. Under the law of one price, trade balance condition is

E B PCC CCB = PBB CBC , so when

E B , PCC and PBB remain unchanged, CCB will decline with the decline of CBC . Since

M B = 2 PCB CCB = 2 E B PCC CCB , if B country tries to avoid devaluation, it must

make

M B fall to the same level with CCB . If M B declines, domestic goods

consumption will also decline, because CB = M / PB . If A country’s consumption of the central country’s goods does not increase, its B

trade balance equation is E rate of

A

B

B

PCC C AC = PAAC AC and at the same time, since the rising

C AC is the same as that of E A ,

CCA will remain unchanged. Since

CCA = M A / 2 PCA = M A / 2 PCC E A , A country's money supply must be increased gradually according to the depreciation rate. But it means that A country will have to increase the consumption of its domestic goods, until the devaluation will not generate inflationary pressure. In short, the consequences brought by A country's currency devaluation can be summarized as follows:

C AA ↑

CCA = YA ↑

CBB ↑

CCB ↓ YB ↓

C AC ↑ CBC ↓ 14243

CCC = YC ↓

(15)

↑

A country's residents consume more domestic products and work more, so that depreciation may increase or decrease their overall welfare. In order to maintain the pegged exchange rate against the central country, B country must accept the contraction in its economy, consumption and welfare, and international and domestic demand for A country's commodities will decrease. In the central country, the improvement of its welfare is obscure as its residents work less but earn more; they can actually achieve higher levels of consumption, and meanwhile reduce the negative effects brought by work, and gain more leisure time. It is because the whole world’s consumption of the central country’s goods shrinks. The above devaluation finally increases A country's products, and its products increased are actually more than those reduced in B country, however, this expansion of production brings great benefits to the central country, for its trade conditions have been improved. The above analysis comes to a clear and fundamental conclusion: for the depreciation of A country, B country will no longer maintain its pegged exchange rate, on the contrary, it will find that its best choice is also devaluation, so as to avoid the decline of export competitiveness and the recession of domestic economy caused by any change of cost competitive advantage, and maintain its market share in the

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central country. In other words, because of trade links between countries, a particular country's currency crisis will spread in the international market and ultimately cause global response.

3 Conclusion The financial crisis triggered by the U.S. subprime mortgage crisis is regarded as "the most severe since World War II". It has caused great damage to global financial market, and has spread to real economy. Research and analysis on the contagion mechanism of real economy will help to block the further spread of the crisis and recover the world economy in an all-round way.

References [1] Rodriguez, J.C.: Measuring fonancial contagion: a copula approach. Journal of Empirical Finance 14(3), 401–423 (2007) [2] Patton, A.J.: Modeling asymmetric exchange rate dependence. International Economic Review 47(2), 527–556 (2006) [3] Wang, C.: Financial crisis: theories and models. Journal of Tianjin University 3, 171–177 (2000) [4] Fu, N., Zhao, J.: Study on the conduction mechanism of financial crisis, with America as an example. Journal of Shanghai Business School 1, 34–39 (2009) [5] Wang, C., Kang, L., Wang, S.: Contagion of currency crisis: theories and models. Studies of International Finance 1, 44–50 (1999)

Teaching and Learning Reform of Visual Foxpro Programming Xiaona Xie1,2 and Zhengwei Chang3 1

Physical Education College of Zhengzhou University Zhengzhou, China 2 School of Computer Science and Engineering 1,2 University of Electronics Science and Technology of China 3 Sichuan Electric Power Test & Research Institute Chengdu, China [email protected]

Abstract. In order to meet the needs of teaching and learning process of physical education majors, this paper develops a student-centered two-stage teaching method for Visual FoxPro (VFP) Programming course. Sample teaching method is used to learn basic knowledge in the first stage. Then grouping and project-oriented teaching method is used to learn systematic knowledge and programming skills in the second stage. The results of this new teaching method demonstrate that the undergraduates had a positive learning attitude and the course achieves good effects. Keywords: Visual Foxpro Programming, physical education college, two-stage method, sample teaching, grouping and project-oriented teaching.

1 Introduction Visual FoxPro, commonly abbreviated as VFP, is a data-centric object-oriented and procedural programming language produced by Microsoft [1]. Due to its efficiency and ease of learning, millions of people are using VFP in the world. VFP has entered the curriculum of many universities in China. Moreover, VFP is one of exam subjects of National Computer Rank Examination of China [2, 3]. One of the characteristics of undergraduates major in Physical Education (PE) is that they need higher score of sports subjects rather than culture subjects to be enrolled into the university. After entering PE colleges, they spend much time and energy on athletic training and make little account of theory courses. The courses of computers are unimportant for PE majors. After learning the course of Introduction of Computer Technology, the freshmen learn VFP Programming in the following semester. As a Database programming language, VFP has its special grammar rules and programming thinking. The students with little programming experience feel hard to learn VFP Programming. As a result, how to cultivate their programming thinking, develop their learning interest and improve programming ability, become an important and difficult problem of VFP Programming course. The two-stage method comes from solving linear programming problems in Combinatorial Mathematics [4]. For some complex linear programming problems, the Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 167–171, 2011. © Springer-Verlag Berlin Heidelberg 2011

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two-stage method is applied when other methods cannot work. Firstly, it works out the fundamental solutions using the general method. Then it obtains the final solution of the original question taken the solution of the first stage as initial basic solution. We apply the two-stage method in VFP Programming teaching because the freshmen of PE majors have weak basic knowledge and lack programming thinking. In the first stage, we aim at teaching them to understand and master basic knowledge using simple sample teaching method. In the second stage, we adopt grouping and projects-oriented method which aims at mastering the advanced knowledge and skills of VFP based on the first stage. Moreover, another objective is developing their team spirit. In this stage, emphasis is put on team projects with authentic tasks chosen by students. To evaluate the feasibility and effectiveness of the two-stage teaching method, 210 students at Physical Education College of Zhengzhou University joined the VFP Programming course. All of these participants have learned Introduction of Computer Technology before. In addition, this paper also describes how to integrate the professional characteristics of PE majors during the teaching process of VFP.

2 The Problems with VFP Programming Teaching of PE Majors As VFP is an object-oriented Database programming language, the learners need to have programming thinking firstly. The undergraduates of PE College have different characteristics compare with other majors. They have weak knowledge background and different educational levels. So our first task is training their programming thinking. Then the SQL command and functions are taught. Finally, some of the students perform more complex tasks such as small scale project development. With the traditional teaching mode of VFP, the teachers firstly introduce the basic commands, functions and widgets, and then take a sample application to explain the usage of these basic components. Lastly, the students develop programs on computer to verify in practice. The traditional teaching mode is shown in Figure 1.

Fig. 1. Traditional teaching mode

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Students major in PE feel boring and do not to know what to do when they face a lot of commands and functions. As a result, the students only passively observe and remember little. Once they try to code themselves, they often do not know how to start. Most of the students can only mechanically repeat the forms or widgets following the teacher taught in the class, and fail to develop program.

3 Two-Stage Method Based Teaching of VFP Programming The traditional teaching mode of VFP focuses on teacher-centered instruction and lacks active involvement of students. In order to create a student-centered, projectoriented and team-based learning environment, we develop a two-stage teaching mode for PE majors. As showed in Figure 2, the two-stage teaching mode can not only be characterized in relation to teams, but also in relation to tasks.

Fig. 2. The two-stage teaching mode

A.

The first stage – sample teaching

Taken the sample teaching method, the teacher firstly setups several specific samples according to the teaching objectives and content. Then the teacher guides the students to analysis, discuss and carry out experiments. The purpose of the stage is to make the students think positively and explore actively. During the earlier time of learning VFP Programming, the main tasks include basic concepts, widget usage, and common data types. In this first stage, we adopt sample teaching method to develop their interest to learn VFP, and build a good base for the second stage.

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According to teaching objectives and content, the teacher may select samples associated with their major or interest. For example, when they were learning the content of labels and text boxes, we designed an athletic club membership registration form since most of the students are familiar with it. It attracted the students’ attention in the classroom. Through this example, students learned the function and property of labels and text boxes. Moreover, they are guided to think about how to read the input data and complete a simple athletic club membership registration form. During this process, the students need to think about how to design and achieve this case related to their major and daily life, so that they become a designer and participant. The teacher just selects the appropriate cases and leads the students to design and achieve it. So, the teacher becomes a designer or a guider. B.

The second stage –grouping and project-oriented teaching

After the first stage, the students have mastered basics knowledge. The teacher could lead the student groups to complete some small development projects using the grouping and project-oriented teaching method. In this stage, we can not only further motivate the students’ initiative, bust also develop their team spirits. This is the second phase of the two-stage method. In the second stage, the teacher is an instructor and better facilitator. This is a necessary precondition for student-centered learning process. For example, the teacher may specify a simple project to develop a curriculum schedule management system which is well-known to students. As shown in Figure 3, this project is involved with the contents as following: 1) Creating curriculum schedule subsystem: sequential flow, variable definition and function definition, etc. 2) Query subsystem: branching and loop flow, array definition and usage, etc. 3) Update subsystem: array and files, etc. In the preparation phase, the teacher explains how to perform requirements analysis and system design, and rebuilds the learned knowledge system. During the teaching process, the teacher firstly introduces how to design each subsystem and explains required knowledge point. Then the class is divided into several groups with the principle that the students within a group can help for each other. Every group develops one of the modules. Finally, the project results, programming ability and team spirit of each group or student are assessed by the teacher.

Fig. 3. Curriculum schedule management system

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4 Conclusion This study developed the students-centric two-stage teaching method for VFP Programming of undergraduate major in physical education. The students learn programming skills in a team-based and project-oriented way with interesting samples or attractive projects. The samples and projects are come from their major and daily life. The result indicated that this two-stage method had a positive impact on the teaching process. It not only developed interests, but also trained competition and team spirit of the students.

References [1] Weng, Z.: Visual FoxPro Database Development Tutorial Programming Microsoft Visual Basic 6.0. Tsinghua Universty Press, Beijing (2004) [2] Tang, Y., Li, T., Zhang, Z.: Survey and Research on VISUAL BASIC Programming Bilingual Teaching. Computer Education 6(13), 91–94 (2008) [3] Yi, D.: Research on Teaching Reforming of Visual FoxPro Programming of Higher Vocational College. Computer Era. 24(7), 67–68 (2006) [4] Sun, S.: Combinatorial Mathematics. Press of UESTC, Chengdu (2003)

Numerical Simulation of a New Stretch Forming Process: Multi-Roll Stretch Forming Process Haohan Zhang, Mingzhe Li, Wenzhi Fu, and Pengxiao Feng Dieless Forming Technology Center, Jilin University, Changchun, Jilin province, China [email protected]

Abstract. Multi-roll stretch forming process (MRSF) is a flexible manufacturing technique to form sheet panels with different curvature. More importantly, the flexible rollers play the role of a nucleus in MRSF. Compared with the traditional stretch forming, MRSF is better industrially applicable to not only the traditional dies but also the Multi-point stretching die (MPSD). In this paper, extensive numerical simulations of the MRSF process of stretching toroidal saddle parts were carried out based on the dynamic explicit finite element analysis. And for the flexible rollers, the analysis of lateral rollers position and discreteness of rollers are used to study the effect of flexible rollers on the quality of formed part. The investigation shows that the two aspects are quite critical for the quality of formed parts. Keywords: materials synthesis and processing technology, plastic processing, flexible forming, stretch forming, numerical Simulation.

1 Introduction Stretch forming operations are extensively used for forming hyperbolic skin parts, such as aircraft outer skin parts and high-speed train skin parts. The stretched parts will have better shape control after forming because the metal have undergone strain-hardening. This could reduce the curvature springback [1]. However, the traditional stretching machine has two deficiencies, that is, low non-defective rate and low flexibility. On the first level of low non-defective rate, the quality of formed part is unstable because of the complexity of the parameters and sports of the process, and variety of part forming defects [2-3]. For the second level, there are two points in the low flexibility of traditional stretching process. Firstly, the traditional stretching rigid die is bulky and costly, which means that it is simply designed for fabrication of a particular part and offers no shape flexibility. Secondly, the traditional stretching machine is unable to achieve the forming precision of any parts when dies change the shapes often. In other words, the forming operation can not be altered with the change of shapes of dies. To enhance the flexibility of stretch forming machine, there has been a lot of research on both dies and machine. For the die, a flexible discrete-die has been developed which can be used to form the parts of different shapes and is cost-saving for small lot production. A concept is proposed as reconfigurable tooling for flexible Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 172–180, 2011. © Springer-Verlag Berlin Heidelberg 2011

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fabrication (RTFF). Hard, Walczyk and others explored a discrete-die mechanical designed for and others explored a discrete-die mechanical designed for rapid response production of sheet metal parts [4]. Cai did a series of research on Multi-point stretching forming (MPSF) which uses matrices of punch elements to replace the traditional rigid dies in sheet metal stretch forming [1, 5]. For the stretching machine, CNC-controlled segmented stretch forming machine with flexible discrete-grippers have been designed by K. Siegert [6]. Furthermore, F. X. Tan did a series of numerical simulation and experiments on stretch forming machine with flexible clamp [7]. These studies lay the foundation for the flexibility of stretching machine. On the basis of previously professional dedication, A MRSF prototype system was developed as shown in Fig.1. It is better industrially applicable to achieve the forming precision of different parts when dies change the shapes often. There are many experiments which prove the feasibility of MRSF. As it is shown in Fig. 2, a spherical part and a saddle part are formed by using prototype. The paper aims to investigate and

Fig. 1. MRSF prototype

Fig. 2. The parts formed by MRSF prototype

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understand the basic physical phenomena in the MRSF process of sheet metal with the help of finite element simulation, including the influence of lateral rollers position and discreteness of rollers on the quality of the formed part.

2 MRSF Concept and FE Model A. Description of the multi-roll stretch forming process As shown in Fig. 3, this core mechanical structure in MRSF flexible machine is composed of two rows of flexible rollers, four rows of flexible clamps and a die. The head of rollers and clamps can swing in the y-z plane in forming process.

Fig. 3. The structure diagram of MRSF

More specifically, a Typical MRSF process is introduced. Firstly, the value of pressure on the upper clamps is the same as that of pressure on the lower clamps in order that the sheet can be clamped. Secondly, a gradually increasing pressure is applied to the flexible rollers, in which the value of the pressure exerted on each roller is the same so that the sheet metal can get the same amount of tension. In this process, with the rollers moving downward, the heads of rollers are swing automatically to adjust them to an appropriate condition. To illustrate, to form the saddle-shaped part, two ends of sheet contact with the die firstly, then the middle of the sheet does. As a result, the lateral rollers can reach a balance to stop moving firstly and the middle rollers can move downward. Finally when the metal sheet completely contacts with the saddle-shaped die, various positions of sheet metal are equally stretched, thus the sheet metal is formed an arc-shaped face with the same shape as that die in the position of rollers, as shown in Fig.3 B-B. Simultaneously , when the rollers is moved downward with a pressure, all clamps under the same value of pressure on the upper and lower ones can tend to follow the metal sheet to get a downward movement until the sheet reaches a balance as shown in Fig. 3 A-A. Under the circumstances, that arc-shaped face is identical to the arc-shaped in stretching die. Whatever the shape of stretching die

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are, flexible rollers as well as clamps can both form the same shape as the specially designated shape of stretching die. In short, the rollers and the clamps have a tendency to automatically adjust themselves to the individual corresponding position in accordance with the individual shape of one of the different stretching dies. Owing to the flexibility of application to various shapes of stretching dies in forming process, MRSF has the advantage in employing MPSD which can quickly adjust itself to a specific condition. B. Material model The material of sheet blank is 08AL steel which are widely used for forming car cover parts and high-speed train skins. This material has the advantage of plasticity and ductility. The relevant mechanical properties are: yield strength σy = 130MPa, Young’s modulus E = 207GPa and Poisson’s coefficient μ= 0.30 and density ρ= 7845 kg/m3. The relation of the uniaxial real stress vs. real strain of 08AL steel was obtained from tension tests are given in Fig. 4.

Fig. 4. Uniaxial stress-strain curve of 08AL

C. FE model The MRSF parts are rectangular sheets of 960×300mm, the shape area is 400×300mm and the thickness of the sheet is 1.0mm. The bi-directional curvatures of the toroidal saddle die are all 300mm and the size is 400×320mm. Owing to the symmetry of the part, only a quarter of the sheet, rollers, clamps and die were modeled. The commercial software ANSYS/LS-DYNA, an explicit FE code, was chosen to simulate the process of MRSF. The finite element shell163 was used to model the sheet metal and the rigid four-node element was used to model clamps, rollers and die. Fig. 5 illustrates a finite element model for the numerical analysis of MRSF process of toroidal saddle part. OA and OB, denoted by x- and z-, respectively, are symmetrical axes of the die. The x-axis agrees with the stretching direction of the sheet blank. On the two symmetrical axes, the displacements normal to the axis and the rotations around the axis were constrained in the finite element simulations.

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Fig. 5. 1/4 Finite element model of MRSF

3 Finite Element Simulation Result

． Effect of the lateral rollers position on the quality of formed parts

A

As discussed above, mainly, MRSF operation is that the rollers move downward to stretch sheet metal parts. The results of finite element simulations show that the position of rollers has very important effect to the quality of formed part. Firstly, the lateral flexible rollers can’t be placed close to the edge of the raw sheet metal. When they do, in Fig. 6a they can turn outside because the stretched part is elongation at the longitudinal (or stretching) direction and shorten at the horizontal direction. That can alter the direction of force exerted by lateral rollers toward a wrong direction and will exert nonuniform stretching force to the part which can lead to a defect or nonuniform stretching. Secondly, the lateral rollers can’t indent so much, or there is a nonuniform stretching, for the stretching in edges of sheet metal is very few and very more the one in middle of sheet metal is. To accurately place the lateral rollers, the amount of horizontal shortening of the part must exactly be calculated. But it is stated that the amount of horizontal shortening of the part depends on many factors such as the part’s size, thickness, tensile degree and material. To make achievement, that amount should be calculated by finite elements simulation. In this investigation, the most appropriate indent formulated-distance of the lateral rollers is 8mm. Fig. 6b shows the appropriate position of lateral rollers, in which the rollers does not turn outside and makes the sheet stretched towards right direction. Actually, in practical design, that different parts are distinct in indent distance of lateral rollers makes the requirement that indent distance of lateral rollers can be adjusted. In order to meet that requirement, a mechanical structure that can easily replace rollers can be developed, which could expand the scope of manufacture.

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Fig. 6. Forming process with different positions of rollers. (a) inappropriate position and (b) appropriate position

(a)

(b)

(c)

Fig. 7. X-strain distributions on the toroidal saddle parts of different discreteness of rollers stretch-formed by MRSF. (a) n=3, (b) n=5, (c) n=7.

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． Effect of discreteness of flexible rollers on the quality of formed parts.

In the finite element simulation, it is demonstrated that the discreteness of rollers influence the quality of formed part. X-strain distribution, thickness of sheet metal and springback amount are considered as the essential indicators reflecting the effect of discreteness of rollers on the quality of formed parts. To be specific, a set of numerical simulation was done, where the rollers are dispersed into 3, 5 and 7 segmentations. In the following figures, n donates the number of the segmentations. Fig. 7a, b and c present the x-strain distributions on the toroidal saddle parts according to different discreteness of rollers. In analysis of the three figures, the minimums x-strain are getting similar, but the maximums x-strain are apparently different by stating that the one that dispersed into 7 segmentations is smallest. Furthermore, that one standing for the rollers with 7 segmentations has most uniform in high quality. Drawing from those figures, when number of the segmentations of roller is most and the degree of discreteness (the more) is larger, the x-strain of formed part is more uniform. Similarly, the thickness data along the symmetric axis OB of the toroidal saddle part was picked to draw the thickness curve in Fig. 8. It shows the simulated results of the thickness concerning different discreteness of rollers. And examining the figure, when number of the segmentations of roller is most and the degree of discreteness (the more) is larger, the thickness of formed part is more uniform. The formed part with n=7 has the best thickness of the three parts. Finally, the center point O was designated as the constraint point so that BD stands for the largest springback amount of sheet metal. Thereby springback amount along BD was picked to draw the springback curve in Fig. 9. From the diagram, the higher the degree of discreteness (or in other words, the more the number of the segmentations of roller) is, the little the springback amount will be. The formed part which using the roller dispersed into 3 segmentations got the biggest springback amount 0.9934mm, but the one dispersed into 7 segmentations got the smallest springback amount 0.8019, additionally, and made the achievement of best shape accuracy.

Fig. 8. Simulated results of the thickness of toroidal saddle parts with different discreteness of rollers. Along the symmetric axis OB.

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Fig. 9. Simulated results of springback of toroidal saddle parts with different with different discreteness of rollers

In summary, the higher the degree of discreteness (or in other words, the more the number of the segmentations of roller) is, the more uniform tension the formed part gets, the more uniform thickness the formed part gets and the better the shape accuracy becomes. However, having a high discreteness can make the flexible roller complicated and expensive. Consequently, the number of the roller being dispersed should reasonably be designed in practical operation in order to obtain the high-quality formed part, by the same token, it should be given a emphasis to economically design the MPSF. In view of the above idea, it is reasonably and economically that the n=5 in this simulation can be viewed as a reasonable selection.

4 Concluding Remarks MRSF is viewed as a new flexible stretch forming process, and flexible rollers are the most vital component. This paper attempts to work out two useful results, drawing support from extensive numerical simulations aiming at examining the effect of position and discreteness of flexible rollers on the quality of formed part. The followings are the main conclusions, can give guidance to optimizing MRSF process in practical production. (1) Because the sheet metal is elongation at stretching direction and is shorten at horizontal direction, the lateral rollers have to indent a reasonable distance. And in MRSF process design, the indent distance should be taken into account. As to the case investigated in this paper, an 8 mm indent distance is suitable for the MRSF process. (2) The higher the degree of discreteness (or in other words, the more the number of the segmentations of roller) is, the more uniform tension the formed part can get, the more uniform thickness the formed part can be and the better the shape

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accuracy becomes. But a high discreteness will make the flexible roller complicated and expensive, for the case investigated in this work, the roller dispersed into five segmentations is a reasonable selection. Acknowledgment. This work was supported by the EU Sixth Research Framework Programme (ASTS-CT-2006-030877).

References [1] Cai, Z.Y., Wang, S.H., Xu, X.D., Li, M.Z.: Numerical simulation for the multi-point stretch forming process of sheet metal. J. Mater. Process. Technol. 209, 396–407 (2009) [2] Bai, D., Zhou, X.B., Li, D.S., Liu, Y.C.: Key Technology Research and System Development of Complex Aircraft Skin Stretch-forming Simulation. Acta Aeronautica et Astronautica Sinica 25, 606–609 (2004) [3] Luo, H.Y., Li, D.S., Zhang, Y.M., Wang, L., Wang, J.T.: Research & Development on Numerical Control Skin Stretch Forming Testing System. Testing Technology and Testing Machine 04, 31–34 (2006) [4] Walczyk, D.F., Hardt, D.E.: Design and analysis of reconfigurable discrete dies for sheet metal forming. J. Manuf. Syst. 17, 436–453 (1998) [5] Wang, S.H., Cai, Z.Y.: Int. FE simulation of shape accuracy using the Multi-Point Stretch-Forming process. J. Adv. Manuf. Technol. 49, 475–483 (2010) [6] Siegert, K., Fann, K.J., Rennet, A.: CNC-Controlled Segmented Stretch-Forming Process. Ann. CIRP 45, 273–276 (1996) [7] Tan, F.X., Li, M.Z., Xiang, X.J., Zhang, G.Z.: Numerical Simulation in the Process of Multi-point Stretch Forming with Flexible Clamp Mode. Journal of Xi’an Jiaotong University 42, 1160–1164 (2008)

Research on the Maturity of the CDIO Capability Evaluation System for Engineering Students Liang Hong1 and XingLi Liu2 1

Economics and Management College, Heilongjiang Institute of Science and Technology (HIST) 2 Computer and Information Engineering College, Heilongjiang Institute of Science and Technology, Harbin, China [email protected]

Abstract. The purposes of this article are to present the design of an evaluation frame about CDIO engineering capability maturity for engineering students and the index system of evaluation, and then put it into practice for the professional of “Software Engineering” in three colleges of Engineering. In addition to theoretical studies, that is to say, the article expatiates on the statistic and analysis of quantity, which is the maturity of various core competencies about CDIO engineering capability. It involved in following several studies: First, drawing multi-graphs about the longitudinal trends of themselves, and then analyzed it; Second, by correlation analysis find the degree of the relation relationship between the various variables; Third, In the process of regression analysis select an variable, which is closely related with the maturity of CDIO capability, it deduce regression formula by method of least squares, and then predict the regular of dependent variable with independent variables. Analyses of qualitative data showed that CDIO maturity evaluation can scientifically predict the trends of CDIO capability and moderately regulate the CDIO process.

，

Keywords: CDIO the maturity of capability, the system of evaluation index, Correlation and regression analysis.

1 Introduction CDIO model used life cycle from production processes, system development to run as the carriers, through the systemic product design fostered students’ capability in technology knowledge, personal capacity, professional ability and attitude, team work and communication skills, ultimately, equipped the students with engineering capabilities such as business concept, design, implementation, and operation of product system in the social or enterprise environment [1]. Currently, CDIO project concept is taken into account by domestic and foreign researchers and institutions, and teaching reform based on CDIO talent cultivation mode is explored and used actively in the engineering colleges, but the research and application lack research and practice in the assessment of CDIO engineering ability. In the CDIO process, the educational assessment is not only an essential part, but also a means of qualitative Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 181–188, 2011. © Springer-Verlag Berlin Heidelberg 2011

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system description and measurement for students’ CDIO capability. Meanwhile, formation process of the CDIO capability is a gradual process, in the process of the formation, the appropriate capability maturity index system should be provided, which gives direction to CDIO growth potential and improvement. At last, the assessment of CDIO capability and CDIO process constituted a feedback loop. Therefore, this paper conducted thorough investigations to CDIO capability maturity assessment framework, evaluation index system and the quantitative analysis of evaluation results, and practices based on theoretical research has proved that CDIOCMM Capability Maturity evaluation played a good part in forecast and regulation and control of the implementation of CDIO project.

2 CDIO Capability Maturity and Evaluation Criteria A. CDIO Capbility Maturity CDIO capability maturity (CMM) is a assessment system that the U.S. Software Engineering Institute of Carnegie Mellon University, SEI presented a software development organization for the software process capability maturity. Research has shown that, CMM assessment system can be applied to organizational knowledge management processes, teaching process capability maturity except for software process capability. So these ways contribute to the smooth implementation of the CDIO for educational institutions engineering education model for references and this paper apply it to CDIO educational evaluation. B. Evaluation Criteria Capability Maturity CDIO process students reflect the direction in CDIO and implementation of the CDIO to improve the capacity. To quantitative measurement of CDIO capability maturity, this article set evaluation criteria that measure the evaluation of the object to reach the end of the scale of the degree-level indicators and criteria. The system of evaluation criteria consist of the Scale and the Grade. The effect of the Scale is separating the extent from evaluation objects; the effect of Grade is to distinguish between degrees of symbols. Usually in Chinese characters (such as A, B, C, D), letters (such as A, B, C, D) or digital (5, 4, 3, 2).But the label has no independent meaning; it is the auxiliary part of the assessment criteria. This paper Referencing the basic idea of the Capability Maturity Model the capability maturity CDIO rated four scales from low to high scale, namely: the initial level, basic-level, professional level and application-level, respectively 1,2,3,4 label distinguish. All levels of capability maturity reflect the students CDIO engineering education in different stages of development of different characteristics and learning content needs.

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1) Initial level: students have good basic English and math, but incomplete understanding of the profession, unclear professional learning goals ,project-based learning is disordered, even chaotic, with the chance of its success; 2) Basic level:students have a certain quality of human moral, theoretical basis of professional disciplines, and they can purposefully chosen professional course of study, have small-scale projects-based learning according to curriculum needs ,and learning process is stable.

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3) Professional level:students master the professional knowledge, familiar with project-based professional learning process, can use expertise to analyze and solve common practical projects, learning processes are independent. 4) Application level:students have professional experience, it can find problems in real projects, innovative on professional knowledge , and learn other knowledge according to needs of project , and learning process is spontaneous. CDIO process through a three Grades project as the key domain to guide learning, and gradually achieve the desired goal of engineering education.

3 CDIO-CMM Assessment Framework The only one ultimate goal of Use of educational evaluation methods or models is to improve the school instruction and improve the quality of teaching comprehensively. No doubt, building an orderly assessment structure is to ensure CDIO-CMM Maturity Assessment smoothly launching, . In CDIO-CMM model, each level provide for a number of key process areas except for initial level, at each stage after the end of the study conducted, should enter the the evaluation stage of CDIO-CMM maturity degree . There are several key issues need to concern on CDIO-CMM assessment in operation Frame. •

First, set up assessment teams, team members should be familiar with CDIO Engineering Education and statistical analysis; and pay attention to division of labo when assigning role.

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Second, determine the assessment index system, pay attention to weight distribution index.

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Third, collected samples of questionnaires and pay attention to the sample to be representative and diversity.

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Fourth, prcesssing the collecting data ,such as consolidation, classification and analysis ; suggested the control of a single variable frequency, multivariate correlation analysis and significant variables in the regression analysis and other related statistical analysis.

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Fifth, draw conclusions and give a scientific evaluation of forecast according to the results.

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Sixth, set out corrective measures to improve the training program, control a domain key of the CDIO process.

4 CDIO-CMM Assessment Index System Evaluation index system is the basis of scientific assessment, this paper Kelongbahe American scholar (1982) theory, through the divergence and convergence of the two phases determine the index system [5].

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C. Divergent phase Task is initially determine the index system, mainly through researching CDIO outline and the relevant literature, and analyzing of content and elements CDIO; According to CDIO framework, personal and professional skills is essential for engineer; At the same time, modern engineering systems increasingly rely on the support of multi-disciplinary background, so students must grasp the appropriate technical knowledge and build up reasoning ability . In order to be able to work in team-based environment, students must also be equipped with the necessary interpersonal skills and have good communication skills; Finally, in order to truly create and run the product / system, students must have capability of conceive, design, implementation and operation (CDIO) product / system both in the enterprise and in the society[6]. Through the above four areas analysis we build up an index of CDIO capability maturity evaluation, and establish two level refinement indicators. D. Convergence phase Task is filter indicator and determines the target weight, mainly through the survey of the key features. In this study, we select more than 50% frequency index as the target to initially establish of evaluation index, and then use normalization formula to determine the relative weight of each index distribution. As shown in Eq.1, it is an indicator to determine the weight of all normalization methods, similarly, by this way we can achieve all levels of target weight distribution.

ri =

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xi

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∑x

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Note ri I-factor in the weight of the main components. xi I-factor in the load weight of main components.

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5 CDIO-CMM Application and Analysis Based on the theory study, the assessment practice and analytical job had finished in three colleges of education for CDIO process engineering, specific procedures are as follows: E. Sample selection and survey Application of evaluation framework, at first, a special evaluation group was setup, producing level measurement form by index system. Using random sample method to take survey sample, we get 634 Software Engineering students in three different engineering schools. After Statistics, the number of distributed questionnaires is 634, where 623 of them were recovered, 617 of them are effective. Effective questionnaires 97% of the total number of distributed ones. F. The method of Statistical analysis This study selects a statistical analysis tool of Statistical Package for the Social Sciences. Firstly, we carry out the work of Statistics and analysis from four

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perspectives, which has guiding significance for reflecting the distribution and the correlation between mature CDIO. Thus, we give up all indicators of the ability of statistics and analysis of frequency distribution, and conduct statistical analysis from main three sides as follows: 1) Trend analysis of the individual indicator variables each year for CDIO-CMM Maturity.To Analysis the students CDIO-CMM trend of core competencies through Statistics of each year students of the indicator variables CDIO and draw multi-line graph variable model[7]. 2) Analysis of correlation on indicator variables for CDIO-CMM Maturity. Main is correlation statistical analysis on indicators and exploration the closeness between the two indicator variables. The aim is to study the changes in the ability of CDIO through these indicators measurable and close to the ability of CDIO. Specific Close degree of correlation is said with r here.The survey data is variable of ordinal, and select Spearman correlation analysis method to compute correlation coefficient r according to formula 2. Assume |r|<0.3 expresses two variables are linearly independent, 0.3=<|r|<0.5 expresses Low correlation of two variables, 0.5=<|r|<0.8 expresses significant linear correlation of two variables and |r| >=0.8 expresses high degree of correlation of two variables.

ri = 1 −

6∑ d 2

n ( n 2 − 1)

(2)

Where the d is each of the observations corresponding to rank second difference; n is number of pairs. Note: Before the Correlation Analysis, need to draw scatter plot of variables for preliminary judging whether there is correlation between two variables and whether a linear trend, and whether there are differences in data often points. the direct analysis of ignoring scatter plot is likely to draw wrong conclusions[8]. 3) Variables significantly related to indicators of the regression analysis and forecasting for CDIO-CMM Maturity.Regression analysis is important method using a deterministic method to study the relationship between the variables non-deterministic. To variables significantly related to indicators, select one or several variables as independent variables, the other variables change with the change of variables as the dependent variable, through the establishment of a linear relationship mathematical model to study their non-deterministic relationship, the analytical method is mainly carried out based on the following steps: a) Firstly, Establishing a linear regression equation according to formula 4, find coefficients a and b in the equation of the regression line using observations by the method of square plot, a is Intercept, b is slope or regression coefficient. Regression equation is solved by formula 3. b) Second, Significance tests to Regression equation. On the one hand, significant regression coefficient test by the method of T test, On the other hand, test to the fit goodness of regression line by the square of correlation coefficient R2 to illustrate the number of the change in the dependent variable caused by the changes in independent variables, R2 approaches 1, the better fitting. If value of R2 is zero, it

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means the relationship of independent variable and dependent variable is unrelated, there is no value with the regression line. c) Third, Test of significance of the regression line. Regression sum of squares, residual sum of squares, and statistics are determined by analysis of variance and F test methods, statistics equals squares of the average regression sum of squares / mean residual. F value is too small to the level of significantly different, that is to say, since the explanatory power of variables to the dependent variable is poor, regression line equation with no significant. d) Finally, Complete independence of residuals by test to Durbin-Watson.

b=

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a=

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n∑ x 2 − ( ∑ x ) n

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(3) _

= y− b x

y = a + bx

(4)

Substituted (3)-type to (4) get a regression equation.

6 Data Analysis and Founds Firstly, Figure 1 shows the individual indicator variables each year trend analysis in the CDIO-CMM maturity. Year discount displays with a different shape, 1,2,3,4 are values of maturity level, it represent separately four ability study levels for "Basic knowledge", "professional competence", "teamwork" and "System capacity", which are four kind of ability. The result is one of four forms: Excellent, good, medium, poor. The analysis results show that the number of first year student at a medium level is more, second year students of good level are much more and corresponding curve is relatively smooth, the most students are third year students at excellent level, who close to 30%; Similarly, as the process of CDIO training, students of "Professional competence", "teamwork" and "System capacity" all show significantly improved state.

Fig. 1. CDIO multi-line graph of the core ability

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Table 1. Spearman’s Correlations total: 621

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Secondly, Correlation analysis between the indicator variables and statistical situation between the investigated variables about the correlation for CDIO-CMM maturity as show as table 1. According to statistical theory known, "Year" and "team work", "System capacity" showed a significant linear correlation, that is with the implementation of CDIO process, students of CDIO capability maturity reflected a gradual improve process, and "Teamwork" and "system integration" also showed a highly linear correlation, which means that student of team ability and capability in systems integration is an direct association with each other. Nots: “CC” is the abbreviation of “Correlation Coefficient”; “BK”is the abbreviation of “Basic Knowledge”; “OS” is the abbreviation of “Occupational Skills”; “TC” is the abbreviation of “Team Coordination” ;”IA” is the abbreviation of “Integration Ability”. Thirdly, In accordance with relevant CDIO-CMM maturity of the analysis results to the significantly related indicator variables, we selected related significantly two variables of "teamwork" and "grade" to regression analysis. Because the CDIO process is increased with grade-depth training, select "Grade" as independent variable, the "teamwork" capacity as the dependent variable processing, besides, statistical analysis to the variable to enter or return to the model and regression model of summary, variance, regression coefficient and residual. Known as statistical analysis results by table 1, "Year" and "teamwork" as two variable regression equation parameters to test results. Show as table, regression equation constant or the intercept is 1.195, standard error of intercept is 0.071, test value T is 16.717, significance level is 0.0000; the slope of the regression equation is 0.407, error of regression coefficient is 0.032, standard regression coefficient is 0.459, test value T is 12.864, significance level is 0.0000. The level of significance above the level of 0.0001 shows that the slope of the overall sense, regression equation as shown in Equation 5, predicted value can be calculated according to the regression equation.

y = 1.195 + 0.407 x

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7 Conclusion By means of theory, practice evaluation, and statistical analysis showed that, CDIO maturity assessment research and practice in Engineering Colleges could promote the scientific understanding to CDIO teaching process, forecast the development trend of CDIO, and appropriate regulation of the CDIO process changes; it has a role of reference to engineering colleges on CDIO engineering education reform. Acknowledgment. The study completed in support of “the New Century Higher Education Reform Project MPE-CDIO Undergraduate Engineering Education Research and Practice in Heilongjiang Province”. Special thanks to project leader Professor Zhang Fengwu for the guidance to the study.

References [1] Wang, G.: Reading and Mode Thinking for CDIO engineering education. Higher Education Research in China (9), 86–87 (2009) [2] Liu, M.R. (translation), Carnegie Mellon University Software Engineering Institute. Capability Maturity Model (CMM): Software Process Improvement Guide. Electronic Industry Press, Peking (2001) [3] Jin, D.: Educational Evaluation and Measurement. Education and Science, 107–121 (2001) [4] Chen, Q., Ren, S., Hu, Z., Wu, B.: Engineering Students of CDIO Capability Maturity Assessment and Study in Improvement System. China Higher Education 8, 31–33 (2008) [5] Crawley, E.F.: The CDIO Syllabus A Statement of goals for Undergraduate Engineering Education: 1, 4, 17, 9 [EB/OL]. (2008-06-30) (2009-04-13), http://www.cdio.Org [6] Yi, H.: Concise Guide to Social Statistics. Social Sciences Academic Press, [7] Gao, X., Dong, H.: Data analysis and application to SPSS. Tsinghua University Press [8] Li, J.: Statistics (revised). Machinery Industry Press

The Web Data Extracting and Application for Shop Online Based on Commodities Classified Jianping Deng1, Fengwen Cao1, Quanyin Zhu2, and Yu Zhang2 1 Department of Electronics Engineering, Suzhou Vocational University, Suzhou, Jiangsu Province, China 2 Faculty of Computer Engineering, Huaiyin Institute of Technology, Huaian, Jiangsu Province, China [email protected]

Abstract. As the Web and its usage develop very fast, the content, structure, and usage data, and the Web mining get more and more useful in everywhere such as e-supermarkets, e-commerce, e-learning, and e-government. Many theories and algorithms are reported for Web mining. This paper shows a novel method for the Web data extracting of shop online. The MVC design model and the Bottle Formwork are opted to build the application system. The opened developing tools: Python language and MySQL database are used to code the system program and construct the system database respectively. The system architecture and the commodity classified are introduced. More then 30 functions coded by Python are designed for implantation the data extracting, analyzing, statistics, and system management. Some of them are described in detail. Experiment demonstrates its performance and proves this case is meaningful and useful for other shop online development. Keywords: Data extracting, Commodity classified, Shop online.

1 Introduction For the Web server and Web mining development, the e-supermarkets, e-commerce, e-learning, and e-government getting more and more advancement. Those e-service specially shopping online support our life convenient day by day. A lot of technologies are used to progress the Web usage application. The classification of Web data mining is divided into content mining, structure mining and log mining three categories [1]. Some education researchers reported their research results on the e-learning using the Web mining, such as Reference [2] and Reference [3]. Some researchers focused on the Web mining algorithms for search engines [4]. However, the most of researchers hammer at the content or text mining such as Reference [4] and Reference [5]. Another important research domain is the behavior of the Web users, the outcomes are reported in the Reference [6] and Reference [7]. Some research based on the feature selection [8], and some research based on the XML technologies [1,5,9]. As we know, the Web mining can be used to discover the Internet news [10], Analyze email communications [11], recommender the music [12], require skill sets for Computing Jobs [13], and even to latent topics from web sites of terrorists or extremists Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 189–197, 2011. © Springer-Verlag Berlin Heidelberg 2011

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[7,14] and so on. As the shop online development very fast in China recently, all shopkeepers want to know more information about the customer and recommend the new products to them using e-mails. So, our group designed a Web data extracting website for the shopkeeper’s online shop based on the Commodities Classified.

2 The Main Technologies A. Python Language Python not only can be used to one kind of program, but also can be used to code the script. It is an Object-Oriented language and has a high performance data structure which can be master easily. Python supports warehouse files for C language, so it can be coded by C and C++ language [15], it suits to a extension language for other application programs as well. For example, it can be used to extend the functions for CAD and DBMaker, etc. Python afford more structural support then the Shell, on the other hand, it furnish strong check errors then C language. Python built up lots of data types, so it has very huge application fields. Python technology holds out tags moving, it can create a record file and provides you with a redeveloping on the foregoing codes [16]. B. Bottle Formwork Bottle is a small formwork for Web development, which is used to the Python program. Bottle formwork constructs script functions show as follow: z

Basic Mapping The Bottle use the mapping to deal with the Web page operations instead of what we use always the background functions for server. Basic mapping call the Bottle functions inside by router() modifier depend on the different URL request on the Web page. The request mode of Web page includes the GET, the POST and the HEAD. z Dynamic Mapping The Bottle can set up a dynamic mapping for variable name that depend on the partial content of the URL. A regular expression can be used to write in the URL too, and as a part of the dynamic variable name. z Document Flow Return and JSON Web Server Gateway Interface (WSGI) can not deal with the file object or character string, but Bottle can transform it to the iter object. The dictionary class is allowed to use in the WSGI criterion, and it will be transformed to the JavaScript Object Notation (JSON) format. z Cookies The Cookies are used to save a few data of server in the client disk. In the Bottle, it will be saved in the request.COOKIES variables, and it can be opened in the function response.set_cookie (name, value[, **params]). z Template The private template can be built in the Bottle; the private functions can be set up by using function of template (template_name, **template_arguments), and the default path of template include the ['./%s.tpl', './views/%s.tpl'].

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z

Template syntax Template syntax of Bottle includes: ¾ %...Python code begin, the Bottle can dispose the indent automatically. ¾ %end is used to close up some sentences like %if ..., %for..., etc., and the others for must be closed up. ¾ {{...}} is used to print the results of the Python sentence. ¾ %include template_name optional_arguments may include other templates. z Key/Value database A database can be built by using the bottle.db module variables. The user can save the database object using the key or the attribute, and insure the database name is correct. For the database operation required, the Bottle suspension stored the all changes in the memory pool, and all changes will be auto saved when the requirement is over. z Using WSGI and Middleware The bottle.default_app() of Bottle be able to return a WSGI application, so the user can use the WSGI or middleware after the bottle.run() function has been declared. z Release You can select the single thread server or multithread server to release in the Bottle. The wsgiref.SimpleServer function is the default single thread server. Multithread server can be achieved by installing the HTTP server and adapter.

3 The Overall Design A. System Architecture The application system includes three main entries, which are Data Extracting, Data Analyzing, and System Management. Figure 1 shows the system architecture.

User Login

User Entry

Password Get back

Portal Selection Web Page

Data Extracting

Commodities Selection

Data Analyzing

System Management

Statistic Reports System Database

Commodities Classified Database

Fig. 1. System architecture

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B. Data Extracting The Data Extracting is one important of all function in the application system. The user can select the URL address what you want for data extracting. The function consists of URL type and URL address which belong to the URL type you have selected above. The extracted data save in the system database which comprises the title, type, text, issue time, capture time, and the URL address. All the URL address can be set up on the system management background. C. Data Extracting The Data Analyzing is another important of all function in the application system. The user select the data type what he/she want, then choose the source URL address, the analysis results will be given after the commodities classified is opted. The analysis report consists of the title, commodity type, commodity name, issue time, capture time, and the URL address. All the analysis results will be saved in the system database. D. Management Background The Management Background is used to set up the administrator, user, and the regular expression of the URL address. It can modify and delete the administrator, user, and the regular expression of the URL address too. The password for the administrator and users can be changed in the Management Background as well. E. Data Statistics The extracted data and the analyzed data can be taken as a statistics. Such as the record times, the URL address, commodities, issue time, amount of each commodity name and numbers, etc. The storekeeper can get the useful data depend on their needs, for example, the information about the new products, the purchaser, and so on. F. Commodity Classified The Commodity Classified database is the basic for the application system. All commodities classified stored in it. We divide all commodities to two top classified as esculent and inesculent class respectively. The esculent include 21 second tier classified, each second tier is divided to 7 classes, the total last level commodities classified of the esculent include 209 class. As the same, the total last level commodities classified of the inesculent include 563 classes. More then ten thousands of the classified commodities are stored. Furthermore, the storekeeper can add and edit the commodities name and the commodities classified depend on their convenient management. As an example, the table 1 shows a part of Electrical Commodity Classified.

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Table 1. The example of electrical commodity classified

Classified washing machine TV accessory

Table Name S1 S3

flat television water heater refrigerator

S5 S7 S9

Mini-audio fanner

S11 S13

equipment of drinking water cleaner electric radiator humidifier

S15 S17 S19 S21

phone Radio/recorder

S23 S25

battery

S27

Electrical switch E-Rice cooker

S29 S31

hearth

S33

electric pan Soymilk machine Coffee machine electric oven Disinfection cupboard toaster

S35 S37 S39 S41 S43 S45

Classified air-condition Gas Water Heater home theater DVD player water dispenser purifier Cleaning machine Foot Bath Heating device electric iron electrical outlet power tool induction cooker electric pressure cooker Juicer/ blender electric water jug microwave oven Multi-pot dishwasher egg boilers …… …… Other electrical equipments

Table Name S2 S4 S6 S8 S10 S12 S14 S16 S18 S20 S22 S24 S26 S28 S30 S32 S34 S36 S38 S40 Sxx Syy S001

4 Main Functions Design The MVC architecture is use to develop the application system. The main functions are more then thirty; some of them are introduced as follow: 1) send_file() The send_file() is designed to transfer the image files to the User Interface (UI), the image files will be read from image folder when the send_file() function is required. The value of control src is “image/image_name.jpg” and used in the UI code. The browser sends out the POST requirement to the server when it load the image, and the server return the GET mode to the UI loading after it received the requirement from the client. 2) login() The login() is a link function which is designed for response all requirement belong to terminal “/login”. It returns the entry page to the user.

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3) select() The select() is a link function which is designed for the different entry. It has three different values, the first entry is the Data Extracting, the value of action for UI form is terminal “/select1”; the second entry is the Data Analyzing, the value of action for UI form is terminal “/select2”; and the third is the System Management, the value of action for UI form is terminal “/select3”. All the select() functions return the corresponding entry page to the user. 4) dataget() The select() is designed for Data Extracting UI. It returns the extracted data and saves in the system database at the same time. It only responds the terminal “/dataget” requirement of action attribute value. 5) datashow() The select() is designed to show the extracted data depend one the filter interval what the user selected. The action attribute value must be terminal with “/dataget”. 6) dataanalyze() The dataanalyze() is designed for Data Analyzing UI. It analyzes the data from the extracted (stored in the system database) which depend on the data type the user selected it and returns the data analyzed results. It only responds the terminal “/dataanalyze” requirement of action attribute value. 7) datacount() The datacount() is designed for data statistics UI. It can count the extracted data and the analyzed data. The server only respond the user action requirement with attribute value of the terminal “/datacount”. 8) admin() The regular expression is the sticking point of the application system. The data extracting works based on the regular expression which edit by the administrator. The information on the Web page should to make a pair with the regular expression corresponding. Many Web pages may need more then one regular expression, so the data extracting matching procedure need time after time. In the application system, we built up four regular expressions corresponding to deal with one Web page, and the system administrator can add it using the management function, so the admin() function has more then one parameters. The function action attribute value must be terminal with “/admin”. In order to return the successful or errors information to UI, we build a file name of web.txt provisionality. It saves the page code, and shows the information to the administrator with the Web page used the open function of Bottle. 9) search() The search() function is another important functions in the system. All the data extracting and data analyzing codes lie on it. When the user select finished for data type and the URL address, the search() function will be run to extract the Web page and analyze it. All the extracted and analyzed data will be saved on the extracted_table and the analyzed_table respectively. The zero record and the same record compared with the table existed record respectively. As every knows, one Web page may include many sub-pages, the strategy consists of seven steps: First step: capture the sub-pages URL and save it at a temporary table name A; Second step: checking the sub-pages URL saved in the table A with the system database URL records;

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Third step: URL filtering. Delete the same URL record in the table A compare with the system database URL records; Fourth step: select one URL address of the table A to extract data; Fifth step: save the extracted data of the step four; Sixth step: depend on the numbers of records in the table A to redo step four and step five; Seventh step: return the results to the UI. The Figure 2 shows the algorithm above. Select the commodity type for extracting Send the request to the server Transfer the selected information to the server Confirming the type of URL address selected Connect the Web page of the URL Data matching using the regular expression for the URL, and save the match condition information to the variable Connect the system database (MySQL) Save the extracted data to the system database Return the results to the UI Fig. 2. Data extracting algorithm

10) show() The show() function is designed for showing the extracted data and the analyzed data. The user can select the data ranges, which include the extracted time, URL address, commodities, issue time, amount of each commodity name and numbers, etc. the showing can be list with specifically order support the user request. 11) Other functions The register() is used to user register, rec()used to return the password back to user, ad() is used only for the system administrator, adm() is used to return the user UI by the administrator, admreg() is returns the password back to the administrator, reg() is used to register the system (the requisition information includes the user name, nickname, password, sex, address, contact phone, and e-mail. All the register information will be saved in the user table in the system database in manner of POST, and it can be achieved by using the request function of Bottle.), log() is used to log in the system for the ordinary users (the function has two parameters: user name and user password), admlog() used to log in the system for the administrator, delete() is used to delete regular expressions which are not used (only the system administrator can do it). showuser() is used to show the user’s name, nick name, password, sex, age, address, telephone, and e-mail and so on, showstyle() is used to show the regular expressions, showtimes() is used to show the numbers of the records of data statistics, showURL() is designed to count the numbers of different URL for data statistics (it needs the filter to

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filtrate the same URL address because of one URL address may has more then one data Corresponding.), lentime() designed for recording the capturing time (it is a read-only data, it can be gotten from gettime field variable of the Bottle.),

5 Implemention and Conclusion We develop an application system using the idea and design introduced above. The Python language is opted to code the system program, the Bottle formwork is used to build the system architecture, and the MySQL is selected as the system database and the commodities classified database. We choose the electrical commodity classified as the second classified, the third and the fourth is the digital products and mobile communication respectively. The URL is the http://www.360buy.com/. Figure 3(a) is the data extracting result without keyword, and 3(b) is the data extracting result with keyword “SONY”.

(a) Without keyword

(b) With keyword “SONY”

Fig. 3. Data extracting result

As the Web and its usage grows, it will continue to generate ever more content, structure, and usage data, and the value of Web mining will keep increasing [17]. Our application system is working on the local shop online now and it can also count the extracted data and analyze it. Our future work is how to compare the price on the other Web page with our online shop for each commodity, and recommend the new products to the registered users depend on their shopping interesting. All the future work will be supporting the shopkeepers more and more convenient.

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References [1] Chen, Q., Hou, M.: XML-based data mining design and implementation. In: ICCDA 2010, pp. V4-610–V4-613 (2010) [2] Lim, E.H.Y., Tam, H.W.K., Wong, S.W.K., et al.: Collaborative content and user-based web ontology learning system. In: FUZZ-IEEE 2009, pp. 1050–1055 (2009) [3] Chanchary, F.H., Haque, I., Khalid, S.: Web Usage Mining to Evaluate the Transfer of Learning in a Web-Based Learning Environment. In: WKDD 2008, pp. 249–253 (2008) [4] Alla, H.A.H.M.A., Al-Ghreimil, N.: A Novel Efficient Classification Algorithm for Search Engines. In: CIMCA 2008, pp. 773–778 (2008) [5] Chan, G.Y., Wong, H.S., Rao, G.S.V.R.K.: An Adaptive Intrusion Detection and Prevention (ID/IP) Framework for Web Services. In: ICCIT 2007, pp. 528–534 (2007) [6] Atanasova, T., Kasheva, M., Sulova, S., Vasilev, J.: Analysis of the possible application of Data Mining, Text Mining and Web Mining in Business Intelligent Systems. In: MIPRO 2010, pp. 1294–1297 (2010) [7] Li, Y., Feiqiong, L., Kizza, J.M., Ege, R.K.: Discovering topics from dark websites. In: CICS 2009, pp. 175–179 (2009) [8] Ramakrishna, M.T., Gowdar, L.K., Havanur, M.S., Swamy, B.P.M.: Web Mining: Key Accomplishments, Applications and Future Directions. In: DSDE 2010, pp. 187–191 (2010) [9] Cheng, Z., Yong, F., Yaping, S.: The Implementation of the Web Mining Based on XML Technology. In: CIS 2009, pp. 84–87 (2009) [10] Xu, C.-Z., Ibrahim, T.I.: A keyword-based semantic prefetching approach in Internet news services. IEEE Transactions on Knowledge and Data Engineering 16(5), 601–611 (2004) [11] Grobelnik, M., Mladenic, D., Fortuna, B.: Semantic Technology for Capturing Communication Inside an Organization. IEEE Internet Computing 13(4), 59–67 (2009) [12] Su, J.-H., Yeh, H.-H., Yu, P.S., Tseng, V.S.: Music Recommendation Using Content and Context Information Mining. IEEE Intelligent Systems 25(1), 16–26 (2010) [13] Litecky, C., Aken, A., Ahmad, A., Nelson, H.J.: Mining for Computing Jobs. IEEE Software 27(1), 78–85 (2010) [14] Chen, H.: IEDs in the dark web: Lexicon expansion and genre classification. In: ISI 2009, pp. 173–175 (2009) [15] Dubois, P.F., Yang, T.: Extending Python with Fortran. Computing in Science & Engineering 1(5), 66, 68–73 (1999) [16] Dulume, P.F.A.: Nest of Pythons. Comouting in Science & Engineering 7(6), 81–84 (2005) [17] Ramakrishna, M.T., Gowdar, L.K., Havanur, M.S., Swamy, B.P.M.: Web Mining: Key Accomplishments, Applications and Future Directions. In: DSDE 2010, pp. 187–191 (2010)

Image Registration Algorithm Using an Improved PSO Algorithm Lin-tao Zheng and Ruo-feng Tong Department of Computer Science and Engineering, Zhejiang University, Hangzhou, China [email protected]

Abstract. Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities, different time points, and/or different subjects. A large number of methods for image registration are described in the literature. Unfortunately, there is no one method that works very well for all applications. Particle swarm optimization is a stochastic, population-based evolutionary computer algorithm. In this paper, we propose a new approach using improved Particle swarm optimization for medical image registration. The algorithm has been successfully used for medical image registration. The feasibility of the proposed method is demonstrated and compared with Standard PSO based image registration technique. The obtained results indicate that the proposed method yields better results in term of both algorithm stability and accuracy. Computational time is also relatively small in the proposed case compared to the other case. Keywords: image registration, improved particle swarm optimization, mutual information.

1 Introduction Medical images are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease progression. Within medical research they are used to investigate disease processes and understand normal development and ageing. In many of these studies, multiple images are acquired from subjects at different times, and often with different imaging modalities. In research studies, it is sometimes desirable to compare images obtained from patient cohorts rather than just single subjects imaged multiple times. Furthermore, the amount of data produced by each successive generation of imaging system is greater than the previous generation. This trend is set to continue with the introduction of multislice helical CT scanning and MR imaging systems with higher gradient strengths. There are, therefore, potential benefits in improving the way in which these images are compared and combined. Image registration technique refers to a process of determining a spatial transform mapping on one image into another. Image registration is a fundamental task in image Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 198–203, 2011. © Springer-Verlag Berlin Heidelberg 2011

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processing used to match two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. It is used in motion correction, multi-modal image fusion, treatment planning and many other tasks. Image registration is an important technology aimed to align two or more images by finding a suitable transformation that relates the involved images. As a pre-step to compare and combine information taken from different images, image registration plays an important role in many medical applications, such as information fusion, 3D volume construction, atlas building and so on. For a mathematical treatment of this problem, image registration can be considered as an optimization problem which tries to maximize an objective energy function with respect to the transformation that measures the similarity between the two involved images under some regularization constraints. There are many approaches to image registration [1]. Much work has recently focused on intensity-based approaches, in which the intensity measure (color, or gray level) are used to compute similarity measures between the images. Intensity-based registration does not generally require extensive preprocessing, such as segmentation or feature extraction. To address image registration problems, this paper focuses on a new improved PSO technique. This technique is relatively recent and is better know for optimization but has not been previously applied to medical images previously. We will introduce this method in more detail, and then apply it for medical image registration.

2 Particle Swarm Optimization Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. [2]The particle swarm concept originated as a simulation of simplified social system. The original intent was to graphically simulate the choreography of bird of a bird block or fish school. However, it was found that particle swarm model can be used as an optimizer. During the past years, PSO has been successfully applied to multidimensional optimization problems, artificial neural network training, and multiobjective optimization problems. Particle Swarm Optimization optimizes an objective function by undertaking a population-based search. The population consists of potential solutions, named particles, which are metaphor of birds in flocks. These particles are randomly initialized and freely fly across the multi dimensional search space. During flight, each particle updates its own velocity and position based on the best experience of its own and the entire population. Mathematical notation of original PSO is defined as follow: An individual particle i is composed of three vectors: its position in r the D-dimensional search space xi = ( xi1 , xi 2 ,..., xiD ) , the best position that it has individually found

r pi = ( pi1 , pi 2 ,..., piD ) , and its velocity

r vi = ( vi1 , vi 2 ,..., viD ) . Particles were originally initialized in a uniform random

manner throughout the search space; velocity is also randomly initialized.

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These particles then move throughout the search space by a fairly simple set of update equations. The algorithm updates the entire swarm at each time step by updating the velocity and position of each particle in every dimension by the following rules

：

vid = vid + c1 * rand () *( pid − xid ) +c2 * Rand () *( pgd − xid ) xid = xid + vid where in the original equations

(1.1) (1.2)

c1 and c2 are a constant with the value of 2.0, rand

and Rand are independent random numbers uniquely generated at every update for r each individual dimension d = 1 to D, and pg is the best position found by any neighbor of the particle. Particle swarm optimization for image registration was introduced in [3]. The basic PSO algorithm can be enhanced in a variety of ways. Although the original PSO algorithm has ability of exploiting the global maximum, it can not guarantee to achieve the maximum but often falls into local maxima. To overcome this shortcoming, In this paper, we use the following equations to instead equation (1.1) and (1.2):

vid = ϕ{vid + c1 * rand () *( pid − xid ) +c2 * Rand () *( pgd − xid )}

ϕ= In an ordinary condition,

xid = xid + vid 2 2 − C − C − 4C 2

(1.3)

(1.4)

, C = c1 + c2

c1 = 2.8, c2 = 1.3 .

3 Mutual Information Mutual information is a basic concept originating from information theory, measuring the statistical dependence between two random variables or the amount of information that one variable contains about the other. The mutual information I of two images X and Y is defines as

I ( A, B ) = H ( A) + H ( B ) − H ( A, B ) where H ( A) and entropy.

H ( B ) are individual entropies and H ( A, B ) is the joint

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4 Image Registration Model

Fig. 1. Framework of medical image registration

Fig. 2. The Pseudo code of the improved PSO procedure

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5 Results and Discussion In order to evaluate the proposed PSO-based image registration, we compared the proposed method with original pso method-based image registration. In this section, we perform several registration experiments with medical image data to evaluate the performance of the proposed technique. Moreover, we also perform conventional PSO for comparison.

Fig. 3. (a) target image. (b) floating image.

Brain CT data and MR data are used as the reference image and the test image, respectively. Table 1. Registration results of experiment 1

Tx

Ty

θ

Ground truth

7

0

2

Standard PSO

9

1

0

Our algorithm

7

0

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Table 2. Registration results of experiment 2

Tx

Ty

θ

Ground truth

-17

-13

0

Standard PSO

-20

-13

0

Our algorithm

-17

-13

0

It can be shown that results obtained using our proposed PSO when compared with the results obtained using original pso method reveals the following facts: Improved PSO has been successfully applied for image registration application and demonstrated that PSO gets better results in a fast, cheaper way compared with original pso method.

6 Conclusions and Future Work In this paper, a new approach to image registration using improved PSO is implemented. We proposed a new global optimization approach based on improved particle swarm optimization for medical image registration. Experimental results with medical image data show that the proposed method performs much better results than conventional PSO.

References [1] Brown, L.: A survey of image registration technique. ACM Comput. Surv. 24, 325–376 (1992) [2] Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science(Nagoya, Japan), pp. 39–43 (1995) [3] Talbi, H., Batouche, M.: Particle swam optimization for image registration. In: Information and Communication Technologies: From The to Applications, pp. 397–398 (2004)

The Research of Network Intrusion Detection Based on Danger Theory and Cloud Model Zhang Ruirui, Li Tao, Xiao Xin, and Shi Yuanquan College of Computer Science, Sichuan University, Chengdu 610065, China [email protected]

Abstract. A new method of intrusion detection based on the danger theory and the cloud model is presented in this paper. The main idea of danger signal generation mechanism of this method is stated as follows. Antigen apoptosis and necrosis will affect antibody concentrations. This paper has defined the concentration variability functions concerned and divided the risk levels. Changes of antibody concentrations in the immune system are determined by the cloud model, and then danger signals will be sent according to the changes. This method has successfully solved the problems of high false positive rate and high false negative rate. The theoretical analysis and experimental results show that the method is effective to intrusion detection with advantages of diversity, real-time and adaptability. Keywords: danger theory, cloud model, danger signal, artificial immune system, intrusion detection system.

1 Introduction With the development of Internet and information technology in recent years, network scale and structure become more complex, and network security become more critical. It has achieved good results by applying the technology of artificial immunity to the field of intrusion detection, but there are some difficult issues. Such as, the self/nonself boundary is too clearly, the rate of false positive and the rate of false negative are high, and so on. In 2002, Dr. Uwe aickelin introduced the danger theory into the artificial immune system, and pointed out a new direction for artificial immunity. In the danger theory, there is no self or non-self. Danger is recognized according to the danger signal which is emitted when the system is violated [3]. The key to danger theory is the definition of danger signal. With the help of the cloud model which is often used to describe uncertainty, the status (safe or in danger) is defined, and risk levels are divided. Danger is identified through the cloud model. Based on the research of the danger theory and the cloud model, the way to generate danger signal is presented, and a new model of intrusion detection is proposed in this paper. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 204–211, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 The Model Theory A. Antibody and Antigen In this model, antigens are defined as network requests, selves are defined as normal network requests, and non-selves are defined as abnormal network activities (network attacks). Selves and non-selves constitute the whole collection of antigens, and characteristics of antigens are expressed by antigen determinants. Antibodies are used for detecting and matching antigens, and characteristics of antibodies are expressed by antibody determinants. Based on the shape-space model, the determinants of antigen and antibody are defined as binary strings. B = {0, 1}len. B is defined as all sets of binary strings whose length is len. N is defined as the set of natural numbers. R is defined as the set of real numbers. The antigen determinant d is composed of m-feature gene segments. d = (d1, d2, …, dm). Antibody Ab is divided into immature antibody, mature antibody and memory antibody. Ab is composed of antibody determinant, age, concentration and count of matched antigen. The antibody determinant is defined as d whose structure is the same as antigen determinant. The immature antibody represents newly-created immune cells which don’t go through self tolerance. The mature antibody represents immune cells which go through self tolerance and aren’t activated by antigens. The memory antibody represents mature immune cells which are activated. B. Gene Lib The determinants of antigen and antibody are composed of gene segments which are parts of IP packet. Put together all the possible values of each gene segment in the gene library, and then select randomly corresponding value of each gene segment to compose a legal gene. The types of gene segments are as follows: service type (8 bit), source address (32 bit), source port (16 bit), destination address (32 bit), destination port (16 bit), protocol type (8 bit), length of IP packet (16 bit), part of packet data (16 bit). C. Calculation of Affinity The affinity between antigen and antibody, antibody and antibody is defined as the matched level between its data structures. The affinity can be computed by Euclidean distance, Manhattan distance, Hamming distance or r-continuous bits [1]. The improved Hamming distance is adopted in this paper. L

faffinity (d1, d2) =

1−

∑δ i =1

L

⎧⎪δ =1,d1i ≠ d2i (⎨ ) ⎪⎩δ =0,other

(1)

d1 ∈ B, d2 ∈ B, L ∈ N (the length of d1 and d2). The larger f is, the more matched (similar) d1 and d2 are, and the larger the affinity between antigen and antibody is.

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D. Consanguinity Class and Consanguinity Class Series Consanguinity Consg = {<x, y> | faffinity (x.d, y.d) ≥ θ ∧ x, y ∈ Ab}, and θ is the matching threshold between x and y. For any antibody set X, if ∀ x, y ∈ X, exist <x, y> ∈ Consg, then X is called a consanguinity class [5]. That is to say that if the affinity between any elements x and y in set X is greater than a given threshold, X is called a consanguinity class. If any element in the set of Ab-X doesn’t have the relationship of consanguinity with any element in the set of X, then X is called the biggest consanguinity class in the set of Ab. π = {A1, A2, …, An}, Ab1 = Ab, Abi = Ab -

U

A j . Ai is defined as a biggest consanguinity class with most elements in the set

1≤ j
of Abi, and Ab =

UA

i

, then π is called a consanguinity class series[5].

1≤i ≤ n

Given 1 ≤ j < i ≤ n , exist Ai I Aj = ∅ . E. The Cloud Model The cloud model is a mathematical transformation model between the qualitative concept expressed by language values and quantitative data, and its digital characteristics are mathematical expectation Ex, entropy En, and hyper entropy He. Ex expresses the best proper point in the domain space which represents the qualitative concept. En expresses the measurable scale of a qualitative concept, and reflects the uncertainty of the concept. He expresses the measurable scale of uncertainty of En, and reflects the randomness of samples which represent the qualitative concept[6]. The most important thing of introducing the danger theory into intrusion detection systems is to estimate danger. In the process of estimating, danger and safety are qualitative concepts which are uncertain, while the resources of artificial immune systems are quantitative data, so the cloud model can be used. While in the immune system which is attacked, the antibody concentration changes most immediately. So, we can use the changes of antibody concentration to constitute the cloud model to estimate danger. First, take samples of the antibody concentrations from different consanguinity class series when the system is in secure state. Limit the values of samples between 0 and 100. According to the algorithm of backward cloud generator [6], we can get the digital characteristics of the cloud of every consanguinity class series in secure state {Exsafe1, Ensafe1, Hesafe1}, {Exsafe2, Ensafe2, Hesafe2}, …, {Exsafen, Ensafen, Hesafen}. Introduce well-known attacks to the system, and collect samples when the system is in danger. Then get the danger-state clouds. If the secure-state cloud and the dangerstate cloud cover the entire space, we can use the two clouds to estimate danger. This is an ideal situation. If they cannot cover the entire space, we should divide the blank area into weak-secure-state cloud and weak-danger-state cloud. Generally, the closer to the center of the domain the value is, the smaller the entropy value and hyper entropy value of the cloud are; the more away to the center, the bigger. Between the entropy values and the hyper entropy values of adjacent clouds, the lesser is 0.618 times the larger by default. This is an empirical value. Therefore, we can get Enlesssafe, Enlessdangerous, Helesssafe, Helessdangerous. Under the “3En rules” of the cloud, we can

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compute expected values Exlesssafe, Exlessdangerous of weak-secure-state cloud and weakdanger-state cloud. Exlesssafe = Exsafe + 3Enlesssafe = Exsafe + 3*0.618*Ensafe

(2)

Exlessdangerous = Exdangerous - 3Enlessdangerous = Exdangerous - 3*0.618*Endangerous (3)

3 The Model Framework A. The Overall Procedure There are two parts of the system, the data modeling module and the intrusion detection module. The aim of data modeling module is to establish the cloud models of consanguinity class series. First of all, more than ten kinds of attacks including syn flood, scanning attack, IP spoofing attack are used to init the system. Then we get the initial set of antigens and the initial set of memory antibodies, and the initial consanguinity class series. Afterwards, under the way described in the chapter of the Cloud Model, we can obtain one-dimensional clouds of each risk level. Then we design the rules listing in the following to build the rule generator. Thus, the data modeling is completed. Rule1: IF the concentration is low THEN the system is safe; Rule2: IF the concentration is less low THEN the system is less safe; Rule3: IF the concentration is less high THEN the system is less in danger; Rule4: IF the concentration is high THEN the system is in danger. The aim of intrusion detection module is to judge whether the system is being attacked, and the procedure is shown in Fig. 1. When the system receives an IP packet, antigen presenting sub module extracts gene segments and encodes them to be an antigen determinant. Then, the antigen comes to the memory antibody set. If it isn’t identified, then it comes to the mature antibody set. If it is identified, concentrations of matched mature antibodies should be increased, and concentrations of consanguinity class series should be calculated, and the membership degree of risk level which the concentrations belong to should be calculated too. According to different membership clouds, different responses should be made. • •

• •

If it belongs to the secure-state cloud, that is, although non-self is detected, but the system is safe. The detection can be regarded as a wrong identification, and the corresponding mature antibody should be deleted. If it belongs to the weak-secure-state cloud, that is, although non-self is detected, but the system is basically safe. The detection can be regarded as a wrong identification, the immune response cannot be provoked, and the concentrations of corresponding mature antibody should be decreased. If it belongs to the danger-state cloud, that is, the antigen is necrotic and should be deleted from the antigen set. The immune response should be provoked, and corresponding mature antibodies should be added to the memory antibody set. If it belongs to the weak-danger-state cloud, that is, the antigen is necrotic and should be deleted from the antigen set. The immune response should be provoked, and the concentrations of corresponding mature antibody should be increased.

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Fig. 1. Flow Chart of Intrusion Detection Module

When the immune response is provoked, the antibody mutates into more antibodies according to the clone selection algorithm. Parts have higher affinity with the antigen in order to identify antigens more quickly, and parts have lower affinity to ensure the diversity of the immune system. B. Changes of Antibody Concentration There are several factors affecting the concentrations of antibodies. •

The initial concentration of mature antibody is δt0, when it comes from immature antibody.

•

Within the lifetime of mature antibody, when a non-self matches with a mature antibody, the concentration of that antibody is increased by δt1, and the concentrations of other unmatched antibodies are decreased by δt2. The changing curve of δt1 is in connection with attack power a, and the stronger a is, the larger δt1 is. Under continuous attack, the time of immune study can be shortened, and the system can make immune response more quickly. δt2 is a function of attack power a as well, and increases slowly with the decrease of attack power. If an attack comes again in a short time, the system can maintain a high security level. Assuming that δt1 and δt2 meet the following equations. δ1(a) = (e ) − 1

(4)

δ2(a) = 0.2*log(a+1)

(5)

a

0.2

•

Within the lifetime of mature antibody, if it matches with an antigen which is known as self, the mature antibody will be deleted; if the immune response isn’t provoked, the mature antibody will be deleted too; if the immune response is provoked, the mature antibody will evolve to be a memory antibody according to the clone selection algorithm.

•

The functions of memory antibody concentrations are similar to those of the mature antibody.

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4 Results and Analysis of Experiments A. Experimental Environment and Parameter Settings Experiments were done in the network security lab in Sichuan University where more than 20 computers are, and the Server provides www service, ftp service, email service and so on. We adopted 10% of KDDCUP 99 data provided by MIT Lincoln lab as experimental data. Take the entire gene segments from the Gene Lib to constitute the antigen determinant which is a binary string with length L=144. The matching threshold of affinity is θ=0.9. We use the count of matched antigens to approximately represent the attack power a of function δ1, and use the time t to approximately represent the attack power a of function δ2. B. Analysis of Experimental Results 1) Efficiency of immune learning is improved. Compared to the traditional intrusion detection system T_IDS based on immunity, the efficiency of immune learning of improved intrusion detection system I_IDS proposed in this paper is improved. The shorter the learning time is, the higher detection rate is. Under the Smurf attack, the immune learning curves of T_IDS and I_IDS are shown in Fig. 2.

Fig. 2. Immune Learning Contrast

From the figure above, two curves increase gradually at the beginning and reach a relatively stable state at the end. The increasing period is the immune learning period. The system is stable, indicates that the system has identified the antigen and added corresponding antibodies into the memory antibody set. The rising slope of the I_IDS curve is obviously higher than T_IDS’s, which indicates that the learning efficiency of I_IDS is higher than T_IDS’s, and the former will make second immune response before the latter. With the increase of variation generation, number of antibodies increases. T_IDS identifies attacks depending on the increased affinity between antigen and newly mutated antibody. I_IDS identifies attack depending on the increased concentration of antibodies. The antibody concentration has greater impact on the danger degree, and is more efficient.

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2) Efficiency of detection is improved. The detection rate TP refers to the detection probability of non-self. The false alarm rate FP refers to the probability of self identified to be non-self. We can use TP and FP to evaluate the system performance. Under the Smurf attack, the TP curves of T_IDS and I_IDS are shown in Fig. 3 and the FP curves are shown in Fig. 4.

Fig. 3. Contrast of TP

Fig. 4. Contrast of FP

From the figures above, the detection efficiency of I_IDS is higher than T_IDS for unknown attacks. The detection rate of I_IDS is higher than T_IDS’s and the false alarm rate is lower than T_IDS’s. The detection rate increases gradually at the beginning and reaches a relatively stable state at the end. The false alarm rate decreases gradually at the beginning and reaches a relatively stable state at the end as well. The system has to learn when an attack occurs, and then the accuracy of detection becomes stable when the system grasps the characteristics of attack.

5 Conclusions A new method of intrusion detection based on the danger theory and the cloud model is presented in this paper. This paper has defined the concentration variability functions and divided the risk levels. Then, changes of antibody concentrations in the immune system are determined by the cloud model, and danger signals will be sent according to the changes. This method has successfully solved the problem of high false positive rate and high false negative rate. The theoretical analysis and experimental results show that the method is effective to intrusion detection with advantages of diversity, real-time and adaptability. This method is an exploration of the immune danger theory

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and the cloud model applied to the intrusion detection field. In the future, the main work is to make further analysis of the related immune mechanism and algorithms, and to apply this model to network security situation forecast and so on. Acknowledgment. It is a project supported by the National Natural Science Foundation of China (60873246) and Dr. Foundation of Chinese Education Ministry (20070610032).

References [1] Li, T.: Computer Immunology. Publishing House of Electronics Industry, Beijing (2004) (in Chinese) [2] Aickelin, U., Cayzer, S.: The Danger Theory and Its Application to AIS. In: 1st International Conference on AIS (2002), pp. 141–148 (2002) [3] Matzinger, P.: The Danger Model: A Renewed Sense of Self. Science, 301–305 (2002) [4] Shifflet, J.: A technique independent fusion model for network intrusion detection. In: Proc. of the Misstates Conference on Undergraduate Research in Computer Science and Mathematics, pp. 13–19 (2005) [5] Li, T.: An Immune Based Model for Network Monitoring. Chinese Journal of Computers 29, 1515–1522 (2006) (in Chinese) [6] Li, D., Liu, C.: Study on the Universality of the Normal Cloud Model. Engineering Science 6, 28–34 (2004) (in Chinese)

A Network Security Situation Awareness Model Based on Artificial Immunity System and Cloud Model Zhang Ruirui, Li Tao, Xiao Xin, and Shi Yuanquan College of Computer Science, Sichuan University, Chengdu 610065, China [email protected]

Abstract. The artificial immune theory and the cloud model theory are applied to the research on situation awareness of network security in this paper. A security situation awareness model is established from three levels, including situation perception, situation comprehension and situation projection. In the model, network attacks can be real-timely monitored by the intrusion detection technology based on the danger theory and the cloud model; network security situation can be evaluated by the calculation of antibody concentration changes which have relationship with the attack power, and can be predicted by a new mechanism of time-series prediction based on cloud models according to the historical and current situations. The theoretical analysis and experimental results show that the model is effective to network security situation awareness with advantages of real-time and high accuracy. Keywords: artificial immune system, network security, network security situation, situation awareness, cloud model.

1 Introduction With the rapid development of Internet, all kinds of network attacks continue to occur. How to perceive the network security situation real-timely and precisely becomes a hot research topic in the network security field. At present, most network security situation awareness models [3,7] can make a rough assessment of network risks, but they have disadvantages of poor real-time and low accuracy. The artificial immune theory and the cloud model theory are applied to the field of network security situation awareness, and a model is presented in this paper. There are several characteristics of the model. Firstly, it can monitor network attacks real-timely, and detect network threats precisely by the intrusion detection technology based on the danger theory and the cloud model. Secondly, a quantitative evaluation algorithm of network security situation is presented, and it can calculate the current network security situation indexes realtimely and quantitatively. Thirdly, it can predict the network security situation by the cloud model technology, and provides basis for making reasonable-response strategies. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 212–218, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 The Model Theory A. The Model Framework There are three levels of situation awareness, including situation perception, situation comprehension and situation projection. So, according to the three levels, the model presented in this paper consists of three modules which are intrusion detection module, situation evaluation module and situation prediction module. The model framework is shown in Fig.1.

Fig. 1. The Model Framework

B. The Intrusion Detection Module In the danger theory [2], the death manners of cells in biological immune system are apoptosis and necrosis. Apoptosis is a natural process, and is the result of environmental regulation in the body. Necrosis is irregular death associated with stress cells. These two different manners of cell death in the immune system produce different danger signals which decide the harm to humans. In this paper, the normal end of the network behavior is known as antigen apoptosis, otherwise, known as antigen necrosis. Antigen apoptosis generates the repression signal which restrains antibody production. Antigen necrosis generates the gained signal which stimulates antibody production. So, the death manners of antigen affect the antibody concentration. In this paper, antibodies (mature antibodies and memory antibodies) are divided according to the idea of consanguinity class series [5]. The cloud model [6,8] is a mathematical transformation model between the qualitative concept expressed by language values and quantitative data, and its digital characteristics are mathematical expectation Ex, entropy En, and hyper entropy He. Ex expresses the best proper point in the domain space which represents the qualitative concept. En expresses the measurable scale of a qualitative concept, and reflects the uncertainty of the concept. He expresses the measurable scale of uncertainty of En, and reflects the randomness of samples which represent the qualitative concept. In this paper, risk levels are divided by the cloud model into secure-state cloud, weak-secure-state cloud, weak-danger-state cloud and danger-state cloud. Then rule generator is constructed, and antibody concentrations are modeled. Afterwards, risks are determined by calculating the membership degree of consanguinity class series’ concentrations to risk levels.

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In the paper, antigens are defined as network requests, selves are defined as normal network requests, and non-selves are defined as abnormal network activities (network attacks). Selves and non-selves constitute the whole collection of antigens. Antibodies are used for detecting and matching antigens. The affinity between antigens and antibodies, antibodies and antibodies is defined as the matched level between its data structures. The affinity can be computed by Euclidean distance, Manhattan distance, Hamming distance or r-continuous bits [1]. The antigen set is dynamic evolved. Antibody is divided into immature antibody, mature antibody and memory antibody. The immature antibody represents newly-created immune cells which don’t go through self tolerance. The mature antibody represents immune cells which go through self tolerance and aren’t activated by antigens. The memory antibody represents mature immune cells which are activated when their concentrations belong to the danger-state cloud. The workflow of intrusion detection module is shown in Fig.2. Antigen Mutation Set of Memory Antibodies Antibody Concentration belongs to Danger-state Cloud Set of Mature Antibodies

Match Self Dead Too old Match Self

Self Tolerance Set of Immature Antibodies

Set of Apoptosis Antigens Non Self

Produce Antibodies Randomly

Fig. 2. The Workflow of Intrusion Detection Module

C. The Situation Evaluation Module In the intrusion detection module, when the computer isn’t attacked, the system is in secure-state cloud, and antibody concentrations of each consanguinity class series is relatively low; when the computer is under attack, the system is in danger-state cloud, and antibody concentrations of related consanguinity class series increase; when the attack recedes, the system is in weak-danger-state cloud or weak-secure-state cloud, and antibody concentrations of all consanguinity class series decrease. So, security situations of hosts or networks can be calculated by the changes of concentrations of mature antibodies and memory antibodies. Assume that at time t, the consanguinity class series of host j ’s immune system (0≤j≤m) is divided into A(t)={A1(t), A2(t), …, An(t)}, which shows the types of network attacks. Compute concentrations of each consanguinity class series realtimely p(t)={p1(t), p2(t), …, pn(t)}. pi(t) is composed of concentrations of all antibodies in consanguinity class series i. Determine that whether pi(t) belongs to the secure-state cloud. If it does, the system isn’t under such attacks. And if it doesn’t, the system is under such attacks. The concentration changes of consanguinity class series i of host j can be calculated by the following formula.

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⎧0

p i ∈ secure-state cloud

cij(t)= ⎨

⎩ p i (t)-Ex safei p i ∉ secure-state cloud

215

(1)

Because the importance of each host in the network is different, and the harmfulness of each type of attack is different, we should take the importance of hosts and the harmfulness of attacks into account when computing the security situations of hosts or networks. Set αj (0≤αj≤1) as the importance of host j, and βi (0≤βi≤1) as the harmfulness of attack type i. Set Rij(t) as the security situation index of host j at time t when attacked by type i, and Rj(t) as the general security situation index of host j at time t, and Ri(t) as the security situation index of network at time t when attacked by type i, and R(t) as the general security situation index of network at time t. The formulas are as follows. Rij(t)= 1 −

1

(2)

1 + ln(1 + cij ( t ) β iα j ) 1

Rj(t)= 1 −

(3)

n

1 + ln(1 +

∑ c (t ) β α ij

i

j

)

i =1

1

Ri(t)= 1 −

(4)

m

1 + ln(1 +

∑ c (t ) β α ij

i

j

)

j =1

1

R(t)= 1 − 1 + ln(1 +

m

n

j =1

i =1

∑∑ c (t ) β α ) ij

i

(5)

j

Obviously, 0≤Rij(t), Rj(t), Ri(t), R(t)≤1. The bigger Rij(t), Rj(t), Ri(t), R(t) is, the greater threat hosts and networks is suffering. D. The Situation Prediction Module The network security situation can be predicted by a new mechanism of time-series prediction based on cloud models, according to the historical and current situations. Assume that we have time-series data set D={(ti, ri) | 0≤i
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Fig. 3. The Expression of Linguistic Variable Time T

high; IF in low risk period 2 THEN security situation R is medium 2; IF in safety period 2 THEN security situation R is low 2. 2) Determine the Historical Cloud and Current Cloud Assume that the cycle length of time series is L. First, the changes of network security situation should be limited in a number of cycle lengths. There exists an integer w and a time value t’ [0, L] to meet the equation tk= t’ +w*L. Then divide D into two parts: HD = {(ti, ri) | 0≤i<w*L} and CD = {(ti, ri) | w*L≤iR1, T2->R2, …, T5->R5}, according to the distribution of HD’s data. Ti and Ri in prediction rule set are atomic concepts of antecedent and consequent linguistic variables expressed by clouds. We can activate corresponding rules by determination of the value t’ belonging to which atomic concept of the antecedent linguistic variable. If t’ belongs to Ti, rule Ti->Ri is most proper to reflect the periodic pattern at time tk. So, the cloud of Ri is called historical cloud as corresponding prediction knowledge. According to the distribution of CD’s data, we can get the current cloud Ik by the algorithm of backward cloud generator [8].

∈

3) Generate the Predictive Cloud By inertia weight of current cloud Ik(Exc,Enc,Hec) adding to historical cloud Ri(Exh,Enh,Heh), we can get the predictive cloud P(Ex,En,He).

Ex=

Exc Enc + Exh Enh Enc + Enh

En=Enc+Enh He=

Hec Enc + Heh Enh Enc + Enh

4) Make the Prediction of Time Series

We can get multiple results by intriguing prediction rule Ai->P many times.

(6) (7) (8)

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3 Results and Analysis of Experiments E. Experimental Environment and Parameter Settings

Experiments were done in the network security lab in Sichuan University where more than 20 computers are, and Servers provide database service, ftp service, email service and so on. We adopted 10% of KDDCUP 99 data provided by MIT Lincoln lab as experimental data. Four computers were monitored in the experiments, including FTP Server A, Print Server B, Database Server C host D, and their importance indexes were 0.5, 0.2, 0.8 and 0.1. The harmfulness indexes of attack syn flood and land were 0.8 and 0.6.

，

F. Analysis of Experimental Results Fig.4 shows the contrast curves of attack power (number of attack packets per second) and security situation index. Fig.4 indicates that when network was suffering continuous high-intensity attacks, security situation indexes of hosts and network were high; otherwise, when the attack power lowered, security situation indexes of hosts and network decreased. The experimental results are consistent with the real network conditions. So, the model can well reflect the changes of current network security situation real-timely and precisely. The contrast curves of real values and predictive values of security situation under multiple attacks are shown in Fig.5.

Fig. 4. Contrasts of Attack Power and Security Situation

Fig. 5. Contrasts of Prediction Values and Actual Values of Security Situation

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From Fig.5, the predictive values of security situation were close to the actual values. So, the situation prediction module has the advantage of high accuracy.

4 Conclusions As a new network security field, network security situation awareness is significant for achieving large-scale network security monitoring. A model of network security situation awareness based on artificial immunity system and cloud model is presented in this paper. The model has three modules. First is the intrusion detection module based on the danger theory and the cloud model. Second is the quantitative situation evaluation module based on the corresponding relationship between the antibody concentration variation of biological immune system and the intrusion rate of pathogen. Third is the prediction module based on the cloud model. The theoretical analysis and experimental results show that the model can quantitatively and realtimely evaluate and predict the network security situation, and is effective. Acknowledgment. It is a project supported by the National Natural Science Foundation of China (60873246) and Dr. Foundation of Chinese Education Ministry (20070610032).

References [1] Li, T.: Computer Immunology. Publishing House of Electronics Industry, Beijing (2004) (in Chinese) [2] Aickelin, U., Cayzer, S.: The Danger Theory and Its Application to AIS. In: 1st International Conference on AIS, pp. 141–148 (2002) [3] Visintine, V.: An Introduction to Information Risk Assessment. SANS Institute (2003) [4] Shifflet, J.: A technique independent fusion model for network intrusion detection. In: Proc of the Misstates Conference on Undergraduate Research in Computer Science and Mathematics, pp. 13–19 (2005) [5] Li, T.: An Immune Based Model for Network Monitoring. Chinese Journal of Computers 29, 1515–1522 (2006) (in Chinese) [6] Li, D., Liu, C.: Study on the Universality of the Normal Cloud Model. Engineering Science 6, 28–34 (2004) (in Chinese) [7] Chu, C.K., Chu, M.: An integrated framework for the assessment of network operations, reliability, and security. Bell Labs Technical Journal 8, 133–152 (2004) [8] Li, D., Liu, C., Du, Y., Han, X.: Artificial intelligence with uncertainty. Journal of Software 15, 1583–1594 (2004)

A Research into the Tendency of Green Package Design Zhang Qi1, Jiang Xilong1, and He Weiqiong2 1 2

Teachers’ College of Beijing Union University School of Foreign Languages, Jimei University [email protected]

Abstract. Starting by introducing the conceptions of Green Design and Green Package Design, this paper analyzes the environmental hazard caused by traditional packaging, and summarizes the problems existing in package design today. Then the authors fully probe into the trends of Green Package Design with the purpose for constructing new viewpoints and methods on the study of Green Package Design, which will promote the development of green package design and make it popularized and applied in packaging circles. Keywords: Green Design, Package Design, Tendency of Design.

1 The Conception of Green Package Design Generally speaking, the Conception of Green Package Design derives from the Conception of Green Design. Firstly, three conceptions will be compared: Green Design, Package Design and Green Package Design. GD (the Conception of Green Design) is also termed as ED (Ecological Design), DEF (Design for Environment), LCD(Life Cycle Design) or ECD(Environmental Conscious Design). In the process of creating and designing, it takes environmental factors and pollution prevention into account so that the products will do lest harm to the environment. Package Design is a kind of creative design which combines the information of products with the main elements of shape, structure, material, color, and image, typesets and other accessorial elements in order to make products better received in the market. The functions of package lies in two aspects: one is to protect the products from being damaged in storage, transportation and usage. The other is to lay out the features and information of products and eventually achieve sales promotion. The Conception of Green Package Design means that the designer considers more factors on energy-saving and environment protection. In other words, they aims to achieve the goal of environmental protection and meanwhile realize the essential packaging functions such as protection, using methods, service life, quality and cost, etc. Moreover, Green Package Designing process includes the whole life span of products, that is, at the beginning of designing, designers should take full consideration of all the processes of producing, selling, using and its scrap’s influence on environment. Therefore, designers should cooperate closely with technicians dealing with producing and reclamation, giving top priority to the feasibility of disassembling and recycling, utilizing natural resource, and reducing trash. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 219–223, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 The Present Situation of Green Package Design Closely related to people’s daily life in many areas, packaging industry also faces serious environmental crisis. For one thing, because of its one-time-consumption, the discarded article is of greater amount compared with its short life span. For another, it’s very inconvenient to garbage collection and disposal due to the complexity of packaging types, functions, materials and techniques. It is reported that in America every year the packaging garbage is about 50,000,000 tons, 80,000, 000 tons in EU, 21,000,000 in Japan, and 16,000,000 in China. All the packaging garbage is increasing year after year and seriously pollutes the environment day by day. It’s obvious that modern package design developing at the same time with industrial revolution has great bad effects on environment pollution. Giving priority to utility, people evaluate the package design by the sale and commercial profit they obtain. While green design counts for little compared with commercial profit. What’s more, in the fierce market competition, the function of sales promotion is intensified, so the designer will make every means to stimulate purchasing, which leads to make much more rubbish. There’s no doubt that modern packaging cannot conform to the development of environment protection and conservation-oriented society. Therefore, the shift from the age of modern package design to green package design age is historically inevitable. As mentioned above, the traditional conception of package cannot meet the requirement of environment protection and sustainable development. Thus, the notion of Green Package Design appeared in the late 1980s and early 90s has become a new tendency of design in resolving the environmental problems caused by packaging. In the recent 10years, Green Package Design has made great achievement: now the conception of Green Package Design is commonly shared among designers; new environmentally-friendly materials are exploited and designed continuously; more and more consumers accept the idea of green design. Nowadays, under the circumstances of increasing environmental crisis, people is reflecting on the influence on environment by their own behavior and culture, and starting to embrace environmentalism and welcome green living style. A new era of Green Package Design will be started soon with the development of new criteria of evaluation on packaging, new consumption concept, neo-conception of design and new employment of environmentally-friendly materials. In addition to bearing notion of green living in mind, a designer should have a keen sight on the green design trends so that he can exert themd in designing, which is his time mission to make better contribution to Green- Package- Design era.

3 The Trends of Green Package Design Considering Green Package Design as a new era of package designing, designers should know the changing trends of designing and get themselves prepared with “Green” thinking ability and other skills. From present designing conception and achievement of Green Package Design, we can generalize four important design currents as follows:

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3.1 Concept of “Green Life” in Package Design Green Package Design which advocates a green life with environment protection, conservation-oriented and sustainable development has become the most crucial trend in packaging industry. Not only should designers have understood and accepted the notion and style of green life, but also they should know how to simplify package and economize materials. In other words, they should know how to use the least resource to meet the consumption demand with the lowest cost and least pollution. It’s necessary to point out that the notion of green life doesn’t advocate asceticism, but promotes an attitude to lead a natural and healthy living style as simple and sustainable as they can. The designers are not only a bridge which connects products and human beings, but also they are responsible for the products and services because they can guide people to change the ways of using products. Therefore, theoretically, the designers would have the opportunity to exert their ability in solving environmental issues and build up sense of responsibility for protecting environment. At present, the green life notion is spread from special group of people to common ones or specific domain to general one, which makes the somewhat idealistic green life notion become more realistic. Meanwhile, this trend inspires the designers to explore in wider designing scope. 3.2 Concept of Moderate Design In the commercial world, almost all the package design is excessively done due to pursuing profit. For instance, on festivals in China, products like moon cakes of Midautumn Festival or tea would be excessively packaged in zooty mosaic, which is a great waste of resource. In order to lead a green life, the principle of Moderate Design should be set up. So what is moderate design? First of all, choose only one single material to design as possible as you can. That is, if the box design can fulfill the function of packaging with one single material, no more type is employed. Secondly, choose materials that are compatible to each other so that they will be convenient to be disassembled and recycled. Take metals and plastics as an example, they are hard to be compatible and cannot be collected together. While clothes and paper are more compatible to be collected together, and that helps improve work efficiency. Thirdly, Green Package Design should phase in simplified package or non-package or be careful in choosing disposable package. The simplified package would just use the least packaging materials and those firm and indestructible products could be unpackaged. Fourthly, products can be packaged in bulk, which also makes the least trash. Now the vogue of design in bulk is thriving in environmentalistic countries like Japan, Germany and other west European countries. Japan, a country dainty about gift package, has been implementing simplified package in shops and supermarkets, which are warmly supported by environmentalist buyers. On the information of type, photo and color in visual design, elements of environment protection can be employed while the zooty and over-decorated design just for eye-catching effect can be avoided. Designers should not employed complicated and diverse technics with the purpose to foster the thought of green

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consumption by treasuring nature, protecting environment, and saving resource. For instance, on photo and color designing, if one color is enough, then two colors is given up, or two colors will be sufficient, others will be saved. 3.3 Revival of “Unity of Heaven and Man” Concept One significant value in Chinese traditional culture is the harmonious unity of heaven and man. The value opposes the opinion that heaven and man are of antinomy but it advocates oneness of heaven and man as explained by Lao Tzu, the great philosopher, in his book “ Tao Te Ching”: “ Man follows the earth, the earth follows heaven, heaven follows the Tao, Tao follows what is natural.” So designer might study and absorb spirit of Chinese traditional philosophy and embody it in green package design. In ancient China, simple package and employing reusable material help to save resource, which is propitious for the harmony of nature and man. One example is the emergence of China’s first trademark in a printed wrapping paper “Ji’nan Liu’s Fine Needle Shop” which is now kept in the National Museum of Chinese History. This legendary trademark in the Northern Song Dynasty was designed with a white rabbit image in the middle, implying the message of the Jade Rabbit pounding medicine, and inscription on both sides of the image, reading “we buy quality steel bars and we make fine sowing needles; you see the white rabbit at doorstep and you find where we are.” This widely-used wrapper not only serves as a trademark to promote selling, but also can be reused for many times. In that period of Song Dynasty, people had been already skillful in printing two colors, however, they always printed with one color, which is consistent with green package notion. However, we still find that few simple and reusable wrappers are used in shops. Many wrappers still cannot satisfy the requirement of green package design. 3.4 The “Re” Thinking of Green Packaging Design The “Re” Thinking of Green Packaging Design is a vital concept in green design trend, which stands for the notions of “Reduce, Reuse, Recycle, Retrogradation and Refill.” As mentioned above, Green Package is defined as a kind of package that is pollution-free, recyclable, and reusable. Because the package processes from selecting materials, designing, making till using and reusing or discarding have met the demand of environment protection that includes saving resource, reducing waste, being recyclable and degradable and trash-free. When the project is designed, the product package’s life-long effect on environment has begun. Thereby at the beginning designers should think over each process of planning, designing, choosing material, framework, technics, and recycling or discarding. In the prospective of “Re” Thinking of Green Packaging Design, designers must take account not only of the aims of package decoration and selling, but of its effect on environment. Only in this way can they carry out the principle of environment protection and conservation which will lead to reduce waste, improve work efficiency and recycle the materials. Guided by the “Re” Thinking principle, packaging can be designed in the form of being easy for reusing or cheap for collecting. To achieve the purpose of dissembling and recycling the package easily, the materials, the structure in modular design or

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standardized design must be well devised. In this way, less time is spent in collecting trash in category and the high efficient collecting will reduce cost of collecting but increase the rate of collection.

4 Conclusion In all perspectives of Green Package Design, any package does not solitarily exist in nature, but starts with using and consuming energy and resource and then ends with rubbish or even as pollutant. As a designer, all the processes in packaging industry will influence the environment directly or indirectly, especially by the materials chosen, the types of technics and methods of disposal. Because of his decisive influence in many aspects such as environment protection, resource saving, and advocating green living style, the Green Package designers need to think with discretion about the relationships between package and man, nature and man, package and environment, package and consumers, and package and manufacturers. From the complicate relationships we can draw the conclusion that green package design is significant in encouraging protecting environment, conserving resource and living a green life. The principle of sustainable development doesn’t not only involve designers’ personal interest, but also the existence of human and other living beings. How Green Package Design follows the sustainable development principle to cooperatively solve the conflicts between f human’s existence and their development has become the crucial task in designers circle, packaging industry and human society. Therefore, we should conduct a new close survey on our living ideology, designing notion and review the wisdom of ancestors for the purpose of grasping the green package design trends, leading a green life and creating a new era of green designing.

References [1] Chen, F.: Conception of Green Design in Eco-Packaging. China Packaging 3, 45–46 (2009) [2] Huang, Y.: How to Implement Green Design in Paper Wrapper. Packaging World 3, 27–28 (2009) [3] Li, K.: Green Designing and Green Living. Hundred Schools in Arts 3, 165–166 (2006) [4] Ma, Z.: Product Packaging Calls for Green Design. Packaging World,11, 86 (2008) [5] Wang, Y.: Green Design and Moderate Package. Journal of Huaihua University 5, 78–79 (2008) [6] Zhang, L., Ma, Y.: Green Design. Journal of Hebei Institute of Architectural Engineering 2, 107–108 (2005)

Time-Frequency Filtering and Its Application in Chirp Signal Detection Xiumei Li1 and Guoan Bi2 1

School of Information Science and Engineering, Hangzhou Normal University, Hangzhou, China 2 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore [email protected]

Abstract. Time-frequency filtering has been widely used in various applications. In this paper, the time-frequency filtering is applied to the LPPHough transform (LHT) which is a method for chirp signal detection. Chirp signals corrupted by heavy Gaussian noises and impulsive noises are considered in the simulations. It shows that the time-frequency filtering can help to greatly minimize the computation complexity of the LHT for chirp signal detection without deteriorating the detection performance. Keywords: Time-frequency filtering, local polynomial Fourier transform, chirp signal detection.

1 Introduction In many practical applications, it is necessary to extract signals from noises. Since the signals and the noises may overlap either in the time domain or in the frequency domain, a conventional time-domain windowing or frequency-domain windowing may not be adequate. Effective methods to process the noisy signals are the time-frequency representations because they usually increase the signal-to-noise ratio (SNR) in the time-frequency domain. While random noise tends to spread evenly into the entire time-frequency domain, the signal energy is usually concentrated in a relatively small region. Therefore, under this situation we can use the time-frequency filtering to extract the useful signal information in the time-frequency domain and suppress the noise effect [1]. Chirp signals, also known as linear frequency modulated (LFM) signals, are often encountered in many applications such as in radar, sonar and communications. Detection and estimation of chirp signals in a noisy environment are of great importance. The time-frequency-based methods, such as the Radon-ambiguity transform and the pseudo-Wigner-Hough transform, have been reported to be effective for detecting and estimating chirp signals [2, 3, 4]. Moreover, the energy distribution of the local polynomial Fourier transform (LPFT),which is the local polynomial periodogram (LPP), has been combined with the Hough transform for chirp signal detection [5,6]. It has been shown that the LPP-Hough transform (LHT) achieves significant improvements on detecting the chirp signals in very low SNR Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 224–232, 2011. © Springer-Verlag Berlin Heidelberg 2011

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environments. However, it is desired to further reduce the computational complexity of the LHT. It will be shown in this paper that by using the time-frequency filtering, the computational complexity of the LHT for the chirp signal detection can be greatly reduced while the detection performance remains almost the same. This paper is organized as follows. Section 2 gives an introduction of the timefrequency filtering. In Section 3, the combination of the LHT and the time-frequency filtering are introduced and investigated. Simulations are given in Section 4 to show that the time-frequency filtering is useful to reduce the computational complexity of the LHT for chirp signal detection. Finally conclusion is given in Section 5.

2 Time-Frequency Filtering The time-frequency filtering has been used in many different applications. For instance, it has been used as an approach for sonar target classification [7], which is more robust to reverberation and background noise perturbations. Combined with the LPFT and STFT, the time-frequency filtering is employed for jammer rejection in the spread spectrum communications to improve the desired signal receiving performances [8], as well as for nonstationary interference suppression in noise radar systems [9]. The time-frequency filtering is also applied to audio time-scale and pitch modification [10]. To design a time-frequency filter, we first need to estimate the instantaneous frequency of the signal, for example, based on peak detection in the time-frequency domain [1].Then the time-frequency filtering, also known as the ``mask'', can be defined as (1) where R is the instantaneous frequency region. In Figure 1, some simulations are given to show how the time-frequency filtering works. Various signals corrupted by Gaussian noises with SNR=0dB are considered, such as the sinusoidal FM signal, the parabolic FM signal, and the signal with two cross chirp components. From Figure 1, it is observed that the time-frequency filtering can help to suppress the noise effect and extract the useful information.

3 The LHT with Time-Frequency Filtering for Chirp Detection Hough transform is a method widely used in image processing for shape detection and feature extraction [11]. The Hough transform associates each point in the timefrequency domain with a sinusoid in the parameter domain [11]. If N points are concentrated along a straight line in the time-frequency domain, they will correspond to N sinusoidal curves intersecting at the same point in the parameter domain. The integration along the line produces a maximum and its coordinates in the parameter domain are directly related to the parameters of the lines. Therefore, the Hough transform converts a difficult global detection problem in image space into a more easily solved local peak detection problem in a parameter space.

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By using the time-frequency representations such as the short-time Fourier transform (STFT) and the Wigner-Ville distribution (WVD), chirp signals can be described as straight lines in the time-frequency domain. Since the WVD can give an optimal concentration for chirp signals in the time-frequency domain, it has been combined with the Hough transform, therefore achieving the Wigner-Hough transform (WHT) to detect chirp signals [12]. However, the WHT cannot provide the desirable performance for signals distorted in a heavy noisy environment, due to the inherent noise threshold effect problem of the WVD [13]. The Hough transform, combined with the linear STFT, was reported to detect a weak and low rate chirp signal [4]. For these methods which combine the time-frequency representation with the Hough transform, a signal representation in the time-frequency domain should be firstly obtained, then it is further processed by the Hough transform. Therefore, for chirp detection, a proper processing method is needed to obtain an appropriate line representation in the timefrequency domain. The local polynomial Fourier transform(LPFT) is the generalization of the shorttime Fourier transform(STFT) [14]. Unlike the Wigner-Ville distribution (WVD), the

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LPFT is a linear transform that is free from the cross-terms for multi-component signals. Moreover, compared with the STFT, the LPFT can achieve significant performance improvements for the chirp signals since it uses a polynomial function to describe the instantaneous frequency (IF) characteristics of the time-varying signals. As shown in [6], the LPFT can achieve higher SNR performance than the WVD, and it can provide better representation for chirp signals corrupted by heavy noises. Therefore, the LHT can provide better performance than the WHT. In this application for chirp signal detection, the time-frequency filtering is used for the representation in the time-frequency domain to obtain a much cleaner representation. Then the filtered representation is followed by the Hough transform for chirp detection. Considerable Hough transform effort can be saved by filtering out these points which do not contribute much to the detection, therefore the computation complexity of the detection can be greatly reduced. The algorithm of the LHT with the time-frequency filtering for the detection of chirp signals, with the unknown parameters and embedded in Gaussian noises, consists of the following steps: (a) estimate the polynomial parameter for the LPFT using the polynomial time frequency transform (PTFT) [15]; (b) compute the LPP of the signal; (c) filter the LPP using the time-frequency filtering; (d) compute the Hough transform of the filtered LPP; (e) compare the values achieved by the Hough transform with a given threshold for each pair of ρ and θ . When a certain value exceeds the threshold, the detection is made on the presence of a chirp signal.

4 Simulations In this section, signals in Gaussian respectively by the combination of the be shown that with the help of the computation complexity of detection remains almost the same.

noises and impulsive noise are processed LHT and the time-frequency filtering. It will time-frequency filtering, the corresponding can be minimized while the performance

A. Signals in Gaussian Noises Consider a mono component 256-point chirp signal

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Fig. 3. The LHT and LHT with time-frequency filtering of a monocomponent chirp signal (SNR = -10dB).

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the signal in (1) embedded in Gaussian noises with different SNRs. We assume that the probability of false alarm is 10-2, from which the threshold of detection can be calculated. Theoretical analysis of the Hough detector was presented in [4]. From Figure4, it is observed that the performance of the LHT for Pd >0.99 is close to that of the theory. Compared with the LHT without time-frequency filtering, the detection performance of the LHT with the time-frequency filtering is almost the

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same. However, with the help of the time-frequency filtering, the computation complexity is greatly reduced, which will be shown later. B. Signals in Impulsive Noise In many practical applications involving communications and imaging, signals are often corrupted by impulsive noise. Clipper is a standard tool to deal with impulsive noises due to its natural ability to eliminate the outliers with a simple computational procedure [16]. Thus in this paper for the signals corrupted by impulsive noise, we first use the clipper to reduce the effect of impulsive noise before applying the LHT to detect chirp signals. In this way, the LHT, which was previously used for signals in Gaussian noises, can be used to process signals corrupted by impulsive noises. Let us consider the α − stable impulsive noise

n(t ) = α ( w13 (t ) + w23 (t )),

where w1 (t ) and w2 (t ) are independent Gaussian random variables with unit variances. Figure 5(a) shows the mono component corrupted by impulsive noises with α = 5, and in Figure 5(b) the time-frequency filtering is used to minimize the effect of the noise. Therefore the noise in Figure 6(b) can be further suppressed than in Figure 6(a). Since the clipping method does not increase the computational complexity significantly, the LPP of the clipped signal requires almost the same computation time as that for the signal corrupted by Gaussian noises. Table 1 lists the average computation time for mono component corrupted by Gaussian noises and impulsive noises, respectively. Similarly, the time-frequency filtering can help to reduce the computational complexity for signal with multiple components, such as multi component signal form

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Table 1. Comparison on computation times required by LHT and LHT with time-frequency filtering, for monocomponent chirp signal Signal in AWGN (s)

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5 Conclusion In this paper, the time-frequency filtering is introduced and combined with the LHT for chirp signal detection. It is shown that for chirp signals corrupted by heavy Gaussian noises or impulsive noises, the time-frequency filtering can be used to further reduce the computation complexity of the LHT without detection performance deterioration.

References [1] Boashash, B.: Estimating and interpreting the instantaneous frequency of a signal-Part 2: Algorithms and applications. Proceedings of the IEEE 80(4), 540–568 (1992) [2] Wang, M., Chan, A., Chui, C.: Linear frequency modulated signal detecting using Radonambiguity transform. IEEE Transactions on Signal Processing 46(3), 571–586 (1998) [3] Cirillo, L., Zoubir, A., Amin, M.: Parameter estimation for locally linear FM signals using a time-frequency Hough transform. IEEE Transactions on Signal Processing 56(9), 4162–4175 (2008) [4] Sun, Y., Willett, P.: Hough transform for long chirp detection. IEEE Transactions on Aerospace and Electronic Systems 38(2), 553–569 (2002) [5] Li, X., Bi, G.: A new transform for chirp detection. In: International Symposium on Information Theory and Its Applications, ISITA (December 2008) [6] Bi, G., Li, X., Meng, C., See, S.: LFM Signal Detection Using LPP-Hough Transform. Signal Processing (in press) [7] Chevret, P., Gache, N., Zimpfer, V.: Time-frequency filters for target classification. Journal of the Acoustical Society of America 106(4), 1829–1837 (1999) [8] Stankovic, L., Djukanovic, S.: Order adaptive local polynomial FT based interference rejection in spread spectrum communication systems. IEEE Transactions on Instrumentation and Measurement 54(6), 2156–2162 (2005)

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[9] Dakovic, M., Thayaparan, T., Djukanovic, S., Stankovic, L.: Time-frequency-based nonstationary interference suppression for noise radar systems. IET Radar, Sonar and Navigation 2(4), 306–314 (2008) [10] Zhu, X., Beauregard, G., Wyse, L.: Real-time signal estimation from modified short-time Fourier transform magnitude spectra. IEEE Transactions on Audio, Speech, and Language Processing 15(5), 1645–1653 (2007) [11] Hough, P.: A method and means for recognizing complex patterns, U.S. Patent 3,069,654 (1962) [12] Barbarossa, S.: Analysis of multicomponent LFM signals by a combined Wigner-Hough transfrom. IEEE Transactions on Signal Processing 43(6), 1511–1515 (1995) [13] Rao, P., Taylor, F.: Estimation of instantaneous frequency using the discrete Wigner distribution. Electronic Letters 26(4), 246–248 (1998) [14] Katkovnik, V.: A new form of Fourier transform for time-frequency estimation. Signal Processing 47(2), 187–200 (1995) [15] Xia, X.: Discrete chirp-Fourier transform and its applications to chirp rate estimation. IEEE Transactions on Signal Processing 48(11), 3122–3133 (2000) [16] Nikias, C., Shao, M.: Signal Processing with alpha-stable distributions and applications. John Wiley and Sons, NY (1995)

Hunting for the ″Sweet Spot″ by a Seesaw Model Haiyan Li1, Jianling Li2, Shijun Li1, and Zhaotian Liu1 1

School of Information Science and Engineering, Yunnan University, Kunming, Yunnan, 650091, China 2 School of Humanities and Social Sciences, Jinling Institute of Technology, Nanjing, Jiangshu, 210038, China [email protected]

Abstract. A seesaw model was proposed to hunt for the sweet spot on a baseball bat. After analyzing the vibration of the bat and the ball, a basic vibration model was abstracted to simulate the interaction of the bat and the ball, which simplifies the vibration of the bat and the ball into two components, one is a seesaw part, whose pivot is the sweet spot of the bat, the other is the source of the vibrator (SOC). The model demonstrate that the sweet spot is not at the end of the bat while is a sweet zone close to the end of the bat and also illustrates that “corking” in the head of the bat will decrease that area of the sweet zone while “corking” in the end of the bat will increase the area of the sweet zone. The model shows that the area of the sweet zone is increased if the material of the bat is metal compared to solid wooden bat. Keywords: baseball bat, sweet spot, seesaw model, vibration model.

1 Introduction Baseball, the national ball in USA and Japan, is popular and is thought as the combination of competition, wisdom, braveness and cooperation. Batters know from experience that there is a sweet spot [1] on the baseball bat, about 17 cm from the end of the barrel, where the shock of the impact, felt by the hands, is reduced to such an extent that the batter is almost unaware of the collision. At other impact points, the impact is usually felt as a painful sting or jarring of the hands and forearm, particularly if the impact occurs at a point which is not sweet spot. Therefore, many researches have been done on bat and ball. Most of these studies are based on the physical phenomena of ball-bat collision. In this paper we proposed a seesaw model by just taking the essence of physical phenomena into consideration rather than the specific process of the physical phenomenon. The model defines the sweet spot as the minimum energy loss area, maximum energy transfer area [2]. After analyzing the vibration of the bat and the ball, a basic vibration model was abstracted to simulate the interaction of the bat and the ball, which simplifies the vibration of the bat and the ball into two components, one is Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 233–241, 2011. © Springer-Verlag Berlin Heidelberg 2011

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a seesaw part, whose pivot is the sweet spot of the bat, the other is the source of the vibrator (SOC). The model demonstrates that the sweet spot is not at the end of the bat while is a sweet zone close to the end of the bat and also illustrates that “corking” in the head of the bat will decrease that area of the sweet zone while ″corking″ in the end of the bat will increase that area of the sweet zone. The model shows that the area of the sweet zone is increased if the material of the bat is metal compared to solid wooden bat.

2 Approach At first, the vibration mode of the baseball bat is summarized through relevant literatures [3, 4], and then we take the vibration of the basic mode caused by the ball colliding with the bat as a simple model, shown in Fig. 1, which the vibration curve is enlarged in Fig. 1, actually the real vibration amplitude is not as large as that shown in fig. 1. Subsequently, we improve the model to enable them to be used in practice based on leverage theory.

Fig. 1. Schematic diagram of fundamental vibration mode

A. The Model without Considering Torque The baseball bat vibrates after colliding with a ball. A Vibration Modes of a Baseball Bat was proposed in [4], which implicated that an impact at the node will not cause the bat to vibrate, and thus none of the initial energy of the ball would be lost to the bat. To illustrate the theory, we have done an experiment called "The effect of the ball rebound height when the ball falls on the seesaw at different locations”, shown in fig. 2. It is clear that the rebound height of the ball falling on the pivot O is the highest. The loss energy of the ball is the minimum at the pivot O. Fig. 2 shows the difference of the rebound height of a ball, whose initial height is the same but the collision point on the seesaw is different. H 1 is the rebound height which the ball falls at the pivot; H 2 is the rebound height which the ball falls at the non-pivot. H 1 > H 2 .

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Fig. 2. The effect of the ball rebound height when the ball falls at different locations

B. The Model Considering the Torque According to the abovementioned conclusion, the node is the smallest energy loss point, shown in fig. 3. Then, there will be three best sweet spots in fig.3, which is contradicted to the actual situation. Therefore, in order to further determine the sweet spot of the baseball bat, we must consider the torque of the baseball bat.

Fig. 3. The second bending mode

From the perspective of torque, the sweet spot should be close to the end of baseball bat, where the energy transmitted to the baseball is the maximum, whether the tangential speed or the torque of the bat is the maximum when the player hits the ball with the same speed. In order to find the real sweet spot out, we divide a baseball bat into two parts based on the above analysis on the vibration and torque of the bat. One part called "SEESAW" is AB segment shown in Fig. 4 whose pivot is near the end of node (O), that is, a sweet spot. The other part called "SOV" (the Source of Vibrator) is BC segment shown in Fig. 4. "SEESAW" part and "SOV" parts have no vibrations when there is no impact between the bat and the ball. Once the collision occurred, "SOV" starts to vibrate and affects "SEESAW" swing up and down. In general, bat has such a

Fig. 4. Basic SEESAW Model

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movement model ,that is, the impact of the ball and the bat prompts bat vibration, while the vibration which in turn affects the total energy of the ball when it is bounced back. In Fig.4 AB is the "Seesaw" part, whose pivot is O, BC is the vibration source. It is concluded that there is a point of minimum energy loss in the seesaw part of the bat, which is the pivot of seesaw, called sweet spot.

3 Simulation and Result Analysis A. Why Isn’t the Sweet Spot at the End of the Bat? The question why the sweet spot is not at the end of the bat is now translated into why the pivot of "Seesaw" is not at the end. Based on the torque balance theory, it can be concluded by the Seesaw shown in Fig. 5: F1 × L1 = F2 × L2

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If the pivot is at the end, then the L1 (or L2 ) tends to 0, so F1 (or F2 ) tends to ∞, while it is impossible to achieve F1 (or F2 ) →∞ . Therefore, the pivot of "seesaw" cannot be at the end, that is, the sweet spot cannot be at the end. C. “Corking” a Bat Affects the “Sweet Spot” Some players believe that "corking" a bat enhances the "sweet spot" effect. Here we use the proposed model to discuss this issue. The previous conclusion only considers the sweet spot of the bat in a vibration mode, but the vibration of a baseball bat is very complex based on many previous studies[3,4,6], which has a lot of bending modes, as shown in Fig. 6. However, the first and second bending modes have significant impact on ball energy, therefore, according to the convention used by Rod Cross [6] , the sweet zone is defined as the region located between the nodes of the first and second modes of vibration (about 4-7 inches from the barrel end of a 30-inch Little League bat), shown in Fig. 7. Since the vibration motion of the bat is small in this region, the impact in this region results in little vibration of the bat but a solid hit results in maximum energy transferred to the ball.

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Fig. 6. Three bending modes of a freely- supported bat

Fig. 7. The sweet zone

In this way, the model is further improved if the sweet spot become the sweet zone, which the vibration amplitude of SOV change will affect the SEESAW vibration amplitude. Particularly, the larger SOV vibration amplitude is, the larger SEESAW vibration amplitude will be, and vice versa. Now we analyze the sweet zone change based on the amplitude of SEESAW changes. In order to make the analysis more reliable, we assume: (1) The relationship between the collision energy loss of ball, bat and their position before the collision is that node has no loss of energy, at other location, the greater the amplitude of the bat is, the greater the energy of ball loss is, and vice versa. (2) The relationship between the oscillation amplitude of vibration and its quality, for a fixed frequency of the vibration source and fixed external factors, can be defined as that the larger of the quality the smaller of the amplitude; and vice verse. Based on the above analysis, it is clear that "corking" the bat decreases the quality of the SOV, so that its amplitude increases. The increased amplitude of SOV results in amplitude increase of SEESAW accordingly. When SEESAW amplitude increases,

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the location where the bat loss the same energy, moves closer to the pivot of the SEESAW, and so the sweet zone becomes smaller. With the above analysis and conclusions, we know that "corking" a bat in the head will not enhance the "sweet spot" effect. Therefore, why Major League Baseball (MLB) prohibits "corking" does not depend on this effect. D. Is the Material Which the Bat is Constructed Matters? We make some more assumptions to predict how different material impacts the performance of a bat: (1) The bat has the same shape and the same volume (2) Only the density of the material affects the sweet spot of bat made of different materials. (3) The baseball bat is solid and the density is uniformly distributed (4) The mass of a bat changes because of the material density, that is, the mass increases as the density increases and vice verse. With the above assumptions, we make a more detailed analysis on the model in the basic vibration model diagram shown in Fig. 1, the basic vibration model. If the material of the baseball bat is changed, then the mass of the bat will be changed. The change finally leads to change the pivot of the "seesaw" part . Now we analyze the specific change. With the "seesaw" shown in Fig. 8, we have the following assumptions: (1) The balance of the see-saw is defined as the status when the seesaw does not swing. The “seesaw” is always in a state of equilibrium as long as the material remains unchanged. (2) The transition from one equilibrium state to another is achieved by shifting the pivot. (3) We only consider the final result of the transition that the see-saw varies from one equilibrium status to another while ignoring the specific process. (4) Left is defined as the positive shift direction. (5) When material changes, Δm denotes the difference between the variation of the mass increment on the left and the mass increment on the right.

Fig. 8. A special "seesaw" model

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When the "seesaw" is balanced, if M denotes the mass of the right, then the mass of the left is M + ΔM and ΔM > 0 . With the above assumptions and theory about torque, we obtain: ( M + ΔM ) gL1 = MgL2 ( M + ΔM + Δm ) g ( L1 − ΔL) = Mg ( L2 + ΔL)

(2) (3)

Equation (3) minus (2) ,we get: ΔmL1 = ΔL( ΔM + Δm )

(4)

As shown in Fig. 8, L1 < L2 and when the seesaw is in equilibrium state, according to (2) we obtain ρV1 gL1 = ρV2 gL2 , therefore V1 > V2 . So if the density of the ‘seesaw” increases, we get Δm > 0 .And because L1 > 0 and ΔM > 0 so the condition that makes the (3) correct is ΔL > 0 . Then, according to the assumption about the shift direction of fulcrum, it is concluded that the fulcrum should be shifted from right to left (positive direction) as the density of “see-Saw” increases or the fulcrum should be shift from left to right (negative direction) as the density of “see-saw” decreases. With above conclusions, we can predict different behavior of wood (usually ash) or metal (usually aluminum) bats. It is clear that the metal density is larger than that of wood. So the fulcrum of a wood seesaw shifts from right to left when the material has been replaced by metal. From the two bending modes (the fundamental bending mode and second bending mode as shown in Fig. 6), it is concluded that the sweep spot [3,4] is greatly impacted because the shift movement are not the same. Now we will figure out the difference of the movement. From (4), we obtain: ΔL =

ΔmL1 ΔM + Δm

(5)

To compare ΔL in the fundamental bending mode and the second bending mode, we set that L1 in (5) is corresponding to L11 and L12 in the fundamental bending mode and the second bending mode, respectively. In the same way, ΔM is corresponding to ΔM 1 and ΔM 2 , ΔL is corresponding to ΔL1 and ΔL2 , Δm is corresponding to Δm1 and Δm2 .Therefore, ΔL1 =

Δm1 L11 ΔM 1 + Δm1

(6)

ΔL2 =

Δm2 L12 ΔM 2 + Δm2

(7)

Then (5) is expressed as (6) and (7) in the fundamental bending mode and the second bending mode .According to our "seesaw" model, we know: L11 > L12 ΔM 1 < ΔM 2 , , Δm1

Δm2

So Δm1 L11 > Δm2 L12 and ΔM 1 + Δm1 < ΔM 2 + Δm2 , then, it is achieved that:

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(8)

Equation (6) shows that the shift movement of the fulcrum in the fundamental bending mode is larger than that in the second bending mode when the material varies from wood to metal. Then with Fig. 7 we can conclude, the sweet zone of a solid metal bat is smaller than that of a solid wood bat. From the above analysis we can conclude that the greater the density of the material, the smaller the sweet spot area when the bat have the same shape and volume. On the contrary, the smaller the density of the material the greater the sweet spot area. Therefore, it is not the reason why MLB prohibit the use of metal bats. However, we must consider that metal (usually aluminum) baseball bat is hollow and its mass is even smaller than the wood bat. From this point of view, although the density of metal (usually aluminum) is larger than the density of wood, but the average density of a hollow metal bat is smaller than that of a solid wood bat. Therefore, the use of metal enlarges the sweet spot area. When the sweet spot area increases, in terms of the competition, to get the same batting effect needs lower batter's batting skill. In other words, when the sweet spot area increases, the fairness and competitiveness of the competition decrease. Therefore, in order to ensure the competitiveness and fairness of the baseball tournament, MLB should prohibit metal bats.

4 Conclusions A vibration model was proposed to simulate the interaction of bat and ball, which simplifies the vibration of the bat and the ball into two components, a seesaw part, whose pivot is the sweet spot of the bat; and the source of the vibrator (SOC). Based on the “SEESAW” model and the leverage theory, the model drew conclusions: (1) the sweet spot is a sweet zone close to the end of the bat. (2) “corking” a bat in the head decreases the area of the sweet zone while “corking” a bat at the end increases the area of the sweet zone; and (3)the sweet zone is increased if the material of the bat is metal compared to the solid wooden bat. Acknowledgment. This work was supported by the Grant (2008YB009) from the Science and Engineering Fund of Yunnan University, the Grant (21132014) from the Young and Middle-aged Backbone Teacher’s Supporting Programs of Yunnan University and the Grant (21132014) from on-the-job training of PHD of Yunnan University.

References [1] Russell, D.A.: The sweet spot of a hollow baseball or softball bat invited paper at the 148th meeting of the Acoustical Society of America, San Diego, CA, November 15-19 (2004); Abstract published in J. Acoust. Soc. Am., 116(4), Pt. 2, pg. 2602 (2004) [2] http://www.exploratorium.edu/baseball/sweetspot.html

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[3] Russell, D.: Vibrational Modes of a Baseball Bat. Science & Mathematics Department, Kettering University (2003) [4] Russell, D.A.: Vibrational Bending Modes of a Baseball Bat. Science & Mathematics Department, Kettering University (2003) [5] Cross, R.: The sweet spot of a baseball bat. American Journal of Physics 66(9), 771–779 (1998)

Multi-objective Optimization Immune Algorithm Using Clustering Sun Fang, Chen Yunfang, and Wu Weimin College of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China [email protected]

Abstract. In this paper, a Multi-objective Optimization Immune Algorithm Using Clustering (CMOIA) is proposed. The mutation operator based on affinity definition can make the generated antibodies develop into a much better group. It combines local search ability of evolutionary algorithm by using crossover and genetic mutation operators to operate on the immune mutated antibodies. Then a clustering based clonal selection operator is used to maintain a balance between exploration and exploitation. Four general multi-objective optimization problems are selected to test algorithm performance according to the widely used four performance indicator. It was shown that the Pareto fronts obtained by CMOIA were better convergence and diversity than the ones from the other four classical multi-objective optimization evolutionary algorithms. The experiment results show the highly competitive in terms of the originality and robustness of the proposed algorithm. Keywords: Multi-objective Optimization, Artificial Immune Systems, Immune Optimization, Clonal Selection.

1 Introduction Multi-objective optimization problem (MOPs) was first proposed by French economist Pareto in 1896, it attempts to find the optimal solution while dealing with multiple objective functions with decision-making variables, and usually subject to equality or inequality constraints. Due to the widespread presence of multi-objective problem and the difficulty of solving, the issue has been always an attractive and challenging. Traditional methods for solving multi-objective optimization is transform a complex multi-objective optimization propositions to a single-objective optimization of propositions. However, in practice most of the multi-objective optimization problems are complex non-linear problems, the use of these methods are either unable to converge, or the speed of convergence is difficult to accept. Therefore, heuristic search strategies have been introduced into the multi-objective optimization to solve these complex non-linear optimization problem, evolutionary algorithm is one of the biggest bright spot, including classics MOGA [1], NSGA [2], PAES [3], SPEA [4] etc. These traditional evolutionary algorithms has made important breakthroughs, but they are

Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 242–251, 2011. © Springer-Verlag Berlin Heidelberg 2011

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always accompanied by lack of one kind or another, so other heuristic strategies attack more focus, of which the immune based heuristic search for a meteoric rise, because some of the inherent features of the immune system in exactly can compensate for multi-objective evolutionary algorithm in solving optimization problems. Immune optimization algorithm is based on the idea and gradually developed a heuristic random search technique, which are simulated adaptive processes by a group of antibodies. Immune algorithm can avoid some defects in multi-objective evolutionary algorithm such as premature convergence, and points out a new solution for traditional non-linear mathematical model of complex multi-objective optimization problem. The paper firstly presents the definition of multi-objective optimization problems and the artificial immune algorithm, analyzes the progress of the study of immune optimization algorithm, and then put forward a novel artificial immune algorithm based on clustering (CMOIA). The CMOIA focused on the clonal selection operator, diversity maintenance strategies based on clustering and the affinity mutation operator. Then a number of widely used multi-objective optimization test problems and performance testing indicators are selected to experiment, optimal solution and performance indicators of the experiment results are analysis in-depth.

2 Backgrounds Information Artificial Immune Systems (AIS) is inspired by biology immune systems. It is a complex computing system constructed to solve various difficult problems based on the functionality, principle and basic traits of biological immune systems. The purpose of research within this area is mainly to penetrate deeply into the information processing mechanism of biological immune system, and then construct some algorithms and engineering models to solve the difficult problem we faced up with in reality. [5, 6] Antibodies always try to best recognize a antigen in biological immune procedure, this is very similar to the evaluation of MOPs. Consequently antigen can be seen as multi-objective problem, antibody is treated as solution to that problem and antigenantibody pair affinity can be seen as the quality of the solution to the problem. After relation mappings like this, biological immune mechanism is brought into the field of multi-objective optimization. The essence of the multi-objective optimization problem is that there always exists contradiction between different sub-objectives; the improving on specific sub object may result in worsening of the other objectives. This means it is nearly impossible to coordinate all the objectives to reach a minimization. It is required to make a concession between all sub-objectives to achieve the “best” required by specific application. Due to its high search efficiency, avoid of premature, keep of individual diversity and other qualities, the research within this area is always a hotspot. According to the underlying principles of the existing AIO methods, they can be classified into two main categories: clonal selection principle-based, and immune networks-based approaches.

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Immune optimization based on immune network was first brought by Jerne [7], which suggests that B-cells are stimulated and suppressed not only by non-self antigens but also other interacted B-cells. Based on this theory, there are two subpopulations in the B-cells. One can be used to create an initial immune network, while the other is trained by the non-self antigens. An important feature of Jerne’s immune networks is their efficient adaptation to changing environments. Based on Jerne’s study Immune networks can be grouped into two sub-classes, the first one is De Castro and Timmis’ discrete immune network model [8] and the second one is Hajela and Lee’s immune network model [9]. The basic idea of clonal selection based immune optimization is that: Antibodies recognizes antigen can be reproduced, these cells will be selected and left for reproduce by immune system, and other antibodies won’t be selected or reproduced. Recent years many researchers started researches from various aspects within this area. Firstly proposed by de Castro and Von Zuben [10], CLONALG is one of the most widely applied optimization method in practice. It can be considered as a computational implementation of the clonal selection principle and affinity maturation of immune response. As we know, for mutation, crossover, and selection, GA only take the fitness of individual chromosomes into account. The CLONALG, on the other band, focuses on the affinities among chromosomes. That is, the proliferation and mutation rates of chromosomes are set to be inversely proportional to their affinities. During the evolution of CLONALG, those chromosomes with higher affinities are suppressed, while the ones with lower affinity are stimulated. Therefore, it can efficiently search in a diverse space of local optimum to find better solutions. Researches within this area mainly focus on the two models mentioned above at present, Improving and application in this area can be grouped into two classes based on their start point: One primarily focus on the improving of performance of MOIAs and the other focus mainly on how to refer more principle from existing biological immune systems.

3 Computational Framework of Cmola Here is the framework of solving multi-objective optimization problems using immune principles. First treat the feasible solutions of specific optimization problem as antibodies, optimization objectives as antigens. Generate random antibodies toward specific antigen as the Immune system. Get the feasible solutions from these random solutions. Then use immune memory pool to hold the Pareto optimal solutions which were just found. These memory cells need to be updated constantly with certain mechanisms to obtain an even distributed Pareto front. At last after sufficient amount of iteration the required Pareto front was fetched from the memory pool. In this paper, a new clustering based multi-objective immune algorithm was proposed in order to fully explore the potential of immune algorithms on multiobjective optimizations. Preliminary experimental results show that our algorithm obtains competitive results in terms of convergence and diversity.

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Table 1. The pseudo code of CMOIA 1. 2. 3.

4.

Define the antibody population size, cross rate, mutation rate and other Algorithm parameters. Initialize the population P with random generated antibodies. While ( Max Number of Iteration not reached ) a) Calculate the objective values and constraints of all solutions b) Perform Enhanced non-dominate sorting on all solutions, then use clustering based selection operator to select elite solution into C(t). c) Use our immune clone strategy to Clone C(t) to form a copy C'(t) d) Use our immune mutation strategy to mutate C'(t) to get A(t). e) Perform GA crossover and mutation strategy on A(t) to get A’(t) f) Generate some random antibodies and finish the objective and constraint calculation to get R(t) g) Merge A(t), A’(t), R(t) into Memory Cell M(t) h) Use M(t) as the new population for next iteration. Get pareto front from Memory Cell M(t).

In order to avoid early convergence caused by excessive local search, we adopted clustering based clonal selection strategy to maintain a balance between exploring new solutions and searching locally. Further more in order to accelerate the information exchange between antibodies; we adopted GA crossover and mutation strategy on antibodies after immune clone. With all these done, after every new solution was found, our algorithm can quickly perform local search around that solution point to get an even and diverse solution set. Referring to the adaptive response to specific antigen stimulus of biology immune system, we generate some random antibodies so that the algorithm can have better chance to find the isolated solutions. With these randomly generated antibodies, the algorithm can avoid early convergence and can cover the search space with a higher probability at the same time. At last, the cloned solution set, the mutated solution set and newly generated random solution set will all be merged into the memory pool for next iteration. As to the memory management, we adopted generally used crowding distance based strategy on memory pool management. Using memory pool in algorithm to maintain elite solutions is a common strategy for multi-objective optimization immune algorithms, generally after the specified amount of solution in the pool was achieved, algorithm can stop, but for better comparative study of CMOIA with other algorithms, we set the termination criteria to predefined number of iterations, generally speaking after these iterations, the memory pool must be full.

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A. Clonal Selection Strategy Referring to the results Wang X. L. [11] et. al.‘s ACSAMO obtained, we define our antibody affinity in CMOIA as follows: A ff i = x i − x c + x i − x g

Affinity is defined as the sum of Euclidean distance between current antibody and best antibody in current generation and previous generation. Xc, Xg represents best antibody in current and previous generation respectively. In order to calculate the best antibody among generations such as Xc and Xg, we defined weighted aggregation approach to evaluate the performance of antibody. Antibody with the least weighted objective represents the best antibody within this generation. WO =

∑

n i =1

ω i x i Where ω i ∈ U (0,1) and

∑

n i =1

ωi = 1

Wi will be regenerated every iteration to avoid shield on some certain solutions caused by concentration on some certain objectives. The immune clone selection generally can be divided into two phases: first is selection, which is to select typical elite from the current generation, this procedure is finished mainly via sorting the solutions in current generation. To fully explore the typical ness of the selected individual we extend the existing Pareto dominating concept and bring out a new Pareto dominating condition. This method can insure the diversity of the population and resolve constraints at the same time. Use the extended Pareto dominance compares procedure to sort the existing population and select first 30% individuals in the population. Then the selecting part of clonal selection part is finished. The second part of the whole clonal selection procedure is to reproduce the elite which have been chosen out in the first phase. The second phase is also called local search. And exploring the unsearched area for potential elite solutions is carried out at the same time. Generally speaking how to maintain a balance between local searches and explore potential Pareto optimal solution is a key problem which Immune algorithms should focus on. CMOIA used clustering based diversity strategy to maintain the balance. B.

Cluster Based Diversity Maintain Strategy

CMOIA used a clustering based density measure strategy to keep the non-dominating solutions distribute evenly. This is different from generally used strategy at present which consider the individual’s information contribution to the whole population. Clustering based density measure strategy is carried out in the objective space while the other is in the variable space. Clustering analysis is carried out by analyze some certain pattern, the pattern is ndimension vector or a point in n-dimension space. We can analyze the Pareto front data which was generated by algorithm at specific time by carrying clustering analysis. The whole procedure is listed below: first cluster currently generated Pareto front into several groups according to its position in the objective space. Then we use these groups to further decide whether more local search or potential optimal solution

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explore should be carried out according to the solution number in specific sub group. If the clustered group contains many solutions then we should reduce search in this area, while if the group have less solutions then more search power should be injected into this area to fully explore the potential optimal Pareto solution. With all these done we can make clonal generated optimal solutions maintain a required diversity. We choose K-means cluster algorithm because of its simplicity and effectiveness. The time complexity of K-means cluster algorithm is only O(kNt), when dealing with massive data sets, we can get the processed data in time. Besides all these benefits it is also easy to implement and understand. The k-means algorithm is an algorithm to cluster n objects based on attributes into k partitions, k < n. It is similar to the expectation-maximization algorithm for mixtures of Gaussians in that they both attempt to find the centers of natural clusters in the data. It assumes that the object attributes form a vector space. The objective it tries to achieve is to minimize total intra-cluster variance, or, the squared error function

∈

where there are k clusters Si, i = 1, 2, ..., k, and µi is the centroid or mean point of all the points xj Si. The general procedure of K-means cluster algorithm CMOIA adopted is described bellow: Table 2. The pseudo code of K-MEANS Clustering algorithm 1. Initialize all the algorithm parameters , for K-means

algorithms: N 2. Randomly choose N solutions as N Clusters and calculate

cluster center for each cluster. 3. While the center of any cluster changes over each iteration

a) b) c)

select a solution, calculate its distance with each cluster center Di select the shortest distance Di and add the solution into that cluster. after that update this cluster center using mean method.

After clustering the Pareto front data, decision of whether to strengthen local search or explore new optimal solutions. Clusters containing a lot of solution indicate that this local space has been searched and putting more searching power within this area will merely be a waste. The representativeness of solution within this area is poor. Clusters contain little solution indicates that this space is not fully developed and It is recommended to append search power into this area. The representativeness of solution within this area is better. With all these strategies, CMOIA can achieve a better balance between local searches and explore new optimal solutions, thus searching a wider objective space and get better results. Besides clustering based diversity maintain strategy, CMOIA also referred to Tan [12] work for better results. They used GA crossover and mutation operators on the

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immune mutated solutions so that we can get fully searched local optimal solutions. Compared with traditional immune algorithms, the antibodies within CMOIA will go through GA crossover and mutation operations, not only traditional immune mutation operations. After this procedure is finished, all the offspring and parent generation is archived and added into the memory archives. C. Immune Mutation Strategy In biological immune systems, the frequently mutated somatic cell plays an important role in the adapted immune response procedure: first it maintains a diversified instruction system by mutation; second combined with immune selection procedure, it will greatly increase the affinity of particular antibody. Artificial immune systems simulated this feature of biology system. On the other hand, the hyper mutation operator is different from the GA’s mutation operator. The difference mainly lies in its adjustment of mutation rate. For GA mutation operator, it usually uses a linear mutation rate which changes over iterations to control the mutation degree of all solutions. While on the other hand immune hyper-mutation operator is more complex. The mutation rate varies between antibodies and is decided by all solutions, those which has smaller mutation rate if they are close to the optimal solution while those which has greater mutation rate if they are far from the optimal solution. During the immune reaction phase, The antibodies undergo different degree of hyper-mutation, Those with superior affinity undergo much less mutation compared with those with inferior affinity. CMOIA made several adoptions to the general immune mutation strategies. For example every antibody going through immune mutation phase will undergo a perturbation, all the decision variable within the solution will vary within normally distributed interval [-S,S]. To further explore new optimal solutions at the beginning and in the proceeding phase, mutation intensity is defined as: S = [U (0,1) −

1 ] × A ffinity i × γ × β 2

(6)

Among which, A ffinity i is the affinity of the solution, γ is mutation rate, β is a adjustment coefficient. in our experiment, γ = 0.8, β = 1 . When mutated decision variable exceeds its bound, we set it to the bound value for simplicity. The mutation intensity is pretty complex because it connects the solution with the whole population, when the affinity is superior, namely A ffinity i is low, it is obvious that the mutation intensity S is small, thus optimal solutions won’t get lost in the procedure because of mutation. While on the other hand if A ffinity i is high, namely the affinity is inferior, then the mutation intensity is much bigger, then the solution will have more chance to evolve into a superior solution.

4 Cmoia Algoright Test and Analysis A. Benchmark Problem and Quality Indicator To fully evaluate the efficiency of the CMOIA algorithm, we compared CMOIA with SPEAII, NASGII, PAES, and PESAII. All the tests were finished on an Intel P4 1.8

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GHz computer with 512MB physical memory and JDK 1.5.0. All the algorithms run 30 times independently for accuracy. To evaluate the performance of CMOIA algorithm as objective as possible, we selected six frequently cited benchmark problem, including two unconstraint multiobjective problems (Kursawe, Viennet3) and two constraint problems (Osyczka2, Srinivas). Besides we used several widely used quality indicator to fully examine the diversity and convergence of CMOIA’s finally generated solution set. 1. Generation Distance (GD): a measure of the distance between the true and generated Pareto front. 2. S (Spread) Pareto front Spread: a measure to evaluate the distribution and coverage of solution point within the calculated Pareto front. 3. H Hyper volume quality: a quality indicator calculates the volume (in the objective space) covered by members of a non-dominated set of solutions Q (the region enclosed into the discontinuous line in the figure below,) for problems where all objectives are to be minimized. B.

Comparative on Performance

The shape of Pareto front can only gives a direct concept of performance toward specific problem, to fully and accurately evaluate the performance of specific algorithm, we need to measure various quality indicators to make further conclusion on them, only in this way can we objectively measure the performance of specific algorithm. 1. Comparation on Time Cost Table 3. CMOIA performances in the aspects of Time Cost Time Cost(ms) Kursawe Viennet3 Osyczka2 Srinivas

CMOIA PAES

NSGAII PESAII

SPEAII

4299 13366 5503 13826

5822 5621 6366 5081

26529 40517 22634 45329

1695 8057 1274 3203

10534 22528 10162 13268

Time cost of CMOIA is superior to three other algorithms on these testing problems including: Kursawe and Osyczka2. It also outperforms other two algorithms on Viennet3. Only on Srinivas CMOIA cost about the same time as average. Judge from time consuming we can see that CMOIA did pretty well than other problems. And at the same time, we can see all the great results SPEAII has achieved are at the cost of great time consuming. 2. Comparation on GD Table 4. CMOIA performances in the aspects of GD GD Kursawe Viennet3 Osyczka2 Srinivas

CMOIA

PAES

NSGAII

PESAII

SPEAII

0.00033 0.00022 0.00559 0.00022

0.00020 0.00238 0.01371 0.00030

0.00012 0.00026 0.00144 0.00020

0.00017 0.00038 0.01027 0.00024

0.00018 0.00029 0.00152 0.00011

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The convergence is the most aspect of all quality indicators, it can be concluded from the table that, CMOIA outperforms three other algorithms on testing problem Viennet3. CMOIA performs fairly well as other algorithms has done on testing problem Osyczka2, Srinivas. On the other hand CMOIA is inferior to other problems on problem Kursawe. Generally speaking CMOIA takes a leading place in the result of GD indicator. 3. Comparation on Spread Table 5. CMOIA performances in the aspects of Spread Spread Kursawe Viennet3 Osyczka2 Srinivas

CMOIA

PAES

NSGAII

PESAII

SPEAII

0.54317 0.75173 1.28333 0.18115

0.84531 0.75701 1.11589 0.61604

0.48745 0.73068 0.56847 0.40671

0.80124 0.76551 0.96264 0.63171

0.21246 0.79225 0.79529 0.17168

On quality indicator S, It can be concluded from the table that CMOIA outperforms most of the other four algorithms on problem Viennet3 and Srinivas. CMOIA performs generally the same as the other four algorithms on problem Kursawe and Fonseca. Only on problem Osycaka2 CMOIA don’t do well than other problems. Generally speaking CMOIA takes a lead in quality indicator S. 4. Comparation on Hyper-area Table 6. CMOIA performances in the aspects of Hyper-area Hyperarea Kursawe Viennet3 Osyczka2 Srinivas

CMOIA

PAES

NSGAII

PESAII

SPEAII

0.78892 0.83615 0.57469 0.53966

0.82088 0.80489 0.47733 0.53542

0.83327 0.83259 0.77765 0.53796

0.81763 0.83203 0.57933 0.53581

0.83315 0.82682 0.76763 0.53999

Quality indicator H is used to measure the coverage of the algorithm. It can be concluded from the above table that CMOIA outperforms all other algorithms on problem Viennet3 and Srinivas. CMOIA performs generally the same as other algorithms on problem Osyczka2. But CMOIA don’t do well as other algorithms on Fonseca and Kursawe. Taking all these into account, CMOIA still dominant other algorithms on indicator H. After comparing all six indicators, a conclusion can be drawn that considering convergence, diversity, coverage and time cost CMOIA does fairly well compared with PAES, PESAII, NSGAII and SPEAII. It is an applicable algorithm which can be used for general purpose multi-objective optimization problem solving.

5 Conclusion In this paper, a novel artificial immune multi-objective optimization algorithm is proposed to make up for the lack of evolutionary algorithm and immune algorithm,

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which is based on clustering clonal selection strategy. Comparing with the existing research on clonal selection strategy, the algorithm is based on the diversity of populations that can better maintain the balance between in exploiting new optimal solution regions and strengthening the local search. CMOIA mutation strategy allows the algorithm convergence to the true Pareto Front with faster rate and is able to avoid premature convergence and diversity loss. Experimental results show that CMOIA has unique advantages on convergence, diversity and distribution uniformity. In highdimensional, complex issues CMOIA bound by poor performance, but in general the CMOIA is a new member of immune algorithms and can completely replace PAES, PESA, and NSGAII to solve the problem of multi-objective optimization.

References [1] Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving MultiObjective Problems (Genetic and Evolutionary Computation). Kluwer Academic Publishers, Dordrecht (2002) [2] Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGAII. Parallel Problem Solving from Nature, Berlin (2000) [3] Knowles, J.D., Corne, D.W.: Approximating the non-dominated front using the Pareto Archived Evolution Strategy. Evolutionary Computation 8, 149–172 (2000) [4] Zitzler, E., Thiele, L.: An Evolutionary Algorithm for Multi-objective Optimization: The Strength Pareto Approach. Computer Engineering and Communication Networks Lab, Swiss Federal Institute of Technology, Zurich, Switzerland, Technical Report 43 (1998) [5] Hart, E., Timmis, J.: Application Areas of AIS: The Past, the Present and the Future. In: International Conferences on Artificial Immune Systems. Springer, Heidelberg (2005) [6] Zheng, J.Q., Chen, Y.F., Zhang1, W.: A Survey of artificial immune applications. Artificial Intelligence Review (34), 19–34 (2010) [7] Jerne, N. K.: Towards a Network Theory of the Immune System. Ann. Immunology, Vol. 125C , pp. 373–389 (1974) [8] de Castro, L.N., Timmis, A.: An artificial immune network for multimodal function optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation Honolulu, pp. 699–704 (May 2002) [9] Hajela, P., Lee, J.: Constrained genetic search via schema adaption: An immune network solution. Structural Optimization 12(1), 11–15 (1996) [10] de Castro, L.N., von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002) [11] Wang, X.L., Mahfouf, M.: ACSAMO: an adaptive multiobjective optimization algorithm using the clonal selection principle. In: 2nd European Symposium on Nature inspired Smart Information Systems, Puerto de la Cruz, Tenerife, Spain, November 29 - December 1 (2006) [12] Tan, K.C., Goha, C.K., Mamuna, A., Eia, E.Z.: An evolutionary artificial immune system for multi-objective optimization. In: Artificial Intelligence Review. Springer, Heidelberg (2002)

A Novel Hybrid Grey-Time Series Filtering Model of RLG’s Drift Data Guo Wei1, Jin Xun2, Yu Wang1, and Xingwu Long1 1 National University of Defense Technology, College of Opo-electronic Science and Engineering, Changsha, China 2 China Satellite Maritime, Tracking and Controlling Department, Jiangyin, China [email protected]

Abstract. In order to shutdown the random drift of mechanically dithered RLG’s output data effectively, a new method named Grey-Time series modeling is proposed, which has integrated the Metabolic GM(1, 1) model and Time series model. Kalman filter is used to filter the drift data based on the model which has been built, and the Allan variance is adopted to analyze the data of gyro before and after modeling and filtering. The results show that: the effect on inhibiting RLG’s random drift by using this new method is better than that of traditional time series modeling and succedent Kalman filter. The method effectively decreases random error in each term of RLG, in which the improvement on quantization error is quite obvious. Keywords: RLG, Grey System, Time series analysis, Kalman filter, Allan variance.

1 Introduction Ring laser gyro(RLG), as one of the core components of strapdown inertial navigation system(SINS), is widely used in many military field at cutting edge, however, the gyro’s drift error accumulated over time is the main factor which affects the accuracy of the navigation system [1][2]. In all the drift errors, the random error term is the real one affecting the performance of RLG, which is generally considered as slow timevarying and weak nonlinearity, and because of the strong randomness of successive start, as well as the interference by the external environment factors, it is not easy to catch the true signal of the RLG. Therefore, through the use of effective modeling and filtering programs to reduce the random drift of RLG is one of the keys to improve its accuracy. At present, one of the common methods to inhibit the random drift of RLG is making time series analysis on the random drift of RLG’s signal with the description of AR (n) or ARMA (m, n) model and then designing Kalman filter based on the model [3][4]. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 252–259, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Chinese scholar Professor Deng Julong created gray system theory in the 80's, focusing on some uncertainty problems, such as "small sample" and "poor information", which are hard to be dealt by probability statistics and fuzzy mathematics. The gray system which is characterized as "modeling by small amount of data" explores evolution law of data through the role of sequence operator according to information coverage[5]. It is believed in grey system theory that any random process is a grey magnitude changing in certain amplitude range and a certain amount of time zone, and the random process is a grey process. With the function of weakening sequence randomness and exploring the law of data evolution, gray model is a kind of excellent dynamic system modeling method and it has been widely used in the economic, control, and other fields [6][7][8]. Because of the working mechanism of RLG, external environmental interference and other reasons, there are also some random errors in the output signal of RLG, which brings uncertainty on the signal to a certain degree. Therefore, this paper adopts the grey-time series modeling, which is fused by metabolic GM (1, 1) grey model and time series model, based on the method of Kalman filter toward the drift data model, and uses Allan variance, which is widely acknowledged by IEEE, to analyze and compare the RLG drift data before and after the modeling filter.

2 Grey-Time Series Model and Kalman Fil Ter A. GM (1, 1) Model GM (1, 1) is a single argument first-order gray model. It implemented to establish a continuous dynamic differential equation model by using discrete data sequence through weakening the randomness of the data series by the sequence operator, exploring potential law of the data, and the exchange of difference equation and differential equations. Setting original data sequence as X = {x (1), x (2), ... , x ( n)} , using first accumulating generation operator (1-AGO) to transform the original sequence (1) (1) (1) (1) into accumulated generating sequence X = {x (1), x (2), ... , x ( n)} , in (0)

which, x ( k ) = (1)

k

∑x

(0)

(i ) , k = 1, 2,..., n

i =1

The close average generated sequence of in which, z (1) (k ) =

(0)

(0)

。 X (1) is Z (1) = {z (1) (1), z (1) (2), ... , z (1) (n)} ,

1 (1) [ x (k ) + x(1) (k − 1)] , k = 2,3,..., n 2

GM (1, 1) model

(0)

。

x (0) (k ) + az (1) (k ) = b , its least squares estimation parameter

list satisfies the equation as below:

aˆ = (B T B)-1 B T Y

(1)

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⎡ x (0) (2) ⎤ ⎢ (0) ⎥ In which, Y = ⎢ x (3) ⎥ ⎢ M ⎥ ⎢ (0) ⎥ ⎣ x ( n) ⎦

，

⎡ − z (1) (2) ⎢ (1) − z (3) B=⎢ ⎢ M ⎢ (1) ⎣ − z (n)

1⎤ ⎥ 1⎥ , ˆ ⎡ a ⎤ , −a is the developmental a=⎢ ⎥ ⎣b ⎦ M⎥ ⎥ 1⎦

factor, b is the amount of grey influence. Developmental factor reflects the developmental trend of the data, and the amount of grey influence reflects the relationship between the changes of data. The whitenization equation of GM (1, 1) model is

dx (1) + ax (1) = b dt

(2)

By solving it we get time response sequence of GM (1, 1) model, and by restoring it we get the estimation of the original sequence. The estimated value of the expression is as below:

b xˆ (0) (k + 1) = (1 − ea )( x(0) (1) − )e−ak , k = 1,2,..., n a

(3)

B. Modeling and Estimation of Metabolic GM (1,1) As time goes on, the meaning of the old data information will be gradually reduced. At the same time of the RLG outputting new data continuously, the old information will be timely removed, which will better reflect the real-time characteristics of RLG drift. Meanwhile, the constant metabolism could also avoid the difficulties brought by the increase of the information, such as the expansion of computer’s memory, the increasing operation quantity of modeling. It could carry out real-time modeling and estimation. The core model of grey system theory—GM (1, 1), can estimate model parameters by only four data, which is up to a certain degree of precision. In order to take into account the estimation accuracy and the real-time character, this paper carry out one modeling and estimation with the output of every 10 gyro’s data points, and store the estimated value of 10th data point which contains the real-time feature of the first 10 data points, then it inputs data by metabolism, constantly does modeling and estimation. The process of modeling and estimation of Metabolic GM (1, 1) is shown in diagram 1. (0)

(0)

In the diagram 1, x (t ) is the real-time data output of the RLG, and xˆ (t ) is the estimated value after GM (1, 1) being modeled. After each new data output by the RLG, it will remove the oldest data in the sequence of 10 elements. With the new data added, it forms a new sequence and GM (1, 1) model will be established. Thus, Metabolic GM (1, 1) model has strong adaptability. In order to investigate the feasibility which Metabolic GM (1, 1) model applied to estimate RLG drift data, authors used a small amount of RLG output data for modeling and estimation by the above methods , and the results are shown in Table 1.

A Novel Hybrid Grey-Time Series Filtering Model of RLG’s Drift Data Metabolic modeling

RLG Output

255

Real-time estimation

GM (1,1) , x (0) (2) , ... , x (0) (10) ⎯⎯⎯→ xˆ (0) (10)

x (0) (1)

GM (1,1) x (0) (2) , x (0) (3) , ... , x (0) (11) ⎯⎯⎯→ xˆ (0) (11) GM (1,1) x (0) (3) , x (0) (4) , ... , x (0) (12) ⎯⎯⎯→ xˆ (0) (12)

M

M

x (t − 9) , x (t − 8) , ... , x ( t ) ⎯⎯⎯→ xˆ (0) (t ) (0)

(0)

(0)

GM (1,1)

Fig. 1. The modeling and estimating process of Metabolic GM (1, 1) Table 1. The estimating results of Metabolic GM (1,1) model for RLG’s drift data

x (0) (º/h) (0) Estimated data x (º/h)

6.9960

7.0752

7.1498

7.0917

7.0647

7.0175

7.0375

7.0828

7.1010

7.0986

Relative error (%) Developing coefficient − a

0.31 4.9×10-4

0.53 1.04×10-4

0.94 1.3×10-3

0.13 1.8×10-3

0.48 1.5×10-3

Original data

As it can be seen from Table 1, the development coefficient −a <0.3, is up to the sphere of application of GM (1, 1) model[9], and the estimated relative error is less than 1%. C. AR Model and Kalman Filter A large number of experiments and statistical tests show that the static output data of high precision RLG can be basically seen as a smooth, normal time series. Due to the existence of ground speed component and bias error of RLG, the above data does not meet the zero mean condition. In this paper, we used the first-order differential signal of the estimated value after the metabolic GM (1,1) modeled to establish the secondorder autoregressive model --AR (2) model, which is commonly used in time series analysis of RLG’s random drift. The first-order differential signal is expressed as

d (k ) = xˆ (0) (k ) − xˆ (0) (k − 1)

(4)

（2）model with differential signal is as below:

The form of AR

d (k ) = ϕ1d (k − 1) + ϕ2 d (k − 2) + ε (k ) Therein, ε ( k ) is the white noise, estimate.

ϕ1 , ϕ2 are

(5)

the parameters for the model to

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Then this signal model is expressed by the estimated value modeled by the metabolic GM (1, 1), which is as follows: xˆ(0) (k) = (1+ ϕ1 ) xˆ(0) (k −1) + (ϕ2 − ϕ1 ) xˆ(0) (k − 2) − ϕ2 xˆ(0) (k − 3) + ε (k ) (6) According to estimates value sequence

xˆ (0) after being

xˆ (0) after the Metabolic GM (1, 1) being

modeled, it can be estimated the model parameters

ϕ1

and

ϕ2

in real time by the

least square method, thereby determine the model. Based on the above signal model, it can be built a Kalman filter model, which filters the RLG random drift in inertial (0)

navigation system. We would use xˆ as the input signal of Kalman filter to establish the following Kalman filtering equation:

⎧ X ( k + 1) = ΦX (k ) + Γw(k ) ⎨ ⎩ y (k ) = HX (k ) + v(k )

⎡1 + ϕ1 ϕ 2 − ϕ1 Φ = ⎢⎢ 1 0 ⎢⎣ 0 1

(7)

− ϕ2

⎤ ⎡1 ⎤ ⎥ In which 0⎥ Γ = ⎢⎢0⎥⎥ H = [1 0 0] . ⎢⎣0⎥⎦ 0⎥⎦ , , The statistical properties of process noise w( k ) and measurement error v(k ) will

，

satisfy following conditions:

E ( wk ) = E (vk ) = 0 ; Autocorrelation function ϕ ww = Qδ kj , ϕ vv = Rδ kj ; Mean value

Cross-correlation function

ϕ wv (k , j ) = 0 .

The overall program of the random drift data of RLG being modeled and filtered is shown in figure 2.

x (0) (t )

xˆ (0) (t )

ϕ1 and ϕ2

x% (0) (t )

Fig. 2. The overall program of modeling and filtering

3 Experimental Results and Analysis On the condition of room temperature, we put the mechanically dithered RLG, which was made by our own teaching and research section, on the platform and kept it relative stationary toward the geographic coordinate system. The electricity job does not begin sampling until the stable output of gyro. The sampling frequency was 1Hz, and the sampling time was 4 hours. Figure 3 (a) is the original sampling data of mechanically dithered RLG; Figure 3 (b) is the estimated value that the original data

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(a) Sampling raw data of mechanically dithered RLG

(b) Estimated value after Metabolic GM (1,1) modeling Fig. 3. The drift data before and after Metabolic GM (1,1) modeling

has been through metabolic GM (1, 1) model. From figure 3 we can find that, the randomness of the RLG drift data was weakened when it has been through metabolic GM (1, 1) model and the noise was obviously reduced. The mean value of the data, before and after the model, has not changed, which was 7.0549 ° / h. We get the data which has been estimated after the modeling of Metabolic GM (1, 1) to make AR (2) modeling, and then for Kalman filtering, the filtering result is shown in Figure 4 (a). In order to facilitate the comparative analysis, we directly get the original data of RLG to be AR (2) modeled and Kalman filtered, the filtering result is shown in Figure 4 (b). Adopting Allan variance, we process the original data of RLG, the data modeled by grey-time series model and filtered, the data modeled by traditional time series model and filtered to be analyzed, and the results are shown in table 2, where Q, N, B, K, R respectively represented the quantization noise, angle random walk, bias instability, angular rate random walk, rate of slope.

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(1) Grey-time series modeling

(b) Traditional time series modeling Fig. 4. The result of Kalman filtering to the drift data

From Figure 4 (a) and Figure 4 (b) we can see that, compared with gyro’s original drift data, the filtering effect based on the grey-time series model and the traditional time series model are both significant, and the filtering effect based on the grey-time series model is slightly better than that based on the traditional time series model. You can see from Table 2, after gray-time series modeling and Kalman filtering, the random errors of RLG, which have been separated by Allan variance, were all significantly reduced. Quantization error decreased to 23% of the original data, angle random walk to 37% of the original data, the stability of bias error instability to 17%, angular rate random walk to the 12%, rate of slope down to the original 20%. After the traditional time series modeling and Kalman filtering, the random errors of RLG were also improved, but the results was not as good as the gray-time series model program, especially for the item of quantization error. Therefore, the program of using the gray-time series modeling effectively reduced each item of RLG’s random errors, and improved its accuracy.

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Table 2. The analytic result of each random error in RLG using Allan variance Error terms Raw data Traditional time series model Grey time series model

Q(μ rad )

2.01×10

-1

N (° / h )

8.96×10

-4

B(° / h) 3.16×10

-3

K (° / h / h ) 7.12×10

R (° / h 2 )

-3

5.66×10-3

1.11×10-1

4.79×10-4

7.52×10-4

9.97×10-4

1.83×10-3

4.70×10-2

3.33×10-4

5.56×10-4

8.41×10-4

1.16×10-3

4 Conclusion In order to reduce the random drift in the output data of RLG, this paper studied a gray-time series modeling method based on metabolic GM (1, 1) model. Experimental results showed that this method could not only weaken the randomness of drift data, but also could establish the online second order autoregressive AR (2) model of drift data, and effectively inhibited the random drift of RLG by Kalman filter, as well as improved the measurement accuracy of RLG.

References [1] Long, X., Tang, J., Yu, W., et al.: Development on Ship’s RLG Inertial Navigation System. In: Proceedings of the 6th Annual Conference of Chinese Society of Inertial Technology, pp. 15–23 (November 2008) [2] CJSCI 6130.01C, Master Positioning, Navigation, and Timing Plan, Washington, D.C (March 31, 2003) [3] Zhang, S., Yan, W.: The Modeling and Filtering of Laser Gyro Drift Data. Journal of Chinese Inertial Technology 7(4), 70–72 (1999) [4] Sang, M.S., Jang, G.L., Chan, G.P.: Equivalent ARMA Model Representation for RLG Random Errors. IEEE Transaction on Aerospace and Electronic Systems 36(1), 286–290 (2000) [5] Deng, J.: A Textbook of Grey System Theory, pp. 10–15. Huazhong University of Science and Technology Press, Wuhan (2002) [6] Liu, S., Lin, Y.: Grey Information Theory and Practical Applications, pp. 1–22. Springer, London (2006) [7] Jiang, C.W., Hou, Z.J., Zhang, Y.C.: Chaos analysis of the grey forecasting model. The Journal of Grey System 12(4), 319–322 (2000) [8] Zhao, X., Huang, Q., Wu, J.: Runoff series pattern mining based on grey Markov chain. Engineering Journal of Wuhan University 41(4), 1–4 (2008) [9] Liu, S., Deng, J.: The range suitable for GM(1,1). The Journal of Grey System 11(1), 131–138 (1999)

The Final Sense Reverse Engineering Electroencephalography Mohammed Zahid Aslam Alpha College of Engineering, Visvesvaraya Technological University, Bangalore, India [email protected]

Abstract. The human brain has the amazing ability to bombard itself with millions of bits of diverse information every day. It must also be able to store and convert these intelligent thoughts. In simpler terms, it does this by evaluating, sorting, figuring and redirecting information based on sequences and relationships.This data being processed by the brain is already being studied by EEG (Electroencephalogram) at various levels of research and for clinical purposes. This paper introduces the idea of reverse engineering the concept used in EEG’s and “write” data into the brain. This device will apply the required voltage in micro volts to the different parts of the brain externally using EEG electrodes. Thus instead of the basic senses converting physical images and characters, sounds etc into certain voltages being transmitted to the brain, an external device (such as an EEG) can convey the information to the brain. Keywords: Brain, Electroencephalography, Medical treatment, Pattern recognition, stochastic automata.

1 Introduction Electroencephalography (EEG) is a neurological diagnostic procedure that records the changes in electrical potential in various parts of the brain. The EEG data recorded in data sets is usually highly dimensional in nature. This data is in terms of electrical potentials obtained on the surface of the scalp. In simpler words when we read a certain character for instance the photoreceptor cells (cone and rod cells) in the retina convert this particular character into a particular voltage which is then transmitted to the brain through the neurons. The same process of converting real time signals into electrical potentials is used by all five senses namely sight, hearing, touch, smell and taste. This particular voltage known as “action potential” for a certain pattern or character is then bombarded onto a part of the brain. Therefore when the brain receives this unique action potential it can understand the character being read. The signals read and transmitted by the brain are usually not exceeding 150µV and occupy a frequency range of 0.5-60 Hz. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 260–264, 2011. © Springer-Verlag Berlin Heidelberg 2011

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The signals can be classified into four frequency bands: Delta (δ) – Below 4 Hz Theta (θ) – 4 Hz to 8 Hz Alpha (α) – 8 Hz to 12 Hz Beta (β) – Greater than 12 Hz The frequency spectrum of a normal EEG is as shown below in figure 1

Fig. 1. Frequency Spectrum of Normal EEG

Beta waves having the greatest frequency among the signals obtained are associated with an alert mental state whereas alpha waves are associated with a relaxed mental state. Both alpha and beta waves are obtained by the EEG when we are awake. When we are asleep we enter into several different states including theta and delta waves whose frequency is comparatively less than alpha waves and beta waves. Delta waves state of mind is the deepest state of sleep which may include dreaming.

2 Transmission of Electrical Signals The core component of the nervous system is the neuron. Neurons are highly specialized for the processing and transmission of cellular signals. Inside the neuron there is a high concentration of potassium ions (K+). This concentration of potassium ions is lower outside the neuron. On the other hand sodium ions (Na+) have higher concentration in the extracellular fluid and lower concentrations in the nerve cell. The cell membrane is more permeable to potassium ions than to sodium ions. This leads to a resting potential of about -70mV and the membrane is polarized. When the sensory organs transmit electrical signals this resting potential gets disturbed and the corresponding electrical potential with respect to the resting potential is transmitted through the neurons to the brain. Therefore information in the form of electrical signals gets transmitted from one part to another through the neural network. Rerouting this information from an external source rather from the sensory organs is the basis of this paper.

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3 Proposed Idea Electroencephalograms are being used to read the data being received and processed by the brain. EEG devices are being used to read data from the brain and control objects in the virtual world. Portable EEG devices are available today which are being used to communicate with patients in paralysis. EEG devices are also being used in the world of gaming to control objects in the virtual world just by thinking about them. Reversing this process of reading signals from the brain, we can be able to write data into the brain from an external device. This device can perform the basic task of converting natural continuous signals into electrical signals which can be directly fed into the brain. This is shown in the figure below

Sensory organs (eyes, ears etc.)

Brain

Reverse engineered EEG device

Fig. 2. Alternate route for information

4 Construction This device can be constructed using an electroencephalogram which uses portable dry electrodes which can be placed in the international 10/20 system of placing the electrodes. After the placement of the electrodes we can send the micro volt signals corresponding to the patterns or characters identified by the brain. When this micro volt signal is received by a particular region of the brain it will automatically understand the character or pattern that we are trying to write into the brain. This micro volt data for different patterns and characters can be obtained from the data sets of the electroencephalograph. This process only replaces the use of the sensory organs and the neural network. It does not alter the functions or the way in which the brain processes data. It is simply trying to re-route data through another path which does not include the neurons. In a way it is trying to create an external wireless neural network which does not use neurons as connectors.

5 Issues Related to an EEG The human brain is the most complex organ of all. Trying to write data into the brain through an external source has to be done with highest precision and accuracy. The micro volt signals may not harm the human body but as the skull absorbs the signal and attenuates it we should be very careful with the application of the right voltage. The Signal-to-Noise ratio of the EEG should also be looked at. Since all these issues are inherent with the EEG they have been dealt with by the EEG designers. As long as data is being read without any problem then it can be written simply by reversing the electrodes from input to output mode. Care should be taken to apply the particular voltage to the particular part of the brain.

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6 Usage of the Device This device will allow you to “read” and get all the theoretical and factual data in a relaxed state. Using this device the concentration levels can be significantly increased. Therefore the brain will not get saturated soon and will be able to read more information. Since this method requires reduced efforts from the human side repetition of patterns can be obtained on a better scale. This would lead to understanding the concepts in a better and faster way. Data or information has been available to us from books in the library in earlier days to the data and information being available on personal computers through the internet. Today this vast amount of data and information can be accessed from portable devices like smart phones etc. Therefore today we can work or research in a much faster way as the availability of information is better and faster. This technology of reversing the functions of an EEG and writing data to the brain would further reduce the gap of information availability. Whether it is research work, teaching or studying the immediate availability of information can do wonders and would create an extended interest in the subject. Teachers will never have to be in doubt about the smallest things while teaching, researchers will never have to go back to the book to check the finest points and students would not have to wonder about the theoretical and factual information. Everyone can only worry about understanding the concepts, think about them and apply them. In medical conditions like dyslexia which are not intellectual disabilities, this device will enable the people with these conditions to think and process data like those who do not have such a condition. Therefore students with such conditions can study and understand much better. Such a device can replace the use of some medicines whose main function is to inform the brain to regulate certain functions of the body. The brain interprets information by pattern recognition. When we understand the pattern of each alphabet of a language then we can read that particular language. Once we understand the pattern of the alphabets in any language then the vocabulary of a person can be increased to standard level using which the communication can be made better. Therefore the difficulty in learning a new language can be significantly reduced and the language can be learnt faster. The applications of such a device are endless such as communication for the people without the basic senses of eyesight, hearing etc. It can also be used to “download” a textbook or encyclopedia to the brain without actually having to read it! Although this device will not give you the power to “think” it just reduces the distance of the source of information from a library, internet accessed on a computer or smart phones available today to your brain. It will reduce the time taken to access information already available as it is present in your brain. This device can be used to “think” efficiently and increase research work at all levels as the theoretical data would be instantly available in the brain. The bottom line being that we would not have to be wrong about any fact that is already available as data.

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7 Conclusion With the advent of such a device the more important and significant things like research based activities can be done on a huge scale. As of now majority of the time is being consumed trying to retain and recall information which is already available. With such a device we can lie down with our eyes shut and in the meanwhile the device can write or ‘download’ the required information to the brain. Since the senses like eyesight, hearing etc. are not being used it would not be easy to get tired or saturated. Therefore more information can be read and the facts can be imprinted onto the brain in shorter amounts of time. People with dyslexia can study and understand concepts in a faster and much better way. Also pain can be relieved by stimulating certain parts of the brain which can be done using this device. It can replace the use of medicines as they mostly inform the brain about erroneous signals and correct them; this can directly be done by stimulating the brain externally. Further research can lead to the technology being totally wireless (without the use of electrodes) and also not just to write the data but may be program the brain to do various tasks voluntarily.

References [1] Vikramvarun, A.: Optimal EEG channels and rhythm selection for task classification. Wright State University (April 2007) [2] Binnie, C.D., Rowan, A.J., Gutter, T.H.: A manual of electroencephalographic technology. [3] James, A.: How much information does the brain hold? (published March 12, 2009) [4] Vidal, J.J.: Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering [5] Ranky, G.N., Adamovich, S.: Analysis of a commercial EEG device for the control of a robot arm [6] Castellanos, N.P., Makarov, V.A.: Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. Journal of neuroscience methods 158, 300–312 (2006)

Eutrophication Assessment in Songbei Wetlands: A Comparative Methods Han Bingxue Xuchang University, College of Urban Planning and Environmental Science, Xuchang, China [email protected]

Abstract. Urban wetland is the important part in urban eco-system, whose judge standard is eutrophication assessment. A comparative method of urban wetland eutrophication assessment was undertaken in Songbei wetlands to study the spatial distribution of eutrophication conditions in urban wetland environments. A modified trophic state index (TLI) consisting of six physical, chemical and biological indicators including total phosphorus (TP), total nitrogen (TN), chlorophyll-a concentration (chl-a) chemical oxygen demand (CODmn), biological oxygen demand (BOD5) and ammonia-nitrogen (NH3-N) was constructed to describe the eutrophication state of the wetland environment. A 0–100 eutrophication scale was also developed to indicate seven different trophic levels within the wetland environment: oligotrophic, lower-mesotrophic, mesotrophic, upper-mesotrophic, eutrophic, hypereutrophic and extremely hypereutrophic. Rank of eutrophication result from scoring method and corresponding weighted eutrophication indexes model (CWEIM) which implies water in Songbei wetlands is deteriorating, no matter what difference between the two methods.

，

Keywords: Songbei wetlands, Eutrophication, score method, CWEIM.

1 Introduction Nowadays, there is a growing interest in restoring the quality of the water of Songbei wetlands, and in protecting the wetlands ecosystems from pollution. Thus, it is necessary to know the level of eutrophication in the wetlands, to identify point sources of pollution, and to determine the risk of eutrophication in order to establish guidelines for environmental protection. Eutrophication is the process of nutrient enrichment (primarily N and P) that stimulates algal blooms, primary productivity and massive growth of macrophytes(R.S.S.WU,1999). Eutrophication can be defined as the sum of the effects of the excessive growth of phytoplanktons leading to imbalanced primary and secondary productivity and a faster rate of succession from existence to higher serial stage, as caused by nutrient enrichment through runoffs that carry down overused fertilizers from agroecosystems and/or discharged human waste from settlements (Khan and Ansari, 2005).Caused by the excessive fertilization of aquatic ecosystems by phosphorus (P) or other nutrients, eutrophication is a serious environmental problem throughout the world (MA,2005; Schindler 2006; Smith and others, 2006). Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 265–272, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Eutrophication causes algae blooms (which can be toxic), oxygen depletion, fish kills, increased costs for purification of water, and loss of economic benefits associated with clean water(MA ,2005; Cooke and others, 2005). So, eutrophication is now regarded as the most important pollution threat to Songbei wetlands waters. Earlier research shows spatial and seasonal factors influence water quality (R.Christian Jones, Donald P. Kelso, Elaine Schaeffer; 2008).According to Xiao-e Yang, et al. (2008), water quality of Songhua river in Harbin segment appears -V class water. In Dayan Island in spring water quality satisfies III class water, its eutrophication is similiar to IV Class. In drainage area of Songhua river, agricultural surface source pollution is serious. There is 6 ten thousand hektares around the Songhua river upstream. More than 70000 ton chemical fertilizers and pesticides are used in each year, because use rate of nitrogen fertilizer is only 30%, about 60% flows with surface pathway into water. In Songhua river upstream, ruining forest, open-up wasteland and impropriate woodland create 2128 km2 eroded areas in drainage area. Owing to scarce data, a preliminary evaluation of eutrophication should be performed to select suitable methods for improving Songbei wetlands. We describe here a multiyear study of spatial and temporal patterns in water quality within Songbei wetlands impacted by an array of natural and anthropogenic factors. The purpose of this paper is to determine the important components of spatial and temporal variation in water quality in this study area to facilitate an understanding of management impacts and allow the most effective use of future monitoring resources.

Ⅳ

2 Study Area Songbei wetlands called “urban lung” lies in the north shore of Songhua river and belongs to the urban wetland. Its eutrophication is the biological response to the excess input of nutrients into a water body. Songbei wetlands located at north bank of songhua river, covers 88km2.There are three monitoring locations: Songbei wetlands source (Wangbao Town), terminal (Dayan Island) and Sun Island artificial wetland. Songbei wetlands belongs to beach wetland, influenced by seasonal supply, covering a total area of over 88km2,part of surface water flow out of wetland. Songbei wetlands with wide surface water flowing slowly lies in the north bank of Songhuajiang River. Its depth is average 1-3m in summer. The three sites sampled included Two natural wetlands (Wangbao Town wetland and Dayan Island) and one constructed wetland (Sun Island).Samples were collected quarterly from three sites in the open water from Spring 2005 to May 2008.

3 Sampling Water were sampled in four seasons: winter (December, January and February), spring (March, April, and May), summer (June, July, and August) and fall (September, October, November).Sampling monitoring in Songbei wetlands water quality takes place in April,2005, August, 2005, November,2005, May,2008.

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A. Sampling Methods

① ③ ⑤

② ④

Monitoring pollutants includes organic pollutants: CODmn, BOD5, DO; inorganic chemistry indices: NH3-N; toxic factors: volatile phenol, arsenic; heavy metal indices:Hg, Pb,Ge; natural index: PH value; Eutrophication indicative factor : chl-a ; alga. Its measure method is adopted by surface water monitor standards stipulated from State Environmental Protection Administration. Table 1 summarize of monitoring methods of the above chemical factors.

⑦

⑥

Table 1. A summary monitoring methods of chemical factors Item

Monitoring methods

PH DO CODmn BOD5 TP TN chl-a Pb volatile phenol mercury cadmium

GB6920-86 GB7489-87 GB11892-89 GB 7488-87 GB11893-89 GB11894-89 spectrophotometer GB7475-87 GB7490-87 GB7468-87 GB7475-87

B. Sampling Data Samples were analyzed for alkalinity, DO, chloride, CODmn, BOD5, TP, TN, Pb, volatile phenol, dissolved solids, chlorophyll a,pheophytin a, turbidity, sulfate, temperature, dissolved oxygen, pH, secchi disk depth, to light extinction,and conductivity. Phytoplankton community structure was analyzed quarterly during the same time period. CODmn change scope is 4.08- 11.30mg/L, average 7.29mg/L.Its maximum in summer,in August, 2005, is 11.30 mg/L. Because in summer excessive rainfall wash out surrounding farmland,with a large number of pollutants into wetland water,which leads to heavy organic pollution.TN maximum is 5.68 mg/L, which appears in spring. In autumn, TN concentration reduces. And, its minimum is 1.12mg/L. TN maximum appears in spring in Wangbao Town which is mainly due to Songhua river spring flood. Surface runoff lead to that Wangbao Town farmland nonpoint source pollution and domestic sewage polution.TP maximum appearing in summer, is 0.267mg/L,which relates to growth in alga and organism in the wetlands. chl-a maximum appearing in August,is 10.0mg/L, because in autumn water temperature is fit for floating alga growth. Except autumn, chl-a concentration along Songhua river descends. C. Judgement of Limiting Nutrient Water eutrophication is due to over nutrients into water,resulting in crazy phytoplankton propagation and bad water quality in the ecosystem. Nitrogen and phosphorus are main chemical factors to affect eutrophication. Theoretically, N/P

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ratios (TN/TP weight) less than 7 which indicates the possibility of nitrogen limitation, whereas ratios above 10 shows the possibility of phosphorus limitation. Here, nitrogen and phosphorus are dissolubility nitrogen and dissolubility phosphorus for alga growth. Table 2 shows in sampling locations of Songbei wetlands in 2005, mean and ratio of total nitrogen (TN) and total phosphorus (TP) concentration. In Songbei wetlands, annual mean ratio of TN/TP concentration is 17.53.And TN/TP ratios in each season is more than 12.84:l. Hence,phosphorus is the limiting nutrient of Songbei wetlands. However, the possibility of cyanobacterial nitrogen fixation complicates the issue at low N/P ratios. TN/TP in Wangbao Town wetland is 22.24. TN/TP in Sun Island wetland wetland is 17.53. TN/TP in Dayan Island wetland is 11.37. Phosphorus (P) pollution and the subsequent eutrophication of water systems is a serious and persistent environmental problem. Phosphorus is not mobile in soils, so it does not present the same sorts of problems that nitrogen does. TP in pollution is characterized as exponential change, because in summer pollution go beyond 7-8 times “Environmental quality standard for surface water” (GB3838-2002) ,which lead to wetland water eutrophication. Phosphorus in summer increase greatly because summer is the main season of rainfall in Harbin. Songhua river flooding remains wetland nutrients and leads to TP maximum in summer.

4 Evaluation Method After obtaining sample data, basic statistical assessment of the results is needed to assessing water eutrophication under the above evaluation criteria. Because correspondence between coefficients and baseline coefficient is different, eutrophication indexes figured out by different coefficient of eutrophication indexes formula has different results. Scoring system of assessing eutrophication can derive a good result (Michael Karydis,1996).The first computational formula named scoring method follows,

M = ∑ i =1 M i n n

(1)

where M is score of wetland eutrophication; Mi is score of evaluation parameters;N is number of evaluation parameters. The methods use 0-100 rank indexes as eutrophication evaluation.The 0–100 scale was divided into ranges, each representing a particular trophic state: 0–30 representing oligotrophic, 30–40 lower- mesotrophic, 40–50 mesotrophic, 50–60 upper-mesotrophic, 60–70 eutrophic, 70–80 hypereutrophic, and 80–100 the extremely hypereutrophic (Fu-Liu Xu , Shu Tao, R.W. Dawson, Beng-Gang Li; 2001).TN,TP,BOD5 and chl-a 4 are choosed as key coefficient of water eutrophication evaluation. Another computational formula is corresponding weighted eutrophication indexes model(CWEIM). Consulting Carlson (1977) and Porcella et al. (1980), a trophic state index (TSI) on a scale from 0 to 100 was constructed. The TSIs was based on total phosphorus (TP, in mg/l of P), total nitrogen (TN, in mg/l of N), chemical oxygen demand (CODmn, in mg/L), NH3-N (in mg/L), chlorophyll-a concentration (chl-a, in mg/m3), and BOD5 (in mg/L). With Status Index TLI(chl-a) of chl-a as baseline, in

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choosing some coefficients of eutrophication indexes near to baseline coefficients (with small absolute deviation), TLI (chl-a) weighted synthesis. The corresponding weighted comprehensive eutrophication indexes are:

TLI (Σ) = ∑ j=1Wj ⋅ TLI ( j ) m

(2)

where, TLI(Σ) is comprehensive eutrophication indexes;TLI(j) is eutrophication indexes of the jth coefficient; Wj is the jth coefficient corresponding weight of eutrophication indexes.TLI(j) are eutrophication indexes formula of nutrients factors:

0.995ln chl-a ⎞ 1.488ln TP ⎞ ⎛ ⎛ TLI (chl-a) = 10 ⎜ 2.5 + ⎟ , TLI (TP) = 10 ⎜ 9.436 + ⎟, ln 2.5 ln 2.5 ⎠ ⎝ ⎠ ⎝ 2.438ln COD mn 4.552 ln TN ⎞ ⎛ ⎛ TLI (TN) = 10 ⎜ 5.453 + ⎟ , TLI (COD mn ) = 10 ⎜ 0.109 + ln 2.5 ln 2.5 ⎝ ⎠ ⎝ 2.363ln BOD5 ⎞ ⎛ TLI ( BOD5 ) = 10 ⎜ 2.118 + ⎟ ln 2.5 ⎝ ⎠

⎞ ⎟, ⎠ ,

1.55 ln NH 3 − N ⎞ ⎛ TLI ( NH 3 − N ) = 10 ⎜ 7.77 + ⎟. ln 2.5 ⎝ ⎠ If define importance of chl-a to eutrophication with 1, then corresponding relationship between the jth coefficient and chl-a is Rij(j=l,2.…m). Because Rij=Rji, the jth coefficient’s relative importance of eutrophication indexes is: Wj = Rij2

m

∑R j=1

2 ij

(3)

Here, correlation coefficient Rij derives from linear regression method of data (xi,yi) using least square method.Rij is corresponding relationship between the jth coefficient and chl-a;M is number of chosed coefficient. ⎛C ⎞ (4) TSI G (i) = 1 + 10.6 ln ⎜ i ⎟ ⎝ Ci0 ⎠ where Ci0 is research factors of “Reference value”, given by chl-a:0.1, TN:0.01, TP:0.001 , CODmn:0.1, BOD5:0.005, NH3-N:0.002.

5 Results and Discussion Form Table 2, Wangbao Town wetland, Sun Island wetland and Dayan Island wetland all suffer from reductions in water quality. Wangbao Town has the highest rank. Eutrophication in spring, 2008 is lower than 2005. This is because in 2005 peasants around the wetland opened up wasteland in fragmentary farmland plot and overgraze near the wetland. In 2008, local government forbid open-up wasteland and overgrazing near the wetland,which improves Songbei wetlands water quality. And sampling in 2005 spring is in May, while sampling in 2008 spring is in the spring ploughing season. CWEIM is used to synthesize in term of different coefficients of eutrophication indexes and correspondence degree between coefficients and baseline coefficients. The application of CWEIM is of primary importance which gives Table 3.

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H. Bingxue Table 2. Songbei wetlands eutrophication evaluation

Monitoring locations

Wangbao Town wetland

Sun Island wetland

Dayan Island wetland

Monitoring results

Monitoring seasons

score

eutrophication

Spring,05

70

eutrophic

Summer,05

65

eutrophic

Autumn,05

64

eutrophic

Spring,08

51.25

upper-mesotrophic

Spring,05

52.5

upper-mesotrophic

Summer,05

54.5

upper-mesotrophic

Autumn,05

56

upper-mesotrophic

Spring,08

47.5

mesotrophic

Spring,05

62.5

eutrophic

Summer,05

42.5

mesotrophic

Autumn,05

50

mesotrophic

Spring,08

53.75

upper-mesotrophic

Direct comparisons of the results in Table 2 and Table 3 is complicated because of the disparity in sampling digestions and procedures of the computations. However,the two tables give us some implications,which decreases uncertainty in predictive models of urban wetlands. All evaluation results show most water in Songbei wetlands are between mesotrophic and eutrophic. Upstream of Wangbao Town wetland is affected by serous nitrogen and potassium permanganate pollution. Because its monitoring location is near Wangbao village, polluted by indirectly upstream domestic sewage and farmland non-point source pollution. Wangbao Town wetland receives so much irrigation and drainage with low disposal rate of sanitary sewage that it exceeds wetland purifying capacity and makes water quality worse. Because weak wind flow reduces greatly water circulation in water area of Wangbao Town wetland,it easily appears eutrophic.Dayan Island wetland, with the lowest water, is between mesotrophic and eutrophic.Dayan Island wetland is characterized with big water acreage and shallow depth. Affected by different wind power and density,its water quality easily mix uniformity. Sun Island artificial wetland and Dayan Island wetland are near the city. Renewal water partly will not create conditions for eutrophication in the area. So, Sun Island artificial wetland often appears upper-mesotrophic. Above all,all kinds of pollutants concentration in Dayan Island wetland and Sun Island wetland are shorter than those in Wangbao Town. Unfortunately, overpopulation, local soil erosion, inadequate water use management, and intensive deforestation have caused major reductions in river water quality. In the deep study, phosphorus is the main nutrients released by internal source in Songbei wetlands where about 70% phosphorus nutrients flows into wetland and sinks into bottom every year. Because Songbei wetlands receive a lot of urban sewage, phosphorus volume in bottom mud is too high to accept.

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Table 3. The calculated results of water body weighted index

Monitoring locations Wangbao Town wetland

Sun Island wetland

Dayan Island wetland

Monitoring results

Monitoring seasons

TLIΣ

eutrophication

Spring,05

70.34

hypereutrophic

Summer,05

67.37

eutrophic

Autumn,05

66.4

eutrophic

Spring,08

56.47

upper-mesotrophic

Spring,05

52.77

upper-mesotrophic

Summer,05

60.13

eutrophic

Autumn,05

59.71

upper-mesotrophic

Spring,08

61.15

eutrophic

Spring,05

54.08

upper-mesotrophic

Summer,05

51.75

upper-mesotrophic

Autumn,05

65.76

eutrophic

Spring,08

59.95

upper-mesotrophic

6 Conclusion In this paper, water eutrophication of Songbei wetlands derived from the score method and modified CWEIM have the same results. It is concluded phosphorusbased eutrophication derive from suitable climate,the relative adequacy of total phosphorus, slow-flowing water and longer update water-cycle. Deeper degree of eutrophication is in the summer than in any other season, the eutrophication decreased from upstream to downstream in space. Water eutrophication can be greatly accelerated by human activities that increase the rate of nutrient input in a water body, due to rapid urbanization, industrialization and intensifying agricultural production. Finally, Songbei wetlands are multifunctional in the sense that they generate several ecosystem services such as, supplying habitat for many plants and animals, including endangered species, mitigating floods, recharging aquifers, and improving water quality by removing organic and inorganic nutrients and toxic metals from the water that flows across the wetlands

References [1] Xu, F.-L., Tao, S., Dawson, R.W., Li, B.-G.: A GIS-based method of lake eutrophication assessment. Ecological Modelling 144(2), 231–244 (2001) [2] Carlson, R.E.: A trophic state index for lakes. Limnol. Oceanog 22(2), 361–369 (1977) [3] Porcella, D.B., Peterson, S.A., Larsen, D.P.: Index to evaluate lake restoration. J. Environ. Eng. Div. ASCE 106(EE6), 1151–1169 (1980)

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[4] Wu, R.S.S.: Eutrophication, Water Borne Pathogens and Xenobiotic Compounds: Environmental Risks and Challenges. Marine Pollution Bulletin 39(3), 11–22 (1999) [5] Khan, F.A., Ansari, A.A.: Eutrophication: An ecological vision. The Botanical Review 71(4), 449–482 (2005) [6] Christian Jones, R., Kelso, D.P., Schaeffer, E.: Spatial and seasonal patterns in water quality in an embayment-mainstem reach of the tidal freshwater Potomac River, USA: a multiyear study. Environ. Monit. Assess. 147, 351–375 (2008) [7] Yang, X.-e., Wu, X., Hao, H.-l., He, Z.-l.: Mechanisms and assessment of water eutrophication. Journal of Zhejiang University SCIENCE B 9(3), 197–209 (2008) [8] MA (Millennium Ecosystem Assessment). Ecosystems and human well-being: summary for decision makers. Island Press, Washington D.C (2005) [9] Schindler, D.W.: Recent advances in the understanding and management of eutrophication. Limnol Oceanogr. 51, 356–363 (2006) [10] Smith, V.H., Joye, S.B., Howarth, R.W.: Eutrophication of freshwater and marine ecosystems. Limnol Oceanogr 51, 351–355 (2006) [11] Cooke, G.D., Welch, E.B., Peterson, S., Nichols, S.A.: Restoration and management of lakes and reservoirs, 3rd edn. CRC Press, Boca Raton (2005) [12] Karydis, M.: Quantitative assessment of eutrophication:a scoring system for characterising water quality in coastal marine ecosystems. Environmental Monitoring and Assessment 41, 233–246 (1996)

Capital Management of Real Estate Corporations under Tightening of Monetary Policy Liu qingling and Li Xia School of Accounting, Anhui University of Finance & Ecnomics, Bengbu, Anhui, 233030, China [email protected]

Abstract. Presently under the circumstances of tight-money policy, numerous corporations are facing the issue of the shortage of cash flow, including real estate industry. Strained capital chain gets the real estate industry to gradually droop. The author thinks that there’s some vitality behind the drooping appearance and corporations should live through this difficult time by expanding the sources of money. Keywords: Capital Chain, Cash Flow, Real Estate Corporations.

1 Drooping Real, Estate Corporations Currently Chinese real estate industry is confronted with stagnated inflation time that the real estate market is insufficient and the buyers are watching and seeing. Real estate market of a lot of big or medium-sized cities has been attacked by this “cold current”. Each developer’s sales volume and saleable area have showed a certain extent of decrease for the first 5 months this year compared with the same time of last year. According to data announced by National Bureau of Statistics, the prosperity index of real estate in May, 2008 was 103.34, continuously falling for 6 months. Based on the report of “Beijing’s real estate market conditions of first half year” released by the Bureau of Statistics in Beijing, for the first half year in Beijing, the commercial residential buildings of this whole city was covering an saleable area of 4571 thousand square meters, dropping by 47.1% compared with the same period of last year. According to “the city land-value report” of Guangdong Province, from February, the building-value of Shenzhen has been consecutively declining with an average price of 11014 Yuan per square meter in May, dropping by 30% or more compared with 15487 Yuan per square meter of the same period of last year. The building-value of Foshan has been drooping consecutively. In April, housing prices was approximately 5209 Yuan per square meter, sharply falling by 25% compared with the climax of 6822 Yuan per square meter in November of last year. Recently, get-togethers of real estate merchants emerge in great quantities under different names like closed conferences, forums and discussions. In spite of the names, we could easily know the only purpose: to look for ways out of current stage of difficulties. For example, at the end of April, the Strategic Alliance Organization Developer of Chinese cities’ real estate developers held the annual meeting in Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 273–279, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Chengdu, discussing the analysis and approaches of Chinese real estate industry market situations. In May, Residential Commercial Association of industry and commerce convened situation analysis conference. It analyzed current market, developers’ capital and policies, regarding price-off promotion just a matter of time. On 25th June, a research centre affiliated of Beijing Construction Committee called the developers for a meeting in order to find out the problems of price-off promotion. Developers were required to report clearly the real situations of discount-promotion and the reasons of that.

2 Strained Capital Chain As a capital-intensive industry, real estate industry generally has a small proportion of its own reserves, leading to a serious phenomenon of hollowing. A large number of real estate developers are pouring most of their profits into the single reserve of land and are even playing with the turnover rate of hypothesized capital. They are excessively depending on the cash flow generated in investment activities, and thus such a big financial risk is hiding behind. According to an recent report released by securities research institute called “Guo Tai Jun An”, based on an assumption that compared with the same period of last year, in 2008, the sales volume of commercial residential building decreased by 10%, the bank loan kept the same, reached as high as 710000 million Yuan, which was equal to two times of the medium and long-term loan of newly-added real estate industry highest in 2007. In average, the funding gap of each domestic capital developer is 12.01 million Yuan, equal to 24% of sales revenue of last year. A. Bank Loan Some research data shows that, in 2004 the capital of real estate industry only 16% or more was its own reserves, and over 80% capital was originated from domestic bank loan. This functioning pattern of challenging the more powerful side directly led to the high assets-liabilities ratio of Chinese real estate developers. From 1997 to 2005, that ratio has always been as high as 75%. Announced in 2007, that ratio of real estate listed companies was even reaching 76.9%. However, in recent years Chinese government has been taking all kinds of measures to regulating and controlling the real estate industry. “8 national regulations”, “6 national regulations”, new controlling policy on real estate of “15 regulations ” proposed by 9 government ministries and commissions, all kinds of notifications, suggestions and implementation details has been functioning one after another. According to the rules, “commercial banks shouldn’t give loans to real estate concerns which don’t have certain qualifications like a project fund ratio of at least 35%”. “To those developers who have a lot of unused land and vacant commercial residential buildings, commercial banks should act upon prudent operation principle and strictly control the offer of loans of an extended time and any form of rolling credit”. “To those commercial residential buildings vacant over 3 years or more, commercial banks shouldn’t accept them as loan collateral.” This is bringing a higher

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threshold to developers when applying for a bank loan, which is to say, a valve providing power to real estate industry is tightened up. On the other hand, within one year, People’s Bank of China raised the ratio of reserves against deposit 5 times, and this ratio in RMB of present deposit-financial institutions reached historical climax 17.5%. The money is tight, banks’ capital is insufficient and the fluidity of fund is inadequate. Under the tightened monetary policy, real estate developers cannot survive the credit crunch. Some small and medium even large sized real estate concerns have been feeling more of the insufficiency of capital. The newly regulated policy that only by paying off all the land prices can the documents like certificate of land be gained, the construction projects be started, the bank loans be applied makes it impossible for developers to make profit of bank loans and buildings selling in advance system like before. The issue of developers’ capital chain will be the key point of drastic changes of market. Especially in 2008, ample capital seemed much more important at the time of market’s return. B. Domestic and Overseas Floating Capital Since the reforming and opening policy was carried out, Chinese local economy has been developing rapidly; a lot of floating funds have been emerging in nongovernmental circles. These floating funds are made active all the time in order to make excessively high profit, for example, speculation on Clivia, stamps, futures, groups in Wenzhou have been stored up. Under the circumstances of current market, without the temptation of high profit, real estate industry investing in local floating funds seldom sees concerns go in. What’s more, if the funds stored up are kind of selfprotecting dumping, a drastic slump of building prices will definitely be triggered. A large amount of hot money began to flow into domestic market due to the continuous ascending of RMB exchange rate, American economic weakness and the depreciation of US dollars. Showed in a research report recently released, between 2005 and 2007, the scale of foreign capital in Chinese real estate industry was expanding promptly with an average annual speed of 34.2%. And the average scale of gross assets increased from 16300 million Yuan to 27600 million Yuan. Some scholars hold that foreign capital is positive about the prospect of Chinese real estate market even under the policy of macro-control. Nevertheless, recently with the closing ceremony of the 4th US-China Strategic and Economic Dialogue in June, relevant situations have been secretly changing. Federal Reserve indicated that the interest rate wouldn’t get any lower and foreign capital’s motive of escaping from risks was reducing. Moreover, hot money is to make profit not to rescue the market. Working in financial research institute of Social and Science College, research fellow Yi Xianrong perceived that “the function of international floating funds puts much emphasis on short-term profitability; therefore industries with best capital negotiability will be most appealing.” Because the real estate market of many places is still suffering from the darkness of bubble economy, future overseas arbitrage capital would not easily step into Chinese real estate industry. Within one half year, Chinese A share price fell approximately 50% in 10 consecutive losing days. Depressed stock market was gradually depriving those developers’ desire to attain capital through going public again and regaining funds.

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C. Sales Revenue Repaying capital with interest is required when applying a bank loan. Both self-owned and other funds aim at profiting, but only sales revenue is the source of terminal repayment and profit of developers. Under the condition that funds from banks and other investment are hard to acquire, developers could only rely on the sales revenue for survival. However, present selling conditions are not optimistic enough. Late in May, People’s Bank of China surveyed urban depositors by questionnaires in 49 cities, and received 19600 effective ones. According to this survey, the proportion of residents who were planning to purchase houses in 3 months dropped to 14.6% last season which was the lowest in history, although it bounced back to 15.1% this season, slightly increasing by 0.5%, however, it was still the second lowest. Among the biggest 7 cities investigated, all the residents’ desire to purchase houses declined except Beijing, compared with last season. While the sales volume is diminishing, the form of promotion in some cities has been changing from giving off all kinds of invisible presents to clearly marking the price-off discount. Developers’ high-price complicity mechanism was broken, now price-off promotion has become an agreement reached by this industry. Smart Wan Ke began to diversify the promotion measures from the very beginning of this year, although the sales volume decreased to some extent compared with the same period of last year, its national programs took the lead in achieving the marketing breakthrough by price-off discount. Recently in Beijing, in the team purchase activities newly run by Zhujiang Augusta city-state, all the purchasers could enjoy 4% discount, a second 4% discount brought by lump-sum settlement and a third 4% discount if there were 50 people or more in the team and another 1% off if donating 100 Yuan to Wenchuan disaster area, which meant that the biggest discount could reach 12.4%. However, the promotion activities failed to arouse strong motivation as in Red May. While the developers were exploring the psychological bottom line of house purchasers by promotion, they had to face the reality that those purchasers started to wait and see as the prices fell, which seemed the commencement of a vicious circle. At this time, the market index kept going down, the funds of some investors or purchasers-to-be got locked up, making partial purchase needs unsatisfied. Even though some people chose to purchase houses at this time, purchasers who were able to guarantee full payment began to apply for bank loans. The once costliness purchasers were acting upon 20%-30% down payment of real estate loans which has been universally carried out in China. The knocked down housing price got most house property a negative asset. Not long after, men of wealth with house property became debtors. And then developers had to accept the situation of lots of returned houses. And the attention of citizens got diverted by the earthquake on 12th May and the 2008 Olympic Games; consequently they paid less attention to purchasing houses. The shrink of volume of trade and regulation of housing prices led to financing difficulties of developers and low confidence of real estate market, which was worsening the situation that developers were having difficulties in gaining funds from banks and capital market. Their cash flow was facing even greater challenges. Many annual report of real estate corporations show that at the end of operation period, the cash flow only equals to 10% of the amount of liabilities. Many figures also show the negative net cash flow at the end of the period.

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3 The Capical Menagementunder Tighteningmonetary Policy A. Vitality Under the circumstances that domestic commercial bank credit is increasing rapidly, fixed assets investment is excessively hot, CPI is so high and bulk commodities on international market are raising speedily, the tightening monetary policy People’s Bank of China will not get changed in the short term. And it’s not that realistic for real estate developers to alleviate capital conditions by applying for bank loans. A large amount of overseas funds faced with depressed stock market in the process of ensuring value and making profit will definitely look for other ways of investment. In the long run, the capital flowed into commercial market will undoubtedly pull up the each item of the cost of house property, bringing pressure of going up to housing price. Or the capital will directly go into real estate industry after house price returns to rationality. Some scholars deem that at present, these funds are going to Midwest cities of second and third class for real estate speculation or waiting for the housing market to reach the lowest point. As to housing price, currently price-off marketing areas mainly are cities of first class in national real estate industry like Beijing, Shanghai, Guangzhou, Shenzhen, Hangzhou. In these cities, the regulation of housing price is inevitable. Firstly, housing price has gone through such a long time of irrational and large increase, and has already been distorted. The developers have once reaped a huge sum of profit. Real estate market is one of the countable industries where operators could afford usurious capital. This is foreshadowing later inevitability of housing price. The land has sold out, the city has been constructed well, houses have been built up, and the housing price has increased. The government, developers, real estate speculators and even local citizens, who own house property, as the “honeymoon” of all satisfaction passed, began to realize that the bubbles of real estate industry were gradually dying out. Secondly, real estate cities of first class were investing more rather than demanding real consumption rigidity. For example, <Shenzhen’s blue book: the report of Shenzhen’s development in 2007> written by Shenzhen’s social and science institute declares that, in Shenzhen, residential buildings transferred to others within half a year after getting the certificate of title account for 30.31% of total houses. Residential buildings transferred to others 3 years or more after getting the certificate of title account for 28.11% of total houses. Consequently, at present adjustment of housing price in big cities is not weird. It’s a normal and technical reverse. Seen from a long term, housing price will not descend endlessly. Although some corporations are dumping in panic due to the unconnected capital chain, it’s just a short-term factor, a turnover process, and buyers will spur the housing price. After all, along with the boost of urbanization process, the market still has much housing need. Real estate market will finally come into a stable, rational and positive developing path. According to sales index of houses from 70 big or medium-sized cities in May 2008 announced by the website of National Development and Reform Committee, most cities’ selling price of houses did not increase considerably and stayed basically stable compared with last month, and increased approximately 1% on a year-to-year basis. It can be seen that sort of vitality exists behind the tight capital chain of developers. A number of speculator and small and medium-sized developers without sufficient

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funds will be knocked out after proper adjustment. Having been reorganized, the real estate market is going to be more mature and living adapters will go on to develop in that domain. B. Expand the Sources of Cash It’s a fixed fact that driven by profit, local capital is gradually coming back to real estate industry. A lot of owners of local capital especially those “Resources Bosses” who are making fortune in resources industry keep buying during the time of real estate depression. On 19th July, 2008, “Sanlitun SOHO”, a new project of SOHO in China, opened quotation with the concluded price of 5000 million Yuan on that day. Up to 24th June, the concluded price has broken through 5000 million Yuan. In these selling records, bills over 100 million Yuan added up to about 2000 million Yuan. Among them, we can frequently see buyers like owners of coal mines and iron mines and other resources industries. And we can also notice that some owner of iron mines has bought a two-story store with 45 units. They are pouring ample profits and cash flow to SOHO. Besides, the gradual legalization of small-sized loan companies created a smooth path for local capital to easily enter real estate industry. Faced with the continuous declining trend of A share market, real estate listed corporations which have taken away trillions of Yuan from capital market through financing measures like adding issues turn their steps to issuing bonds. On 21st and 22nd May, Baoli Real Estate and China Vanke Co. were respectively allowed to issue debentures worth no more than 4300 and 5900 million Yuan. On 5th June, Beijing North Star Company (601588) got across its application of issuing debentures worth less than 1700 million Yuan. Besides, Gemdale Corporation (600383) and COFCO Property (000031) were starting financing by issuing bonds. What’s more, a lot of other companies were planning bonds financing. Bonds financing are currently extremely popular with investors on account of its interest rate which is higher than bank deposit rate and less risky than stock market investment. Xinhuzhongbao (600208) issued “08 XINHU Bonds” worth 900 million Yuan online on 2nd July. Only a few minutes after issued, the bonds got all sold out, and off-line subscribed ones worth 500 million Yuan also got sold out. Real estate corporations could make an active avail of overseas funds, financing by loans in foreign currency or from foreign banks. And they can also finance by transferring stock ownerships or assets. In the meantime, the developers should strengthen company management, change operation mode and develop new ideal products which suit consumers’ needs. Also, they should spare no effort to cut marketing cost, speedup capital reflow in order to accelerate capital turnover.

4 A Brief Summary Real estate industry with insufficient funds is presently an epitome of high debt industry in China. Under the condition that Central Bank pushed up deposit reserve rate several times, capital flow of these corporations has been shorter and shorter. Small and medium-sized companies without abundant funds have been facing much more obvious financial risk. Due to factors like ascending cost, many of those small

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and medium-sized companies in Wenzhou and Guangzhou have already been out of production or reluctantly maintaining the production without any profit. Many other corporations chose the operation mode of expanding the scale when bank loans were easy to get. Now the situation has changed and it’s not that easy to get the money from banks. However, they should still continue to invest funds, but the funds invested cannot be withdrawn temporally, so funding difficulties are first to be settled. Some companies are even financing by trading metals like ships. The tight capital chain and market adjustment will definitely pick out superb companies which are much more adaptable. Now high debt companies should internally strengthen financial management and increase the speed of capital flow. Externally they should finance in different legal medium in order to overcome the difficulties.

References [1] The city land-value report of Guangdong Province (June 2008) [2] Beijing’s real estate market conditions of first half year, Bureau of Statistics in Beijing (April 2008) [3] Shenzhen’s blue book: the report of Shenzhen’s development in 2007. Shenzhen’s social and science institute (2007)

Scotopic Visual Image Mining Based on NR-IQAF* Fengbo Tian1, Xiafu Lv1, Jiaji Cheng1, and Zhengxiang Xie2,** 1

Chongqing University of Posts and telecommunications, Chongqing 400065, China 2 Chongqing Medical University, Chongqing 400016, China [email protected]

Abstract. In this paper, a new image mining method is proposed considering the contrast resolution limitation of human vision under scotopic visual environment. First, a valid gray distribution of an image is taken from scotopic vision condition. Second, the gray values of a target image pixels are computed from the original image via Zadeh-X transformation. Third, a no reference image quality assessment function (NR-IQAF) is used to estimate the image after transformation. Experiments demonstrate that this method greatly improves the visual contrast and luminance of the image, so image quality and visual effects have been improved significantly after mining; in addition, the optimal quality image can be obtained by the NR-IQAF. Keywords: Scotopic vision, image mining, Zadeh-X transformation, averaging contrast, no reference image quality assessment.

1 Introduction With the development of network, more and more video surveillance and image devices are used. Every day we will face a large number of image information. How to get valuable information from the image especially the image taken from low-light or scotopic vision that the light stimulus intensity is lower than 3cd/m2 or below, has been one of study focus [1]. Image mining is a kind of interdisciplinary research topic which can automatic obtain the implicit image content and can be divided the higher layer image mining and the lower layer image mining. The fundamental task is efficiently access to highlevel image space objects and their mutual relations from the feature description of lower layer image, in order to extract the implicit, previously unknown and potentially useful information, image relations or other implicit image mode in image sequence [2]. There is no image without vision and light. If there is light without contrast, there is also no valuable image [3]. As the contrast resolution limitation of human vision in low-light environments, making the image with rich information under scotopic visual environment couldn’t be distinguished clearly. If the contrast resolution of the images * **

The project is supported by National Natural Science Foundation of China. Grant No.60975008. Corresponding author.

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could be improved with appropriately, the human eyes could recognize the information within it. In this paper, a lower layer image mining method is proposed by means of ZadehX transformation. The image taken from the scotopic condition or called as lower light level condition are processed by this method. First, it can improve the visual contrast of an image by the Zadeh-X transformation. Then, the optimal quality image is obtained by a NR-IQAF [4]. In addition, the algorithm is simple and easy to meet the requirement of image processing for real-time.

2 Theoretical Basis The achievement of image mining under scotopic condition mainly based on nonlinear and gradually flattening theory of image gray spectrum and Zadeh-X transformation. A.

Non-linear and Gradually Flattening Theory of an Image Gray Spectrum [5]

The non-linear and gradually flattening theory of an image gray spectrum is used to mining the information hidden in an image, which is in order to see whether or not there is information that could be mined. The image gray spectrum indicates a distribution of the pixel number of an image by the gray levels. A method called nonlinearly and gradually flattening a gray spectrum is used to measure the gray spectrum distribution of an image as equation (1):

T( g ) =

∑

O1 m ( g )

255

255

g =0

O g =0

1m

∑ (g)

O( g )

(1)

where O(g) and T(g) represent the gray level of pixel number in the original image and the target image respectively. M is called flattening order. B.

Image Structure Mining: Zadeh-X Transformation

If the image information exists but can not be distinguished by human vision, the Zadeh-X transformation can be used to complete the lower layer mining. In order to the structure information of the image mined can be obtained clearly. The Zadeh-X transformation as equation (2) [6]:

T( x, y ) = K

O( x, y ) − Sita Delta

(2)

and the constraint condition is:

⎧ 255, T( x, y ) = ⎨ ⎩ 0,

T( x, y ) > 255 T( x, y ) < 0

(3)

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where T(x, y) and O(x, y) represent the gray values of the target image and the origin image at point (x, y) respectively. Sita [0,255] is the gray/chroma value settled, and delta [1,255] is the gray/chroma scope that can be used to decide the gray/ chroma hierarchy of image mined. K is the space stretching factor. Here we let K=255.

3 Image Quallty Evaluation Parameters In order to ensure the image quality after mining is optimal. We first should learn the association of human subjective vision and objective physical quantities. There are a lot of factors that influence human subjective vision. The factors can be described by some suitable objective physical quantities. They are averaging gray, information entropy and averaging contrast. An image with high quality should include appropriate gray, enough information, right contrast, low noise level and uniformity trending gray spectral distribution. A. Averaging Gray, AG The quality of an image or a photograph is considered bad by human subjective vision if it is too bright or darkness. Therefore, the image quality is linked to the proper brightness of the images (the gray of images) [7]. The averaging gray (AG) of an image is calculated as equation (4): N −1 M −1

g (m, n) n =0 m =0 M * N

AG = ∑ ∑

(4)

where AG means average grays. Gray(x, y) is the gray of the pixels at point (x, y). The averaging gray of an image with the uniform distribution histogram as equation (5):

AG =

1 M*N

255

∑ i* i =0

M*N = 127.5( gray level ) 256

(5)

This is the optimal auxiliary index evaluating the image quality. B. Image Information Entropy, InEn The gray information contained in an image is richer, the quality is better. A nature scenery image with good visual effect has nearly 256 different gray-levels information. Information entropy (InEn) is used to denote the information contained in the image. InEn is calculated as equation(6): 255

InEn = −∑ p(i )Log2 p(i ) i=0

(6)

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where p(i) denotes the probability of pixel distribution at the gray level(i). Let Log2p(i) =0 when p(i)=0. An image with uniform distribution histogram has the biggest information entropy as equation(7): 255

I nEn=-

1

Log ∑ i=0 256

2

1 = 8( bit ) 256

(7)

C. Image Averaging Contrast, AC Human perceiving different things by distinguishing the difference among these things. Without different gray-scale, an image has no contrast. Here the contrast discussed is the simultaneous contrast. There are a variety of definitions about contrast relate to the image processing [8]. Here the definition of the simultaneous contrast is adopted as equation(8) [9]:

Csimul = abs [ Lt − Lb ]

(8)

where Csimul means the simultaneous contrast. Lt and Lb denote the gray level of the target and the background respectively. The following formula is used to calculate the averaging contrast (AC) of an image. N −2 M −2

AC =

∑ ∑ Gray( x, y ) − Gray( x + 1, y ) y =0 x =0

( M − 1 )* ( N − 1 )

(9)

Where Gray(x, y) is the gray of the pixels at a point(x, y). M and N are the number of pixels in the directions of x and y respectively. The above three objective physical quantities are very important to obtain the optimal quality image. A NR-IQAF can be established by them.

4 Acquirement of Optimal Quality Image Consistency is the most important aspect in measuring the effective of image quality assessment method. If the image after mining is consistent with human visual system(HVS), then the optimal quality image could be obtained. The built of NRIQAF is to get the right image which is consistent with the results of visual perception. An image’s quality can be depicted by the above NR-IQA parameters AG, IE, AC. In addition, their product is a function with maximum value. The image with better quality should have right averaging gray under keeping enough information and

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appropriate contrast. Considering these factors, A Comprehensive Assessment Function (CAF) to obtain the optimal quality is designed as follow:

CAF = InEnα ∗ AC β ∗ NGDγ

(10)

where NGD denotes normalized gray distance (NGD). It can be obtained by the following formula (11):

NGD = ( 127.5 − dist( 127.5 − AG )) / 127.5

(11)

where dist (·) is distance operator. Scotopic visual image input Gray spectrum analysis Obtain the value of Delta and Sita Zadeh-X transformation Is CAF maximum?

Change the value of Delta

N

Y

Image output Fig. 1. Flowchart of image mining process

Here α, β, γ is the weight value of CAF, the image quality is different when the CAF with maximum under different weight value. If the optimal image is right the maximum of CAF under certain α, β, γ weight value. So an objective image quality evaluation function can be obtained. The result of statistical experiments is estimated and reported as α=1, β=1/4 and γ =1/2 respectively. Hence CAF can be also expressed as equation (12):

CAF = InEn ∗ AC 1 4 ∗ NGD 1 2

(12)

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CAF is the function of theta and delta. The value of CAF and the image quality variation with the value of sita and delta as follows: • • •

For any sita, CAF has only a maximum value when sita increases gradually form 1 to 255. For any sita, the image quality first becomes better, and then becomes worse with delta gradually increases form 1 to 255. The maximum value of CAF corresponding to the relatively optimal quality image. The image quality will become worse with the increase of sita; hence the optimal quality image is the image that CAF has a maximum value when sita equal to 0.

Therefore, to obtain the optimal quality image, Let sita=0. The maximum value of CAF could be gotten by changing the value of delta from 1 to 255. Meanwhile, the optimal quality image can be obtained after mining[10].

5 Experimental Results The mining process is shown in Fig. 1. The results of the image mining are listed in table 1 and shown in Fig. 2 (a)~(f). Fig. 2(a) is the original image, (b) ~ (f) are the images mined by various values of deltas respectively. Each image of (a) ~ (f) consists of three parts, the above is the original image or the image mined, the middle is the gray spectrum corresponding to the above image and the below is the parameters of image quality assessment. As show in Table 1 and Fig.2, the image(c), whose CAF value is maximum (6.184), can be considered the optimal quality image. As shown in Table 2 and Fig. 3, the image quality mined is improved; the results of evaluation and the trends of human visual perception are consistent. Therefore, NR-IQAF(CAF) model is effective to improve the contrast resolution limitations of human visual under the scotopic vision, and achieve the optimal image effect. Table 1. The variations of evaluation parameters with delta Sita

0

0

0

0

0

0

Delta

255

3

6

9

12

15

InEn

3.154

1.710

2.688

2.947

3.027

3.059

AG

3.584

183.3

120.1

85.60

65.92

59.95

AC

0.945

35.34

31.55

23.96

18.69

15.23

NGD

0.028

0.563

0.942

0.671

0.517

0.423

CAF

0.521

3.128

6.184

5.342

4.526

3.932

Image

(a)

(b)

(c)

(d)

(e)

(f)

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(a) Sita=0, Delta=255, CAF=0.521

(b) Sita=0, Delta=3, CAF=3.128

(c) Sita=0,Delta=6, CAF=6.184

(d)Sita=0,Delta=9,CAF=5.342

(e) Sita=0,Delta=12,CAF=4.526

(f) Sita=0,Delta=15,CAF=3.932

Fig. 2. The variation of an image quality with Delta. Table 2. The evaluation parameters of original images and optimal images Sita Delta InEn AG AC NGD CAF Image

0 255 4.331 8.401 0.958 0.066 1.100 (a)

0 17 4.007 124.5 12.49 0.977 7.444 (b)

0 255 4.141 6.883 0.670 0.054 0.871 (c)

0 13 3.686 121.9 10.93 0.956 6.553 (d)

0 255 4.023 10.38 0.855 0.081 1.103 (e)

0 20 4.018 131.9 10.87 0.966 7.168 (f)

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(a) Sita=0, Delta=255, CAF=0.521

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(b) Sita=0, Delta=3, CAF=3.128

(c) Sita=0,Delta=6, CAF=0.871

(d)Sita=0,Delta=9,CAF=5.342

(e) Sita=0,Delta=12,CAF=1.103

(f) Sita=0,Delta=15,CAF=3.932

Fig. 3. Original images and optimal images Note: The image (a)(c)(e) are the original images, and (b)(d)(f) are the corresponding optimal images with them respectivity

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6 Conclusions The built model of image quality assessment function (CAF) can be used to assess the image quality taken from scotopic condition. The CAF possesses a convex feature shown in Fig. 2 and Fig. 3, and its maximum corresponding to the optimal quality image. The assessment results are well consistent to the results of the subjective assessment by human vision. Therefore, this paper proposes NR-IQAF(CAF) model is feasible to obtain the optimal images.

References [1] Li, J., Narayanan, R.M.: Integrated spectral and spatial information mining in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing 42(3), 673–685 (2004) [2] Zhang, J., Hsu, W., Lee, M.: Image mining: issues, frameworks, and techniques. In: Proceedings of the 2nd International Workshop on Multimedia Data Mining (MDM/KDD 2001), pp. 13–20 (August 2001) [3] Wang, Z.F., Liu, Y.H., Xie, Z.X.: Measuring contrast resolution of human vision based on digital image processing. Journal of Biomedical Engineering 25(5), 998–1002 (2008) [4] Wang, Z., Li, Q.: Video quality assessment using a statistical model of human visual speed perception. J. Opt. Soc. Amer. 24(12), B61–B69 (2007) [5] Xie, Z.X., Wang, Z.F., Liu, Y.H.: The theory of gradually flattening gray spectrum. Chinese Journal of Medical Physics 23(6), 15–17 (2006) [6] Xie, Z.X., Wang, Y., Wang, Z.F.: A method for image hiding and mining based on Zadeh transformation. Chinese Journal of Medical Physics 24(1), 13–15 (2007) [7] Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multi-scale structural similarity for image quality assessment. In: Proc. IEEE Asilomar Conf. Signals, Syst., Comput., pp. 1398– 1402 (November 2003) [8] Li, W.J., Zhang, Y., Dai, J.R.: Study on the Measurement Techniques of MRC in Visible Imaging System. Acta Metrologica Sinica 27(1), 32–35 (2006) [9] Agostini, T., Galmonte, A.: A new effect of luminance gradient on achromatic simultaneous contrast. Psychonomic Bulletin and Review 9(2), 264–269 (2002) [10] Wang, Z., Lu, L., Bovik, A.C.: Video quality assessment based on structural distortion measure. Signal Processing: Image Communication 19(2), 121–132 (2004) [11] Albonico, A., Valenzise, G., Naccari, M., Tagliasacchi, M., Tubaro, S.: A reducedreference video structural similarity metric based on noreference estimation of channelinduced distortion. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Taipei, TW (April 2009)

Extraction of Visual-Evoked Potentials in Rat Primary Visual Cortex Based on Independent Component Analysis Zhizhong Wang, Hong Wan, Li Shi, and Xiaoke Niu School of Electric Engineering, Zhengzhou University, Zhengzhou, China [email protected]

Abstract. The visual-evoked potentials(VEPs) is very important and meaningful to study the brain function and the information processing mechanism of visual systems. In the paper first the characteristics of electromyography (EMG), electro-oculogram (EOG), electroencephalogram (EEG) and VEPs in rats were obtained respectively in time and frequency domain. Then a novel abstracting algorithm based on independent component analysis (ICA) was proposed and applied to extract the VEPs from the mixed above under different colors’ stimulation. The correlation coefficient between the extracted and original signals is 0.9944. The experiments demonstrated this new method could extract VEPs correctly and efficiently Keywords: Visual-evoked Potentials, Rat, Primary Visual Cortex, Independent Component Analysis.

1 Introduction Visual-evoked potentials (VEPs) is an important way to assess the functional integrity of visual pathways in the nervous system [1]. VEPs can be easily recorded from the visual cortex of the experiment animal which responses to different visual stimuli. VEPs consist of electrical signals generated by the nervous system in response to a stimulus. There are several types of VEPs, including flash evoked potential (FEP) and pattern evoked potential. The FEP is produced by a visual stimulation with a brief and diffuse flash light, which is frequently used to evaluate the neural activity and sensory processing in the visual system and to identify and characterize the changes occurring in the retina and the occipital cortex [2,3]. VEPs can also provide a further therapeutic approach through the stimulate of monitoring neurophysiologic changes related to diseases [4]. The pattern evoked potentials have been used to assess parametric characteristics of visual perception, detect neuronal irritability and diagnose neurological diseases [5]. With the development of brain-computer interface (BCI), the electrophysiological activity of the brain can be obtained from implanted electrodes in the cortex. At least five kinds of brain signals have been detected for BCI so far: visual evoked potentials,

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slow cortical potentials, cortical neuronal activity, β rhythms, and event-related potentials [6]. One of the main issues in designing a BCI is to find the patterns of brain activity, which could be easily explored. One of these patterns is VEPs which can be directly stimulated by light and extract from the brain electrical activity through a number of methods [7] such as Fourier analysis [8], Wavelet Transform [9] and independent component analysis (ICA) [10]. Continuous visual stimulation, however, could cause fatigue or tiredness of subjects’ visual system. Therefore, the feature extraction of EEG has been widely used in BCI. ICA is one of the most important methods to obtain the VEPs by single extraction. A lot of hard works have indicated that ICA could separate VEPs, EEG, EOG and EMG from the mixed signals. But the VEPs won’t be recognized by the algorithm [11] because there are some problems such as the irregular order and random when the ICA algorithm is applied to extract the VEPs. In the paper a novel improved VEPs extraction algorithm based on ICA was proposed. First we detected the signals from the primary visual cortex with red, green, blue and white LED stimulus. Second we analyzed the characteristics of the electromyography (EMG), the electro-oculogram (EOG), the electroencephalogram (EEG) and the VEPs of the rats in time and frequency domain respectively. Third we got the valuable components from the test signals by ICA . At last we extracted the VEPs from the components considering the characteristics obtained above.

2 Materials and Methods A. Animals

～

3-4 month-old male Sptrague-Dawley rats with weighting 270 300g were used in the experiments, 8 of the total 16 rats for the VEPs testing, and the rest for the EEG recording. The rats were born and raised in Henan Experimental Animal Center of Zhengzhou University. The animals were maintained at 12 h light–dark cycles and a constant temperature of 23±2 °C. Water and food were allowed free access. All aspects of the care and treatment of laboratory animal were approved by the Animal Care Center of Zhengzhou University. B. Surgery Animals were anaesthetized with sodium pentobarbital (50 mg/kg, i.p.) and fixed on a stereotaxic apparatus. During the experiment, the rectal temperature was maintained at 37°C using a thermostatically controlled heating pad. For EEG recording, a pair of Teflon-covered tungsten electrodes (0.4 mm diam./3mm length each) were implanted into the left visual cortex (7.3 mm from Bregma,AP-7, ML 1.5 3, DV 0.7 from dura mater surface) and served as EEG electrodes. The EMG and EOG electrodes were made by stainless steel wires soldered onto stainless steel springs with a 0.5 mm outside diameter. The springs were sutured onto the nuchal muscles and lateral of orbita respectively. The reference electrode was placed on the skull far away from the recording electrodes, and the ground electrodes were stitched on the scalp of the rat. All wires were connected to a 12 pin connecter and bonded onto the skull with a dental acrylic mixture.

～

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For EVPs testing, silver wires soldered to the stainless-steel screws as recording electrodes (1.2mm diam./3mm length each ) were screwed into the skull over the left visual cortex (7mm posterior to bregma and 2.5mm lateral to the midline). Similar screws placed over the ipsilateral and the contralateral frontal cortex served as reference and grounding electrodes, respectively. All electrodes were led to an 8-hole plastic cap, and secured to the skull with dental acrylic. After surgery, the animals were individually housed in the experimental cage with free access to water and food, and allowed to recover for 1 week before EEG or VEPs testing. C. Visual Stimuli VEPs were elicited by a flash visual stimulator designed by Electrical Engineering School of Zhengzhou University. Luminance control device is composed of different color light emitting diodes (LED) with red, green, blue and white. The stimulator can provide multiform stimulations in different color and intensity by changing the connection mode or the parameters of the stimulator. For VEPs experiment, four different color flashes (the intensity of the flash: 5 cd ⋅ s / m , duration: 100ms, frequency: 0.5Hz) were used to stimulate the left eye of the rat in an order of white, red, green, and there is 5 min interval between different color flashes. 2

D. Electrical Recording Rats were habituated to the experimental environment for at least 24 h prior recording and connected to long recording leads which allowed them to move freely in a soundproof observation box. Four LEDs, parallel to the level of the rat’s eyes, were fit on each side of the box in order to catch the flash stimuli. The EEG, EOG and EMG of rats were continuously recorded from Multi-channel Physiological Signal Acquiring System (model RM6240CD, Chengdu Instrument Co.) at least 30min (sampling frequency: 10 kHz, high pass barrier frequency: 100Hz, low pass barrier frequency: 0.8Hz) before the testing. Five days after implantation, rats were placed in the stereotaxic frame under anaesthesia (10% chloral hydrate, 35 mg/kg, intraperitoneal ) for VEPs recording. The pupils of the rat were dilated with a freshly prepared mixture of 0.75% tropicamide and 2.5% phenylephrine drops (Sigma Chemical Co., St. Louis, MO) prior visual stimulation. Ten minutes later, VEPs were detected by the flash stimulator that was located approximately 15 cm in front of the eyes of the rat. VEPs were calculated by averaging 60 electrical responses of extracellular field potentials over the 3 min stimulation period (trains of 100 msec visual stimuli, 0.03 Hz). Evoked signals were amplified (5000x) and filtered at 3 Hz, 1 kHz and collected in the Multi-channel Physiological Signal Acquiring System (model RM6240CD, Chengdu Instrument Co). E. Analysis of VEPs in Time and Frequency Domain Power spectrum was used to analyze the frequency characteristic of VEPs. The purpose of the VEPs spectral analysis is turning the time-varying amplitude wave to

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the frequency-varying power spectrogram. Here, AR (Auto Regression) parameter model [12] was used to carry out the power spectrum. AR parameter model regards the current output x(n) as the weighted sum of the current excitory input u (n) and the past p output, which can be represented as p

x ( n) = −

∑ a x( n − k ) + u ( n)

(1)

k

k =1

where p is the order and a k is the weighted number, k = 1,2, L p . When the parameter of the model is obtained, the power spectrum is computed by ) σ 2 Δt G ( jw) = 2 p (2) 1+ a k exp(− j 2πfΔt )

∑ k =1

where σ 2 is the variance of the excitory white noise, Δt is the sample interval. In time domain, the key point of the VEPs will be located based on the waveform characteristic.

3 Results A. VEPs and EEG Recorded from the Rat Primary Visual Cortex

To analyze the signal feature of VEPs , the evoked responses of the rat primary visual cortex were examined by the different color stimulations. VEPs were elicited by different colors.The VEPs waveforms were composed of a negative peak followed by a positive deviation corresponding to electrophysiological signals recorded in the visual cortex. Furthermore, the EEG ,EMG and EOG were recorded in order to extract the signal feature induced by light stimuli in the condition of awake and moving. The energy distribution histogram above 5Hz of the each signals were shown in Fig.1.

70.00%

The energy above 5Hz %

）60.00% （50.00% 40.00% 30.00% 20.00% 10.00% 0.00% EOG

EM G

EEG

VEP(white) VEP(green) VEP(blue)

VEP(red)

Fig. 1. The energy distribution histogram above 5Hz of different signals

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Fig.1 compared with the VEPs, the EOG, EEG and EEG were all low-frequency signals, the energy above 5Hz is under 20%. The energy distribution of VEPs was mostly higher than other signals; the energy above 5Hz is over 40%. The difference of the energy distribution in these signals has fully illustrated that the diversity between VEPs and EOG, EEG, etc. in frequency domain is obviously. The VEPs elicited by blue, red, green and white light were shown in Fig.2.

Fig. 2. B. Example of VEPs elicited by blue light; R. Example of VEPs elicited by red light; G. Example of VEPs elicited by green light; W. Example of VEPs elicited by white light.

Fig.2 was shown see that EEG, EOG and EMG were series of messy and irregular signals. On the contrary, the latency of P2 peaks in VEPs was very steady, the mean value of the P2 peaks latency were shown in Table 1. Table 1. The mean value of the P2 peaks latency in our experiments

Mean value of the P2 peaks latency(s)

Blue

Red

Green

White

0.093±

0.109±

0.082±

0.094±

0.013

0.014

0.012

0.012

Most of the P2 peaks appeared from 0.04s to 0.16s, which was regarded as a temporal characteristic of VEPs. B. The Improved VEPs Extraction Algorithm Based on ICA

AR parameter model was used to estimate the power spectrum of each independent components separated by ICA. If the latency of P2 peaks in this component was consistent with the range 0.04-0.16s, it will be regard as the VEPs extracted. The procedure of the improved VEPs extraction algorithm based on ICA was described as following:

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Step1: First of all, extract the independent component of the mixed signals, and then estimate the power spectrum of each independent components, marked as Pf i , (i = 1,2, L n) ; Step2: Calculate the energy distribution above 5Hz of each component, which is shown as follows:

∫ P( f )df , (i = 1,2,L n) ∫ P( f )df 30

Pg i =

5 30

1

i

(3)

i

Step 3: Choose the maximum value of Pg j , marked as Pg j . Step 4: Locate the moment of the maximum peak in the jth component, marked as t m . Step 5: If t m ∈ [0.04,0.16] , export the component and regard it as the VEPs we expected. If t m ∉ [0.04,0.16] , we should turn back to Step 1. In this paper, EEG, EOG, EMG and VEPs were applied in different ways. The mixed signals were shown in Fig.3. Then, we extract the VEPs by the improved VEPs extraction algorithm based on ICA proposed, the result is shown in Fig.4. The correlation coefficient between between the extracted and original signals is 0.9944. The results were proved correctly.

Fig. 3. The mixed signals

Fig. 4. The result of improved VEPs extraction algorithm based on ICA

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4 Discussion In the paper a novel improved VEPs extraction algorithm based on ICA was proposed. First we detected the signals from the primary visual cortex with red, green, blue and white LED stimulus in the awake and move freely rats. Second we analyzed the characteristics of the electromyography (EMG), the electro-oculogram (EOG), the electroencephalogram (EEG) and the VEPs of the rats in time and frequency domain respectively. Third we got the valuable components from the test signals by ICA . At last we extracted the VEPs from the components considering the characteristics obtained above. The experiments demonstrated this new method could extract VEPs precisely and efficiently. The correlation coefficient between the extracted and original signals is 0.9944. As mentioned in the introduction, the methods extracting VEPs based on Fourier always bring some signal errors because they can not get the optimal resolutions in time domain and frequency domain simultaneously, which can be avoided by using Wavelet Transform(WT). But this method also has its own disadvantages, for example, the noise with the same frequency cannot be eliminated and it is difficult to choose the proper basis function, both of which are crucial but difficulty when analyzing VEPs with WT. All of these problems can be solved by ICA. However, there are some problems when using ICA to extract the VEPs, such as irregular order and randomicity. In order to extract the VEPs from the mixed signal detected from the primary visual cortex, the ICA algorithm combined with AR parameter model to overcome the problems. Comparering with Fourier-based methods and WT, there are three advantages for our method. First, the VEPs can be extracted effectively. Second, signal-to-noise ratio is increased significantly. Thrid, every component of the extracted VEPs can be detected precisely under single-trial extraction. The improved extraction algorithm based on ICA can also apply to other brain signals if the signals are statistic independent.

References [1] Regan, D.: Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine. Elsevier Science Publishing Company, New York (1989) [2] Parisi, V., Uccioli, L.: Visual electrophysiological responses in persons with type diabetes. Diabetes Metab Res. Rev. 17, 12–18 (2001) [3] Zhou, X., Shu, H.L.: Analysis of visual aculity with VEP technology. Int. J. Ophthalmol. 7, 124–126 (2007) [4] Guarino, I., Lopez, L., Fadda, A., Loizzo, A.: A Chronic Implant to Record Electroretinogram, Visual Evoked Potentials and Oscillatory Potentials in Awake, Freely Moving Rats for Pharmacological Studies. Neural Plasticity 11, 241–250 (2004) [5] Boyes, W.K., Bercegeay, M., Ali, J.S., Krantz, T., McGee, J., Evans, M., Raymer, J.H., Bushnell, P.J., Simmons, J.E.: Dose-Based Duration Adjustments for the Effects of Inhaled Trichloroethylene on Rat Visual Function. Toxicologica Sciences 76, 121–130 (2003) [6] Piccione, F., Giorgi, F., Tonin, P., Priftis, K., Giove, S., Silvoni, S., Palmas, G., Beverina, F.: P300-based brain computer interface: Reliability and performance in healthy and paralysed participants. Clinical Neurophysiology 117(3), 531–537 (2006)

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[7] Middendorf, M., Mcmillan, G., Calhoun, G., Jones, K.S.: Brain-computer interfaces based on the steady-state visual-evoked response. IEEE Trans., Rehab. Eng. 8(2), 211–214 (2000) [8] Duhamel, P., Vetterli, M.: Fast Fourier transform:a tutorial review and a state of the art. Signal Processing 19, 259–299 (1990) [9] Quian Quiroga, R., Sakowitz, O.W., Basar, E., Schürmann, M.: Wavelet Transform in the analysis of the frequency composition of evoked potentials. Brain Research Protocols 8, 16–24 (2001) [10] Lee, P.-L., Hsieh, J.-C., Wu, C.-H., Shyu, K.-K., Chen, S.-S., Yeh, T.-C., Wu, Y.-T.: The Brain Computer Interface Using Flash Visual Evoked Potential and Independent Component Analysis. Annals of Biomedical Engineering 34, 1641–1654 (2006) [11] Barros, A.K., Vigário, R., Jousmaki, V., Ohnishi, N.: Extraction of event-related signals from multichannel bioelectrical measurements. IEEE Transactions on Biomedical Engineering 47(5), 583–588 (2000) [12] Faust, O., Acharya, R.U., Allen, A.R., Lin, C.M.: Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques. IRBM 29, 44–52 (2008)

A Novel Feature Extraction Method of Toothprint on Tongue in Traditional Chinese Medicine Dongxue Wang, Hongzhi Zhang, Jianfeng Li, Yanlai Li, and David Zhang School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China [email protected]

Abstract. The toothprint on tongue is an important objective index for revealing the human sub-health state, and thus the extraction and description of toothprint is of great significance in clinical applications. Current toothprint detection methods are only based on concave point. These methods, however, heavily depend on the accuracy of the segmentation of the tongues from the background, and are difficult to detect the weak toothprint on tongue. In this paper, we propose an effective method to make toothprint detection more robust and accurate. The proposed method first extracts both the curvature feature and the color feature around the contour of the tongue, and then analyses the toothprint using these two kinds of features. Experimental results show that the proposed method is promising for the detection of both the obvious and the weak toothprints. Keywords: tongue diagnosis, toothprint, curvature feature, color feature, contour.

1

Introduction

The tongue with toothprints on its contour is a kind of abnormal tongue appearance, and is of great diagnostic significance to clinical applications [1, 2]. Compared with other diagnostic features, toothprint is easy to be identified in the practice of tongue diagnosis, and is robust against external factors, such as food, medicine, and so on. In traditional Chinese medicine (TCM), the toothprint on tongue is an important objective index for revealing the human sub-health state. It has attracted many researchers’ attention in TCM tongue diagnosis research. With the progress in medicine science, image processing, and pattern recognition, the research of the toothprint on tongue is going towards microcosmic, quantitative, and objective. Zhong et al. [3] proposed two feature extraction methods for the detection of the toothprint on tongue, which are based on convex closure structure and curve fitting, respectively. These two methods, however, seriously depend on the accuracy of the segmentation of the tongues from the background, and are difficult to detect the weak toothprint on tongue. Moreover, the methods would also perform poor for detecting the toothprint with serious deformation. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 297–305, 2011. © Springer-Verlag Berlin Heidelberg 2011

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In this paper, we first investigate the characteristics of curvature and color on the contour of the tongue with toothprints, and propose a novel feature extraction method of the toothprint on tongue by using these two features. Then we carry out several experiments to show the effectiveness of the proposed method. The remainder of the paper is organized as follows. Section 2 describes the factors and definition of the toothprint on tongue. Section 3 describes the feature extraction method of the toothprint on tongue using the feature of curvature on the contour of the tongue. Section 4 presents the feature extraction method using the feature of color on the contour of the tongue. Section 5 uses the two methods together to make a comprehensive analysis to the toothprint on tongue. Section 6 provides the experimental results and Section 7 offers the conclusion of this paper.

2

Factors and Definition of Toothprint on Tongue

In TCM, the formation of the toothprint on tongue can be attributed to splenic asthenia, where the spleen cannot transmit and distribute the fluids, and the fluids is stopped in the tongue, then the tongue becomes big and fulfills the alveolus, finally the contour of the tongue is pressed and the toothprint is left. The substance of the toothprint on tongue is the conjunctive tissue hyperplasia and edema caused of obstacle in the circumfluence of blood or lymph in the tongue [4]. On one hand, because of the edema of the tongue, it belongs to deficiency of spleen yang. On the other hand, because of the laxity of the tongue’s muscle, it belongs to deficiency of spleen qi. From the observation of numerous tongue images with toothprints from the tongue image data set of Bio-computing Research Center of Harbin Institute of Technology, we summarize the characteristics of the toothprint on tongue from the following three aspects [5]: • Location: toothprint is usually found on the two sides of the tongue, sometimes on the tongue tip; • Shape: toothprint has obvious prints of tooth pressing, and usually exhibits dentate contour; • Color: the toothprint region usually has a dull-red color, whereas the nontoothprint region has a white color.

Fig. 1. Tongue with toothprints

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Fig. 1 shows two typical tongue images with toothprints, where obvious prints of tooth can be observed on the two sides of the tongues.

Fig. 2. The R component values along the tongue contour

Fig. 3. The ci’ values along the tongue contour

Fig. 4. An contour curve of the left side of a tongue image

3

Curvature Feature Extraction of Toothprint on Tongue

The contour points of the tongue are picked out from tongue images using the snake algorithm developed by Bio-computing Research Centre of Harbin Institute of

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Technology [6, 7]. Using the snake algorithm we can obtain 120 contour points from a tongue image, and the curve through these points makes up the contour of the tongue. Assume that there is no toothprint on the root of the tongue, and the probability that the toothprints appear on the two sides of the tongue is higher and the toothprint feature is more obvious than the tongue tip. So we focus on the contour information on the two sides of the tongue. Fig. 2 is a contour curve of the left side of a tongue with toothprints. The x-axis and y-axis represent the vertical and horizontal coordinate of the pixel of the tongue contour, respectively. If there is no toothprint on the tongue, the curve should be concave. That is to say, the gradients of the curve are monotonically increasing. If there are toothprints, the corresponding regions of the curve might be convex (see the red-framed regions in Fig. 2). So we intend to utilize this obvious characteristic to detect the toothprint candidate. In order to make estimate of the toothprint candidate more reliable, we assume that for any non-toothprint segment on the curve the second derivative should be lower than 0. If the average curvature is higher than the predefined threshold ThresholdC (empirically determined as 0.002), we should call this region a toothprint candidate. The definition of the discrete curvature ci of the point i is given as follow:

⎛ Δx Δx ⎞ ⎛ Δy Δy ⎞ ci = ⎜ i − i+t ⎟ + ⎜ i − i+t ⎟ ⎝ Δsi Δsi+t ⎠ ⎝ Δsi Δsi+t ⎠ , 2

2

(1)

where Δxi=xi−xi-t , Δyi=yi−yi-t , and Δsi=(Δxi 2+Δyi 2)1/2, and t is the step parameter with t=15. Then we add the sign of the second derivative y’’ before the curvature ci :

⎡⎛ Δxi Δxi+t ⎞2 ⎛ Δyi Δyi + t ⎞ 2 ⎤ ci' = sgn ( y'' ) ⎢⎜ − − ⎟ +⎜ ⎟ ⎥  ⎣⎢⎝ Δsi Δsi + t ⎠ ⎝ Δsi Δsi + t ⎠ ⎥⎦

(2)

The result of ci’ obtained is shown in Fig. 4. The x-axis represents the vertical coordinates of the tongue contour in the tongue image, and the y-axis represents the curvature value with the sign. Because there is some noise on the contour curve and the contour curve is not absolutely smooth, the concavity and convexity of the contour curve may be unstably. Through the experiments, we found that the toothprints in tongue images are commonly between 12 and 76 pixels. So we set the threshold Threshold1 and Threshold2 12 and 76, respectively, representing the maximum and the minimum of the length of the toothprint candidate. Thus we could obtain four toothprint candidates in Fig. 3 (marked as the red - framed regions), and the positions coincide with the positions of the convex curve segments in Fig. 2.

A Novel Feature Extraction Method of Toothprint on Tongue

(a)

(c)

(e)

301

(b)

(d)

(f)

Fig. 5. Toothprints obtained only use the curvature or color feature: (a) toothprints obtained use the curvature feature (b) toothprints obtained use the color feature (c) curvature value of the left side of the tongue (d) R value of the left side of the tongue (e) curvature value of the right side of the tongue (f) R value of the right side of the tongue

4

Color Feature Extraction of Toothprint on Tongue

From the characteristics of the toothprint described in Section 2, we find that the color of the non-toothprint region would prefer to be white because of edema, and the color of the toothprint region would prefer to be dull-red because of the obstruction of blood flow. Considering that red is the principal color of tongues, we only use the R component to represent the color of the pixels along the tongue contour. In the proposed method, we use a diamond region which is 5 pixels away from the tongue contour and contains 25 pixels.

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By averaging the R component of the diamond region, we obtain a description of the color of the pixel along the tongue contour, which would be valuable for toothprint detection. As shown in Fig. 4, the values of the R component would become lower when the diamond region is close to the toothprint. On the contrary, the values would become higher when the diamond region is far from the toothprint. In Fig. 4, the x-axis represents the vertical coordinate of the diamond region in the tongue image, and the yaxis represents the average value of the R component of the pixels in the diamond region. The R component value would be lower in the toothprint region, and result in a fluctuation. Thus we can utilize this characteristic to select toothprint candidate. If a curve segment is in the fluctuation region, there is more probable that this segment is a toothprint. On the contrary, if a curve segment is not in the fluctuation region, the probability would be much less. That is to say, if the difference of the minimum of the R component values with the R component of the two endpoints of the curve segment is higher than the threshold ThresholdR (with the optimal value 8), and the length of the curve segment is within the range [Threshold1, Threshold2], we set this curve segment as a toothprint candidate. As shown in Fig. 4, we could obtain two toothprint candidates (marked as the redframed regions), and the positions coincide with the positions of the first two convex curve segments in Fig. 2.

5

Comprehensive Analysis of Toothprint on Tongue

Because the extracted contour curve of the tongue may be not smooth, and the light and angle would cause some interference during capturing the tongue image, it is not sufficient to judge the toothprint only use the curvature feature or the color feature, and it is likely to make error, as shown in Fig. 5. So we intend to use these two features together. In the result of toothprint regions obtained by the curvature value, if the average curvature value of a toothprint candidate is higher than a high threshold ThresholdC2 (the best value is 0.03 by experiment), that is to say if the average curvature value with the sign of the second derivative is lower than the threshold -ThresholdC2, we set it a toothprint directly. Else, in the result of toothprint regions obtained by the R component value, if there is a toothprint candidate whose position is close to some one obtained by the curvature value, then we set it a toothprint. Else, we do not set it a toothprint. The result obtained is shown in Fig. 6. In Fig. 6, the average curvature values of the second and the third toothprint candidates in (a) are higher than the threshold ThresholdC2. So we directly set them toothprints (as is shown in (e)). Since there is no toothprint candidate in (c) which is close to the first and the forth tothprint candidates in (a), we do not set them toothprints. There is no toothprint candidate in (b) whose average curvature value is higher than the threshold ThresholdC2, and only the first toothprint caididate in (d) is close to the second one in (b), and thus we set the second toothprint candidate in (b) is a toothprint, and the others are not toothprints (as is shown in (f)).

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6 Experimental Results and Discussion To evaluate the proposed method, we build a data set of 200 tongue images with toothprints, which includes 534 obvious toothprints and 329 weak toothprints. The result is shown in Table 1. From Table 1, one can see that 507 of 534 obvious toothprints and 273 of 329 weak toothprints are correctly detected, with the correct rates 94.9% and 83.0%, respectively.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

Fig. 6. The result of the comprehensive analysis: (a) curvature value of the left side of the tongue (b) curvature value of the right side of the tongue (c) R value of the left side of the tongue (d) R value of the right side of the tongue (e) the result of toothprints of the left side of the tongue (f) the result of toothprints of the right side of the tongue (g) the result of toothprints of the tongue

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The results above indicate that the proposed method can obtain satisfactory detection performance for not only the obvious toothprint but also the weak toothprint. Among the toothprints which are not correctly detected, most can be attributed to that the curvature feature of these toothprints is not very obvious. Fig. 7 shows one tongue image with toothprints which are not correctly detected. As shown in Fig. 7, there is no obvious concave region on the tongue contour, so we should detect toothprint only using the color feature. However, if we only use the color feature to detect the toothprint, we cannot achieve satisfactory for the general toothprint. Thus it is valuable to further investigate more effective method to simultaneously make use of the contour and color features. Table 1. Result of toothprint detection

Type of Toothprints

Identification Result Actual number

Detected number

Correct rate (%)

obvious toothprints

534

507

94.9

weak toothprints

329

273

83.0

total

863

780

90.4

Fig. 7. A tongue with toothprints which are not correctly detected

7

Conclusions

The detection of the toothprint on tongue is a very important component in tongue diagnosis. In order to identify the toothprint on tongue automatically and accurately, we propose a novel method that could analyze the tongue image by using the two kinds of features on the contour of the tongue, curvature feature and color feature.

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The effectiveness of the proposed method has been experimentally demonstrated. The method can act as assistant diagnostic tool and be applied in TCM clinical applications and be useful in future computational tongue diagnosis research. Acknowledgment. This work is partially supported by the NSFC under Contract Nos. 60902099, 60871033 and 61001037, and the National Science & Technology Major Project of China under Contract No. 2008ZXJ09004-035.

References [1] Shen, Z.: Reference standard of the diagnosis of deficiency syndrome in Traditional Chinese Medicine. Journal of Integrated Traditional and Western Medicine 3, 117 (1983) [2] Medicine Bureau of Health Ministry, Guiding principles in the clinical research of spleen deficiency treatment by Chinese Medicine. Acta Medica Sinica 3, 71–72 (October 1988) [3] Zhong, S., Xie, Z., Cai, Q.: Researches on tooth-marked tongue recognition method. Journal of Hanshan Normal University 29, 34–38 (2008) [4] Li, M.: Clinical researching situation of the formation mechanism of tongues with toothprints and the correlation with diseases. Hunan Journal of Traditional Chinese Medicine 21, 80–82 (2005) [5] Gong, K.: Research on feature extraction and classification of tongue shape and toothmarked tongue in TCM tongue diagnosis, Master Thesis of Harbin Institute of Technology, pp. 31–41 (June 2008) [6] Pang, B., Wang, K.: Time-adaptive Snakes for tongue segmentation. In: Proc. 1st. Int’l. Conference on Image and Graphics (ICIG 2000), Tianjin, China, pp. 228–331 (August 2000) [7] Pang, B., Wang, K., Zhang, D.: On automated tongue image segmentation in Chinese Medicine. In: Proc. Int’l. Conf. Pattern Recognition, pp. 616–619 (2002)

Stability and Bifurcation of an Epidemic Model with Saturated Treatment Function* Jin Gao1 and Min Zhao2,** 1

College of Mathematics and Information Science, Wenzhou University,Wenzhou, China 2 College of Life and Environmental Science, Wenzhou University,Wenzhou, China [email protected]

Abstract. In this paper, we studied an epidemic model with nonlinear incidence and treatment. We described and analyzed by elementary means of the model, a limited resource for treatment is proposed to understand the effect of the capacity for treatment. It is shown that a backward bifurcation will take place if the capacity is small. The dynamical behaviors of the SIR epidemic model with nonlinear incidence and treatment were also studied. Keywords: Epidemic model, Backward bifurcation, stability analysis, Treatment.

1

Introduction

The classical Kermack-McKendrick epidemic system models the infectivity of an individual depends on the time since the individual became infective [1]. In the study, the saturated incidence rate is expressed in numbers forms , such as β IS , kI 2 S 1+α I 2

kIS 1+ α I 2

,

([2,3,4,5,6,7,8,9]).

In the paper of Wang and Ruan [10], they studied the model with treatment, several authors consider different kinds of treatment( [11, 12, 13,14]. Giving the patients timely treatment will reduce the numbers of infective patients. We must try our best to avoid the delayed effect for treatment by improving our medical technology and investing more medicines, beds and so on. In compartment models for the transmission of communicable there is usually a basic reproductive number R0 , representing the mean number of secondary infections *

**

This work was supported by the National Natural Science Foundation of China (Grant No. 30970305). Corresponding author.

Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 306–315, 2011. © Springer-Verlag Berlin Heidelberg 2011

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caused by a single infective introduced into a susceptible population [15]. Papers [14, 15, 16, 17,18] found backward bifurcations due to social groups with different susceptibilities, pair formation, nonlinear incidences, and age structures in epidemic models. Backward bifurcation is important to obtain thresholds for the control of diseases. we restrict our attention to the following model: dS λ SI = A − dS − + μR 1+ α I dt dI λ SI εI = − (d + r ) I − 1 + kI dt 1 + α I dR εI = rI + − (d + μ ) R 1 + kI dt

(1)

where S ( t ) , I (t ), R (t ) denote the number of susceptible, infective, and recovered individuals at time t respectively, A is the recruitment rate of the population, d is the natural death rate of the population, μ is the rate at which recovered individuals lose immunity and recovery and return to the susceptible class,

r

is the natural

recovery rate of the infective individuals. ε I / (1 + kI ) is the removal rate of infective individuals due to the treatment sites. The organization of this paper is as follows. In the following section, we present equilibrium analysis, mathematical analysis of this model and the bifurcation and some numerical simulation . A brief discussion are given in section 3.

2

Main Results

A. Equilibrium Analysis We consider the existence of equilibria of model (1). For any values of parameters, model (1) always has a disease-free equilibrium E0 = ( A / d , 0, 0) . In order to find the positive equilibria, set λ SI + μR = 0 1+ α I λ SI εI − (d + r ) I − =0 1+ α I 1 + kI εI rI + − (d + μ ) R = 0 1 + kI A − dS −

(2)

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We sum up the three equations of (4), this yields S =

A − I − R d

(3)

we eliminate R using the third equation of (2) and substitute it into the equation of (1), then substitute S into the second equation to give the form

aI 2 + bI + c = 0.

(4)

a = k[α (d μ + rd + r μ ) + λ (d + μ + r ) + d 2α ],

(5)

b = (d + ε + r )λ + d 2 (α + k ) + μα (d + ε ) + r (k + α )(d + μ ) + d (εα + dk ) − λ kA(1 + μ ),

c = (μ + d )(ε + r + d ) − λ A(1 + μ ) Define the basic reproduction number as follows: R0 =

λ A(1 + μ ) . (μ + d )(ε + r + d )

(6)

It means the average new infections caused by a single infected individual in a whole susceptible population [17]. From Eq.(4), we can see that (1) If

k = 0 , Eq.(4) is a linear equation with a unique solution I =−

which is positive if and only if R0 > 1 (i.e. (2) If

c b

c<0

)

k ≠ 0 , Eq.(4) is a unary quadratic equation,

(i) If R0 = 1 , then

c=0

and there is a unique positive root of (4), I = − b , a

which is positive if and only if b < 0 ; (ii) If R0 > 1 (i.e.

c < 0 ), then there is a unique nonzero solution of (4) and thus

there is a unique endemic equilibrium (iii) If R0 < 1 (i.e.

I1 =

c > 0 ), then there are two positive equilibria

−b − b2 − 4ac , 2a

I2 =

−b + b 2 − 4ac . 2a

(7)

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B. Mathematical Analysis To study the dynamics of model (1), we first present a lemma. Lemma 1. The plane S + I + R = A / d is an invariant manifold of system (1), which is attracting in the first quadrant. Proof. Denoting N (t ) = S (t ) + I (t ) + R (t ) , then summing up the three equations of (1), we have dN = A − dN dt

(8)

It is clear that N (t ) = A / d is a solution of Eq.(8) and for any N (t0 ) ≥ 0 , the general solution of Eq.(8) is N (t ) =

When

t

1 [ A − ( A − d N ( t 0 ) e x p ( − d ( t − t 0 )))] d

tends to infinity, N (t ) = A / d ,

which we can get the conclusion. This means that the limit set of system (2) is on the plane S + I + R = A / d . Thus, we focus on the reduced system dI λI A εI Δ = ( − I − R) − (d + r ) I − = P( I , R) dt 1 + α I d 1 + kI Δ dR εI = rI + − ( d + μ ) R = Q( I , R ). dt 1 + kI

Theorem 1. If

α > k , system (9) does not have nontrivial periodic orbits.

Proof. In system (9), taking into account the practical significance, we know that I > 0 and

R>0.

Take a Dulac function D ( I , R ) =

1+α I . λI

We have ∂ ( DP ) ∂ ( DQ ) α (d + r ) 1+ α I α −k + = −1 − − (d + μ ) − ∂I ∂R λ λI (1 + kI ) 2

If

α >k, ∂ ( DP ) ∂ ( DQ ) + <0 ∂I ∂R

The conclusion follows.

(9)

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From the above discussion, we can see that model (9) always has a disease-free ' equilibrium E0 = (0, 0) , To study the dynamical behavior of the disease-free

equilibrium, the Jacobian matrix of system (9) at (0,0) is

⎛ λA ⎞ − d −ε − r 0 ⎟ ⎜ J0 = d ⎜⎜ ⎟ −d − μ ⎟⎠ r +ε ⎝ Lemma 2. The disease-free equilibrium (0,0) of system (9) is (i) a stable hyperbolic node if λ A − d − ε − r < 0 ; d

(ii) a saddle-node if

λA − d −ε − r = 0 d

(iii)a hyperbolic saddle if λ A − d − ε − r > 0 d

Next we consider the local stability of the epidemic equilibrium when R0 > 1 . The Jacobian matrix of (9) is

⎛a J = ⎜ 11 ⎝ a21 a11 =

a12 = −

A d

a12 ⎞ ⎟ a22 ⎠

λ ( − I − R) − α I (1 + α I ) (1 + α I )

2

−

ε (1 + kI )2

−d −r,

ε λI , a21 = r + , a22 = − d − μ . 1+ α I (1 + kI )2

From the first equation of (9), we have λI A εI ( − I − R ) = (d + r ) I + 1+ α I d 1 + kI

substituting (10) into a11

a11 =

1 [−α I (1 + kI )2 (1 + d + r ) + ε I (k − α )] (1 + kI ) (1 + α I ) 2

So, det( J ) =

(1 + kI ) 2[α I (1 + d + r )(d + μ ) + λ rI ] + ε I (k − α )(d + μ ) + ελ I (1 + kI ) 2 (1 + α I )

(10)

Stability and Bifurcation of an Epidemic Model with Saturated Treatment Function

which is positive if and only if (1 + kI ) 2 [α I (1 + d + r )( d + μ ) + λ rI ] + ε ( d + μ ) kI + ελ I > εα ( d + μ ) I

Note that I ≠ 0 , it is equivalent to (1 + kI ) 2 [α (1 + d + r )( d + μ ) + λ r ] + ε (d + μ ) k + ελ > εα ( d + μ )

in fact, we have, (1 + kI ) 2 [α (1 + d + r )( d + μ ) + λ r ] + ε (d + μ )k + ελ > α (1 + d + r )(d + μ ) +λ rε (d + μ )k + ελ

So det( J ) > 0 if

α (1 + d + r )( d + μ ) + λ r + ε (d + μ ) k + ελ > εα ( d + μ ) Then we get

k >α −

tr ( J ) =

λ (ε + r ) − (1 + d + r )( d + μ )α = k1 ( d + μ )αε

−(1 + kI )2 [α I (1 + 2d + r + μ ) − d − μ ] + ε (k − α )I (1 + α I )(1 + kI )2

which is negative if

(1 + kI )2 [α I (1 + 2d + r + μ ) − d − μ ] > ε (k − α ) I in the same way, we have (1 + kI )2[α I (1 + 2d + r + μ ) − d − μ ] > α I (1 + 2d + r + μ ) − d − μ So,

α I (1 + 2d + r + μ ) > ε (k − α )I , Note that I ≠ 0 , then

α (1 + 2d + r + μ ) > ε (k − α ) , We can get

k<

α (1 + 2d + r + μ ) = k2 ε

So we have the flowing theorem by Routh-Hurwitz criterion

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Theorem 2. When R0 > 1 , suppose k1 < k < k2 , then there is a unique endemic equilibrium of model (9) which is locally asymptotically stable. Theorem 3. The system (9) has a backward bifurcation at R0 = 1 if and only if

b < 0 . Then we will give an explicit criterion in terms of the parameters A ,

d, ε, r

for the existence of backward bifurcation at R0 = 1 . When R0 = 1 ,

λ, μ, c=0

so that

λ A(1 + μ ) = (μ + d )(ε + r + d ) The condition

(11)

b < 0 is

[(d + μ)ε + λ A(1+ μ)]k > λ(μ + r + ε + d ) + α[(r + d )(d + μ) + ε d ] With λ A(1 + μ ) determined by (11), which reduces to ( d + μ )(2ε + r + d ) k > λ ( μ + r + ε + d ) + α [( r + d )( d + μ ) + ε d ]

that is

k>

λ (μ + r + ε + d ) + α [(r + d )(d + μ ) + ε d ] = k0 (d + μ )(2ε + r + d )

(12)

A backward bifurcation occurs at R0 = 1 with λ A given by (11) and only if (12) is satisfied. From the above discussion, we can point out that when

k is larger than

k0 ,

the backward bifurcation will take place, that is to say, when the effect of the infected being delayed for treatment becomes stronger than some level, the backward bifurcation occurs. Thus, we can say,

k , the parameter which represents the effect of

the infected being delayed for treatment is one of the factors that lead to the backward bifurcation. C. The Bifurcation Curve In order to draw the bifurcation curve(the graph of I as a function of R0 ), we will consider

ε

as a independent variable and keep other parameters fixed. Implicit

differentiation of the equilibrium condition (4) with respect to

ε

gives

Stability and Bifurcation of an Epidemic Model with Saturated Treatment Function

(2aI + b)

313

dI = −( μ + d ) − (λ + μα + d α ) I < 0 dε

It is clear that the right hand of the equation is negative and the sign of dI is dε

determined by 2aI + b , this implies that the bifurcation curve has positive slope at equilibrium values with 2aI + b < 0 and a negative slope at equilibrium values with

2aI + b > 0 . The bifurcation curve is as showed in Fig.1.

Fig. 1. Backward bifurcation with A = 8 , λ = 0.05 , d = 0.1 , ε

= 1 , k = 1 , α = 3 , μ = 0.3 ,

r = 0.2

3

Conclusions and Remarks

In this paper, we have dealt with an SIR epidemic model with saturated incidence and saturated

treatment

function. We set a basic reproduction number R0 = λ A(1 + μ ) (μ + d )(ε + r + d ) and find that it has a great impact on the model. The result indicate that when R 0 < 1 , the disease-free equilibrium is asymptotically stable (see Fig.2). When R0 > 1 , the endemic equilibrium exists and is asymptotically stable(see Fig.3). Biologically, these indicate that when the recruitment rate of the population A is large enough or the natural death rate d and removal rate( rate of recovered individuals lose immunity μ sums the natural recovery rate of the infective individuals r ) is sufficiently small such that R0 > 1 , then the disease persists.

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Fig. 2. When A = 2 , λ = 0.05 , d = 0.1 , ε = 1 , k = 1 , α = 3 , μ=0.1 r = 0.2 , which indicates tha I (t ) and R (t ) approach zero as time goes to infinity, R0 = 0.42 < 1 , S (t ) approaches to its

steady state value while I (t ) and the disease dies out. R (t ) approach zero as time goes to infinity, the disease dies out.

Fig. 3. When A = 4 , λ = 0.2 ,

, d = 0.1 , ε = 1 , k = 1 ,

α =3 ,

μ = 0.1 , r = 0.2 ,

R0 = 3.384 > 1 ,all three components, S (t ) , I (t ) , and R(t ) approaches to their steady state as time

goes to infinity, the disease becomes endemic.

Corresponding, if the recruitment rate of the population , such that R0 < 1 , then the disease dies out. We have showed that the parameter k have is one of the factors that lead to the backward bifurcation, which represents that the effect of the infected being delayed for treatment can lead to the backward bifurcation. Therefore, in order to radiate the disease, we have to give the patients treatment timely by improving our medical technology and investing more medicines and so on[17].

Stability and Bifurcation of an Epidemic Model with Saturated Treatment Function

315

References [1] Štep’an, J., Hlubinka, D.: Kermack–McKendrick epidemic model revisited. Kybernetika 43(4), 395–414 (2007) [2] Capasso, V., Serio, G.: A generalization of the Kermack-Mckendrick deterministic epidemic model. Math. Biosci. 42, 43 (1978) [3] Derrick, W.R., Driessche, P.: A disease transmission model in a nonconstant population. Journal of Mathematical Biology 31(5), 495–512 (1993) [4] Hethcote, H.W., Driessche, P.: Some epidemiological models with nonlinear incidence. Journal of Mathematical Biology 29(3), 271–287 (1991) [5] Hethcote, H.W., Levin, S.A.: Periodicity in epidemiological models. Applied Mathematical Ecology, 193–211 (1989) [6] Liu, W., Levin, S.A., Iwasa, Y.: Influence of nonlinear incidence rates upon the behavior of SIRS epidemiological models. Journal of Mathematical Biology 23(2), 187–204 (1986) [7] Ruan, S., Wang, W.: Dynamical behavior of an epidemic model with a nonlinear incidence rate. Journal of Differential Equations 188(1), 135–163 (2003) [8] Wang, W., Ruan, S.: Simulating the SARS outbreak in Beijing with limited data. Journal of theoretical biology 227(3), 369–379 (2004) [9] Xiao, D., Ruan, S.: Global analysis of an epidemic model with nonmonotone incidence rate. Mathematical Biosciences 208(2), 419–429 (2007) [10] Wang, W., Ruan, S.: Bifurcations in an epidemic model with constant removal rate of the infectives. Journal of Mathematical Analysis and Applications 291(2), 775–793 (2004) [11] Feng, Z., Thieme, H.R.: Recurrent outbreaks of childhood diseases revisited: the impact of isolation. Mathematical Biosciences 128(1-2), 93–130 (1995) [12] Hyman, J.M., Li, J.: Modeling the effectiveness of isolation strategies in preventing STD epidemics. SIAM Journal on Applied Mathematics 58(3), 912–925 (1998) [13] Wu, L.I., Feng, Z.: Homoclinic bifurcation in an SIQR model for childhood diseases. Journal of Differential Equations 168(1), 150–167 (2000) [14] Zhang, X., Liu, X.: Backward bifurcation of an epidemic model with saturated treatment function. Journal of Mathematical Analysis and Applications (2008) [15] Brauer, F.: Backward bifurcations in simple vaccination models. Journal of Mathematical Analysis and Applications 298(2), 418–431 (2004) [16] Van den Driessche, P., Watmough, J.: A simple SIS epidemic model with a backward bifurcation. Journal of Mathematical Biology 40(6), 525–540 (2000)

Study of Monocular Measuring Technique Based on Homography Matrix Jia-Hui Li and Xing-Zhe Xie Robotics Laboratory, School of Information Engineering, Southwest University of Science and Technology, Mianyang, China [email protected]

Abstract. This paper presents a real-time and effective measuring technique to determine the location of moving object on a fixed plane using homography matrix with a single camera. Based on OpenCV, this paper firstly calibrates the camera's intrinsic parameters, and then obtains homography matrix between the plane of moving object and the image through specific template, and meanwhile the origin position of plane coordinates and coordinate axis direction are gained. According to the camera perspective projection model, the actual coordinates of the moving object in the fixed plane can be calculated while the coordinates in the image are known. Using the research results in on-line location of mechanical parts shows that the system has high accuracy and its precision meets for practicality. Keywords: Homography Matrix, OpenCV, Monocular measurement, Perspective Projection.

1

Introduction

Developing from the image processing and patternrecognition , the main purpose of computervision is to make computer identify and cognize 3d world by graphics and image sequence . The ultimate objective is to make computer understand 3d scenery , namely make computer have some functions of pepole’s vision system. With the rapid development of visual sensor and the computer hardware devices and the thorough study of the image processing and projection theory, computervision technology has developed sufficiently. Vision measuring technique, one new technology based on computervision, focuses on geometric size and spacial position of an object. Monocular visual measurement refers to only using one digital camera or vidicon taking single pictures. Since it needs only one visual sensor, thus the advantage of this method is the structure is simple, the same is too with the camera calibration. Meanwhile, the method avoid the smaller view field of the stereo vision and difficult three-dimensional matching, therefore, in recent years, the research of this aspect is more active. In this paper, the target object is a mechanical part moving on a fixed plane, the inside parameter of the camera is got through the camera demarcation, then through specific template, determing the Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 316–324, 2011. © Springer-Verlag Berlin Heidelberg 2011

Study of Monocular Measuring Technique Based on Homography Matrix

317

Homography Matrix of the mechanical part whose movement plane to image, furthermore, identifying coordinate origin and coordinate axile of the mechanical part, according to the principle of geometrical imaging, the coordinate of the mechanical part can be obtained which is relative to the coordinate origin on movement plane.

2

Camera Perspective Projection Model

In computer vision, geometric parameters of tested objects in 3d space are calculated through screened pictures. Image is a reflection through a space object feflecting in the plane through imaging system, namely the projection space object reflected in a plane. Each pixel image gray-scale reflects the intensity of reflex of a certain point on the surface of a space object, while the position of the point on the picture bear on the geometric position of the corresponding points on the surface of the space object. The relationship between the positions is determined by the geometric model of the vidicon’s imaging system. The so-called linear camera model, that is, without considering the problem of nonlinear aberrance during camera imaging process, and with a group of the basic linear constrained equation, demonstrates linear transformation relationship between camera coordinate and the 3d object space coordinate, as shown in figure 1.

o

x

y o

u o’ x’ p’(x’,y’)

L1 y’

p(u,v)

y Z O Y

L2

X

P(X,Y,Z) p(x,y,z) z Fig. 1. Linear camera model

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J.-H. Li and X.-Z. Xie

In this model, a view is to make the piont of 3d space project on image plane through the perspective transformation. Projection formula is as follows:

⎡X ⎤ ⎡u ⎤ ⎡ au 0 u0 ⎤ ⎡ r11 r12 r13 t x ⎤ ⎢ ⎥ ⎢ ⎥ Y s ⋅ ⎢ v ⎥ = ⎢⎢0 av v0 ⎥⎥ ⋅ ⎢ r21 r22 r23 t y ⎥ ⋅ ⎢ ⎥ ⎢ ⎥ ⎢Z ⎥ ⎢⎣1 ⎥⎦ ⎢⎣0 0 1 ⎥⎦ ⎢⎣ r31 r32 r33 t z ⎥⎦ ⎢ ⎥ ⎣1 ⎦

(1)

or s ⋅ p = A ⋅ [ R | t ] ⋅ P Here (X,Y,Z) are the world coordinates of a point, (u,v) are coordinates which that pionts project in the image plane, in pixels. A is called camera matrix, or internal parameter matrix. (u0,v0) is the image datummark (the intersection coordinates between the light axis and the image plane ), au,av focus on pixels. Internal parameter matrix doesn't rely on the view of the scene, once calculated, can be used repeatedly (as long as the focal length fixing). Rotation - translation [R|t] matrix is called external parameter matrix which is used to describe the movement of a camera relative to a fixed scene, or a rigid motion of an object around the camera. In other words, [R|t] switches the coordinates of point (X,Y,Z) to another coordinate system which is fixed relative to the camera. This is the most basic total line equation of photogrammetry, explaining that the object point, the light heart and the image point must be in the same line, which is also the pinhole model or central projection model expression. According to the total line equation, on the condition of the internal parameters of the camera is certain, by using the known point coordinates and the corresponding image coordiantes, the six external parameters of the camera can be worked out, namely the information light heart coordinate and the position of the light axis of the camera. The above transformation is equivalent to the form below (z≠0): ⎡X ⎤ ⎡x⎤ ⎢ y ⎥ = R ⋅ ⎢Y ⎥ + t ⎢ ⎥ ⎢ ⎥ ⎢⎣ Z ⎥⎦ ⎢⎣ z ⎥⎦

x' = x / z y' = y / z

u = f x ⋅ x' + cx = f x ⋅ x / z + cx v = f y ⋅ y' + cy = f y ⋅ y / z + cy

(2)

(3)

(4)

Here (x,y,z) shows the coordinates of the point in world coordinate system in the camera coordinate system.

Study of Monocular Measuring Technique Based on Homography Matrix

319

In actual work, because of optical lens’ distortion, there will be nonlinear effect on linear model, and the experiment results show that , linear model can not describe imaging geometry relationship accurately, especially when using wide-angle lens, there are large distortion away from the center of the image. Considering the effects of various distortions, nonlinear distortion model should be established. Here the primary deformation is radial deformation, alsothere will be slight shear deformation. So the above model can be expanded as following:

⎡x ⎤ ⎡X ⎢ y ⎥ = R ⋅ ⎢Y ⎢ ⎥ ⎢ ⎢⎣ Z ⎣⎢ z ⎦⎥

⎤ ⎥+t ⎥ ⎦⎥

x' = x / z y' = y / z

x"= x'⋅(1+ k1 ⋅ r 2 + k2 ⋅ r 4 ) + 2⋅ p1 ⋅ x'⋅y'+ p2 ⋅ (r2 + 2x'2 ) y" = y'⋅(1+ k1 ⋅ r 2 + k2 ⋅ r 4 ) + p1 ⋅ (r2 + 2⋅ y'2 ) + 2⋅ p2 ⋅ x'⋅y r 2 = x'2 +y'2 u = f x ⋅ x"+cx

k1 and k2 are radial

v = f y ⋅ y"+c y deformation coefficients, p1 and p2 are tangential deformation coefficients. Here we don’t consider higher-rank deformation coefficients, for they have nothing to do with the scenes of screen, or the resolution of shooting since they are internal parameters.

3 The Monocular Measuring Principle Based on Homography Matrix Homography matrix is an important concept of 3-d vision. Assuming π as an arbitrary plane in a 3-d space, in which a 2-d projective coordinate system is established, using x record the homogeneous coordinates of an arbitrary point in the planeo π, with m1 m2, remember the corresponding points on two images, thus the following formula come into existence:

m1 ≅ H1 ⋅ x m2 ≅ H 2 ⋅ m1

(5)

" ≅ " denotes there lacks a constant factor on both side of the equation . When the plane π doesn’t pass by any light heart, both H1 and H2 are 3×3 non odd matrix, customarily, we say that H1 is the homography matrix from π to the first image, and H2

320

J.-H. Li and X.-Z. Xie

is the homography matrix of π between two images. The homography matrix in this paper refers to the former. In actual application, mechanical parts move on the same plane, therefore, a monocular camera measuring method is designed on the basis of homography matrix. In actual measurement, the movement plane of the mechanical parts should be in the plane in which the third coordiante axis is zero, thus a single camera plays its role mostly in obtaining 2-d information at best in one measurement. By (1):

⎡u ⎤ s ⋅ ⎢⎢ v ⎥⎥ = A ⋅ [ R | t ] ⋅ P = M ⋅ P ⎢⎣1 ⎥⎦ ⎡ m11 ⎢ = ⎢ m 21 ⎢⎣ m31

m12

m13

m 22

m 23

m32

m33

⎡X ⎤ m14 ⎤ ⎢ ⎥ ⎥ Y m 24 ⎥ ⋅ ⎢ ⎥ , ⎢Z ⎥ m 34 ⎥⎦ ⎢ ⎥ ⎣1 ⎦

M =A ⋅ [ R | t ]

(6)

(7)

Asumed Z=0, then

⎡X ⎤ ⎡u ⎤ ⎡ m11 m12 m13 m14 ⎤ ⎢ ⎥ Y s ⋅ ⎢v ⎥ = ⎢⎢ m21 m22 m23 m24 ⎥⎥ ⋅ ⎢ ⎥ ⎢ ⎥ ⎢0 ⎥ ⎢⎣1 ⎥⎦ ⎢⎣ m31 m32 m33 m34 ⎥⎦ ⎢ ⎥ ⎣1 ⎦

⎡u ⎤ ⎡m11 m12 m14 ⎤ ⎡ X ⎤ s ⋅ ⎢⎢v ⎥⎥ = ⎢⎢m21 m22 m24 ⎥⎥ ⋅ ⎢⎢Y ⎥⎥ ⎢⎣1 ⎥⎦ ⎢⎣m31 m32 m34 ⎥⎦ ⎢⎣1 ⎥⎦

(8)

Namely sp = M1P , M1 is the homography matix of the matching relation image and the template. After getting the internal parameter of the camera, the homography matrix can be obtained by specific demarcation templates. The followinf can be got after expuncting S:

m11 X + m12Y + m14 − uYm32 = um34 m21 X + m 22Y + m 24 − vYm32 = vm34 y Matrix:

⎡ m11 m12 − um32 ⎤ ⎡ X ⎤ ⎡ um34 − m14 ⎤ ⎢m ⎥⎢ ⎥ = ⎢ ⎥ ⎣ 21 m22 − vm32 ⎦ ⎣Y ⎦ ⎣ vm34 − m 24 ⎦

(9)

(10)

Study of Monocular Measuring Technique Based on Homography Matrix

321

This equation can work out:

X =

( um34 − m14 )( m 22 − vm 32 ) − ( vm34 − m 24 ) m11 m11 ( m 22 − vm 32 ) − m 21 ( m12 − um 32 )

( um 34 − m14 ) m 21 − ( vm 34 − m 24 )( m12 − um 23 ) Y = m 21( m12 − um32 ) − m11 ( m 22 − vm32 )

(11)

If the coordinates of mechanical parts in the image are known, through (11), the coordinates of mechanical parts in real movement plane can be obtained, thus ending the measurement.

4

The Monocular Measurement Steps Based on Homography Matrix

Single camera measurement system mainly consists of five modules. The relationship between each module is serial, and the whole system is divided into the following several major parts: A. Image Acquisition This paper uses Philips’ SPC900 camera with image resolution 320 x 240 and frame rate 25 frames per second. OpenCV is used to capture vedio and process images. B. Camera Calibration This paper used the calibration method based on OpenCV camera which takes the plane board as calibration template. In order to improve the success rate of corner detection, in the periphery of the squares calibration, it is still required to retain a blank area of a cube size. Fig.2 shows the use of 11 x 11 in the plane calibration template, and the border length the square board is 40mm, what the camera needs to do is to grab some pictures of planar calibration template from different angles, thus to realize the camera

Fig. 2. Plane Calibration Templates

Fig. 3. Corner Detection Successful Image

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J.-H. Li and X.-Z. Xie

calibration. Figure 3 is the picture extracted after successful corner detection. Obviously, thanks to the least-square method, the more pictures are grabbed, the more precise the calibration result is. C. Determination of Homography Matrix In order to determine the coordinate origin of the movement surface of mechanical parts, and the direction of coordinate axis, the calibration template is designed as shown in figure 4 (a), the point of intersection of diagonal lines of the rectangules is the origin of the movement plane for mechanical parts, one o diagonal line is as the X axis, the other is as the Y axis. Figure 4 (b) is the results of element extracting: through special calibration template to determine rotation matrix and parallel moving matrix, using formula (7) to get matrix M, furthermore determining homography matrix M1.

(a)

(b)

Fig. 4. (a) Template image, (b) Extracting elements

D. The Extraction of Target Image Coordinate and Actual Coordinate Calculation Process a frame of image captured by camera, extract the position of goal in image to get image coordinates, and use formula (11) to calculate the actual coordinates of the target on motion plane.

Fig. 5. Monocular Calibration and Measuring Interface

Study of Monocular Measuring Technique Based on Homography Matrix

5

323

Experimental Results

According to the above calibration principle, on Windows flat, using Visual studio 6.0, a single calibration and measurement procedures are developed based on a homography matrix which is also based on an OpenCV1.0. The programming interface is shown in figure 5, this calibration procedures can complete the calibration of homography matrix and the calculation of coordinate of the target object. According to actual application, the measurement target of this paper is a brown pot. There is no human’s participation during the calibration and measurement process.

Fig. 6. Model of Monocular Measurement Table 1. Experimental Data (Unit: mm) Number

Measurements of X

Measurements of Y

Real value of X

Real Value of Y

0

2.30

-0.47

0

0

5

199.86

-0.141

200

0

9

361.72

-1.987

360

0

15

194.05

38.02

200

40

23

109.10

74.56

120

80

38

304.80

104.71

320

120

47

262.18

143.48

280

160

52

72.66

189.55

80

200

69

346.33

225.83

360

240

75

187.10

264.96

200

280

86

227.67

301.62

240

320

93

106.97

338.73

120

360

99

335.01

337.96

360

360

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J.-H. Li and X.-Z. Xie

When experimenting, after getting homography matrix, the coordinate origin and matrix of the motion plane of an object, use the calibration template shown in fig.6, overlap one angle of the calibration template with the coordinate origin, and makes the calibration panels in the first quadrant of the whole calibration plane. From the formula (11), according to the image coordinates of the intersection of each line on the template, the measurement value of the actual coordinates can be calculated and compared with the real coordinates, the experimental results are shown in table 1.

6

Conclusion

This image resolution adopted 320 x 240, imaging size of view was 1600mm x 1200mm, under the premise of no subpixel processing, the maximum error caused by the pixels was 1,600/320 = 5mm. The experimental data shows that this method, in the condition that the lens’ nonlinear distortion is small, can achieve the calibration of homography matrix in monocular measuring through specific calibration template, thus to satisfy the simplicity real online measurement. We apply the results in on-line positioning of mechanical parts, the measurement error is less than 10%, meeting the practical orientation requirements, and its calibration cost is low, and the real measuring range is large. If the number of pixels of the CCD is improved or the image subpixels are processed, the imaging scene is reduced, and the accuracy of the calibration target is improved, this calibration target system can improve the accuracy of measurement.

References [1] [2] [3] [4]

Zhang, Y.J.: Image Engineering, 2nd edn. Tsinghua University Press, Beijing (2007) Zhang, Z.: A flexible new technique for camera calibration IEEE Transactions on Pattern Analysis and Machine Intelligence 22(11), 1330–1334 (2000) Shakunaga, T.: An Object Pose Estimation System Using a Single Camera. Intelligent Robots and Systems, 1053–1060 (1992) [5] Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)

An AHP Grey Evaluation Model of the Real Estate Investment Risk Ba Xi, Zhang Yan, and Wu Yunna Dept. of Economic & Management, School of North China Electric Power University, Beijing, China

Abstract. Risk evaluation has been an important task for real estate investments in uncertain economic condition. In order to find an effective method to deal with the uncertainty of risk evaluation, a model based on the AHP and the Grey system theory was proposed. Though an example, we draw the conclusion that the evaluation result is in accordance with the fact. To break through conventional method, the calculation of the given demonstration example uses MATLAB 7.8, making the process more simple and quick. The result of the research has manifested that the experiences of experts can be fully absorbed. The evaluation results become more accurate and conform to the realistic situation. Keywords: real estate, investment risk, grey theory, risk evaluation.

1

Introduction

In recent years, the real estate investment has been subject to various risks, for example macro-control policy, concerning macro environment, regional environment, consumptive environment, which will cause increasing in costs or time delay, or even project failure. In fact the characteristics of high input, long cycle and great income in real estate industry must go with the huge risk. Facing the unprecedented challenges and opportunities, in order to make the investment decision more scientific and win better investment benefit, how to improve investment risk analysis and management level becomes a current earnest problem and a lot of effort has been made to settle it. The risk assessment of the real estate investment can properly carry out the analysis and evaluation on the instability and the effect caused by the instability and the degree of effect on the real estate investment [1]. The investment risk analysis and management seem to be very essential for the decreasing loss caused by risks. In fact, based on the summarization about the investment risk analysis and managing management theories in domestic and international real estate market, we know there are many risks of real estate investment, such as the social risk, economical risk, technological risk, operating risk, and natural risk according to the sources [2]. There are too many complex risk factor, and there are different cites in different place, such as, according to the lifecycle theory, we can decompose the investment risk into risk in Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 325–334, 2011. © Springer-Verlag Berlin Heidelberg 2011

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X. Ba, Y. Zhang, and Y.Wu

making strategic decision period, risk in preparing for building period, risk in building period, risk in salting period and risk in in-service period[3]. Additionally, the risk in real estate investment is also can be divided into systemic risk and non-system risk, based on the principles that whether this kind of risk influences all investment projects and whether there have investors try to avoid or dispel it . The real estate market includes complex and dynamic factors, such as human, policy environment, etc, each factors play an important role on the performance of the real estate. These characteristics cause that the real estate industry is different with other product investments, so we have to carry on a through analysis and science guard, in order to hold the source, essence and change trend and law of the risk, and then take the corresponding measures or countermeasure to reduce risk losses. In deed, it is necessary to master the nature of risk and the changing law of risk. However, the information of some factors is inadequacy, so it is grey. According to this point, we adopt the method of Grey system theory. Professor Julong Deng first proposed the Grey theory in 1982, which has been applied to various areas such as forecasting, system control, decision making. The good examples of these studies which have been published recently are as follows: Shang Haofa and Li Dan [4] had introduced the grey system theory into the comprehensive risk evaluation, to avoid the subjective factors and fuzzy factors. Through a systematic and logical index evaluation system, they had a grey synthetically model is established by combining qualitative analysis and quantificational computability. To break through conventional method, the calculation of the given demonstration example uses Visual Basic 6.0, making the process simple and quick. Zhang Jian-kun, Zhang Pu [5] analyzed the influence factors of project investment risk, and used the grey fuzzy comprehensive evaluation method to evaluate. Moreover, Zhou Shujing and Zhu Zhi [6] apply the group AHP and hierarchical assessment system, which can avoid the subjectivity. The grey-relevant method was used to present a real estate investment decision method based on grey synthesis assessment. In deed, the research on the Grey system theory and risk evaluation is still insufficient; we still have a long way to go. In this paper, an AHP grey evaluation model has been proposed. The paper is organized as follows: In section 2, we have a scientific classification to all kinds of risks which may exist. In Section 3, a new improved methodology is presented, including an example for the model. Finally, concluding remarks are offered in Section 4.

2

Risk Factors Analysis of Investment

There are many real estate investment risk classifications, including subjective factors, objective factors, inner factors and external factors, which is dynamic, hierarchical, open and systemic[7].To scientifically evaluate the investment risk in real estate , and make evaluation index system is easy to operate , the design principle of the index system are system, science, feasibility, comparability. The ultimate purpose of project assessment is making decision, by contrast the returns and risk of investment of. According to the nature of risk, it can be divided into: systemic risk (risk of the market

An AHP Grey Evaluation Model of the Real Estate Investment Risk

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system) and non-system (project-specific risk).Here, the evaluating indicators system is shown in Fig 1, which includes 9 sub-indicators. In deed, the evaluating indicator of every level may be various because every investment risk in different real estate project or each stage and are different and various, which is very difficult to generate an universally evaluating indicators system.

Fig. 1. Evaluating Indicators System of Investment Risk in Real Estate

(1) Inflation risk Due to long cycle of the real estate investment, the investors will face the risk of inflation risk, which may be reducing the return of investment. (2) Market supply and demand risk Market supply and demand risk refer to investors will meet some uncertain factors, which may make influence on the supply and demand in the market after the real estate goods are built up. The existence of these uncertain factors may influence the introduction of the real estate goods and then make developer suffer losses. (3) Interest rate risk The interest rate risk refer to the possibility of loss, brought by the change of interest rate, which maybe make a loss of earnings and increase the cost and risk of land reserve. (4) Accounting liquidity risk The accounting liquidity risk refer to the investors suffers losses because of discount, the reason of which is the poor liquidity and a complex sales process. (5) Periodicity risk The periodicity risk refer to the investors suffers losses because of Periodical fluctuation of real estate, including recovery, prosperity, decline and depression four stages. It enables the investors who are not strong and are weaker to resist risk go bankrupt because of the problems such as the fund debt, etc.

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(6) Natural risk The nature risk is natural uncertain factors such as earthquakes, floods, tornadoes, etc, that bring risk to real estate investment and people are out of control. The chance of nature risks is less, but once appear, the damage is very serious. (7) Policy risk Policy risk is the risk that a government will discriminatorily change the laws, regulations or contracts governing an investment-or fail to enforce such covenants, in a way that reduces investors’ financial returns [8]. The changing of policy will make overall risk significant impact on the real estate market. (8) Management risk The management risk refers to expected income can not be got or insufficient compensation, because of poor management of the real estate investors. These risks can be attributed to the prediction and management error, with quite a lot considerable subjective factors. (9) Financial risk Financial risk arises from the realization of the fact that manifold and diverse might be the causes, or factors, of risks around a specific project or business, which produce the possibility of financial risk. Real estate are facing with a series of risks that attribute to such characteristics as high investments, continual reward, long time construction and running, dependence on the professional management etc[9].The risk factors' range very wide and source numerous, all of which should not be underestimated.

3 An AHP Grey Evaluation Method The assessment model which consists of eight main steps will be presented in this section. The main framework of the proposed methodology is given in as follows Step 1: Determine risk factors: The possible risk factors ale determined by the expert group by brain storming, literature survey and case analysis. We can construct the evaluation as follows

U = [u1 , u 2 , L , u i , L, u m ]  Where,

(1)

m is the number of factors.

Step 2: Constructing the set of evaluation criteria: Here, the risk of real estate investment is divided into five levels: highest risk, higher risk, general risk, lower risk and lowest risk, they are expressed by

X = ( x1 , x2 , x3 , x4 , x5 ) 

(2)

An AHP Grey Evaluation Model of the Real Estate Investment Risk

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Here we have

x1 = 0.5, x 2 = 0.4, x3 = 0.3, x 4 = 0.2, x5 = 0.1

(3)

Step 3: Confirm the index weight This paper adopts AHP. It is a management tool for decision making in multi-attribute environments. The fundamental approach of AHP is to breakdown a “big” problem into several “small” problems, while the solution of each small problem is relatively simple. Its step is as follows: at first, adopt the form of expert's questionnaire to seek the opinion of some authoritative person opinions in real estate, constructing the matrix of main factor level and sub factor level. Then figure out characteristic number and characteristic vector of every matrix using MATLAB to receive index weight of main factor level and sub factor level[10] The AHP will be better to reduce the subjectivity during the evaluating. The index weight of factors in Fig.1 is shown as follows

tw1 = [0.6,0.4] tw2 = [0.32,0.16,0.32,0.06,0.06,0.06,0.02] 

(4)

tw3 = [0.67,0.33] Step 4: Organize estimators for Grade. Inviting 5 experts on real estate investment to discern the risks of this project, every person gives a mark to every index in the index system separately, according to the point scaleTgiven on step to and fills in pointing form, obtained matrix of appraising sample D as follows

⎡ 0 0.1 0.3 0.4 ⎢0.3 0.3 0.3 0.3 ⎢ T D =⎢0.2 0.3 0.3 0.2 ⎢ ⎢0.1 0.2 0.1 0.1 ⎢⎣0.4 0.1. 0 0

0 0.2. 0.3 0.1 0.3⎤ 0.1 0.1 0.3 0.3 0.2⎥⎥ 0.4 0.4 0.2 0.4 0.2⎥ ⎥ 0.3 0.3 0 0.1 0.3⎥ 0.2 0.2 0.2 0.1 0 ⎥⎦

(5)

Step 5: Confirm appraisal grey species Suppose serial number of appraisal grey is

e(e = 1,2,3,4,5)  which means highest risk, higher risk, general risk, lower risk and lowest risk.

(6)

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The 1st grey species ( e = 1) , where the grey number

⊗1 ∈ [0,0.1,0.2] 

(7)

The winterization weight function is

⎧0.1 ⎪ f 1 ( d i jk ) = ⎨0.2 − d i jk ⎪ ⎩0

d i jk ∈ [0, 0.1] d i jk ∈ [0.1, 0.2]

(8)

d i jk ∉ [0, 0.2]

, where the grey number The second grey species（e = 2）

⊗2 ∈ [0,0.2,0.4]

(9) 

The winterization weight function is

⎧ di jk 0.2 di jk ∈[0, 0.2] ⎪ f 2 (di jk ) = ⎨(di jk − 0.4) (−0.2) di jk ∈[0.2, 0.4] ⎪ di jk ∉[0, 0.4] ⎩0

(10)

The third grey species（e = 3）, where the grey number

⊗ 3 ∈ [0,0.3,0.6] 

(11)

The winterization weight function is

⎧ di jk 0.3 di jk ∈[0, 0.3] ⎪ f3(di jk ) = ⎨(di jk − 0.6) (−0.3) di jk ∈[0.3, 0.6] ⎪ di jk ∉[0, 0.6] ⎩0 The 4th grey species ( e

(12)

= 4) , where the grey number ⊗ 4 ∈ [0,0.4,0.8] 

(13)

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The winterization weight function is

⎧ di jk 0.4 di jk ∈[0, 0.4] ⎪ f 4 (di jk ) = ⎨(di jk − 0.8) (−0.4) di jk ∈[0.4, 0.8] ⎪ di jk ∉[0, 0.8] ⎩0

(14)

The 5th grey species（e = 5）, where the grey number

⊗ 3 ∈ [0,0.3,0.6]  

(15)

The winterization weight function is

⎧di jk 0.5 di jk ∈[0, 0.5] ⎪ f1 (di jk ) = ⎨0.1 di jk ∈[0.5, ∞) ⎪ di jk ∉[0, ∞) ⎩0

(16)

where d i jk is the score that the k ( k = 1,2,L ,5) estimator appraises index Ai , j . Step 5: Calculate weight vector of grey appraisal. For index U1,1 , the grey appraisal coefficient of the

e grey species is

5

M 111 = ∑ f1 (d i jk ) k =1

= f1 (0) + f1 (0.3) + f1 (0.2) + f1 (0.1) + f1 (0.4)

e =1

(17)

= 0.2 5

M112 = ∑ f 2 (di jk ) k =1

= f 2 (0) + f 2 (0.3) + f 2 (0.2) + f 2 (0.1) + f 2 (0.4)

e=2

(18)

=2 5

M113 = ∑ f3 (di jk ) k =1

= f3 (0) + f3 (0.3) + f3 (0.2) + f3 (0.1) + f3 (0.4) =

8 3

e=3

(19)

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5

M114 = ∑ f 4 (di jk )

(20)

k =1

= f 4 (0) + f 4 (0.3) + f 4 (0.2) + f 4 (0.1) + f 4 (0.4) =

e=4

5 2 5

M115 = ∑ f5 (d i jk ) k =1

= f5 (0) + f 5 (0.3) + f 5 (0.2) + f5 (0.1) + f5 (0.4)

e =5

(21)

=2

The weight vector of grey appraisal is

q11 = (0.03,0.21,0.28,0.27,0.21) 

(22)

We also could have

q12 = (0.02,0.11,0.37,0.28,0.22) q13 = (0.02.0.20,0.33,0.25,0.20) q14 = (0.02,0.21,0.29,0.27,0.21)

q15 = (0.02,0.21,0.29,0.27,0.21) 

(23)

q16 = (0.01,0.25,0.28,0.25,0.21) q17 = (0.01,0.29,0.32,0.19,0.19) q21 = (0.03,0.21,0.29,0.26,0.21) q22 = (0,00,0.29,0.33,0.19,0.19) So, we get grey appraisal matrix

⎡0.03 0.21 0.28 ⎢0.02 0.11 0.37 ⎢ ⎢0.02 0.2 0.33 ⎢ Q1 =⎢0.02 0.21 0.29 ⎢0.02 0.21 0.29 ⎢ ⎢0.01 0.25 0.28 ⎢0.01 0.29 0.32 ⎣

0.27 0.21⎤ 0.28 0.22⎥⎥ 0.25 0.2⎥ ⎥ 0.27 0.21⎥  0.27 0.21⎥ ⎥ 0.25 0.21⎥ 0..19 0.19⎥⎦

⎡0.03 0.21 0.29 0.26 0.21⎤ Q2 = ⎢ ⎥ ⎣ 0 0.29 0.33 0.19 0.19⎦

(24)

(25)

An AHP Grey Evaluation Model of the Real Estate Investment Risk

Step 6: Do comprehensive appraisal for The appraisal result is

333

U1，U 2

B1 = tw2 • Q1 = (0.02,0.20,0.31,0.26,0.21)

(26)

B2 = tw3 • Q2 = (0.02,0.24,0.3,0.24,0.2）

(27)

Step 7: Do comprehensive appraisal for

According to the step 6, we have R

U

⎛ B1 ⎞ = ⎜⎜ ⎟⎟ ⎝ B2 ⎠

The appraisal result is

B = tw1 • R ==(0.02,0.22,0.31,0.25,0.21) 

(28)

Step 8: Calculate the total comprehensive appraisal value

The result B of comprehensive appraisal is a vector, which shows the description comprehensive state of the project on every grey degree. Appraisal grade value vector of every grey species is C :

C = (0.1,0.2,0 .3,0.4,0.5 ) 

(29)

To enable B uniformization, we could make

L = B • CT ⎡0.1⎤ ⎢0.2⎥ ⎢ ⎥ = (0.02,0.2,0.32,0.25,0.21) • ⎢0.3⎥ ⎢ ⎥ ⎢0.4⎥ ⎢⎣0.5⎥⎦ = 0.307

(30)

Finally, the appraisal result is 0.307, showing that the project is feasible from the angle of risk.

4

Conclusion

The risk elements that may appear in the process of real estate investment are analyzed, a scientific evaluation index system is formulated. The AHP Grey model is used to

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evaluate the risk in real estate investment. The AHP Grey model is not only superior and scientific on theory aspect but also reliable in application. Through case study, managers will identify the key risk factors and be conscious of these risks tracking control. The AHP Grey model has a good application value..

References [1] Fan, X.: Study on Risk Assessment Methods for Real Estate Investment. North China Electric Power University (2008) [2] Shao, C.: The Analysis and Comprehensive evaluation of Investment Risk for Real Estate. Central South University (2008) [3] Li, J.: Research on Investment Risk for Real Estate. Jiangsu University (2007) [4] Shang, H., Li, d.: Study on Application of Grey System Theory in Risk Evaluation of Real Estate Project. Construction Management Modernization, 11–13 (2008) [5] Zhang, j., Zhang, P.: The Grey Fuzzy Comprehensive Evaluation of Financing Risk in Real Estate Investment. Construction Management Modernization, 39–42 (2004) [6] Zhou, S., Zhu, Z.: Study on the real estate investment decision using grey hierarchical analysis. Journal of Hebei University of Engineering, 83–86 (2010) [7] Deng, R.: Fuzzy Evaluation of Investment Risk in Real Estate, http://d.g.wanfangdata.com.cn/Conference_WFHYXW270617.aspx:9 28-929 [8] Mu, L., Chen, L., Liu, P.: Real Estate Investment Risk Analysis Based on Fuzzy AHP. IEEE (2008) [9] Wang, X., Yang, Z.: Establishing Support System for Risk Management in Real Estate Investment. Industrial Engineering Institute of Chinese Mechanical Engineering Society (2008) [10] Kang, H., Zhao, J., et al.: AhP-Based Weighting Factors Affecting Electrical Saftey on construction Sites, http://d.g.wanfangdata.com.cn/Conference_WFHYXW377297.aspx

The Error Analysis of Automated Biochemical Analyzer Chen Qinghai, Wu Yihui, Li Haiwen, Hao Peng, and Chen Qinghai Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, CIOMP, Changchun, China Graduate School of the Chinese Academy of Sciences, GSCAS, Beijing, China [email protected]

Abstract. In order to find out the relationships between the four error sources, namely, locating error, optics system fluctuations, sampling error and temperature fluctuations, and the accuracy of the instrument, we analyzed each relationship of them, and found that the effect of locating error is so tiny that it can be ignored, finally established the total error model and the model of the allowed noise of the optics system of the instrument. Analysis and the models may supply a reliable theory evidence for the improvement of the instrument and the designing of similar instruments. Keywords: biochemical analyzer, optical absorbance, error analysis, optics system fluctuations.

1

Introduction

Automated biochemical analyzer is an instrument used to make the sampling, mingling, temperature controlling, color comparing and result calculating automated. It is extensively used for medicament, food, environmental medicine and clinical diagnosis, especially in clinical diagnosis, by means of detecting dozens of biochemical parameters in serum or other body fluid, it can help to form a true estimation of the organs of the patients, so it provides bases for making correct surgical plans. The accuracy of the detection was based on the accuracy of the instrument. Currently, the analysis of the accuracy of the instrument was mostly by experiments, rarely based on theoretical bases. This paper combined theory with practice to make a thorough discussion on the accuracy of it, built the total error model.

2

Principle and System Scheme

Biochemical analyzer is based on the bill rule expressed as bellow, Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 335–341, 2011. © Springer-Verlag Berlin Heidelberg 2011

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A = lg

I0 = ε bC I

(1)

In above equation, A was absorbance, I0 was the intensities of the incident light, I was the intensities of the thorough beam, ε was Moore absorption coefficient, b was optical distance, and C was molar concentration. We could see that, the absorbance was proportionate to the molar concentration, which was the fundamental basis of the quantitative analysis of the instrument. The structure model of the instrument built by 3D designing software Solidworks, was shown in Fig. 1. Colorimetric cuvettes were placed in a frame, the centre of which was the light source. The beam shined from the light was made parallel, then filtered through the colorimetric cuvette, broke into the spectrograph. The calculation system would sample the intensities of the light, at last, calculated the absorbance, and then calculated the concentration. Because there was a light hole in the heat-resistant ring between the light and the spectrograph, the teat radiation was acuter than other parts, which made the temperature there was different from other parts of the heat-resistant ring. In order to eliminate the bad effect caused by different temperature, the frame was rotating all the time except the sampling time, so the locating error turned up.

Fig. 1. 3D entity model of the Biochemical Analyzer

3

The Error Analysis of the System

From equation (1), we knew that the aspects affecting the accuracy of the instrument including: 1) Optical distance b, decided by locating error; 2) The intensities I0and I, controlled by the noises of optical system, including light, CCD and ADC, etc. ;

The Error Analysis of Automated Biochemical Analyzer

337

3) Concentration C, depending on sampling error; 4) Temperature fluctuation. A. The Locating Error of the Frame When the slit of the code wheel arrived at the photo-electric sensor, the sensor detected the optical signals, and then the sensor sent signals to MCU, the MCU stopped sending pulses to drive, so the stepper motor stopped rotating and kept holding. When locating, there were two extreme cases: one was that the slit had just passed the edge of the light receiver of the sensor, and the sensor received the signals; the other was that before locating, the slit had just arrived the edge of the light receiver of the sensor, needed to move one more step to pass the edge. Just that one step caused the maximum locating error. Secondly, the error of gear drive was also a factor. It came from manufacturing errors and assembling errors of the gear, shaft and bearing, among which the manufacturing error of the gear Tm was the main factor, the error Tc caused by the fit clearance between shaft and gear hole was a secondary factor [2]. So the error of single gear drive was Ti = ∑ Tm + Tc 2 . 2

Lastly, the index error of code wheel Tw and the index error of the frame Tf were also affecting factors. So the total locating error was [3]:

Tsystem = Ts 2 + δ 2 + ∑ Ti 2 + Tw 2 + T f 2

(2)

The locating error mainly affects the optical distance. As shown in Fig. 2, when the frame was not located in right position, the beam would not be perpendicular to the optical surface of the colorimetric cuvette, which would result in longer optical distance; b −b ΔA Δb cos Δϕ 1 = = = −1 A b b cos Δϕ

(3)

In above equation, Δφ was the error of rotatory angle transformed from the total locating error of the frame.

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Fig. 2. Positioning error’s effect on optical path

B. The Effect of the Noises of the Optical System The noises of the optical system were made up of the fluctuations of the power supply, the noises of CCD, the fluctuations of the reference voltage of the ADC and transformation error, etc.

Fig. 3. Schematic circuit diagram of the direct constant-current source

If the power supply fluctuated 1%, the luminous flux of the light would fluctuate 3.4%. We designed a direct constant-current source, made up of an operational amplifier and a field effect transistor, constituting a closed loop feedback system. Its circuit diagram was as shown in figure 3. We applied reference voltage max6325 (temperature coefficient was 1ppm/°C), low temperature drift operational amplifier op37 and high-power wire-wound resistor to make the direct constant-current source. Experiment result showed that when the current was 1.667 A, it fluctuated 2 mA, accuracy was 0.12%. From equation (1), we knew

ΔA=

1 ΔI0 ΔI ( − ) ln10 I0 I

(4)

The Error Analysis of Automated Biochemical Analyzer

339

In above equation, the distribution of ΔI0 and ΔI were the same normal distribution N (0, σ 2 (lamp )) , and they were mutually independent; therefore, the distribution of ΔA was normal distribution N (0, ( 1 + 1 )( σ (lamp ) ) 2 ) , so ln10 I 02 I 2

σ 1 ( A) =

σ (lamp ) I 0 ln10

1 + 10 2 A

(5)

In above equation, σ1(A) was standard deviation of the absorbance caused by the noises of optical system, σ(lamp) was standard deviation of the relative intensities of light. From equation (5) we knew magnifying I0 helped to minish σ1(A). C. Sampling Error and Its Effect to Test Result If sample had a error ΔV, equation (1) was transformed to A ' = ε b

σ 2 ( A) =

ΔV + V C , so V

A σ (V ) V

(6)

In above equation, σ2(A) was standard deviation of absorbance caused by sampling error, V was accurate sample quantity, and its unit was μL, A was absorbance in ideal condition, A ' was concrete absorbance. Equation (6) showed that absorbance error was proportional to sampling error. D. The Effect of Temperature Fluctuations Suppose in a small temperature range, there was a relationship as below between absorbance and temperature:

A = A0 + k ΔT

(7)

In above equation, A0 was absorbance under temperature 37°C, ΔT was temperature differentials from 37°C, k was the average slope of the absorbance with the change of temperature. From equation (7), we knew absorbance fluctuation was δ lim3 A =k ΔT . Suppose

（）

fluctuation of temperature was uniform distributiton [4], the standard deviation of absorbance was

（）

σ3 A =

k ΔT 3

(8)

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Synthesis and Analysis of Errors

The rotatory angle Δφmax calculated from equation (2) was Δφmax =110.6", applied it to equation (3), we got ΔA = Δb = A

b

1 −1 = 1.4376 ×10−7 , so the relative error of cos Δϕ

absorbance was so tiny that we could neglected it. Confirmatory experiment, as shown in figure 4, we fixed a laser pointer on the center of the frame, and laid a receiver panel at 6.3 meters. We regarded the first locating position of a colorimetric cuvette we chose as the zero point, and then repeated it 49 times, recording the position of every locating, positions were as table 1.

Fig. 4. Locating error verification schematic diagram

Table 1. Displacement of the Laser Spot Center in 50 Times Locating NO. 1~5 6~10 11~15 16~20 21~25 26~30 31~35 36~40 41~45 45~50

0 -0.2 0 -1.3 0 -0.2 0 0 -0.2 0

Displacement of facula/mm 0 0 0 0 -0.2 0 0 -0.2 0 0 0 0 0 0 0 0 0 0 0 -1.3 0 -0.2 0 0 0 0 0 -0.2 0.5 0 0 0 -0.2 0 0 0 0 0 0 -0.2

From table 1, we knew the maximum displacement of the facula was 1.8mm, namely, the maximum rotatory angle error of the frame was 58.93″, less than Δφmax. The above three errors were mutually independent, so the total absorbance error was

The Error Analysis of Automated Biochemical Analyzer

σ (A) =

σ 12 ( A ) + σ

2 2

(A) + σ

2 3

341

(A) ,

Namely,

σ ( A) = (1 + 102 A )

σ 2 (lamp)

A k 2 ΔT 2 2 + ( σ ( V )) + 3 I 02 ln 2 10 V

(9)

In addition, there were clear requirements about sampling error and fluctuations of temperature in industry standard on biochemical analyzer [5], but not about the noises of optical system that was hidden in other stipulations. Therefore, designers of instruments were not clear about it. In order to change this phenomenon, we deduced the permitted noises from equation (9), and it was

σ ( lamp ) = I 0 ln 10

5

(

σ ( A) A

)2 − (

σ (V )

)2 −

V 1 + 10 2 A A2

k 2 ΔT 2 3 A2

(10)

Conclusion

This paper made a detailed analysis about the four errors affecting the absorbance error, and results showed that: 1) Under the condition that the accuracy of gears met the requirements of GB/T10095.1-2001/7 precision, there was a closed loop feedback system and subdivided the step-angle into 64, the locating error of the frame was so tiny that it could be ignored. 2) Had a clear understanding of the relationship between the four errors and the accuracy of the instrument. In addition, we built the model of total error and the model of noises of the optical system, both of which supplied theory evidence to the improvement of instruments and provided reference to the design of similar instruments.

References [1] Li, C.: Ultraviolet/Visible Spectrometer (1st edit), vol. 6, p. 9. Chemical Industry Press, Beijing (2005) [2] Yang, P., Lian, Z.: Mechanical and Electronic Engineering. National Defense Industry Press, Beijing (2001) [3] Chen, Y., Hu, H.: Analysis of rotatory locating system of automated analyzer and parameter optimization. Mechanical Design (2006) [4] Mao, Y.: Theory of error and Analysis of accuracy (1st edit), 1st edn. National Defense Industry Press, Beijing (1982) [5] Standard of Medicine industry in People’s Republic of China YY/T 0654-2008

LOD-FDTD Simulation to Estimate Shielding Effectiveness of Periodic Structures Chen Xuhua, Yi Jianzheng, and Duan Zhiqiang Department of Ammunition Engineering, Mechanical Engineering College, Shijiazhuang, Hebei, China [email protected]

Abstract. A three-dimensional unconditionally stable locally-one-dimensional FDTD (LOD-FDTD) method is developed and is extended to analyze periodic structures. The number of equations to be computed in LOD-FDTD is the same as that with the three-dimensional alternating-direction implicit FDTD (ADI-FDTD) but with reduced arithmetic operations. For periodic LOD-FDTD method, the cyclic matrix lead by the periodic boundary condition can be converted into two auxiliary linear systems that can be solved using the tridiagonal matrix solver. Numerical simulations to calculate the shielding effectiveness of a wire mesh screen are compared with those using the conventional FDTD and ADI-FDTD method, which further demonstrate the effectiveness of this periodic LOD-FDTD method. Keywords: LOD-FDTD, shielding effectiveness, periodic structures, FDTD method.

1

Introduction

Periodic structures find many applications in engineering electromagnetics, such as filters, polarizers and shielding, etc.[1]. For optimal cost efficiency, it is important to estimate their electromagnetic shielding effectiveness during the design phase when periodic structures are applied in electromagnetic protections. Because they are usually large, numerical methods that incorporate the periodic characteristics are typically more appropriate. In these methods, instead of analyzing the entire structure, only a single-unit cell needs to be modeled. Among these techniques, the periodic FDTD is well known as being one most useful numerical technique for such problems[2]. Furthermore, its solving procedure is more straightforward than other numerical methods, such as the method of moments (MoM) and the finite element method (FEM)[3]. Since the periodic FDTD method is derived from the conventional FDTD method, the time-step size of the periodic FDTD method cannot exceed the Courant– Friedrichs–Lewy (CFL) limitation[4]. This leads to computational inefficiency because fine meshes would result in unwanted small time-step sizes. In fact, many Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 342–350, 2011. © Springer-Verlag Berlin Heidelberg 2011

LOD-FDTD Simulation to Estimate Shielding Effectiveness of Periodic Structures

343

micrometer-thick periodic structures are often used today, thus, the inefficiency of the FDTD method is a very serious issue for optimizing the design process. To overcome the CFL constraint, some unconditionally stable techniques, such as alternating direction implicit FDTD (ADI-FDTD)[5] method and Crank Nicolson FDTD (CN-FDTD)[6] are proposed. The CN-FDTD method is more accurate than the ADI-FDTD method but much more expensive in terms of computational expenditures. And more recently, the locally-one-dimensional FDTD (LOD-FDTD)[7], have been proposed. In LOD-FDTD, the number of equations to be computed is the same as ADI-FDTD but with reduced arithmetic operations. In this paper, we propose using the LOD–FDTD method to solve the shielding problems of periodic structures. The LOD-FDTD method that consists of two steps as ADI-FDTD does but with fewer arithmetic operations is extended to analyze periodic structures. And the shielding effectiveness of a wire mesh screen, are calculated using LOD-FDTD method. The numerical results are presented in a comparison of results for the conventional FDTD and ADI-FDTD to demonstrate the effectiveness of the proposed method.

2

LOD-FDTD Formulations

A. The Standard LOD-FDTD Method Maxwell’s equations in isotropic and lossy media are given as ∇× H = ε

∂E + σ E , ∇ × E = − μ ∂H − σ m H ∂t ∂t

(1)

These equations can be expressed in the Cartesian coordinates as ∂U = [ A] U + [ B ] U ∂t

(2)

where U = ⎣⎡ Ex , E y , Ez , H x , H y , H z ⎦⎤′ ⎡ −σ ⎢ 2ε ⎢ ⎢ ⎢ 0 ⎢ ⎢ 0 ⎢ [ A] = ⎢ ⎢ 0 ⎢ ⎢ ⎢ 0 ⎢ ⎢ ⎢ ∂ ⎢ μ∂y ⎣

0

0

0

0

−σ 2ε

0

∂ ε∂z

0

0 ∂

−σ 2ε

0 −σ m 2μ

∂

ε∂x

μ∂z

0

0

μ∂x

0

−σ m 2μ

0

0

0

0

∂

0

∂ ⎤

ε∂y ⎥

⎥ ⎥ 0 ⎥ ⎥ 0 ⎥⎥ ⎥ 0 ⎥ ⎥ ⎥ 0 ⎥ ⎥ ⎥ −σ m ⎥ 2μ ⎥⎦

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and ⎡ −σ ⎢ 2ε ⎢ ⎢ 0 ⎢ ⎢ ⎢ 0 ⎢ [ B] = ⎢ ⎢ 0 ⎢ ⎢ ⎢− ∂ ⎢ μ∂z ⎢ ⎢ 0 ⎢ ⎣

0

0

0

−σ 2ε

0

0

0 0 0 −

∂

μ∂x

−σ 2ε ∂ − μ∂y

−

∂

ε∂y −σ m 2μ

−

∂

ε∂z 0 0 0

0

0

−σ m 2μ

0

0

0

⎤ 0 ⎥ ⎥ ∂ ⎥ − ε∂x ⎥ ⎥ 0 ⎥ ⎥ ⎥ 0 ⎥ ⎥ ⎥ 0 ⎥ ⎥ ⎥ −σ m ⎥ 2 μ ⎥⎦

Applying ∂U ∂t ≈ (U n +1 − U n ) Δt and U n+0.5 ≈ (U n+1 + U n ) 2 to (2) leads to the so-called Crank-Nicolson formulation, which can be further approximated with the following formulations Δt Δt Δt Δt ⎛ ⎞⎛ ⎞ n+1 ⎛ ⎞⎛ ⎞ n ⎜ [ I ] − [ A] ⎟ ⎜ [ I ] − [ B ] ⎟ U = ⎜ [ I ] + [ A ] ⎟ ⎜ [ I ] + [ B ] ⎟ U 2 2 2 2 ⎝ ⎠⎝ ⎠ ⎝ ⎠⎝ ⎠

(3)

With substitution of field components into U , it can be computed into two sub time steps as follows Sub-step 1: ⎛ ∂H n+0.5 ∂H zn ⎞ + Exn+0.5 = ca ⋅ Exn + cb ⋅ ⎜ z ⎟ ∂y ⎠ ⎝ ∂y ⎛ ∂H n+0.5 ∂H xn ⎞ Eyn+0.5 = ca ⋅ E yn + cb ⋅ ⎜ x + ⎟ ∂z ⎠ ⎝ ∂z

(4) (5)

⎛ ∂H yn+ 0.5 ∂H yn ⎞ + E zn+ 0.5 = ca ⋅ E zn + cb ⋅ ⎜ ⎟ ⎜ ∂x ∂x ⎟⎠ ⎝

(6)

⎛ ∂E n +0.5 ∂E yn ⎞ + H xn +0.5 = cp ⋅ H xn + cq ⋅ ⎜ y ⎟ ⎜ ∂z ∂z ⎟⎠ ⎝

(7)

⎛ ∂E n+0.5 ∂Ezn ⎞ + H yn+0.5 = cp ⋅ H yn + cq ⋅ ⎜ z ⎟ ∂x ⎠ ⎝ ∂x ⎛ ∂E n+0.5 ∂E n ⎞ H zn+0.5 = cp ⋅ H zn + cq ⋅ ⎜ x + x ⎟ ∂y ⎠ ⎝ ∂y

(8) (9)

Sub-step 2: ⎛ ∂H yn +0.5 ∂H yn +1 ⎞ + Exn +1 = ca ⋅ Exn+0.5 − cb ⋅ ⎜ ⎟ ⎜ ∂z ∂z ⎟⎠ ⎝

⎛ ∂H n+0.5 ∂H zn+1 ⎞ E yn+1 = ca ⋅ E yn+0.5 − cb ⋅ ⎜ z + ⎟ ∂x ⎠ ⎝ ∂x ⎛ ∂H n+0.5 ∂H xn+1 ⎞ Ezn+1 = ca ⋅ Ezn+0.5 − cb ⋅ ⎜ x + ⎟ ∂y ⎠ ⎝ ∂y

(10) (11) (12)

LOD-FDTD Simulation to Estimate Shielding Effectiveness of Periodic Structures ⎛ ∂E n+0.5 ∂Ezn+1 ⎞ H xn+1 = cp ⋅ H xn+0.5 − cq ⋅ ⎜ z + ⎟ ∂y ⎠ ⎝ ∂y ⎛ ∂E n+0.5 ∂E n+1 ⎞ H yn+1 = cp ⋅ H yn+0.5 − cq ⋅ ⎜ x + x ⎟ ∂z ⎠ ⎝ ∂z ⎛ ∂E n+0.5 ∂E yn +1 ⎞ H zn+1 = cp ⋅ H zn +0.5 − cq ⋅ ⎜ y + ⎟ ⎜ ∂x ∂x ⎟⎠ ⎝

345

(13) (14) (15)

The coefficients are defined in the same way as in the conventional FDTD method and they are as follows ca =

4ε − σ t 2 t 4μ − σ m t , cb = , cp = , cq = 2 t 4ε + σ t 4ε + σ t 4μ + σ m t 4μ + σ m t

None of these equations can be used for direct numerical calculation because they include the components defined as synchronous variables on both the left and right hand side; thus, modified equations are derived from the original equations. In the sub-step 1, the Ex component on the left hand side and the H z components on the right hand side are defined as synchronous variables in (4). Thus, a modified equation for the Ex component is derived from (4) and (9) by eliminating the H zn+ 0.5 components as follows α Exn+0.5 ( i + 0.5, j − 1, k ) + β Exn+0.5 ( i + 0.5, j, k ) + γ Exn+0.5 ( i + 0.5, j + 1, k ) = b

(16)

where α =−

cb ( i + 0.5, j , k ) ⋅ cq ( i + 0.5, j − 0.5, k )

β =1−α − γ γ =−

( Δy )

2

cb ( i + 0.5, j , k ) ⋅ cq ( i + 0.5, j + 0.5, k )

( Δy ) n b = −α Ex ( i + 0.5, j − 1, k ) + ( ca + α + γ ) Exn ( i + 0.5, j , k ) − γ E xn ( i + 0.5, j + 1, k ) +

2

2cb n ( H z ( i + 0.5, j + 0.5, k ) − H zn ( i + 0.5, j − 0.5, k ) ) Δy

In the same way, the modified equation for the E yn+0.5 and Ezn+0.5 components can be derived from (5), (7) and (6), (8), respectively. By solving their simultaneous linear equations, we can get the values of the electric field components at the time of n+0.5. Thereafter, the values of the magnetic field components at the time n+0.5 can be got directly from (7)-(9). For sub-step 2, the similar procedure as above would be executed to deduce the field components at the time n+1.Since the simultaneous linear equations such as (16) can be written in a tridiagongal matrix form, it can be solved efficiently by Gaussian elimination[8]. Note that in the above formulations, there are six implicit and six explicit equations to be computed in total for a full time step, the same number as those of the ADI-FDTD method. However, as shown in (16), the right hand sides of the equations have fewer terms than those with the ADI-FDTD method. As a result, the computational time with the proposed LOD method will be less.

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B. The Periodic LOD-FDTD Method The implementation of the periodic boundary condition in the explicit updates is the same as that of the FDTD method. Because the treatment of Ex , E y and Ez in the implicit updates of the periodic LOD-FDTD method is identical, we shall only use Ex as an example. Considering a linear system of equations

[M ] x = b

(17)

where b represents the right hand vector and x the unknown in general. When the periodic boundary condition is applied in the y direction and normal incidence is assumed, [ M ] is obtained from (16) for each column of Ex as ⎡β ⎢α ⎢ ⎢M [M ] = ⎢ 0 ⎢ ⎢0 ⎢ ⎢⎣ γ

γ β

0 L L α⎤ 0 L 0 ⎥⎥ O O O M⎥ ⎥ O O O 0⎥ 0 α β γ⎥ ⎥ L 0 α β ⎥⎦

γ

O O L L

(18)

The above is a cyclic matrix. By applying an indirect solving procedure, (17) can still be solved by Gaussian elimination with O(n) operation count. To do this, we consider [M] as a perturbation of matrix [N] by the following relation:

[ M ] = [ N ] − v1v2T

(19)

where v1 and v2 are two vectors. The Sherman-Morrison formula prescribes a solution of [ M ]−1 in terms of [ N ]−1 as[9]

[M ]

−1

= [N ] + −1

[N ]

−1

v1v2T [ N ]

−1

1 − v [ N ] v1 T 2

−1

(20)

To evaluate (20), we need to define two auxiliary linear problems, i.e.

[ N ] x1 = b

(21)

[ N ] x2 = v1

(22)

and

After solving x1 and x2 , the solution of the original linear problem defined by (18) is obtained via x = x1 + ζ x2

(23)

v2T x1 1 − v2T x2

(24)

where ζ=

When applying the Sherman-Morrison formula, it is important to find a matrix [ N ] that is easy or efficient enough to solve in the auxiliary problems. Therefore, the major

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347

differences between the periodic LOD-FDTD method and the stand LOD-FDTD method are: in solving the matrix equation in the implicit updates, the traditional LOD-FDTD method solves it directly while the periodic LOD-FDTD solves it indirectly in two steps. The first step is to solve (22) and store the solution beforehand. The second step is to solve (21) at each time step with an updated right hand vector b , and then obtain the final solution according to (23) and (24).

3

Shielding Effectiveness Simulation

In order to demonstrate the periodic LOD-FDTD method, numerical example to compute the shielding effectiveness of a wire mesh screen is presented. Simulations were carried out using both the LOD-FDTD and the conventional FDTD method for comparison. Fig. 1 shows the physical model and numerical model of the mesh screen, a periodic structure material. The mesh screen is a periodic array of individual square meshes with the wire junctions assumed to be bonded. The individual square meshes have a dimension of 1 cm × 1 cm, and the radius of the mesh wire is 0.2 cm. The mesh is made of certain composite metal, whose electromagnetic characteristics are ε r = 2.0 , μr = 5.0 and σ = 1×106 S m . The wire mesh is set in the x-y plane, and the periodic

boundaries are applied in the x and y direction, while the UPML absorbing boundary condition is set in the z directional terminals. A Gaussian pulse is excited at the Ex components on the excitation plane, and the Ex components at the observation points would be recorded. The waveform of the applied pulse is as follows

r a

(a) Mesh screen Periodic boundary

UPML

UPML Excitation plane

Periodic boundary

(b) Fig. 1. Wire mesh screen (a) Physical model (b) Numerical model

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C. Xuhua, Y. Jianzheng, and D. Zhiqiang ⎛ − ( t − t0 ) 2 ⎞ ⎟ , t0 = 0.6 ns , τ = 0.8 ns Ex ( t ) = E0 exp ⎜ ⎜ ⎟ τ2 ⎝ ⎠

(25)

Uniform cells with δ=0.05 cm are applied in the whole simulation space, thus, the cell numbers in x, y and z directions are 20, 20, and 56. Observe points A, B and C are set at cell 13, 21 and 37 in z direction. In this model, the CFL stability condition is Δt ≤ 0.96225 ps , therefore, the time step size is set as 0.962 ps for the conventional FDTD and ADI-FDTD methods, and 0.96225 ps, 9.6225 ps and 14.4338 ps for the LOD-FDTD method. The observed electric fields, which are normalized by the value of the incident pulse amplitude, are shown in Fig. 2. The reflected field, total field and transmitted field are observed at point A, B and C respectively. By applying the Fourier transformation of the incident Gaussian pulse and the transmitted electric field signal, the shielding effectiveness can be calculated. The results calculated by conventional FDTD, ADI-FDTD and LOD-FDTD are in good agreement as shown in Fig. 3. It also can be seen clearly that an increase in the time step size may result in numerical error. However, the numerical error is not too big and the results could be used to make certain estimation.

Normalied E-field

1 incident

0.5 total 0

transmitted

-0.5 reflected -1

0

0.5

1 Time(ns)

1.5

2

Fig. 2. Normalized electric field of incident, reflected, total and transmitted fields

FDTD

60

SE(dB)

ADI-FDTD 50

LOD(1*dt) LOD(10*dt)

40

LOD(15*dt)

30 20 10

0

1

2 3 Frequency(GHz)

4

5

Fig. 3. Shielding effectiveness versus frequency of wire mesh simultion

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349

These simulations were performed on Pentium IV 2.0 GHz PC. The CPU time and required memory size are shown in Table I, with the time step size and total time steps. It can be seen that the LOD-FDTD runs a bit faster than the ADI-FDTD method but with the same memory cost. For the LOD-FDTD method, if the time step size is set 15 times as large as that of the conventional FDTD, total time steps can be reduced by a factor of 15, and the CPU time is also reduced to about 40%. Required memory size, which is about 1.88 times, is increased because of the necessity for extra array storage. Table 1. Time stepping modeling and comutational effort comparison

4

Δt

Steps

CPU Time

Memory

FDTD

0.96225 ps

2400

607.9 s

9.55 Mb

ADI-FDTD

0.96225 ps

2400

5488.3 s

23.6 Mb

LOD-FDTD

0.96225 ps

2400

5232.1 s

23.6 Mb

LOD-FDTD

9.6225 ps

240

524.1 s

23.6 Mb

LOD-FDTD

14.4338 ps

160

348.8 s

23.6 Mb

Conclusion

Periodic LOD–FDTD method has been developed for solving the shielding problems of periodic structures. Compared with ADI-FDTD method, LOD-FDTD method has the same number of equations to be computed but with fewer arithmetic operations required in each equation, which results in overall less simulation time. Numerical simulations of a wire mesh screen are provided as an example. The results compared with those from the conventional FDTD method and ADI-FDTD method agree quite well. The LOD–FDTD method guarantees a stable calculation with any time-step size, and a large time-step size reduces both the number of time-loop iterations and the required CPU time for the calculation. Therefore, the periodic LOD–FDTD method is an efficient and accurate numerical technique for estimating the shielding effectiveness of periodic structures.

References [1] Munk, B.A.: Frequency Selective Surface: Theory and Design. Wiley, New York (2000) [2] Yee, K.S.: Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media. IEEE Trans. Antennas Propagat AP-14, 302–307 (1996) [3] Peterson, A.F., Ray, S.L., Mittra, R.: Computational Methods for Electromagnetics. IEEE Press, New York (1998) [4] Namiki, T.: A new FDTD algorithm based on alternating direction implicit method. IEEE Trans. Microw. Theory Tech. 47(10), 2003–2007 (1999) [5] Zheng, F., Chen, Z., Zhang, J.: A finite-difference time-domain method without the Courant stability conditions. IEEE Microw. Guided Wave Lett. 9(11), 441–443 (1999)

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[6] Sun, G., Trueman, C.W.: Approximate Crank-Nicolson schemes or the 2-D finite difference time domain method for TEz waves. IEEE Trans. Antennas Propag. 52(11), 2963–2972 (2004) [7] Tan, E.L.: Unconditionally stable LOD-FDTD method for 3-D Maxwell’s equations. IEEE Microw. Wireless Compon. Lett. 17(2), 85–87 (2007) [8] Namiki, T.: 3-D ADI-FDTD method—unconditionally stable time-domain algorithm for solving full vector Maxwell’s equations. IEEE Trans. Microw. Theory Tech. 48(10), 1743–1748 (2000) [9] Thomas, J.W.: Numerical Partial Differential Equations: Finite Difference Methods. Springer, Berlin (1995)

Lossless Compression of Microarray Images by Run Length Coding A. Sreedevi1, D.S. Jangamshetti2, Himajit Aithal1, and A. Anil kumar3 1

Department of EEE, VCE, Bangalore, India 2 Department of EEE, EC, Bagalkot, India 3 Department of EEE, VCE, Bangalorer [email protected]

Abstract. Microarray image technology is a powerful tool for monitoring thousands of genes simultaneously. The size of microarray images is very large; hence efficient compression routines that take advantage of the way in which spots are represented in a microarray image are required. This paper discusses a lossless image compression technique that aims to minimize the number of pixels to be stored, to represent a spot. Every row in each spot is represented by a single coded value and also the number of coded data in columns is further reduced. Image is processed in such a way that it does not require addressing and spot segmentation. The compressed data is then used to reconstruct the microarray. The results of the implementation of this method are compared with other lossless image compression methods. This method requires least number of bytes, as less as approximately 56kB when applied to a microarray image of size 657kB. Keywords: A microarray, gridding, lossless image compression, spots.

1

Introduction

A DNA microarray is a complex technology used in genome engineering to study complete gene of an organism. DNA microarrays play a very important role in analyzing thousands of genes simultaneously. Microarrays have become powerful tool in bioinformatics laboratories, basically used for identifying and analyzing the function of gene. DNA microarrays contain thousands of DNA sequences, called probes, fixed to a glass slide [1]. Thousands of microscopic spots of DNA, each containing a specific DNA sequence are represented by a series of arrays. This can be a short section of a gene or other DNA elements that are used to hybridize a cDNA or cRNA sample depending on different manufacturing technology. Microarrays are available in single or two colour arrays. DNA from different sources are labelled with different fluorescent colours (molecules) and hybridized together on to the same array (control and reference samples are labelled with red and green colours respectively) to form spots. The colour and intensity of the spots depend on the expression levels of the DNA in that particular environment. Hybridization experiments yield to gene expressions and gene interactions. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 351–359, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Affymetrix chip DNA microarrays are prepared on a solid surface such as glass or silicon chip using robotic techniques. In standard microarrays, the probes are attached via surface engineering to a solid surface by a covalent bond to a chemical matrix. Instead of the large solid support, if microscopic beads are used then such a platform is known as Illumina. Microarray images are very large, containing large number of pixels of the order similar to 1100 X 700. Since an array can contain tens of thousands of DNA sequences, a microarray experiment can complete many genetic tests in parallel. DNA microarrays can be used to detect DNA (as in comparative genomic hybridization), or detect RNA (most commonly as cDNA after reverse transcription) that may or may not be translated into proteins. DNA microarrays can be used to measure changes in expression levels, to detect single nucleotide polymorphisms (SNPs), to genotype or re-sequence mutant genomes [2]. Microarrays can also be used for clinical studies like cancer diagnosis, comparison of healthy and cancerous tissue, and identification of subtypes of cancer [3]. The quality of microarray data depends on the type of algorithm used to represent it and is highly variable. Till now there is no standardization of microarray processing tools available. Therefore the microarray image processing varies from one experiment to other, unless its format and coding and decoding units are known. Noises in these images may be inherent or due to manufacturing methods. The challenges to face in microarray analysis are: The multiple levels of replication in experimental design, no single platform or standard for representing microarrays, statistical analysis for gene interactions, accuracy (Relation between probe and gene), the sheer volume of data and the ability to share it (Data warehousing). Run length coding technique uses an approach to replace the consecutive occurrences of a data item by a single encoded data value, leading to data compression. The present work aims at minimizing the size of the microarray without considerable loss of data, thus providing an efficient means to store and compare these microarrays.

2 Compression Data compression is the process of converting an input data stream into another data stream (the output or the compressed stream) that has a smaller size. A stream is either a file or buffer in memory. The microarray images will be frequently transmitted for comparisons or reference. To reduce the transmission time and space required by the image, it must be compressed without any loss of essential data. Thus not only the image compression but even its reconstruction is vital. Compression is achieved by the removal of one or more of the three basic data redundancies

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1. Coding redundancy when less than the optimal code words are used 2. Inter-pixel redundancy which results from correlation between the pixels of the image 3. psycho-visual redundancy which is due to data that is ignored by the human visual system To view or use the compressed image it must be fed to a decoder where a reconstructed output image will be generated which may or may not be equal to the original image. If the reconstructed image is same as the original image the system is called as error free, information preserving or lossless compression. If there is some level of distortion present in the reconstructed image then the system is called lossy compression [4]. The performance of a compression method can be expressed in terms of compression ratio, which can be defined as the ratio of the size of the output stream to the size of the input stream, or it can be expressed as its inverse called as compression factor [5]. In one of the previous work on lossless compression, Faramarzpour et al. [6] have employed a spiral path scanning technique to convert 2-D array into 1-D sequences which requires the exact centre of the spot. Variable length coding is used to store the foreground and background pixels. They have achieved a compression factor of 2.13. In the present paper inter-pixel redundancy and psycho-visual redundancy have been concentrated on to get a lossless compression.

3

Methodology

Given the data sizes involved, compression is mandatory in efficient storage of microarrays. Various characteristics of microarrays make them difficult to compress, most notably the noise and uneven spot shapes. Median filtering is a nonlinear operation often used in image processing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges. The background information of the filtered image can be neglected as it does not represent the data. In matrix form the spot is represented as a series of 1’s which are placed so as to form a closed circular path. In the present work we are storing only the vital information of all the spots, which includes centre of the individual spots with its respective intensity and the essential edge pixel information. These essential edge pixels are coded such that they make maximum use of the inter-pixel correlation. The 1’s representing a spot occurs as a series along a row. In the present paper we scan the 2-D matrix representing the image row wise and store the location of only the starting 1 along with the number of 1’s after it, thus reducing the number of pixels to be stored. Hence we reduce the inter-pixel redundancy. For a spot the centre intensity is crucial. Therefore we store the centre location and its intensity and use it for reconstructing the entire image. For finding the centre we use the edge function in MATLAB. Using the edge of a spot we encode the edge pixels in such a way that each pixel value depends on the value of its neighboring pixels. Once a spot is encoded it is easier to find the central row and central column of

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a spot which gives us the centre of the spot. Our output reconstructed image consists of spots with uniform intensity. The code has been implemented using MATLAB-2007. A part of the original image is shown in Fig. 1 and Fig. 2 represents the pixel intensities of a spot.

Fig. 1. Cropped original image showing the spots and their intensities

0 0 2 130 126 132 1 1

0 1 135 142 144 142 138 2

1 107 140 147 146 144 141 111

1 122 145 149 150 147 144 131

1 118 146 148 145 144 140 120

0 1 140 144 141 139 136 1

0 1 1 138 135 134 1 1

Fig. 2. Pixel intensities of a spot in the original image

4

Algorithm

The algorithm of the entire code can be divided into two subparts. A.

Algorithm for Compression

It contains two stages. In both the stages the image is read and represented in the form of a 2-D matrix. Median filter is applied to reduce the noise. All non-zero pixel values are represented by 1. The resultant image is as shown in Fig. 3. 1. Algorithm for coding the required edge (i) The 2-D matrix is scanned row wise and for the pixel with the value 1, which indicates it is part of the edge of a spot. (ii) In the case where series of pixels having the value 1 in a row, only the location of the starting pixel with value 1and the number of pixels having non zero value after it are stored.

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(iii) All the 1’s are replaced with 0’s. (iv) If the pixels encoded values along a column are the same, then 100 is added to the first pixel encoded value and the last pixel is retained. This result is a 2D matrix which stores the coded information of the binary image, shown in Fig. 4 and Fig. 5. The output will contain a 2-D array storing the non-zero pixel row and column details along with its encoded value. The encoding is necessary for reconstruction. 0 0 0 1 1 1 0 0

0 0 1 1 1 1 1 0

0 1 1 1 1 1 1 1

0 1 1 1 1 1 1 1

0 1 1 1 1 1 1 1

0 0 1 1 1 1 1 0

0 0 0 1 1 1 0 0

Fig. 3. Binary image representing a spot

Fig. 3 shows noise that was associated with the original image has been removed and only the pixels whose values are comparable with that at the centre have been considered to form the spot, or the foreground of the spot [7]. 0 0 0 7 7 7 0 0

0 0 5 0 0 0 5 0

0 3 0 0 0 0 0 3

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

Fig. 4. Image showing the edges

0 0 0 107 0 7 0 0

0 0 5 0 0 0 5 0

0 3 0 0 0 0 0 3

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

Fig. 5. The encoded edges of a spot to be stored

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

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2. Algorithm for Extracting the Centre and its Intensity This stage also consists of two parts, finding the complete edge of a spot and encoding it and finding the centre. Algorithm for coding the spot Edge function is used to find the edges of the spots. (i) 2-D matrix is scanned row wise and checked for a pixel value 1as it indicates that it is part of the edge of a spot. (ii) For continuous series of pixels have the value 1 only the extreme pixel locations are stored. The steps are as shown below (iii) If three or more pixels along a row have the value 1, then only the extreme pixel locations are stored with their value as 1. All intermediary pixels are replaced with 0’s. (iv) When two or more pixels along a column have the value 1, then only the extreme pixel locations are stored. All intermediary pixels replaced with 0’s. (v) For a pixel with a value 1 which does not have any adjacent pixel having a nonzero value either along the corresponding row or column, then its location and the value 3 are stored. (vi) If two adjacent pixels have their values equal to 1 and the adjacent pixel to the left and to the right of these pixels is 0, then the location of these pixels and their value as 4 are stored. 0 0 0 2 0 2 0 0

0 0 3 0 0 0 3 0

0 1 0 0 0 0 0 1

0 0 0 0 0 0 0 0

0 1 0 0 0 0 0 1

0 0 3 0 0 0 3 0

0 0 0 2 0 2 0 0

Fig. 6. The encoded data of a spot for centre extraction

Algorithm for finding the centre (i) The coded 2-D matrix containing the edge of the spot, shown in Fig. 6 is read. (ii) The column corresponding to the centre of the spot is calculated when a continuous series of pixels having the value 1 is found. (iii) Extreme pixel whose value not equal to zero is identified (iv) Row corresponding to the centre of the spot is calculated. (v) Knowing the location of the centre, its intensity is extracted from the original image. (vi) The pixels corresponding to edge of the spot is replaced by 0’s.

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The output will be a 2-D array containing the row and column details along with the intensity of the centre of all the spots. The encoded data of a spot, the edges as well as the centre are shown in matrix form in Fig. 7. The compressed image data is stored in two files, one file containing the row and column information of the spots in the image, while the other contains the row and column details of the centre and its intensity value. 0 0 0 107 0 7 0 0

0 0 5 0 0 0 5 0

0 3 0 0 0 0 0 3

0 0 0 0 150 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0

Fig. 7. The coded spot

B. Algorithm for Reconstruction (i) A 2-D matrix is constructed from the matrix containing the information concerning the encoded edges. (ii) Depending on the encoded value of the pixels the edge of the spot is reconstructed. (iii) When pixel value is greater than 1, this pixel and 0’s on its right are replaced with 1. The number of 0’s to be replaced with 1 is given by the encoded value. (iv) The output corresponds to reconstructed binary image in which the foreground of the spots is completely represented by 1’s. (v) The image is reconstructed by replacing all 1’s of the spot by the centre intensity value.

Fig. 8. The reconstructed image

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Result For original image: Number of rows = 1042 Number of columns = 642 Total number of pixels in original image: 1042x642 = 668964 Number of pixels to be stored: Edge Pixels Data+ Centre Pixel Data = 34170 Compression factor = (668964/34170) = 19.58 Table 1. Size of Image When Stored in Different Formats

6

SL NO

FILE TYPE

FILE SIZE

1

Bitmap Image

657 KB

2

TIFF Image

229 KB

3

JPEG Image

60.3 KB

4

Proposed Method

55.5 KB

Conclusions

From Table 1 it can be seen that the proposed method has a higher compression ratio compared to other methods. The JPEG format for storing the image consumes 60.3 KB of memory space while the proposed method takes only 55.5 KB of space. The efficiency of storage is almost 8% more comparatively. The Bitmap format has no compression, whereas in TIFF format we see a compression factor of 2.87. While the proposed method gives a compression factor of 19.58 in terms of number of pixels needed to represent microarray image. This method does not require gridding for addressing the spots. Also spot extraction is eliminated as the image is scanned row-wise and the algorithm takes care of individual spot identification. The reconstructed image is as shown in Fig. 8.

References [1] Bajcsy, P.: An Over view of DNA Microarray Image Requirements for Automated Processing. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005) [2] Paolo, Fortuna, L., Occhipinti, L.: DNA chip image processing via cellular Neural Networks, 0-7803-6685-9/01© 2001 IEEE

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[3] Hautaniemi, S., Lehmussola, A., Yli-Harja, O.: DNA Microarray Data Preprocessing, 07803-8379-6©2004 IEEE [4] Gonzalez, R.C.: Digital Image Processing using Matlab, 3rd edn. Prentice Hall, Englewood Cliffs [5] Salomon, D.: Data Compression, 3rd edn. Springer, Heidelberg [6] Faramanpour, N., Shirani, S., Bondy, J.: Lossless DNA Microarray Image Compression, 0-7803-8104-1/03 © 2003 IEEE [7] Neekabad, A., Samavi, S., Razavi, S.A., Karimi, N., Shirani, S.: Lossless Microarray Image Compression Using Region Based Predictors, 1-4244-1437-7/07©2007 IEEE

Wavelet-Based Audio Fingerprinting Algorithm Robust to Linear Speed Change Jixin Liu1 and Tingxian Zhang2 1

Department of Computer and Information Science, Ujian University of Technology, FuZhou, China, 2 ireless Center, hina TeleCom GuangDong Branch, GuangZhou, China [email protected]

Abstract. This paper, a novel audio fingerprinting scheme for content-based audio retrieval was presented. To efficiently utilize the time-frequency characteristics of audio signal, the 8-levels wavelet transform is adopted to obtain the detail time-frequency features. Experiments show that the proposed scheme not only has good robustness to content-preserving operations and additive white Gaussian noise (AWGN) but also shows highly robustness against large linear speed change. Furthermore, compared to other existing schemes, it requires less storage space and computational costs. Keywords: saudio, fingerprinting, wavelets.

1 Introduction As the development of computer network and multimedia technologies, especially the compression technology of digital audio, audio transmission has become conveniently and widely. Consequently, the copyright protection and security problems are becoming urgently. Digital audio watermarking provides an alternative way to solve this problem. But due to its application limitations, such as its robustness to attacks and the evidence should be approved by third party. The audio fingerprinting technique was proposed. Audio fingerprinting is a compact content-based signature that summarizes the essence of an audio clip. It has attracted much attention because it can implement audio identification regardless of its format and without the need of meta-data or watermark embedding [1]. In recent years, many efforts have been made in the field of audio fingerprinting and a good overview can be found in [1]. And the previous efforts based on wavelet transform can be divided into two categories. The first method performs the wavelet transform on each audio frame directly to extract time-frequency features as audio fingerprints. In [2], 1-dimensional continuous Morlet wavelet is adopted to extract two fingerprints for authentication and recognition purposes, respectively. In [3], a robust perceptual audio hashing scheme using balanced multi-wavelets (BMW) is proposed. This scheme performs 5-levels Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 360–368, 2011. © Springer-Verlag Berlin Heidelberg 2011

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wavelet decomposition on each audio frame, and divides the 5 decomposition subband coefficients into 32 different frequency bands. Then estimation quantization (EQ) with window of 5 audio samples is adopted. Finally, 32 bits sub-fingerprinting is extracted according to the relationship between the log variances of each sub-bands’ coefficients and the mean of all the log variances for each audio frame. Experiments demonstrate that the proposed scheme is robust to several signal processing attacks and manipulations except for linear speed change. The other method introduces computer vision technique, converts the audio clip into a 2-D spectrogram and then wavelet transform is applied. In [4], treat the spectrogram of each audio snippet as a 2-D image and uses wavelet transform extracting 860 descriptors for a 10-seconds audio clip. Then apply pair-wise boosting to learn compact, discriminative, local descriptors that are efficient in audio retrieval. The proposed algorithm achieves very quick and accurate retrieval in practical system with poor recording quality or significant ambient noise. In [5, 6], the so-called Waveprint, combining of computer vision and data stream processing, is proposed. Haar wavelet is used for extracting the t top magnitude wavelets for each spectral image. And the selected features are modeled by Min-Hash technique. In retrieval step, locality sensitive hashing (LSH) technique is introduced. The proposed algorithm exhibits an excellent identification rate against content-preserving degradations except for linear speed changes. Furthermore, the tradeoffs between performance, memory usage, and computation are analyzed through extensive experimentation. As an extension, the parameters of the system are analyzed and verified in [7]. The system outperforms in terms of memory usage and computation, while being more accurate when compared with [4]. It is clear that the existing works based on wavelet transform are not robust against large linear speed changes in common. So a novel framework of audio fingerprinting which is robust against large linear speed changes is proposed in this paper. Experimental results show that the proposed framework can achieve an excellent identification rate though the percentage of the linear speed change up to ±5% . The paper is organized as follows: section 2 describes the details of the proposed framework, while experimental results are presented and discussed in section 3 and section 4 gives conclusions of the whole paper.

2

Proposed Audio Fingerprinting Scheme

In this section, we focus on the design and the implementation of the proposed audio fingerprinting scheme. There are two processes: the fingerprint extraction and the retrieval procedure. A. Fingerprint Extraction One of the most widely used systems [8] is based on Fourier Transform. The spectral range of 300-2000kHz is divided into 33 non-overlapping bark-frequency bands and the sub-bands’ energy are selected for fingerprint modeling. It extracts 32-bit subfingerprints for every interval of 11.6 milliseconds. Improvements have been made in

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[9-11]. Unlike these works [8-11], we utilize the sub-band coefficients of the Daubechies wavelets. An overview of the proposed fingerprint extraction procedure is shown in Figure 1.

Audio Clip cA8 cD8 Preprocessing

Wavelet

Variance Computat ion &

cD2 cD1

Finger-

Fig. 1. Overview of fingerprinting extraction

Details of the extraction procedure are given below: 1) Preprocessing Down sample the input audio signal to mono audio stream with a fixed sampling rate of 5kHz. 2) Framing, windowing and overlapping The preprocessed signal is segmented into overlapping frames which are weighted by a Hanning window with an overlap factor of P. The length of the overlapping frame is 0.37 seconds. 3) Wavelet transformation As known, two sets of coefficients are got for single wavelet decomposition, approximation coefficients (cA) and detail coefficients (cD). In this paper, we apply 8 levels wavelet decomposition with wavelet basis Daub6 to get 9 sub-band coefficients for each audio frame. 4) Variance computation Compute the variance of each sub-band coefficients. The variance of sub-band m of frame n is denoted by σ (n, m) . 5) Fingerprint modeling We extract 7-bit sub-fingerprint for each audio using formula (1).

⎧1, var(n, m) − var(n + 1, m) > 0 F (n, m) = ⎨ ⎩0, var(n, m) − var(n + 1, m) ≤ 0 Where F (n, m) denotes the defined as follows:

m -th

(1)

sub-fingerprint bit of frame n and var(n, m) is

Wavelet-Based Audio Fingerprinting Algorithm Robust to Linear Speed Change

var(n, m) = Δσ (n, m) − Δσ (n, m + 1)

363

(2)

Where Δσ (n, m) stands for the difference of variance among adjacent sub-band coefficients and the definition is given below:

Δσ (n, m) = σ (n, m) − σ (n, m + 1)

(3)

It is obvious that one sub-fingerprint of a single frame does not contain sufficient information for audio identification. As a result, we choose a granularity of 3.3 seconds audio signal to extract the basic unit for audio identification referred to as fingerprint-block. And its size depends on the overlap factor P. For a given P=31/32, 30/32, 28/32, 24/32, 16/32, the size of the fingerprint-block is L × 7 bits, where L =256, 128, 64, 32, 16, respectively. Among the 9 sub-band coefficients obtained from wavelet decomposition, detail coefficients cD1 and cD2 include most of the wavelet coefficients. So these two variances can mostly reflect the changes of the audio signal. As verification, we introduce the relationship between the variance of cD1 and cD2 to extract one bit denoted by F (n,8) for each frame and construct a fingerprint-block of L × 8 bits.

⎧1, Δσ (n,8) − Δσ (n + 1,8) > 0 F (n,8) = ⎨ ⎩0, Δσ (n,8) − Δσ (n + 1,8) ≤ 0

(4)

For convenience, we use Wavelet1 to denote the algorithm extracting fingerprintblock of L × 7 bits while Wavelet2 for the algorithm extracting fingerprint-block of L × 8 bits in the remaining of this paper. Generally, the similarity of two audio signals is measured by Hamming distance which reflects the number of bit errors of these two audio fingerprints. So, Bit Error Rate (BER) and the normalized Hamming distance are used to assess the robustness and the quality of audio signal in this paper. B. Retrieval Procedure In the retrieval procedure, we use the search strategy proposed in [10]. The main idea is described below for convenience. 1) Synchronization Point Selecting As known, an extremely peak exists among the cross-correlations if two signals are similar. Otherwise, no peaks are presented. As a result, we can use the peak of the cross-correlations for selecting synchronization point between two audio fingerprints. Given R and Q, where R is one fingerprint from the database and Q is the fingerprint of the audio for querying, compute the average of the cross-correlations while preserving coherence between wavelet sub-bands. We choose the first s peaks as the candidates of synchronization points. Figure 2 shows an example of the normalized average of cross-correlations. It is obviously that the peak is still recognizable in Figure 2-(b) though the query audio suffering linear speed change. Bellettini assumes that s = 10 is enough for finding the right alignment point. We choose s = 50 in the experiments for safety measure.

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(a) No attack version

(b) Linear speed change -1%

Fig. 2. Normalized average of cross-correlations

2) Normalized Hamming distance computing For the chosen s possible synchronization points, compute the according normalized Hamming distance then choose the minimum distance as the distance of R and Q. And for each R from the database, the distance between R and Q is computed and the retrieval results can be attained.

3

Experimental Results

In this section, the robustness of the proposed audio fingerprinting framework is verified and compared with the existing schemes in [8-10]. For efficiently comparing in the remaining of this paper, we select the same audio experts (Stereo, 44.1kHz, 16bps) used in [8]. The four songs are “O Fortuna” by Carl Orff, “Success has made a failure of our home” by Sinead o’Connor, “Say what you want” by Texas and “A whole lot of Roise” by AC/DC. All of the experts are 30s long and subjected to the following signal manipulations or attacks [8]: (1)128 Kbps and 32 Kbps; (2) Band Pass Filtering ; (3)Amplitude Compression; (4)Echo Addition; (5) Equalization using a typical 10-band equalizer; (6)Time Scale Modification of ±2% and ±5% (Only the tempo changes while the pitch remains unchanged); (7)Linear Speed Change of ±1% and ±5% (Both tempo and pitch change); (8) AGWN of 20dB and 5dB. A. BER Analysis BER is used for declaring whether the two audio are similar or not in the experiments. The threshold T , which directly determines the false positive rate and the false negative rate, must be set properly. T is set to be 0.35 in [8, 10] while T is 0.25 in [3]. In this subsection, the value of T is exploited through the BER histogram of the matching audios and the non-matching audios. The audio experts are subjected to 128 Kbps, 32 Kbps MP3, LS ± 1% , LS + 3% and TS + 2% audio degradations. For each type of attack, we randomly choose 100 audio clips of 3.3s long from each audio expert and the BER of the match and non-match are computed, where the overlap factor P is set to be 28/32. The histogram of the BER is shown in Figure 3.

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From Figure 3, we can know that the BER of match mostly lie in the range of 0.10.25 and partly cross 0.25 due to the randomly starting attack while the BER of nonmatch all cross 0.25 and appear to be the approximate normal distribution. It is obvious that T = 0.25 can well distinguish the match audios from the non-match audios.

(a) Match

(b) Non-match

Fig. 3. BER histogram of match and non-match

B. System Robustness In this subsection, we verify the influences of the overlap factor P on system robustness, for P=31/32, 30/32, 28/32, 24/32, 16/32. For each type of attack, 100 random audio clips of 3.3s from each audio expert are chosen, resulting in 400 trials in total. BER is used for measuring the robustness of the proposed framework. Figure 4 and Figure 5 show the robustness of the proposed framework against linear speed change attacks. In the case of best performance, that is P= 28/32, Wavelt1 can resist linear speed change between −6% and +8% while Wavelt2 between −5% and +7% , that is one percent less than Wavelt1. In summary, the experimental results in this subsection indicate that the system can achieve its best performance when the overlap factor P is set to be 28/32 which is used in the retrieval experiments.

Fig. 4. Robustness of Wavelet1 against linear speed changes

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Fig. 5. Robustness of Wavelet2 against linear speed changes

C. Performance of Retrieval Using the fingerprint extraction described in the previous section, we build a fingerprint database containing 200 songs with an average of 30s. For each song in the database, 26 degradations described before is applied to form the test sets. During retrieval, we randomly choose 5 snippets from each degraded version resulting in 1000 trials for each type of attack. Then the same method generating the fingerprint database is used for extracting the fingerprint of the query snippet. The results of our comparison to references [8, 10] are shown in Table 1. And ROC curve is also presented in Figure 6. As can be seen by these results, schemes proposed in [8, 10] outperform Wavelet1 and Wavelet2 against the degradations except for linear speed change. The performance of Haitsma’s method and Bellettini’s method drops sharply when the linear speed change increases. In contrast, the performance of Wavelet1 and Wavelet2 Table 1. Performance results of fingerprinting schemes Attacks 128K MP3 32K MP3 BPF Compression Echo Equalization LS-1 LS+1 LS-5 LS+5 TS-5 TS+5 20dB 5dB

Haitsma’s 100 100 100 98.7 98.8 98.9 99.7 99.6 40.5 38.9 100 99.9 98.7 95.4

Bellettini’s 100 99.9 100 99.1 99.2 99.2 100 99.9 44.1 45.3 99.9 99.8 99.3 96.1

Wavelet1’s 95.3 94.6 77.3 79.4 83.3 77 93.5 91.8 79.1 74.9 87.6 90.9 83.6 84.7

Wavelet2’s 98.8 98.6 93.2 87.6 88.8 87.4 99 98.1 90.9 88.9 94.8 96 91.8 90.5

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vary much slower and can achieve approximately 75% and 89% for linear speed change +5%, respectively. It is because that Wavelet1 and Wavelet2 are sensitive to the changes of the audio signal. It is also interesting to note that Wavelet2 performs better than Wavelet1 uniformly. On the other hand, it proves the robustness of introducing the relationship between the variance of cD1 and cD2. Compared with Wavelet1, the IDR using Wavelet2 can be improved by 15.9% when subjected to band pass filtering. Finally, the memory usage must be taken into account. For 3.3s audio clip, using Wavelet1, Wavelet2, methods in [8] and [10] for fingerprint extraction, the sizes of the fingerprints are 64 × 7 , 64 × 8 , 256 × 16 and 256 × 32 respectively.

Fig. 6. ROC curve comparing with Haitsma’s and Bellettini’s schemes

4

Conclusion

In this paper, we have proposed a novel audio fingerprinting scheme based on wavelets. We use 8 levels wavelet decomposition for extracting time-frequency features and two fingerprint extraction algorithms are designed. Experiments show that the proposed algorithm Wavelet2 is more robust to various audio degradations, especially for large linear speed change. Furthermore, the fingerprint extracted by Wavelet2 is much compact than the state-of-art system presented in [3, 8, 10]. As a result, it can reduce storage space and computational costs greatly. In future work, we will mainly focus on the scaling properties of our system and proposing a search strategy corresponding to the fingerprint extraction.

References [1] Cano, P., Batlle, E., Kalker, T., Haitsma, J.: A Review of Audio Fingerprinting. Journal of VLSI Signal Processing 41, 271–284 (2005) [2] Lu, C.-S.: Audio Fingerprinting Based on Analyzing Time-Frequency Localization of Signals. In: MMSP 2002, pp. 174–177 (2002)

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[3] Ghouti, L., Bouridane, A.: A Robust Perceptual Audio Hashing Using Balanced Multiwavelets. In: ICASSP 2006, vol. 5, pp. 209–212 (2006) [4] Ke, Y., Hoiem, D., Sukthankar, R.: Computer Vision For Music Identification. In: CVPR 2005, pp. 597–604 (2005) [5] Baluja, S., Covell, M.: Content Fingerprinting Using Wavelets. In: Proc. CVMP 2006 (2006) [6] Baluja, S., Covell, M.: Audio Fingerprinting Combining Computer Vision and Data Stream Processing. In: ICASSP 2007, vol. 2, pp. 213–216 (2007) [7] Baluja, S., Covell, M.: Waveprint: Efficient Wavelet-based Audio Fingerprinting. Pattern Recognition 41(11), 3467–3480 (2008) [8] Haitsma, J., Kalker, T.: A Highly Robust Audio Fingerprinting System. In: ISMIR 2002 (2002) [9] Haitsma, J., Kalker, T.: Speed-Change Resistant Audio Fingerprinting Using AutoCorrelation. In: ICASSP 2003, vol. 4, pp. 728–731 (2003) [10] Bellettini, C., Mazzini, G.: On Audio Recognition Performance via Robust Hashing. In: ISPACS 2007, pp. 20–23 (2007) [11] Bellettini, C., Mazzini, G.: Reliable Automatic Recognition for Pitch-Shifted Audio. In: ICCCN 2008, pp. 1–6 (2008)

Design and Implementation for JPEG-LS Algorithm Based on FPGA Yuanyuan Shang, Huizhuo Niu, Sen Ma, Xuefeng Hou, and Chuan Chen College of Information Engineering, Capital Normal University, Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing, China [email protected]

Abstract. As the accuracy of imaging devices has improving, there is an increasing amount of image data. And it makes image compression play an important role. This paper discusses some key points of the standard JPEG-LS. A JPEG-LS image compression system is taken out for real-time data processing. The whole system was fulfilled in the DE2-70 embedded framework. The handling of boundary points is also illuminated. Then it can be concluded that JPEG-LS is an effective compression algorithm for hardware implementation. Keywords: Image Compression, JPEG-LS, DE2-70.

1

Introduction

With the improvement of image sensor resolution, a growing amount of data is brought out in the modern image process. And it leads to an increasingly heavy burden for data storage and transmission. Therefore, the image data compression also becomes increasingly important. Because some of the system requires a higher integrity for the image detail, the lossless compression is necessary for the images. This article discusses an image compression method, verifies this algorithm in MATLAB and implements it on hardware. JPEG-LS (ITU-T T.87 ISO / IEC 14495) is an international standard for image lossless or near lossless compression which is proposed in 98[4-5]. And what we focused on is its lossless compression performance. This algorithm is used because there is no data conversion such as FFT and other complex data transformation process. As only the data subtraction, shift and other similar processing should be realized, the algorithm is relatively simple and suitable for hardware implementation. The hardware system is implemented in the DE2-70 platform. Then use UDP protocol to transmit the compressed images to PC. PC completed the acceptance process for the compression image and decompressed it. The algorithm for encoding consists of seven modules. The paper describes several key points of the deal in detail. Also the whole hardware implementation flow is analyzed. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 369–375, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 JPEG-LS Compression Algorithm A. Description for JPEG-LS JPEG-LS algorithm is based on LOCO-I algorithm [1]. On the basis of the data prediction, encoding uses run-length coding model and entropy coding based on prediction model. During the encoding process, the original image pixel data is put into the encoder in turn. Then the encoder calculated the local gradient for the current coding pixel according to the context of the pixel. According to the context, the encoder selects the encoding method, whether the conventional mode or the runlength encoding mode. If the local gradients reflect that the current pixel is in a flat area (that is, the local gradients are “0”), choose the run-length encoding mode, or run into the conventional mode coding. In the conventional mode, several context pixels which have been already encoded inspected through the context model. The encoder uses a cause-effect template for fixed prediction, and context is taken into account to correct the predicted values adaptively. Then calculate the forecast error for Golomb coding. In the run-length model, first scan for length, and encode the lengths and termination of the pixel which breaks the run-length encoding. The specific implementation principle is shown below.

Fig. 1. Block diagram for JPEG-LS

B. Key Technologies JPEG-LS algorithm specified in the standards is taken into account the hardware as simple as possible. The main compression techniques are non-linear prediction, adaptive coding, run length coding and entropy coding. Following explained several key points of this technique. 1) Non-linear prediction: In the prediction uses a causal-type template. Related pixels including a, b, c, d are at the Left, top, top left and top right respectively. Because the coding sequence order is from left to right, from top to bottom. Therefore, the compressed code four neighboring pixels are already being encoded. Consider the reversibility of JPEG-LS encoding, it should refer the already encoded value that the pixel to be predicted can be resumed in the decoding process. Predictive model uses adaptive edge detection predictor MED for de-correlation. In fact it selects the best or second best prediction function during the three predictive adaptive to calculate the current pixel to be the predicted value. It is better than the

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vast majority of linear prediction models. For the image already scanned into the encoder, they may be with the same feature. If the compression image is analyzed through statistical feature and apply it in the next pixels to be compressed, the redundancy will be greatly reduced. Then the predictor can forecast more accurate and the prediction residual is reduced. It can successfully achieve the objective of improving the compression ratio [2]. Therefore, it adds the correction value after the forecasts in the normal, which is accumulated by the context of the parameters. 2) Golomb encoding: Suppose to take positive integers x for Golomb coding. Select parameters m. Set b = 2^m, q = INT ((x-1)/b), r = x-qb-1. Then x is encoded into two parts. One is made up of q a 1 and a 0. The other is an m-bit binary number, and its value is r. Following shows the Golomb coding table when m=0, 1, 2, 3. Table 1. Golomb Coding table x

M=0

M=1

M=2

M=3

1

0

00

0 00

0 000

2

10

01

0 01

0 001

3

110

10 0

0 10

0 010

4

1110

10 1

0 11

0 011

5

11110

110 0

10 00

0 100

6

111110

110 1

10 01

0 101

7

1111110

1110 0

10 10

0 110

8

11111110

1110 1

10 11

0 111

9

111111110

11110 0

110 00

10 000

The table shows that Golomb coding is not only consistent with the prefix code law, and smaller x can be expressed with fewer bits, but with a longer bit that represent larger x values. So if x prefers smaller values, Golomb coding can effectively save space. Of course, according to different distributions, different m is selected for the best compression. 3) Run-Length Encoding: RLE is a symbolic value or string length instead of the continuous symbol with the same value. When encoding image data, pixels with the same gray value and along a certain direction can be regarded as continuous symbol. Using string instead of the continuous symbols can significantly reduce the amount of data. The encoding process should skip the process of prediction and prediction error under the Run-length mode. Encoder starts the search from x for a continuous sampling sequence which has the same value with reconstruction of a. The run-length

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is stopped when catches a different value or reaches the end of a line [3]. If experiencing a different value, it needs a run-length suspension coding. It codes the value of x and the subtraction of reconstruction value of a, b. And the value for c, d does not matter. C. The Compression Ratio MATLAB simulation is done for the compression ratio of JPEG-LS algorithm. Compress different pieces of image, and the lossless compression ratio is shown below. From the figure we can conclude that the contents of the image smoothness in particular have a greater impact on the compression ratio. This is because the compression of images has two different methods. And run-length coding of images requires a higher degree of smoothness. Therefore the compression ratio for JPEG-LS algorithm is unpredictable. The statistical values for a large number of images can be drawn between 2 and 5.

(a)

(b)

1.98

2.19

(c) 30.66

Fig. 2. Compression ratio of different images

3

The Implementation of JPEG-LS Algorithm

A. System Design The entire system contains a CMOS image capture system, the image compression system, the image receiving and decoding system. As the following figure shows, CMOS image capture system is made up of a 5 mega pixel digital camera

TRDB_D5M

DE2-70

Fig. 3. System design

PC

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development kit. The image is saved in SRAM of DE2-70. Then the JPEG-LS compression is completed by VHDL. After this, compressed data is transformed to PC. While in the terminal PC, use VC to complete the capture, decoding and displaying. B. JPEG-LS Encoder The hardware implementation for JPEG-LS using VHDL language. This system contains seven modules. MCU is the core of the entire system, and it is in charge of timing control and the choice for encoding method. RAM_IO calls for 8KB space to store the context data and environment variables. Parameter_model module calculates the model parameters under the conventional mode. The data which are calculated from this module are code length parameter (data_k) and the data which needs to be encoded (data_merrval). Then the golomb_transform module does the conventional model encode. If run-length encoding mode is required, first the code length is calculated by the MCU and it also determined the need for run-length break encoding. The runlength_transform module encodes the length. At the same time, the breakpoint is encoded by the break_encoding module. The encoded data from the above three modules is imported into the data_fifo module in a serial way. This module converts the final output data to 8-bit.

Fig. 4. JPEG-LS encoding modules

Among the above seven parts, MCU module controls the entire coding process and it plays the most critical role. It completes for the following three features: first, it make the data to be compressed write into the RAM_IO module and read them out. It provides the address and read or write enable signal. Judge the context data to select the method for encoding. If it is run-length model, the calculation of the length is necessary. Set a counter to give each module enable signal to start the appropriate

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treatment. For the pixel data input and read out processing, this module has two pointers to control a reasonable order. The encoding mode is selected by computing the gradient of the neighborhood data. If all of them are zero, encode by RLE mode, or encode by conventional mode. For the control of each module, it makes use of a process control state machine. C. Handling for the Context JPEG-LS do the requirements for the context of the boundary points as shown below.

0

0

Rb Ra Xm

0

0

Rb Rd Xn

Fig. 5. Schematic diagram of boundary points

To handle the first line of data, set Rb = Rc = Rd = 0. And for the first data of the first line set Ra=0. The following pixel is treated as re-coding of the latest one. The first data for other line set Ra = Rb. So Rc can be set as Rb in the latest line. Then set a variable to store the value of the first line of Rc. Under hardware timing control, update it in the end of the line. While encoding the current pixel it only needs Rc to be output. For the last column pixel Rd, make it equal to a neighborhood with the value of Rb. This completes the set for boundary points.

4 Conclusions By the implement a whole system on DE2, JPEG-LS standard is certified to be a more suitable algorithm for hardware implementation. This article explains several key points about this algorithm. The disadvantage of this method lies in the ability to distinguish error. In the future design some error correction algorithms can be added to make the compression more efficient, which is a focus of research at home and abroad. Acknowledgment. This work was supported by a grant from the National Natural Science Foundation of China (No.10603009), Beijing Nova Program (No.2008B57) and Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality.

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References [1] Zhang, T., Zhou, S., Zeng, Y.: Lossless Image Compression Algorithm based on FPGA. Journal of Systems Engineering and Eletronics 26(10), 1340–1343 (2004) [2] Liu, B.: Image compression technology in satellite engineering research, Master’s thesis, Tongji University (January 2007) [3] Hu, D.: Still image coding methods and international standards, Beijing University of posts and telecommunication press (December 2003) [4] Weinbenger, M.J., Senoussi, G., Sapiro, G.: The LOCO-I Lossless Image Compression Algorithm; Principles and Standardization into JPEG-LS. IEEE Trams. on Image Processing 8(9), 1309–1324 (2000) [5] Weinbenger, M.J., Serouasi, G., Sapiro, G.: LOCO-I: A Low Complexity, Context-Based, Lossless Image Compression Algorithm. In: Data Compression Conference (March 1996)

A Comparative Study on Fuzzy-Clustering-Based Lip Region Segmentation Methods Shi-Lin Wang, An-Jie Cao, Chun Chen, and Ruo-Yun Wang School of Information Security Engineering, Shanghai Jiaotong University, Shanghai, China Nicolas MACHABERT Ecole Supérieurs d'Ingénieurs de REcherche en Matériaux et infotronique(ESIREM) University of Burgundy, Dijon, France

Abstract. As the first step of many lip-reading or visual speaker authentication systems, lip region segmentation is of vital importance. And fuzzy clustering based methods have been widely used in lip segmentation. In this paper, four fuzzy clustering based lip segmentation methods have been elaborated with their underlying rationale. Experiments have been carried out evaluate their performance comparatively. From the experimental results, SFCM has the best efficiency and FCMST has the best segmentation accuracy. Keywords: lip segmentation, fuzzy clustering, spatial information, temporal information, visual speech recognition.

1

Introduction

Recent research [1] has demonstrated that lip motion can be regarded as a visual information source to enhance the accuracy and robustness of many speech recognition and speaker authentication systems. Lip region segmentation as the first step of such systems is of vital importance and its accuracy and robustness will greatly affect the overall performance. Many researchers have proposed various lip segmentation techniques in recent years [2-7], which can be roughly divided into four categories: i) color space analysis [2], which makes classification on certain color spaces; ii) edge based analysis [3], which assumes there should be a noticeable boundary between lip and background; iii) Markov Random Field (MRF) based methods [4], which takes the spatial neighbourhood information into account to reduce the pepper-noise; iv) fuzzy clustering based algorithm [5-7], which employed fuzzy clustering framework to perform lip segmentation. Since fuzzy clustering is an unsupervised algorithm without any assumption of color distribution of lip and non-lip pixels, it can well handle various lip colors due to make-up or low color contrast. In addition, fuzzy clustering provides an efficient scheme to incorporate other useful information (spatial or temporal) into color information so as to improve the accuracy and robustness. Hence, various fuzzy clustering based lip segmentation algorithms [5-7] have been proposed in recent year. In this paper, the authors aim to provide a comparative study on these Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 376–381, 2011. © Springer-Verlag Berlin Heidelberg 2011

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fuzzy clustering based methods and elaborate their advantages and disadvantages. After an in-depth introduction on the rationale behind the algorithms, a series of experiments have been carried out for evaluation. Computation complexity and segmentation accuracy are adopted as two key issues to assess the performance of the algorithm and then corresponding conclusions are drawn. The paper is organized as follows: in section 2, the details of four widely-used fuzzy clustering based lip segmentation algorithms are introduces with the rationale behind the algorithms. The experiment results are given in section 3 with corresponding discussions. Finally, section 4 draws the conclusion.

2

Details of the Fuzzy Clustering Based Algorithms

A. The Traditional Fuzzy C-Means (FCM) Clustering Algorithm The traditional FCM algorithm is an unsupervised learning algorithm which aims to make classification based on their feature similarity. In lip region segmentation, taking the color vector of each pixel as the discriminative feature and the traditional FCM runs as follows: Notations: Consider an image of size N by M. Let X={x1,1, , , , xr,s , , , , xN,M} denotes the feature vectors where xr,s ∈ Rq is a q-dimensional color vector for the pixel located at (r,s). Let di,r,s be the Euclidean distance between the color feature vector xr,s and the color centroid vi of the ith cluster. Note that i=0 stands for the lip cluster and i ≠ 0 stands for the background clusters. Objective function formulation: N

M C −1

J FCM = ∑∑∑ uim,r ,s d i2,r , s

(1)

r =1 s =1 i =0

C −1

subject to

∑u i =0

i ,r , s

= 1, ∀(r , s ) ∈ I

(2)

where the N×M×C matrix U ∈ Mfc is a fuzzy c-partition of X, V={v0,v1,…vC-1}∈ Rcq with vi ∈ Rq represents the set of fuzzy cluster centroids, m∈(1,∞) defines the fuzziness of the clustering, and the value ui,r,s denotes the membership of the (r,s)-th pixel in the fuzzy cluster Ci. The formulation of the objective function in (1) implies that the minimization point is reached when the pixel whose color is similar to the lip is assigned high lip-cluster membership and vice versa. And the final fuzzy segmentation result can be achieved by minimizing the objective function and an iterative approach is adopted for optimization. Optimization: i) With the color centroids fixed, update the membership map by:

(

ui,r ,s = di2,r ,s

)

−1 /( m−1)

∑(d ) c −1

j =0

−1 /( m−1) 2 j ,r , s

(3)

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ii) With the membership map fixed, update the color centroids by: N

M

vi = ∑∑ r =1 s =1

uim,r ,s x r ,s

N

M

∑∑ uim,r,s

(4)

r =1 s =1

iii) Iterate i) and ii) until converge. Rationale: Color feature of each pixel is the only discriminative feature in FCM. The probability of a certain pixel belonging to the lip cluster only depends on its color information. B. The Spatial Fuzzy C-Means (SFCM) Clustering Algorithm SFCM [5] aims to employ “local” spatial information to enhance the robustness of the segmentation algorithm. The basic assumption of SFCM is very simple: if the neighbouring pixels around a certain pixel are very likely to be lip pixels, the pixel is more like a lip pixel. Such assumption can be observed in the modified dissimilarity measure:

DS i , r , s = where

λir,,sj

[

]

1 1 1 2 r ,s ∑ ∑ d i,r , s λl1 ,l2 + d i2,r +l1 , s +l2 (1 − λlr1,,sl2 ) , (l1, l2) ≠ (0,0) 8 l1 = −1l2 = −1

(5)

is the weighting factor controlling the degree of influence of the

neighboring pixels (r+i,s+j) on the center pixel (r,s). The higher similarity between (r,s) and (r+i,s+j), the larger value

λir,,sj

is. The optimization procedure is almost the

same as FCM with the dissimilarity measure,

d i2,r ,s , replaced by DS i ,r ,s .

Rationale: In addition to the color feature, SFCM takes the neighbourhood information into consideration and thus pepper noise can be reduced. Moreover, the authors stated in [5] that considering the neighbourhood information will help faster convergence and lead to more robust segmentation results. C. The “Multi-Class, Shape-Guided” FCM (MS-FCM) Clustering Algorithm Compared with SFCM, MS-FCM [6] employs “global” spatial information. Since the lip is more like an ellipse than other fundamental shapes, an elliptic distance has been adopted to describe the spatial location of a certain pixel. And the dissimilarity function is formulated as:

DM i , r , s = d i2,r , s + gs(i, r , s, p)

(6)

where p = {xc, yc, w, h, θ} denotes the set of parameters that describes the approximate elliptic lip boundary, gs is the spatial function to describe the location of pixel (r,s) with respect to the lip. Exponential form can be employed for gs. The main objective of the function is to penalize the lip membership outside the lip boundary and enhance

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it inside the boundary. The optimization of MS-FCM is a little different from that of FCM and SFCM: in step ii), the spatial parameter set p is also updated. Rationale: Compared with FCM and FCMS, MS-FCM exploits the natural shape of the lip and is specified for lip image segmentation which leads to much higher accuracy. D. The Fuzzy C-Means with Spatial and Temporal Information (FCMST) Algorithm Since lip movement is continuous in the time domain, lip sequences are the common input for many visual speech recognition and speaker authentication systems. The lip region will not change much between successive frames. Taking the lip region of the previous frame as a reference may help improve the robustness of lip segmentation on current frame. Hence, in our previous work [7], such temporal information is introduced and the new dissimilarity measure is formulated as:

DFi,r ,s = di2,r ,s + gs(i, r, s, p) + tem(U prev, p prev )

(7)

where gs is the spatial function and tem is the temporal function. It aims to enhance the lip membership inside the lip boundary of last frame and reduce it outside the boundary. An exponential function is adopted for the temporal function and details can be found in [7]. Since U prev , p prev will not change during iterations of the current frame, the optimization scheme of FCMST is similar to MS-FCM. Rationale: Since lip motion is continuous in nature, FCMST aims to exploits such information to enhance performance. And thus it is suitable dealing with lip sequences.

3

Experimental Results

In order to evaluate the performance of the fuzzy clustering based lip segmentation algorithms introduced in section 2, a database containing one thousand lip image sequences collected from five individuals in our laboratory has been built. Each lip

(a)

(b)

(c)

Fig. 1. Segmentation results using different fuzzy clustering based methods.(red denotes errors and blue denotes correctly classified pixels)

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sequence contains thirty lip images lasting for one second. The converged color centroids and spatial parameter set p in the previous frame is adopted to initialize the corresponding parameters in the current frame. Fig. 1 shows the segmentation results using SFCM (which is very similar to that of FCM), MS-FCM and FCMST. From Fig.1, it is observed that due to image blurring caused by lip motion, even MS-FCM will introduce many segmentation errors while FCMST can well handle the problem. In order to obtain quantitative result, two criterions, i.e., the average number of iterations and the segmentation error (SE) [7], are adopted to evaluate the performance of the four algorithms. And the results are given in Table.1. From Table. 1, it may be observed that: i) Compared with FCM, SFCM cannot improve much accuracy, as pepper noise seldom appear in our lip images. Its advantage lies in it reduce the total number of iterations which fasten the optimization procedure; ii) For MS-FCM and FCMST, the total number of iterations are about 2.5 times compared with FCM and SFCM. And it is observed that when the lip movement is rapid, the algorithms need more iterations to converge. Since FCM only consider color information which does not change much during lip movement, initializing using the lip location of the previous frame may not be so accurate in such case. The major advantages of these two methods are their high segmentation accuracy; iii) FCMST can provide best segmentation accuracy and it converges a little bit faster than MSFCM; iv) It may be concluded that in some real-time applications, FCM or SFCM may be a good choice and in some applications which need high accuracy, FCMST may be the best choice. Table 1. Performance comparison of four methods

No. Iterations SE (%)

FCM 7.47

SFCM 7.14

MS-FCM 18.78

FCMST 18.35

8.06

8.02

3.65

3.46

4 Conclusion Lip region segmentation in images with low color contrast, complex background, image noise and blurring somewhere, is always a difficult problem. Fuzzy clustering based lip segmentation methods can well handle the color variances in lip and skin. And it can also provide a general framework to incorporate other kinds of information. Four fuzzy clustering based lip segmentation methods are introduced and comparatively studied in this paper. Their merits and short-comings are also discussed. Future works of our group aims to extend the FCMST algorithm in both efficiency and accuracy. Prediction using Kalman-filtering may be a feasible direction to deal with the temporal information. Acknowledgment. The work described in this paper is supported by the NSFC Fund (60702043) and Sponsored by Shanghai Educational Development Foundation.

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References [1] Kaynak, M.N., Zhi, Q., Cheok, A.D., Sengupta, K., Chung, K.C.: Audio-visual modeling for bimodal speech recognition. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Tucson, AZ, USA, vol. 1, pp. 181–186 (October 2001) [2] Eveno, N., Caplier, A., Coulon, P.Y.: New color transformation for lips segmentation. In: Proceedings of IEEE Fourth Workshop on Multimedia Signal Processing, Cannes, France, pp. 3–8 (October 2001) [3] Eveno, N., Caplier, A., Coulon, P.Y.: Accurate and quasi-automatic lip tracking. IEEE Trans. on Circuits and Systems for Video Technology 14(5), 706–715 (2004) [4] Zhang, X., Mersereau, R.M.: Lip feature extraction towards an automatic speechreading system. In: Proceedings of IEEE International Conference on Image Processing, Vancouver, BC, Canada, vol. 3, pp. 226–229 (September 2000) [5] Liew, A.W.C., Leung, S.H., Lau, W.H.: Segmentation of color lip images by spatial fuzzy clustering. IEEE Transactions on Fuzzy Systems 11(4), 542–549 (2003) [6] Wang, S.L., Lau, W.H., Liew, A.W.C., Leung, S.H.: Robust lip region segmentation for lip images with complex background. Pattern Recognition 40(12), 3481–3491 (2007) [7] Wang, S.L., Machabert, N.: Sequential lip region segmentation using fuzzy cluster with spatial and temporal information. Submitted to 2nd World Congress on Computer Science and Information Engineering

A Novel Bandpass Sampling Architecture of Multiband RF Signals Fachang Guo and Zaichen Zhang National Mobile Communications Research Laboratory, Southeast University, GUOT Nanjing, China [email protected]

Abstract. In the software defined radio (SDR) front end design, the radiofrequency (RF) bandpass filter is a knotty problem. To widen the passband of the RF filter is a better solution to alleviate the design challenge of RF bandpass filter. However, it will result in significantly increasing the sampling frequency in terms of the conventional bandpass sampling theory. In this paper, we propose a novel method to find the ranges of valid bandpass sampling frequency for multiband RF signals based on the bandwidth of the signal and the passband of the bandpass filter. Compared to the conventional bandpass sampling, the proposed method has more valid sampling frequency ranges and lower sampling frequencies. Keywords: software defined radio, bandpass sampling, analog-digital conversion, valid sampling frequency.

1

Introduction

Software defined radio (SDR) has the agility of changing its functionality through replacement of the application program and hence provides a relatively economical way to accommodate the rapidly emerging mobile communication standards [1]. One goal of SDR architecture is to sample the radio-frequency (RF) signal as close as possible to the antenna. To achieve the goal, one can use the bandpass sampling [2] instead of directly down-converting the desired RF signals to baseband or low intermediate frequency (IF) digital signals [3][4]. According to the theory of SDR, a single radio can communicate simultaneously with many radios using different RF bands and different modulation schemes [5][6]. To fulfill this purpose, one possible SDR receiver front end design is shown in Fig. 1 (a) (b) [3][7], where the input signal to the analog-to-digital converter (ADC) would be a multiband RF signal. The method to determine the ranges of valid sampling frequency for the ADC has been intensively investigated [3][4][7]. However, the design of RF bandpass filter is still difficult. On one hand, the RF bandpass filter at

Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 382–391, 2011. © Springer-Verlag Berlin Heidelberg 2011

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the front end may not be able to achieve the required high Q. On the other hand, its passband may have to change with the dynamic carrier frequency from time to time [8]. To widen the passband of the RF bandpass filter, as shown in Fig. 1 (c), is a better solution to alleviate the design challenge of RF bandpass filter. However, it will result in significantly increasing the sampling frequency in terms of the conventional bandpass sampling theory. Furthermore, this can increase the demand for ADC and digital signal processors (DSP) in a SDR design. In this paper, a novel bandpass sampling method of multiband RF signals is proposed. In the method, aliasing is allowed but only to the undesired RF signals. The ranges of valid sampling frequency are of great interest in the paper. In Section 2, a novel and efficient method to find the ranges of valid bandpass sampling frequency for two-band RF signals is described, and Section 3 extends the analysis described in Section 2 to RF signals with an arbitrary number of bands. The benefits of the proposed method are demonstrated through comparison and application on direct sampling GSM signals in Section 4. Finally, Section 5 concludes this paper.

(a)

B1

− fHVN− f LVN

− f HV 2− f LV 2 − f HV 1− f LV1

f LV1 f HV1

BN

B2

fLV2 fHV2

fLVN f HVN

X 2+ ( f )

X N+ ( f )

W2

WN BLN BN BRN

f

(b) X N− ( f )

X 2− ( f )

X1+ ( f )

X1− ( f )

BL1

− fHN

− f LN − fH2

− f L 2 − fH1

− f L1

W1 B1 BR1

BL2 B2

BR2

f L1 fLV1 f HV1 fH1 f L 2 fLV2 fHV2 fH2 f LN fLVN f HVN f HN

f

(c) Fig. 1. (a) The SDR receiver front end (b) The spectrum of the multiband RF signal at the input of the ADC (c) The spectrum of the multiband RF signal at the input of the ADC after widening the passband of the RF bandpass filter

384

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Valid Bandpass Sampling Frequencies for Two-Band RF Signals

Consider the problem of sampling a dual-band RF signal whose spectrum is shown in Fig. 1(c) with N=2. Bi = f HVi − f LVi ( i = 1, 2 ) and Wi = f Hi − f Li ( i = 1, 2 ) are the bandwidth of desired signals and the passband of the RF bandpass filter, respectively. BLi and BRi ( i = 1, 2 ) are the bandwidth of undesired signals. The i th RF signal can be denoted as X i ( f ) in frequency domain, with its positive spectrum X i+ ( f ) and negative spectrum X i− ( f ) , where i = 1, 2 . To be immune from aliasing to the desired signals after sampling, the sampling frequency requires to be chosen without causing spectral overlapping in the sampled desired signal spectrum. This leads to the eight possible cases shown in Fig. 2.

X1+ ( f − n1 fs )

0 L1

LV1

X2−[ f + (n2 +1) fs ] X1−[ f + (n1 +1) fs ] X2+ ( f − n2 fs )

HV1 L2 H1

LV2

fs 2

HV2

⎧ HV ≤ L NC : ⎨ 1 2 ⎩ LV2 ≥ H1

BC :

{

H2

fs

(a)Case1

L1 + LV1 ≥ 0 H 2 + HV2 ≤ f s

fs 2

fs

fs 2

fs 2

fs

fs

Fig. 2. The dual-band signal shown in Fig. 1(c) (assuming N=2) after bandpass sampling. The 8 possible replica orders are shown in (a) to (h).

A Novel Bandpass Sampling Architecture of Multiband RF Signals

fs 2

385

fs

fs 2

( f )Case 6

fs 2

fs

f

fs

fs 2

fs

Fig. 2. (continued)

As shown in Fig.2, X i+ ( f ) and X i− ( f ) ( i = 1, 2 ) are replicated into a segment located between 0 and f s . They are symmetric with respect to the midpoint of the segment. One can only focus on the first half of the segment. For a given replica order, the sampling frequency must satisfy two constrains: one is referred to as the neighbor constraint (NC) and the other is referred to as the boundary constraint (BC) [7]. For simplicity, Li and H i (±1, ±2) shown in Fig. 2(a) are denoted as the relative lowest and highest frequencies of positive-frequency and negative-frequency replica in the segment. As the same, LVi and HVi (i = ±1, ±2) are defined as the relative lowest and highest frequencies of the desired positive-frequency and negativefrequency replica in the segment. One can easily see that

Li = f Li − ni f s ,

i = 1, 2.

L−i = (ni + 1) f s − f Hi , H i = f Hi − ni f s ,

i = 1, 2.

i = 1, 2.

H −i = (ni + 1) f s − f Li ,

i = 1, 2.

(1) (2) (3) (4)

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LVi = f LVi − ni f s ,

i = 1, 2.

LV−i = (ni + 1) f s − f HVi , HVi = f HVi − ni f s ,

i = 1, 2.

LV−i = (ni + 1) f s − f HVi , ⎢ f LVi + f HVi ⎢ 2 Note that, ni = ⎢ f s ⎢ ⎣⎢

i = 1, 2.

i = 1, 2.

(5) (6) (7) (8)

⎥ ⎥ ⎥ , where ⎣⎢•⎦⎥ denotes the floor function. ⎥ ⎦⎥

Let’s first consider the neighbor constraint. Taking Case 1 shown in Fig. 2(a) as an example, to keep from aliasing between two desired signals in the half of segment, the sampling frequency must satisfy two inequalities as follows: ⎧⎪ HV ≤ L NC : ⎨ 1 2 ⎪⎩ LV2 ≥ H1

(9)

Inequality (9) can be simplified as fs ≤

f L 2 − f H 1 min { BR1, BL 2 } + n2 − n1 n2 − n1

(10)

Next, the boundary constraint will be considered. To cause no aliasing between desired positive-frequency replica and desired negative-frequency replica in the segment, the sampling must satisfy inequalities below: BC :

{

L1 + LV1 ≥ 0 H 2 + HV2 ≤ f s

(11)

Inequality (11) can be simplified as BR 2 B f L1 + L1 2 ≤ f ≤ 2 s 1 n 1 n2 + 2

fH 2 −

(12)

Combining (10) and (12), one can get BR 2 B ⎧ ⎫ f L1 + L1 f − f + min { B , B } ⎪ H1 R1 L2 ⎪ 2 ≤ f ≤ min ⎪⎪ 2 , L2 ⎨ ⎬ s 1 n2 − n1 ⎪ n1 ⎪ n2 + 2 ⎩⎪ ⎭⎪

fH 2 −

(13)

A Novel Bandpass Sampling Architecture of Multiband RF Signals

387

Table 1. The Ranges of Valid Sampling Frequency for the Dual-band Signal of An N-B and RF (N=2) Range of Valid f s

Case 1

2

3

4

5

6

7

8

BR 2 2 1 n2 2 BR 2 fH 2 2 n2 1 fH 2

fH 1

fs

fs

BL1 ⎧ ⎪ f L1 2 min ⎨ ⎪ n1 ⎩ BL1 ⎧ ⎪ f L1 2 min ⎨ ⎪ n1 1 ⎩ 2

f H 2 min{BR1, BR 2 }

f H 2 min BR1 , BR 2 n1 n2 1

BR1 ⎧ ⎪ f H1 2 f H 2 max ⎨ , ⎪ n1 1 n2 ⎩ BR1 ⎧ ⎪ f H1 2 f H 2 max ⎨ , n2 ⎪ n1 1 ⎩ 2 BR1 ⎧ ⎪ f H1 2 f H 2 max ⎨ , ⎪ n1 1 ⎩ BR1 ⎧ ⎪ f H1 2 f H 2 max ⎨ , ⎪ n1 1 2 ⎩

BR 2 2 1 2 BR 2 2 1

fs ⎫ ⎪ ⎬ ⎪ ⎭

fs

⎫ ⎪ ⎬ ⎪ ⎭

fs

f L2

⎫ f H 1 min BR1 , BL 2 ⎪ ⎬ n2 n1 ⎪ ⎭

⎫ f H 1 min BR1 , BL 2 ⎪ ⎬ n2 n1 ⎪ ⎭ BL1 BL 2 ⎫ ⎧ ⎪ f L1 2 f L 2 2 ⎪ min ⎨ , 1 ⎬⎪ ⎪ n1 n2 2 ⎭ ⎩ BL1 BL 2 ⎫ ⎧ ⎪ f L1 2 f L 2 2 ⎪ min ⎨ , ⎬ n2 ⎪ n1 1 ⎪ 2 ⎩ ⎭

,

fs

n1 n2 1

f H1

,

f L2

f L1

f L2

min BL1 , BL 2

n1 n2 1 f L1

f L 2 min BL1 , BL 2 n1 n2 1

⎫ f L1 min BL1 , BR 2 ⎪ ⎬ n2 n1 ⎪ ⎭

fs

⎫ f L1 min BL1 , BR 2 ⎪ ⎬ n2 n1 ⎪ ⎭

fs

BL 2 2 1 2 BL 2 2

f L2 n2

f L2 n2

From (13), one cannot obtain ranges of valid sampling frequency of Case 1 unless n1 and n2 are given. n1 and n2 can be derived in terms of a feasible procedure as follows. From (13), one get ⎧ f H 2 − B2R 2 f L1 + B2L1 ≤ ⎪ 1 n1 ⎪ n2 + 2 ⎨ f − BR 2 ⎪ H 2 2 ≤ f L 2 − f H 1 + min{BR1 , BL 2 } n2 − n1 ⎪⎩ n2 + 12

(14)

Combining inequalities above and considering that n1 and n2 are integer, one have ⎥ B ⎧ ⎢⎢ fH 2 − R 2 ⎥ 1 2 − ⎥ ⎪ n2 ≤ ⎢⎢ ⎡ BL1 + BR 2 ⎤ 2⎥ ⎪ ⎢⎣ 2 ⎢⎣W1 +W2 − 2 − min{BR1, BL 2 }⎥⎦ ⎥⎦ ⎨⎢ BL1 ⎥ ⎥ ⎢ ⎪ ⎢⎢n2 −( n2 + 1 ) f L 2 − f H 1 + min{BR1, BL 2 } ⎥⎥ ≤ n1 ≤ ⎢⎢( n2 + 1 ) f L1 + 2 ⎥⎥ B BR 2 ⎥ 2 2 ⎥ ⎢ ⎪ ⎢⎢ fH 2 − R 2 f − H2 2 2 ⎦⎥ ⎦⎥ ⎣⎢ ⎩⎣

(15)

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According to (13) and (15), one can finally obtain the valid sampling frequencies of Case 1. Following the same procedure, one can get the valid sampling frequencies of other cases. Hence, the corresponding results are shown in Table 1. From Table 1, it can be seen that: particularly, for BLi = 0 and BRi = 0 (i=1, 2), only desired signals exist, the ranges of valid sampling frequency are the same to the ranges obtained in [7].

3

Valid Bandpass Sampling Frequencies for N-Band RF Signals

In this section, the analysis described in Section 2 is extended to RF signals shown in Fig. 1(c) with an arbitrary number of bands. According to the bandpass sampling theory, when the N-band RF signal is sampled at f s Hz, the spectrum of the sampled signal can be obtained by replicating the spectrum of the original signal at multiples of f s . Just like analysis in Section 2, the spectrum of the original signal can be replicated into the segment located between 0 and f s . Then, we can find that the total number of possible replica orders in the segment is equal to 2M × M ! [7]. For each possible replica order in the segment, there are N − 1 neighbor constraints and 2 boundary constraints. One can obtain ranges of valid sampling frequency in terms of the combination of all the constraints. However, the computational complexity is very large. Furthermore, for typical applications, one may find that examining all the possible replica orders eventually ends up with only few valid replica orders which yield nonempty sampling frequency ranges. A feasible procedure to find sampling frequency ranges for an N-band RF signal is to modify the standard procedure as follows. Firstly, for any pair of bands, one can follow the procedure analyzed in Section 2 to determine ranges of valid sampling frequency and ranges of valid ni and n j for all the possible replica orders. The result is similar as the Table 1. For each pair of bands in the N bands, a similar table can be constructed. Thus, there are C N2 tables like Table 1. Then, one considers only those N-band replica orders which can be concatenated with the valid dual-band replica orders in the C N2 tables instead of all the possible N-band replica orders [7]. Following this procedure, it is easy to obtain the valid sampling ranges of N-band RF signal.

4

Comparison and Application

Now, the benefits of our proposed method are demonstrated through comparison and application on direct sampling GSM signal [10]. Usually, the bandwidth of the RF signal of interest is very narrow compared to the carrier frequency. Furthermore, the carrier frequency is dynamic. For example, an RF signal from GSM1800 typically has a bandwidth of 200 kHz, whereas its dynamic carrier frequency is about 1.8 GHz. Although micro electro-mechanical system (MEMS) technology allows dynamic adjustment of the RF component value, such a high selective and tunable bandpass filter is still practically unavailable today. Thus, the RF bandpass filter may cover the

A Novel Bandpass Sampling Architecture of Multiband RF Signals

Table 2. The Ranges of Valid Sampling Frequency for the Dual-Band GSM Signal C A S E

n1

n2

5 2 7 5 4 2 7 5 4 2 6 7 5 4 2 6 3 1 7 5 4 2 6 3 1 7 5 4 2 6 3 1

5 5 4 4 4 4 3 3 3 3 3 2 2 2 2 2 2 2 1 1 1 1 1 1 1 0 0 0 0 0 0 0

11 10 9 9 9 8 7 7 7 6 6 5 5 5 4 4 4 4 3 3 3 2 2 2 2 1 1 1 0 0 0 0

Range of Valid f s (MHz) In Terms Of The Procedure Proposed In [7]

Range of Valid f s (MHz) In Terms Of The Novel Procedure 161.8522 ≤ f s ≤ 161.9059

NONE NONE NONE NONE NONE NONE

169.2091 ≤ f s ≤ 171.1273 190.7600 ≤ f s ≤ 191.9684 195.9263 ≤ f s ≤ 196.6000 201.9714 ≤ f s ≤ 202.6333 206.8111 ≤ f s ≤ 209.1556

240.0000 ≤ f s ≤ 240.6667

238.4500 ≤ f s ≤ 243.1600

NONE NONE NONE NONE

248.1733 ≤ f s ≤ 250.2182 257.0545 ≤ f s ≤ 260.5286 265.9000 ≤ f s ≤ 268.9143 272.5143 ≤ f s ≤ 275.2400

320.0000 ≤ f s ≤ 328.1818

317.9333 ≤ f s ≤ 331.5818

341.8182 ≤ f s ≤ 342.5000

338.4182 ≤ f s ≤ 344.0500

355.0000 ≤ f s ≤ 361.0000

353.4500 ≤ f s ≤ 364.7400

NONE

372.2600 ≤ f s ≤ 376.4800

384.0000 ≤ fs ≤ 391.4286

381.5200 ≤ f s ≤ 393.2000

NONE

403.9429 ≤ f s ≤ 405.2667

417.7778 ≤ f s ≤ 422.5000

413.6222 ≤ f s ≤ 428.7000

480.0000 ≤ f s ≤ 515.7143

476.9000 ≤ f s ≤ 521.0571

537.1429 ≤ f s ≤ 548.0000

531.8000 ≤ f s ≤ 550.4800

568.0000 ≤ f s ≤ 601.6667

565.5200 ≤ f s ≤ 607.9000

NONE

620.4333 ≤ f s ≤ 627.4667

640.0000 ≤ f s ≤ 685.0000

635.8667 ≤ f s ≤ 688.1000

710.0000 ≤ f s ≤ 722.0000

706.9000 ≤ f s ≤ 729.4800

752.0000 ≤ fs ≤ 845.0000

744.5200 ≤ f s ≤ 857.4000

0.9600 ×10 ≤ f s ≤ 1.2033×10

0.9538 ×103 ≤ f s ≤ 1.2158 ×103

3

3

3

1.2533×10 ≤ fs ≤1.3700×10

1.2409 ×103 ≤ f s ≤ 1.3762 ×103

1.4200×10 ≤ fs ≤ 1.8050×10

1.4138 ×103 ≤ f s ≤ 1.8237 ×103

NONE

1.8613×103 ≤ f s ≤ 1.8824 ×103

3 3

3

1.9200×103 ≤ fs ≤ 2.7400×103

1.9076 ×103 ≤ f s ≤ 2.7524 ×103

2.8400×10 ≤ fs ≤ 3.6100×10

2.8276 ×103 ≤ f s ≤ 3.6474 ×103

3.7600 × 10 ≤ f s

3.7226 × 103 ≤ f s

3

3

3

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entire service bands of GSM 900(935-960 MHz) and GSM 1800 (1805 - 1880) systems [8]. In the application, the SDR receiver front end in Fig.1 (a) with N=2 was used to directly sample the entire service bands of GSM 900(935-960 MHz) and GSM 1800 (1805-1880) systems. Then, one can obtain the valid sampling frequencies shown in Table 2 in terms of the procedure proposed in [7]. However, it is more probable that GSM 900(935-960 MHz) and GSM 1800 (1805-1880) systems have only two desired RF signals. It can be assumed that the RF signal with f LV 1 = 947.4MHz and f HV 1 = 947.6 MHz and the RF signal with f LV 1 = 1842.4MHz and f HV 1 = 1842.6 MHz are the desired signals. Then, one can obtain the valid sampling frequency ranges shown in Table 2 according to the procedure proposed in Section 2. From Table 2, it can be found that the latter has more valid sampling ranges and lower sampling frequencies than the former.

5

Conclusion

In this paper, the simple formulas for the ranges of valid bandpass sampling frequency are derived in terms of the bandwidth of the desired signal and the passband of the bandpass filter. The result can be used to efficiently choose an appropriate bandpass sampling frequency for the ADC in a multiband SDR receiver front end. Compared to the conventional bandpass sampling, the proposed method has more valid sampling frequency ranges and lower sampling frequencies. Acknowledgment. This work is supported by NSFC projects(60802005 and 60902010), 863 projects (2007AA01Z2b1 and 2009AA012000), National Science & Technology Major projects of China (2009ZX03006-008-02 and 2009ZX03003-009), and the NSF of Jiangsu project (BK2009266).

References [1] Mitola, J.: Software Radio Architecture. IEEE Commun. Mag. 33(5), 26–38 (1995) [2] Vaughan, R.G., Scott, N.L., White, D.R.: The theory of bandpass sampling. IEEE Trans. Signal Processing 39(9), 1973–1984 (1991) [3] Akos, D.M., Stockmaster, M., Tsui, J.B.Y., Caschera, J.: Direct bandpass sampling of multiple distinct RF signals. IEEE Trans. Commun. 47(7), 983–988 (1999) [4] Wong, N., Ng, T.-S.: An efficient algorithm for downconverting multiple bandpass signals using bandpass sampling. In: Proc. IEEE Int. Conf. Commun., Helsinki, Finland, vol. 3, pp. 910–914 (June 2001) [5] Cook, P.G., Bonser, W.: Architectural overview of the SPEAKeasy system. IEEE J. Select. Areas Commun. 17(4), 650–661 (1999) [6] Zangi, K.C., Koilpillai, R.D.: Software radio issues in cellular base stations. IEEE J. Select. Areas Commun. 17(4), 561–573 (1999) [7] Tseng, C.-H., Chou, S.-c.: Direct Down Cownconversion of Multiband RF Signals Using Bandpass Sampling. IEEE Trans.Commun. 5(1) (January 2006)

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[8] Yu, S., Wang, X.: Bandpass Sampling of One RF Signal over Multiple RF Signals with Contiguous Spectrums. IEEE Sig.Process. Lett. 16(1) (January 2009) [9] Wang, X., Yu, S.: A Feasible RF Bandpass Sampling Architecture of Single-Channel Software-Defined Radio Receiver. In: Proc. IEEE Int. Conf. Commun. (2009) [10] Digital Cellular Telecommunications System (Phase 2+); Radio Transmission and Reception (GSM 05.05 Version 8.5.1 Release 1999), ETSIEN 300 910 Ver. 8.5.1 (2000-11)

Classification of Alzheimer’s Disease Based on Cortical Thickness Using AdaBoost and Combination Feature Selection Method Zhiwei Hu, Zhifang Pan, Hongtao Lu, and Wenbin Li* Department of Computer Science, Department of Radiology, Shanghai Jiaotong University, Wenzhou Medical College, Shanghai Sixth People's Hospital, Shanghai, China [email protected]

Abstract. In this research, using the idea of ensemble, we designed and applied a new supervised learning algorithm for classification of Alzheimer's disease (AD). Using MRI cortical surface-based analysis, cortical thickness of AD patients and normal controls were measured. All these data were retrieved from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We mainly used the cortical thickness data in our research. As human brains can be divided into many lobes and regions, which are of different distinctive capability, we adopted an ensemble feature selection method that filters these lobes according to their discriminative ability, and randomly selects the features for certain times to create several subsets. Each part of the data owns a classifier for training. And then we combined all the classifiers to form a more powerful classifier using AdaBoost. Linear discriminate analysis were used to build up these classifiers. The generalization accuracy using test data set can achieve about 0.86 if selected the parameters well. Our classification method based on ensemble feature selection was therefore proposed and could be used in AD classification problems or other related areas. Keywords: Alzheimer's disease, feature selection, ensemble, AdaBoost, magnetic resonance imaging, cortical thickness.

1

Introduction

In recent years, lots of researches have been done about Alzheimer's disease (AD) for it is the most prevalent cause of dementia in elderly people. Various methods can be adopted to diagnose Alzheimer's disease. Clinically, the Mini-Mental State Examination (MMSE)[12] is widely used as a diagnosis tool. However, using MMSE or other similar tests are time-consuming. Brain imaging has been increasingly important in research of dementias in the past decade. With the help of magnetic resonance imaging (MRI), researchers can differentiate patients from normal elderly *

Corresponding author.

Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 392–401, 2011. © Springer-Verlag Berlin Heidelberg 2011

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controls according to their brain images. Some studies have already investigated the capability and potential of using * Data used in the preparation of this article were obtained from the Alzheimer\'s Disease Neuroimaging Initiative (ADNI) database (www.loni. ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at http://www.loni.ucla.edu/ADNI/Collaboration/ ADNI_Manuscript_Citations.pdf ). machine learning algorithm to detect and predict AD from normal elderly controls [9,14,15]. There are many researches about morphological classification based on voxel of MRI [13,16]. Although this kind of methods may achieve high classification accuracy, but they are usually difficult to be implemented. Another situation is that we've already got some attributes which can be used to separate AD from normal controls. And one of the classification criteria that most frequently used would be cortical thickness, for it has been proved that AD patients usually have thinner cortices than normal elderly controls in some brain regions[7,9-11,15]. While sometimes researcher cannot get enough subjects whose number is smaller than the number of regions which will lead to unstable result, so it is indispensable to select some effective features to build a robust classifier. One of the objectives of this paper is to introduce a feature selection method which can be applied in AD classification and get a more robust result. Some classical feature selection algorithms were used to compare with our algorithm. Based on our feature selection result, we successfully implemented classification algorithm to separate the AD patients from healthy controls.

2 Material and Methods A. Data Source Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a 60 million, 5-year public-private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials. The Principal Investigator of this initiative is Michael W. Weiner, M.D., VA Medical Center and University of California - San Francisco. ADNI is the result of efforts of many co-investigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the

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U.S. and Canada. The initial goal of ADNI was to recruit 800 adults, ages 55 to 90, to participate in the research. Thirty two subjects were studied. There are 16 patients (mean age is 75.9) and 16 normal controls (mean age is 75.7) in this dataset. All these MRI data are obtained from Alzheimer’s disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu/ADNI). B. Participants Selected from ADNI database, thirty two subjects were studied. There are 16 patients and 16 normal controls, and all of them are right-handed. The demographic characteristics of the observations are summarized in Table 1. Table 1. Statistics of the subjects

Age(Mean) Age(SD) MMSE(Mean) MMSE(SD) Male/Female

AD 75.9 8.3 22.6 2.4 4/12

Controls 75.7 6.7 29.4 0.9 9/7

C. Image Acquisition MRI examinations were performed on a 3.0T Philips Medical Systems. All subjects were investigated with a volumetric T1-weighted sagittal oriented MRI sequence (TR= 3.1584ms, TE= 6.8028ms). D. Image Processing Cortical reconstruction and volumetric segmentation was performed with the Freesurfer image analysis suite, which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). The technical details of these procedures are described in prior publications [17, 18, 20–28, 32]. Briefly, this processing includes motion correction and averaging of multiple volumetric T1 weighted images (when more than one is available) , removal of non-brain tissue using a hybrid watershed /surface deformation procedure[32], automated Talairach transformation, segmentation of the subcortical white matter and deep gray matter volumetric structures (including hippocampus, amygdala, caudate, putamen, ventricles)[22, 23] intensity normalization[34], tessellation of the gray matter white matter boundary, automated topology correction [21,33], and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class[17,18,20]. Once the cortical models are complete, a number of deformable procedures can be performed for in further data processing and analysis including surface inflation[24], registration to a spherical atlas which utilized individual cortical folding patterns to match cortical geometry across subjects [25], parcellation of the cerebral cortex into units based on gyral and sulcal structure

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[19,26], and creation of a variety of surface based data including maps of curvature and sulcal depth. This method uses both intensity and continuity information from the entire three dimensional MR volume in segmentation and deformation procedures to produce representations of cortical thickness [20]. Procedures for the measurement of cortical thickness have been validated against histological analysis [30] and manual measurements [29, 31]. Freesurfer morphometric procedures have been demonstrated to show good test-retest reliability across scanner manufacturers and across field strengths [27]. After all the process above ran, 68 lobes (except unknown regions) for left and right hemisphere which is far more than the number of subjects. This situation will lead the result unstable if using all the lobes in classification, so we must reduce the dimension of the data before classification. E. Feature Selection In medical research, sometimes it is very difficult to find enough samples whose number is smaller than the number of disease features. This will make the statistical result unstable. Methods of dimension reduction, especially feature selection should be applied in this situation. Feature selection, or sometimes be called as subset selection, has also been researched for decades, and many successful algorithms have been proposed [3], such as sequential forward selection and sequential backwards selection algorithm. However, unlike the feature extraction methods, traditional feature selection algorithms listed above only select one subset of all features, so maybe some discriminative information will be lost. Here, we proposed a feature selection algorithm that has the advantage of the feature extraction. We applied the idea of combining features in feature selection framework [2]. Although the ensemble method mentioned contributes greatly to our method, we updated some details of the ensemble method. In our method, firstly we used the ratio of the variance between the classes to the variance within the classes (BW-Ratio) to get the discriminative ability of the features. The ratio is described in (1) below. Some features with bad discriminant ability could be removed. And then, similarly with attribute bagging, randomly select certain number of features into subsets for several times. BWR

∑ ∑ ∑ ∑

(1)

Some features with small ratio value, such as the values which are smaller than 1, could be removed before further operation. After that, we put a certain number of features into a subset and repeated for several times. For each subset, we assigned a classifier to be trained using the feature in the subset. So it is very convenient to use AdaBoost[1] to combine all these classifier together to get a better performance.The idea behind this procedure is very similar to Bootstrap aggregating (bagging)[4]. However, the main difference is, bagging is based on samples while our method used here is based on features.

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F. Discriminant Analysis AdaBoost[1], short for Adaptive Boosting, combines several weak classifier to form a stronger classifier. It repeatedly updates the weights of classifiers for T times according to the changing importance of the samples. Under the AdaBoost framework, our classification procedure was performed using linear discriminant analysis. That's because of the fact that AdaBoost can combine weak classifiers into a "stronger" one and uncomplicated algorithms can simplify the model and enhance the speed of computing. Our analysis was performed using leaveone-out validation to prevent over-training and get a robust validation result. As the age of these two groups of subjects are not perfectly matched, and what's more, regression on the residuals of age could not significantly enhance the discriminate result[7], age was omitted in the preprocess of the analysis. Given the number of iterations T, the number of selected feature in each classifier v, the t-th feature subset Vt, training data and corresponding label for i=1,..N and j=1,..M , the whole process can be described briefly below. a) Calculate the BW-Ratio for all the features, remove some features if they got very low BW-Ratios b) For t=1,2,...,T, randomly select v features into Vt c) Initialize the data weighting coefficients = 1/N, i=1,...N. d) For t=1,2,...,T: <= / ∑ Fit a classifier by minimizing the error function: ∑

,

, / 1 exp α , The prediction of the final model: <=∑ α <=ln

e)

∑

3 Experiment Result and Discussion Before we ran test, two parameters need to be confirmed: T and v. They are the number of classifiers (iterations) and the number of features in each classifier. After ran leave-one-out validation for several times, we got the general relationship between T, v and the accuracy of final model(Fig. 1). We wrote Matlab code to implement all the algorithms mentioned before. For each T and v, we ran the program for ten times to get average generalization accuracy. From Fig. 1 we could see, it got poor result when iteration and features were both low. The accuracy was around 0.6. The result improved greatly as these two parameters increased. When T =6-18 and v big enough, it can reach to a best situation, 0.86, in this test. However, as the feature in each subset kept on increasing, the result got a little bit poorer than before, because classifier included too many features and got mislead by some of the features in classification. For another parameter, although

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Fig. 1. Accuracy of Different Parameters

Fig. 2. Comparison with Other Feature Selection Methods

accuracy remained at high level when v greater than 3, the computing time increased linearly while the accuracy didn't get much better and sometimes even got worse. That was mainly because of AdaBoost are likely to get over-fit. Next, we compared our ensemble method with some other traditional feature selection methods. As a classical feature selection method, the forward feature selection procedure was chosen as a benchmark in this experiment. In this procedure, the subset of features adds features increasingly by the classification result. Another practical method, random selection method was also chosen to be compared with. We

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set T=6 for our ensemble method since T around this value was proved as a successful parameter. The feature number in subset was from 1 to 20, because the number greater than 20 tends to make data matrix non-positive definite. For most of time, our feature selection method performed more robust and got better result than the other methods (Fig. 2). Although when T was small, the accuracies of different classifiers were almost the same, our method performed much better than others as T increased.

4 Conclusion In this paper, we adopted a ensemble feature selection method and applied it on classification of Alzheimer's disease based on cortical thickness. Unlike selecting one subset in traditional feature selection algorithm, it selects several subsets of features and use AdaBoost to train. In our experiments, it showed good and robust result than traditional methods. Therefore, our method can get more robust classification outputs than selecting only one subset.Besides being used on classification of Alzheimer's disease, this feature selection method may be applied successfully in many other domains. Acknowledgment. This work is supported by Shanghai committee of Science and Technology (08411951200) and 863(2008AA02Z310) and NSFC (60873133). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.

References [1] Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting. Journal of Computer and System Sciences 55, 119–139 (1995)

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[2] Bryll, R., Osuna, R.G., Quek, F.: Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognition 36, 1291–1302 (2003), doi:10.1016/S0031-3203 [3] Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research 3, 1157–1182 (2003) [4] Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996), doi:10.1007/BF00058655 [5] Ho, T.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998), doi:10.1109/34.709601 [6] Oliveira Jr, P.P.M., Nitrini, R., Busatto, G., Buchpiguel, C., Sato1, J.R., Amaro Jr, E.: Use of SVM Methods with Surface-Based Cortical and Volumetric Subcortical Measurements to Detect Alzheimer’s Disease. Journal of Alzheimer’s Disease 19(4) (January 2010), doi:10.3233/JAD-2010-1322 [7] Lerch, J.P., Pruessner, J., Zijdenbos, A.P., Collins, D.L., et al.: Automated cortical thickness measurements from MRI can accurately separate Alzheimer’s patients from normal elderly controls. Neurobiol. Aging 29, 23–30 (2008) [8] Sanchez-Benavides, G., Gomez-Anson, B., Quintana, M., Vives, Y., Manero, R.M., Sainz, A., Blesa, R., Molinuevo, J.L., Pe?a-Casanova, J.: Problem-solving abilities and frontal lobe cortical thickness in healthy aging and mild cognitive impairment. Journal of the International Neuropsychol Society (July 2010) [9] Singh, V., Chertkow, H., Lerch, J.P., Evans, A.C., Dorr, A.E., Kabani, N.J.: Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer’s disease (2006), doi:10.1.1.123/8501 [10] Du, A.T., Schuff, N., Kramer, J.H., Rosen, H.J., Gorno-Tempini, M.L., Rankin, K., Miller, B.L., Weiner, M.W.: Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain 130, 1159–1166 (2007) [11] Dickerson, B.C., Feczko, E., Augustinack, J.C., et al.: Differential effects of aging and Alzheimer’s disease on medial temporal lobe cortical thickness and surface area. Neurobiology of Aging (September 2007) [12] Folstein, M., Folstein, S.E., McHugh, P.R.: Mini-mental state: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 12(3), 189–198 (1975) [13] Long, X., Wyatt, C.: An automatic unsupervised classification of MR images in Alzheimer’s disease. Computer Vision and Pattern Recognition, 2910–2917 (2010) [14] Duchesne, S., Caroli, A., Geroldi, C., Barillot, C., Frisoni, G.B., Collins, D.L.: MRIbased automated computer classification of Probable AD Versus Normal Controls. Medical Imaging 27(4), 509–520 (2008), doi:10.1109/TMI.2007.908685 [15] Lehmann, M., Crutch, S.J., Ridgway, G.R., Ridha, B.H., Barnes, J., Warrington, E.K., Rossor, M.N., Fox, N.C.: Cortical thickness and voxel-based morphometry in posterior cortical atrophy and typical Alzheimer’s disease. Neurobiology of Aging (September 2009) [16] Peng, H., Susan, R., Shen, D., Davatzikos, C., Herskovits, E.: Bayesian Analysis of Morphological Changes Associated with Mild Cognitive Impairment A Cross-Sectional Study, doi:10.1.1.19.4314 [17] Dale, A.M., Fischl, B., Sereno, M.I.: Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999)

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[18] Dale, A.M., Sereno, M.I.: Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci. 5, 162–176 (1993) [19] Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S., Killiany, R.J.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006) [20] Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U.S.A. 97, 11050–11055 (2000) [21] Fischl, B., Liu, A., Dale, A.M.: Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Trans. Med. Imaging 20, 70–80 (2001) [22] Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., Dale, A.M.: Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 33, 341–355 (2002) [23] Fischl, B., Salat, D.H., van der Kouwe, A.J., Makris, N., Segonne, F., Quinn, B.T., Dale, A.M.: Sequence-independent segmentation of magnetic resonance images. Neuroimage 23(1), S69–84 (2004a) [24] Fischl, B., Sereno, M.I., Dale, A.M.: Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999a) [25] Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain. Mapp. 8, 272–284 (1999b) [26] Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., Caviness, V., Makris, N., Rosen, B., Dale, A.M.: Automatically parcellating the human cerebral cortex. Cereb Cortex 14, 11–22 (2004b) [27] Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R., Maguire, P., Rosas, D., Makris, N., Dale, A., Dickerson, B., Fischl, B.: Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32, 180–194 (2006) [28] Jovicich, J., Czanner, S., Greve, D., Haley, E., van der Kouwe, A., Gollub, R., Kennedy, D., Schmitt, F., Brown, G., Macfall, J., Fischl, B., Dale, A.: Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. Neuroimage 30, 436–443 (2006) [29] Kuperberg, G.R., Broome, M.R., McGuire, P.K., David, A.S., Eddy, M., Ozawa, F., Goff, D., West, W.C., Williams, S.C., van der Kouwe, A.J., Salat, D.H., Dale, A.M., Fischl, B.: Regionally localized thinning of the cerebral cortex in schizophrenia. Arch. Gen. Psychiatry 60, 878–888 (2003) [30] Rosas, H.D., Liu, A.K., Hersch, S., Glessner, M., Ferrante, R.J., Salat, D.H., van der Kouwe, A., Jenkins, B.G., Dale, A.M., Fischl, B.: Regional and progressive thinning of the cortical ribbon in Huntington’s disease. Neurology 58, 695–701 (2002) [31] Salat, D.H., Buckner, R.L., Snyder, A.Z., Greve, D.N., Desikan, R.S., Busa, E., Morris, J.C., Dale, A.M., Fischl, B.: Thinning of the cerebral cortex in aging. Cereb Cortex 14, 721–730 (2004) [32] Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K., Fischl, B.: A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 1060–1075 (2004)

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A Robust Blind Image Watermarking Scheme Based on Template in Lab Color Space YunJie Qiu, Hongtao Lu, Nan Deng, and Nengbin Cai Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai, China [email protected] Shanghai Institute of Forensic Science and Technology, Shanghai, China [email protected]

Abstract. A robust image watermarking scheme is proposed. The scheme is composed of two parts: non-informative watermark embedded in b component of Lab color space, which is used to recover embedded region from the modified image; Informative watermark embedded in DCT domain that carries information can be used to protect copyright. This scheme is a blind watermarking scheme, extraction can be proposed without the presence of original image. Experiments show that the scheme is resilient to typical image processing like cropping, scaling, and translation. It is used in practical project for image copyright protection. Keywords: Image processing, Template based watermarking, Self-synchronize, Blind extraction.

1

Introduction

Robust image watermarking is the process of embedding information that is difficult to remove and retrievable after certain kind of image processing. This technique is developed to meet the need of copyright protection. Blind watermarking is the watermarking scheme that the information can be retrieved without the knowledge of original image, which is correspond to practical situation. In practical situation, scaling, cropping and translation are frequently used as basic image processing methods. Lots of research focuses on resilience against these attacks. In early ages, research focuses on recovering watermark upon loss of image content while keeping the size information. For example, in [1], a scheme using SVD and DWT is proposed. It is claimed to be cropping and scaling resilient. In simulation, scaling is tested by first scale to some size, then scale back before extracting; cropping is tested by replacing a portion of embedded image with pure black; translation is simulated by padding the region moved out with pure white. In fact, if we cut the cropped region off, or does no padding after translation, the scheme fails. This Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 402–410, 2011. © Springer-Verlag Berlin Heidelberg 2011

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indicates that synchronization information, for example size and aspect ratio of image, position of origin (top left corner), is needed in extraction, so it is not a real “blind” scheme. To solve the synchronization problem, new methods are proposed mainly in three directions: •

•

•

Fourier-Mellin transformation based scheme. In [2], the invariants in FourierMellin transformation are first outlined. Fourier-Mellin domain is invariant after scaling and translation, and the rotation results in circular translation in transformed domain. Watermark embedded in Fourier-Mellin domain holds strong robustness. Drawback of Fourier-Mellin transformation is it involves log-polar mapping. It will be difficult to implement and keep the fidelity after embedding. Interest point based scheme. Interest points are points detected from local region of image, and is stable under local and global perturbations. In [3], Harris-Laplacian feature points and corresponding interest region are extracted from image; the scheme embeds 1 bit binary information in interest regions around each interest point. On extraction, watermark is extracted from the voting result of bits extracted from those interest regions. Problem of these schemes is that the occurrence of interest points is hard to control, if interest points concentrate in some region, then cropping off such region will destroy all the watermarks. The capacity of watermark is also a problem. Template based schemes. [4]Template refers to some artificial changes on image (RGB space, or some frequency domain). When the image is transformed, these changes are used to recover the original shape. Difficulty lies in the stability of template.

In this article, a template based watermarking scheme is proposed. We choose some square region as the template region. Template regions serve as vertices of watermarking region. For template region, we modify the value of some b component of Lab color space; for watermarking region, we embed information in DCT domain. The rest of the paper is organized as follow. In Section 2, the detailed algorithm will be described. Simulation result will be shown in Section 3; at last, a conclusion is drawn in Section 4.

2 Algorithm Description A. Distribution of Template and Watermark In our scheme, templates are defined as square blocks uniformly distributed in the whole image. Watermark regions are also square blocks, which are bounded by templates, as illustrated in Figure 1. The size of template region and watermark region is defined upon the need for extraction effect and watermark capacity. In our experiment, each template region is a square with 100*100 pixels, and the watermark region is a square with 256*256 pixels.

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Fig. 1. Distribution of template and watermark

We use certain template distribution for two reasons: • •

Multiple copy of watermark is embedded. Extraction succeeds as long as any single copy can be extracted successfully. Uniform distribution makes it easy to find all the watermark regions from a single template region. Trials can be focused around the true watermarking region.

B. Embedding and Extracting Template We choose Lab color space for template embedding. Lab color space is a coloropponent space with dimension L for lightness; a and b for the color-opponent dimension. We made this choice because lightness is separated into a single dimension, thus embedding in the other two components will have the resilience against contrast modification. The idea is from our previous work in [5]. We mark template region in b dimension of the pixel. The value range of b dimension is [0, 360]. To distinct a point from being template or not template, we quantize b component value to 19 ladders, say 0, 20, 40… 360. Each template pixel’s b component value is quantized to the nearest ladder value. Another thing needs to be noticed is that the pixel outside template region may coincide with the quantized value; these pixels will be misclassified as template pixel. So we need to check all the non-template pixels and do some reverse-quantization if needed. The positions of template just start from the top left corner of image in our scheme.

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The embedding and extracting of template is described as follow: For each pixel p in I, denote its b dimension value as B a) If p is in template region, do quantization

p ' = [ p / 20 ] * 20

(1)

b) If p is not in template region, and satisfies

p − [ p / 20] * 20 < 20 * δ

(2)

Where δ is the threshold to determine whether a pixel has been quantized. If equation (2) is satisfied, the pixel needs reverse-quantization. As shown in Equation 3.

20*δ { pp ''==tt +−20* δ

p ≥t p
(3)

t is the nearest quantization value from p. According to the description in embedding template, we can identify each pixel in the image as template pixel or not by using Equation 2. We check every pixel of the image, marking the template pixels as 1, non-template pixels as 0, which results in a binary matrix. We call it template distribution matrix. Problem is that through image saving (JPEG compression and color space conversion involved) and image modification, value of the pixel may change so that some pixels change their kind. We use erosion/dilation to reduce these noises. Firstly, use erosion to make concentrated template pixels form a continuous region. Then use dilation to restore the size of template region. Repeat erosion/dilation several times. The effect of noise reduction is shown in Figure 2.

Fig. 2. Noise Reduction

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After noise reduction, we need to determine the position and size of template; these two parameters may vary after scaling, translating, or cropping the watermarked image. We fix them by voting on the size of template. This is done in following steps: a) Take 10 rows from the template distribution matrix. We name the fetched sub matrix as a sample stripe. It should be a matrix with 10 rows and same number of columns as the image. b) Sum the sample stripe column by column. We get the density of template pixel in each column. c) We treat the density array as a discrete signal. Template blocks will result in blocks of high density, which can be considered as a square wave. We detect the start and end of square wave by differentiating adjacent density values. We denote a threshold s. (set as 5 in our experiment) If the difference large than s, the position is recognized as the start or end of a square wave. Both the length (side length of template region) and start position (position of template region) of square wave are stored for further extraction. d) Repeat a) to c) to gather statistics on every sample stripes. e) Repeat a) to d), taking 10 columns to form a sample stripe each time, to gather statistics in vertical direction. f) Pick 3 lengths with most frequency; they are regarded as most possible side length of Template region after image is modified. The corresponding start position gathered from horizontal and vertical sample stripes composes the position of template region. We can infer the position and size of watermark region from position and size of template region. C. Embedding and Extracting Watermark In this scheme, our watermark information is a 24*24 binary icon. We embed it pixel by pixel. We embed watermark into DCT transformation of V component in RSV color space of image. The method is innovated from [6] For each Watermark Region in image: a) Get the V component of RSV color space from this image region. Take DCT transformation 2 times on the image; b) Divide the DCT domain into 8*8 blocks; in each block a watermark bit b will be embedded. c) Calculate the mean value m of the block, update the central point p of the block as Equation 4:

+α ) { pp ''== mm*(1 *(1−α )

b =1 b=0

(4)

Here α denotes the intensity of watermark. We set it as 0.2 in our experiment. d) Perform inverse-DCT 2 times to transform the image back to its original color space.

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Let’s consider the extraction. Watermarked image may be cropped, scaled or translated. The size and position of template and watermark regions may change, but notice that these transformations does not change the ratio between size of template region and watermark region. In the previous section, we have found possible templates. We can use the ratio to find possible watermark regions. Then recover the transformed region to its original size. The whole extraction process is defined as follow: a) b) c) d)

Pick a (size, position) pair from template extraction Use size, ratio and position, to infer the watermark region. Resize the watermark region to its original size (in this paper, 256*256). Get the V component of RSV color space from this image region. Take DCT transformation 2 times. e) Divide the DCT domain into 8*8 blocks; f) For each DCT block, calculate its mean value m and get the central point value p. A bit of watermark can be extracted by:

{bb =1 =0

p≥m p <m

(5)

g) Compare the extracted binary watermark w’ with original watermark w by using correlation function

Correlation =

w⋅ w' w w'

(6)

h) Use a threshold θ to determine whether the extracted watermark is acceptable. (In this paper, θ = 0.7) If correlation exceeds θ, the rest trials are unnecessary to do, otherwise go back to a) and try with another (size, position) pair. In practical situation, error may exist in the size and position of extracted template. In each (size, position) trial, we will try extracting by using values that are close to the (size, position) pair.

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Simulation

We have chosen the size of template and watermark region to be 100 and 256 respectively, in order to take advantage of embedding multiple copies of watermark in an image. We choose test images that are relatively large. We choose the original image with size 2816*2112, as in Figure 3. First we perform a cropping on the embedded image, like illustrated in Figure 4. 1536*964 pixels are remained; about 75% of the image is cut off. Through extraction, we can still get an icon with correlation 0.997. Then we test scaling attack. We scale original image to 70% in height and width. The correlation of extracted icon is 0.765. The degradation is mainly caused by the loss of information in scaling. Half of the templates preserve their shape.

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Fig. 3. Original Image

Fig. 4. Cropping

Fig. 5. Crop from scaled image

Finally we perform a hybrid attack by cropping on the scaled image. As illustrated in Figure 5. We finally get an image with size 1017*932. The extraction result is 0.773. The rise of correlation is caused by the extracting algorithm. The algorithm terminates once an acceptable watermark is extracted. So a better watermark may be ignored.

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Overall performance is outlined in Table 1. Table 1. Experiments Attack

Extracted Watermark

Correlation

Crop 75% off

0.977

Scale to 50% area

0.765

Crop 50% after 50% Scale

0.773

Translation is already involved in the simulation because our cropping test is equivalent to crop a part from the image and translate it to the top left corner. All the experiment images are stored in JPEG format, thus our scheme can survive general JPEG compression.

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Conclusion

This paper proposed a template based robust blind watermarking scheme for color image. Templates are embedded in Lab color space by using quantization, and watermarks are embedded in HSV color space by DCT transformation. Simulation shows that the scheme can survive from cropping, scaling and translation attack. Robustness of this scheme increases with the size of the image to be watermarked. For larger images can hold more copies of templates and watermarks. More templates ensure the probability to find watermark region. More watermark regions may give rise to the final correlation. On the other hand, performance can be further adjusted by adjusting the size of watermarking region and the watermark. In this paper, we embed watermark in partitioned DCT domain, thus the capacity should be (256/8)*(256/8) = 32*32 bits. Under this condition, our 24*24 bits watermark can only be embedded once per watermark region. If we make the watermark smaller, or the watermark region larger, then it is possible to embed multiple copies of watermark in one region, then some voting strategy can be adopted to enhance the correlation, especially in the case of scaling. Furthermore, noticing that rotation also keeps the ratio between template region and watermark region, it should be able to extend this scheme to be rotation-resilient. To do so, we can estimate the direction of template block by using Hough transformation. But the result will be affected by noise pixels in DCT. Acknowledgment. This work is supported by the Shanghai Committee of Science and Technology (08JG05002, 08411951200) NSFC (No. 60873133) and 863 (No. 2008AA02Z310).

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References [1] Nasir, I., Weng, Y., Jiang, J.: Novel Multiple Spatial Watermarking Technique in Color Images. In: Fifth International Conference on Information Technology: New Generations (ITNG 2008), pp. 777–782 (2008) [2] O’Ruanaidh, J., Pun, T.: Rotation, scale, and translation invariant digital image watermarking. Signal Processing 66(3), 303–317 (1998) [3] Wang, X., Wu, J., Niu, P.: A New Digital Image Watermarking Algorithm Resilient to Desynchronization Attacks. IEEE Transactions on Information Forensics and Security 2(4) (December 2007) [4] Pereira, S., Pun, T.: Robust template matching for affine resistant image watermarks. IEEE Transactions on Image Processing 9(6), 1123–1129 (2000) [5] Bhatnagar, G., Raman, B., Swaminathan, K.: DWT-SVD based Dual Watermarking Scheme. In: First International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2008, August 4-6, pp. 526–531 (2008) [6] A Novel Blinding Digital Watermark Algorithm Based on Lab Color Space. In: Proc. SPIE, vol 7546, 75460C (2010)

Digital Circuit Design and Simulation of a New Time-Delay Hyperchaotic System Zhang Xiao-hong and Zhang Zhi-guang School of Information Engineering Ganzhou, China 61769 Troops of PLA, Jiangxi University of Science and Technology, Taiyuan, China [email protected]

&& chaotic Abstract. A four-dimensional hyperchaotic system based on Lu system is designed by linear feedback expansion. Dynamic characteristics of the system have been further studied after the introduction of time delays. A method of designing time-delay hyperchaotic digital circuit based on DSP Builder is presented. The new hyperchaotic system is discretized prior to overcome the inherent shortcomings by analog circuit. By optimization of digital circuit design and rational configuration of parameters, the signal amplitude is reasonably controlled. In computer simulations, results by DSP Builder fully consistent with results of the continuous system in Matlab, which shows the development of digital hyperchaotic system based on FPGA possesses practical applications. Keywords: Time-delay hyperchaotic system, Digital circuit, DSP Builder, FPGA.

1 Introduction Chaotic system receives broad applications in complex network, security, secure communication and control engineering. Classical lower-dimensional chaotic system with poor complexity is limited in practical applications due to the narrow highmagnitude bandwidths [1]. Hyperchaos generated by linear feedback expansion has more than one positive Lyapunov exponent, yet its dynamics expand in more different directions simultaneously. It means hyperchaos possess complex dynamical behaviors which could better meet the practical needs. However, time delays do exit among state variables of the real dynamic system, as the evolutions of which are not only associated with the current state also related to the state of the past. Time-delay chaotic system is of infinite-dimension with high randomness and unpredictability, therefore has recently become a focal topic in research [2]. Currently, designing analog circuit with discrete components to generate chaotic signals is well documented. However, analog circuit is easy aging and vulnerable to environmental impacts (Such as temperature, operating voltage). It is sensitive to component deviations with inflexible system configurations as well as troublesome operation and maintenance which limit its practical applications. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 411–417, 2011. © Springer-Verlag Berlin Heidelberg 2011

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FPGA (Field-Programmable Gate Array) is a modern digital signal processing technology. It supports hardware description language as programming language, which can only handle digital signals, unable to do direct floating-point operations. Using FPGA technology to generate and process chaotic signal has broad applications while the continuous chaotic signals should be processed by floating-point algorithm. Most algorithms can be achieved in digital systems (such as FPGA) now with short development process. Systems generated by FPGA are stable and erasable with low cast and ready improvement of algorithm. Normal chaotic systems have been realized by digital circuits in relative papers [34]. And time-delay Lorenz system by analog circuit is also studied in paper [5]. However, in view of the complex dynamic characteristics of hyperchaotic ones, there are rarely reported design hyperchaotic by digital circuit. In this paper, attempts to process time-delay hyperchaotic system with discrete-time algorithm are shown, as well as following a method to design its digital circuit.

2 A.

A New Hyperchaotic System && Chaotic System Evolution of the Lu

&& system is different from Nonlinear functions of the new four-dimensional Lu conventional 3D system, Characteristics of 4D system have been mentioned under scrutiny in paper [5-7]. Time delay perturbation is added into the fourthly equation which is described follows: ⎧ x& (t ) = a( y(t ) − x(t )) + w(t ) ⎪ y& (t ) = by(t ) − mx(t ) z (t ) ⎪ ⎨ 2 ⎪ z&(t ) = −cz(t ) + mx (t ) ⎪⎩w& (t ) = dy(t ) − px(t − τ )

(1)

where a, b, c, d , m are constant parameters, variables p,τ present delay control parameters. The new system could be brought to chaotic even hyperchaotic if variables p,τ being assigned appropriately. Given p = 0.5,τ = 1ms initial value x0 (t ) = 0, y0 (t ) = 1, z0 (t ) = 1, w0 (t ) = 0, t ∈ [−τ ,0] , the system shows new chaotic motions, generating chaotic attractors as Fig.1.

Fig. 1. Phase portraits of time-delay system (1) with τ=1ms

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B. Theoretical Analysis of the New Time-Delay System This new time-delay system is dissipative with its divergence:

∇V =

∂x& ∂y& ∂z& ∂w& + + + = −( a − b + c − d )  ∂x ∂y ∂z ∂w

V (t ) = V (0)e −( a−b+c−d )

(2) (3)

When (a − b + c − d ) > 0 , and t → ∞ , each volume element containing the system trajectory is shrinking at an exponential rate of − (a − b + c − d ) , till the initial volume V (0) of the volume element to contract on an attractor. The newly founded time-delay system possesses four Lyapunov exponents, of which are two positive ones. Its Lyapunov exponents are LE1=0.2726, LE2=0.29609, LE3=-0.68635, LE4=-8.16358. And its Lyapunov dimension is as following:

DL = j +

1 LE( j+1)

j

∑ LE i =1

i

= 3+

LE1 + LE2 + LE3 LE4

= 3+

0.2726 + 0.29609 − 0.68635 − 8.16358

(4)

= 3.0144 One can observed that peaks of system (1) are very clear in the power spectrum, and they emerge as broad peak of the continuous spectrum with background noise, which indicates the possibility of chaotic motions.

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Generation of the Digital Time-Delay Hyperchaotic Signals

C. Discretization of the Time-Delay Hyperchaotic System For discretization of continuous chaotic system, the value of the sampling time ΔT should be rational. Maintaining the same dynamics of discrete chaotic system as continuous one, sampling frequency should be at least 2 times greater than the cutoff frequency, according to Nyquist sampling theorem. In case the sampling frequency is too low, the accuracy would be very limited, resulting in the system prone to jagged trajectory in phase space [8-9]. In view of the complex dynamic characteristics of hyperchaotic system, there are rare reports that designed by digital circuit. D. Digital Difference Method of First Order The following first-order differential discrete logarithm is used: x − xk dx = f ( x1 , x 2 , L , xk ) = lim k +1 Δ T → 0 ΔT dt

(5)

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By discretization the differential equations, the chaotic system with delay is presented as follows. Time quantum of delay τ is defined as N sampling times here as to facilitate signal processing in the next implementation of digital circuit.

⎧ x k +1 = ΔT [ a( y k − x k ) + wk ] + x k ⎪ ⎪ y k +1 = ΔT (by k − mxk z k ) + y k ⎨ 2 ⎪ z k +1 = ΔT (−cz k + mxk ) + z k ⎪w = ΔT [ y − px ] + w k k−N k ⎩ k +1

(6)

Obviously, delays can be set to an integer multiple of sampling time in discretization. Previous value of system vectors as much as N samples of time acts as the amount of delay [8]. With its feedback to the system, the new time-delay system is constituted. Certainly, the dynamic behavior of the system can be adjusted and controlled by multi-vectors. Delays can be introduced as pyk −N , pzk −N , even p( xk −N − zk ) . It means the delay can be altered while the sampling time is adjusted. By setting rational parameters, initial values and restricting signal amplitude, the discrete hyperchaotic system would generate richer dynamics. Notablely, in discrete equations (4), the sampling time act on both linear and nonlinear terms, which become global gains in the circuit design. In this case, it facilitates the control of signals as thus optimizes the circuit design. Since the sampling time with no direct involvement of discrete transformation operations, it will be very convenient to adjust the sampling time as wish in experiments.

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Configuration and Design of Digital Circuit

E. Environment Configuration of Software Platform Considering the characteristics of discrete chaotic systems, Matlab/Simulink R2010a of The Mathwork and Altera's Quartus II 9.1/DSP Builder 9.1SP2 software platform are adopted to complete the design. DSP Builder as an expansion module library (Blocksets) of Simulink, can directly call the elements and blocks of the library to complete the circuit design. With DSP Builder, we can accomplish system-level and algorithm-level design, avoiding the complexity involved in the underlying hardwarelevel algorithm design and hardware description language programming [10]. We can achieve a shorter research and development period with lower cost. When chaos realized in digital system with limited precision, it may result in degeneration of the dynamic characteristics. Chaotic sequence generated would be many short-period chaotic trajectories, namely short-period problem [11]. In order to avoid such problem and ensure calculation accuracy, the sampling time is reasonably set to 1ms. The 32bits width bus is applied in the digital circuit, which make the dynamic behavior of discrete systems is more evident. F. Optimal Design of Hyperchaotic Digital Circuit Fig. 2 is the hyperchaotic digital circuit by DSP Builder. The circuit is a feedback network . Each vector signals x, y, z, w is fed back to the Multiplexer constituting a

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digital integrator. Delay Block is set to delay the vector x that taking operation with vector w next. One can observe that it could be set to a multiple value of sampling time. Even the delays can be altered and shifted to all vectors as we need. In traditional analog circuit, delays should be designed by some discrete components, yet only Delay Block needed to handle all the work in digital circuit. As Fig. 5 showing, the sampling time ΔT is 1ms, the XY/XZ/YZ/YW Graph are common blocks of Simulink. the modules are primarily used to view the x, y, z, w signal trajectories in phase space of the simulation. Also, Scopes are added to monitor the waveform of vector y, w.

Fig. 2. Time-delay hyperchaotic digital circuit by DSP Builder

Block Gain, Gain2, Gain3, and Gain4 in the circuit play key roles in circuit configurations. These blocks are not only gains, but determine the sampling frequency of the digital circuit. That means the accuracy of the circuit can be adjustable, without any external disturbance. G. Simulation and Experiment Results of the Hyperchaotic Digital Circuit To accomplish the design, the file is saved as mdl format. Digital simulation of the time-delay hyperchaotic attractor is started in the Simulink. The phase space trajectories fully consist with results of the continuous system. Both of them are with exactly the same signal amplitude. Due to the adoption of optimal circuit design and high enough sampling frequency, it appears no jagged shape of the portraits. Curves of the digital chaotic sequence show smooth without jagged shape. Phase portraits of each plane in simulation are shown in Fig. 3.

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Fig. 3. Phase portraits of time-delay hyperchaotic digital system (6) with τ=1ms, sampling frequency=1000Hz

H. Prominence and Extension of the New Hyperchaotic Digital Circuit Redesign chaotic system of the same type by analog circuit is inconvenient, for components must be replaced. Conventional operational amplifier, analog multiplier and resistance of other components are of limited models with certain errors. And these sets are not satisfactory in practical applications. However, digital circuit of this paper possesses the following advantages: • Linear and nonlinear parameters of the chaotic system can be reconfigured by gain changes. Likewise, the initial value can be adjusted by constant blocks conveniently. • The digital circuit is of less module types. And it is simple structured yet with high stability and expandability. • The digital circuit completely adopts the latest DSP Builder modules. It can also be compiled to generate VHDL code, and further burnt into a FPGA hardware rewritably by Quartus II. Development costs lower, making the development of more convenient.

5

Conclusions

Based on modern DSP technology, a new time-delay hyperchaotic digital circuit is presented. The circuit system can do operations on real numbers, generating stable discrete chaotic signals. Configuration parameters of chaotic systems are adjusted directly by the gain blocks and the constant blocks. The circuit can be applied to other digital realization of chaotic systems by adjustments and expansion with generability and versatility. The sampling frequency of the digital circuit system can be flexibly adjusted by the gain blocks. Accuracy of the system being improved one order higher than other papers, which eliminates the jagged trajectory and avoid the problem of short periodic orbits. The experimental results are very satisfactory; the phase portraits are exactly the same with the continuous one by Matlab simulation. Different from the continuous one, the discrete chaotic system suffers no interference of external conditions, achieves self-synchronization between transmitter and receiver, hence possesses practical value. The digital system is easy to control and synchronization, can self-starting, taking more advantages than the traditional analog circuits. The system can provide direct and practical chaotic sequences with proved theoretical and practical value.

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Acknowledgment. This work is jointly supported by the National Natural Science Foundation of China (Grant No. 11062002, 10947117).

References [1] Qi, G.-y., van Wyk, M.A., van Wyk, B.J., Chen, G.-r.: A new hyperchaotic system and its circuit implementation. Chaos. Solitons and Fractals 40, 2544–2549 (2009) [2] Kal’yanov, E.V.: Autoparametric delay system with time lag. Theoretical and Mathematical Physics 52(8), 963–967 (2007) [3] Ding, Q., Pang, J., Fang, J.-q., Peng, X.-y.: Designing of chaotic system output sequence circuit based on FPGA and its applications in network encryption card. International Journal of Innovative Computing, Information and Control 3(2), 449–456 (2007) [4] Sadoudi, S., Azzaz, M.S., Djeddou, M., Benssalah, M.: An FPGA real-time implementation of the Chen’s chaotic system for securing chaotic communications. International Journal of Nonlinear Science 7(4), 467–474 (2009) [5] Jiang, S.-q., Hu, G.-s., Dong, J.-m., Li, Y.-p.: Chaotic characteristics and circuit implementation in time-delay feedback Lorenz system. Control Theory & Applications 26(8), 911–914 (2009) [6] Chen, G.-r., Lü, J.-h.: Dynamic analysis, control and synchronization of Lorenz family system. Science Press, Beijing (2003) [7] Liu, Y.-z.: A new hyperchaotic Lü system and its circuit realization. Acta Physica Sinica 57(3), 1439–1443 (2008) [8] Wu, L., Jiang, S.-q.: The Experiment Methods for Time—delay Lorenz System Based on DSP Builder. System Simulation Technology 3(1), 20–24 (2007) [9] Li, G.-h., Li, Y.-a., Yang, H.: Design Method for Chaotic Attractor Based on DSP Builder. Journal of Detection & Control 31(16), 60–63 (2009) [10] Pan, S., Huang, J.-y., Wang, G.-d.: Modern DSP Technology. Xidian University Press, Xi’an (2003) [11] Zhou, H., Ling, X.: Realizing Finite Precision Chaotic Systems via Perturbation of mSequences. Acta Electronic Sinica 25(7), 95–97 (1997) [12] Wang, G.-y., Bao, X., Wang, Z.-l.: Design and FPGA implementation of a new hyper chaotic system. Chinese Physics B 17(10), 3596–3602 (2008)

Hospital Information System Management and Security Maintenance Xianmin Wei Computer and Communication Engineering School of Weifang University, Weifang, China [email protected]

Abstract. Hospital information system (HIS) in the hospital management operations is playing an irreplaceable role. The number of information in hospital information system is huge, some of them is sensitive, confidential and real-time information, the database server is running for 24 hours a day, if the whole network system is in the failure of man-made or accidental, this will cause huge losses and social impact. From the view point of server maintenance, data security and backup, user management, management and maintenance of network security, this paper combined with the actual situation of work and maintenance in the management has made some useful discussion. Keywords: hospital information system (HIS), network security, data security, Firewall.

1

Introduction

With the deepening of the health system reform and the implementation of health care and the new rural cooperative medical care, the hospital is increasingly in large scale, more and more patients are difficult to manage rapidly and increasingly. Therefore, the hospital information management systems have become essential hospital infrastructure. Weifang peoples’ hospital is a general and strong technical force hospital, the annual outpatient visits was about 90 million. Hospital in 1995 started using the computer in charges and pharmacies. Since the old information system could not meet the new situation, in 2005 the hospital reconstructed the hospital information management system. This system covers medical, nursing, medical technology, charges, drugs, administration and other departments, with powerful functions, convenient and practical features, it was welcomed by the majority of medical staff and management, and in our hospital has become the core of medical work systems. In this case, how to ensure safe and effective running of hospital information management system and normal operation, and to ensure effective management of the hospital and proper running has great significance.

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Hospital Information System Structure and Compositions

The main function of the hospital information system for hospitals and their affiliated departments is to provide collection, storage, processing, extraction of patient medical Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 418–421, 2011. © Springer-Verlag Berlin Heidelberg 2011

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information, financial accounting, analysis, executive management information and decision analysis of statistical information and data communications, and can meet information needs of all authorized users. Through the computer network to complete advanced management model of the organic integration with out-patient management, in-hospital management, medical technology management, functional departments and management departments, to improve the overall hospital information utilization and hospital efficiency, and to establish a president-centered hospital information comprehensive network management and decision-making, as well as for hospital management, medical, research, new-type rural cooperative medical care, health insurance, etc. to provide a complete, efficient and useful quantitative basis. System uses the most advanced and most popular structure of B/S. Software system is divided into registration management, designated price charges, outpatient pharmacy management, agency management of out-patient department, hospital management, prescription management, hospital pharmacies, medical supply store management, hospital billing, new-type rural cooperative medical management, medical technology Division management, health care management, drug equipment management, preparation management, operating room management, functional line managers, directors management, system maintenance and other subsystems. Relatively independent subsystems are not only easy to implement their own function, but also to facilitate the achievement of inter-linked network of run-time resource sharing features. In system platform, to use dual server with disk array, two servers use IBM xSeries 260, the operating system is Windows2003 Server, and the hospital information system (HIS) software and Sql_server 2003 database.

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Hospital Information System Management and Maintenance

As hospital information system covered in the content all hospital operations and management, and the compositions contains software, hardware, network and other subsystems, management and maintenance workload is very important and difficult, which including server maintenance, data backup, user management and network security and maintenance. A. Server Maintenance 1) server daily maintenance,server maintenance focuses on software maintenance, including regular or occasional monitoring of memory, disk space monitoring, security access control, computer virus checking and so on. System is running for a certain time, in order not to degrade system performance and impact velocity, to burn the historical data from the online server periodically to a disc and take good care of discs. 2) backup and recovery of server disaster recovery data is an important issue must to be considered in the system safe operation, the system can not guarantee reliable with no problems, hardware failure, software crash, virus effect and the irresistible or unpredictable disaster, may cause the failure of the system, threatening the data

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security. Hardware backup is the most effective means to recover from a disaster and the crash. To satisfy the real time services and data protection requirements, to realize high data availability, the system automatically switching and minimum recovery time, to design out system backup program- Hot Standby. That is two servers, with a shared disk array, in which the work of a server in active state (host) and control the disk cabinet, another server for the standby mode (preparedness machine). When the host as active or passive because the reasons (failed), can not continue to provide services, the prepared machine in time can replace the host service, in the shared disk cabinet the established database will automatically take over by the prepared machine, continue to provide services to achieve non-stop service. Double backup technology is able to ensure the normal operation of the system and basic non-stop, because it uses two servers and related technology for the control of cluster system. Two servers through the "heartbeat" as known of the network cable connected. "Heartbeat" to monitor the operation of each other, once the equipment is operating normally which found in the host machine to crash, then immediately to take over the "death" running tasks. The disk array cabinet dealed RAID, RAID working principle is: using several low-cost hard disks arrays to make a performance far better than a single hard disk to achieve fault tolerance. To sacrifice extra disk capacity for recording, or even repeating the information to ensure the reliability of the disk, this called a faulttolerant disk systems. RAID5 also allows multiple disk heads to read and write data, breaking the bottleneck of a single disk access, can improve the efficiency, increase data security, to avoid crashes caused because the server can not read the disk data. B. Data Security and Backup Currently reliability of computer software and hardware system has been greatly improved, also disk arrays and other equipment can be used to improve system fault tolerance. These techniques improve the reliability of the system, but can not guarantee system security foolproof, just to a certain extent, to reduce the losses caused by media failure. For accidental errors or intentional destructive operation, destructive virus attacks, natural disasters, system failures caused by regular database backup is to ensure the security of the other items re-measure. When an accident happens, can rely on the backup to restore data. We have taken the backup programs as follows: less daily traffic once each morning and afternoon to backup hard drive were prepared on the server and disk array. A weekly CD-ROM backup. Regularly sent to the Registry of the data storage disc, which makes in the event of a disaster situation, in order to protect the security of data. C. User Management HIS system are many and scattered sites, the user involving physicians, nurses, medical technicians, management personnel, financial, etc., on the toll system and financial data with high security and confidentiality requirements. System uses the operating system of Windows Server 2003, database and application level user permission to run the limit of the triple control mechanism to provide a unified rolebased user management tools in the system so that each user has a unique account number, password, and given different levels of permissions, so that can only operate

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their own procedures and call-related data, can not just access to the data without knowing the file. At the same time, we also have the user control program. Through these measures can effectively prevent the illegal invasion of network users, to ensure the safe operation of the network. D. The Management and Maintenance of Network Security Network security is closely related with the application architecture, network security mainly refers to the network when users access the application server and database server, how to ensure server security. From all levels of use and security analysis, the data need of special protection layer, can not provide direct access. Application server layer is also the need for protection, the need to control user access. Developed a strict network security management system, from the management of network equipment and lines to the server, workstation use, the user's login has done a strict requirement. Technically, mainly in the following measures: use of firewall technology to prevent illegal access to the machine; strict control of the internal address of the management; the use of complex password system; rigorous identity authentication. E. Others To ensure normal safe operation of servers and network equipments, and also need to make management of machine room. Management systems and operating facilities of machine room must to be complete, reliable, such as the installation of access control systems, strict personnel access; installation of air conditioning equipment to ensure safety of equipment; install an uninterruptible power supply, power cuts to ensure the normal operation of the system. F. Conclusion For hospital information system (HIS) to play more desired effects, in addition to the management and maintenance of the above, it also asked the hospital leadership attaches great importance to form a strong technical team of system management, and to standardize the management system.

References [1] Liu, Y., Li, J.: Information Systems Audit - backup and disaster recovery programs. Medical Information 07 (2005) [2] Wang, S., Liu, P.: Hospital Management Information System Design and Implementation. Sichuan Institute of Light Industry and Chemical Technology 15(2), 32–33 (2002) [3] Li, Q., Geng, S.: China Rehabilitation Research Center hospital information management system backup program analysis. Medical Intelligence 3, 29–30 (2003) [4] Chen, G., Li, J.: Hospital information system efficiency and operation of security protection program. Computer Knowledge and Technology (Natural Science) 02 (2005)

Design and Research of a Multi-user Information Platform Interface in Rural Areas Xianmin Wei Computer and Communication Engineering School of Weifang University, Weifang, China [email protected]

Abstract. Multi-user platform development in rural areas, not only to meet demand in function, but also to apply to the characteristics of rural information platform. This paper developed a multi-functional system which can serve farmers, not whether it is difficult to achieve functions, but whether it applies to farmers. Interface design plays an invaluable role in these cases,. This paper summarizes the development of practical experience in projects for rural-based platform for multi-user templates, which not only satisfied the functional needs of users, to make the whole design more standard, but also to accelerate the development efficiency of platform. Keywords: multi-user, rural platform, interface design.

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Introduction

The development of the Internet has accelerated the process of rural information, based on B / S structure rural information platform has mushroomed all over the town, the adapted rural information platform multi-user interface is the focus of rural information platform. Unique interface design is not only to highlight in human-computer interaction a unique personalized service; in the display of the system characteristics, but also it is irreplaceable role.

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Interface Design Ideas

Interface design is not beautiful in its appearance and gorgeous special effects, but focuing the system's personality easily and highlightly, which is very friendly to meet the needs of users, design way of User-Centered Design[1] is better to express this. User needs is the source of survival for each project, how to use users’ interface design requires to reflect the function of the system, so customers need to easily achieve, the interface developer for each project must be a top priority. On the basis of user needs are met, to use some of the theories to dig potential customers demand should also be considered by developers, commonly used in interface development as mental model and user modeling. One definition of mental model, Professor JayWrightForrester, in our minds around the world on the picture is just a model, no one was really in the Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 422–426, 2011. © Springer-Verlag Berlin Heidelberg 2011

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mind to imagine or state governments around the world. They selected only those objects around them and concepts and relationships. Developers through the user role distribution and design what the user can use the software which they wish to be able to use the selected system functions. This paper summarizes the undergraduate learning and innovative experimental projects, Java EE-based multi-functional integrated information service platform of rural experience and comparison with the individual sites, based on the past, rural platform optimized page layout.

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Rural Platform Page Layout Design

A. System Overall Design In order to enhance the platform's scalability and robustness, this platform used popular MVC development framework to achieve the performance level (UI), business logic layer (BLL), Data Access Layer (DAL) phase separation, which is the whole system and the presentation layer user interactivity channel. From customer point of view, on the basis of meeting the users basic function, UI is the key to connect user interface with system friendly, devising page layout is particularly important.

Fig. 1. System overall architecture diagram

B. The Characteristics of User Role in the Rural Platform 1) Multi-user The system is based on understanding of the basic needs of farmers, based on demand from farmers to high-end personal assistance in the design of life, personnel participating in system except farmers, computer technicians, agricultural technicians, legal experts and business users and administrators, all participants in this system has its unique role, but the center is the farmers, therefore, in the design of interface-based, the system should strongly consider the needs of farmers, but they can not ignore the other participants needs. Therefore, it will be in the interface is a big challenge. How the user can complete role assignments and permission assignments and linking the needs of the system interface, it will be the interface design is key to success. Here, good page layout design is particularly important.

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2) Specificity of the User Role The interface development is mainly directed against the peasants, but from current China's rural information point of view, rural information education is not optimistic, but farmers are apparent for their daily needs, the developer needs to convert these realities physical model to be appropriate guidance. User roles in addition to farmers, there are five type of people involved, these are farmers or daily maintenance required in the necessary personnel needed, therefore, interface developers in different roles are for different user interface designs in different styles, But the system as a whole style is to be consistent. C. The Basic Principles of Interface Design Layout is within a specified area and reasonable to arrange graphics and text position, to group and summarize messy pages, mixed content need according to the overall layout, these were intrinsically linked to organizational arrangement, repeatedly refined text, graphics and spatial location relations, so that viewers have a smooth visual experience, which with good page layout structure should be clear, with beautiful and easy to operate [2]. The basic principles of interface design are the followings. (1) interface design consistency. Consistent interface design not only to a certain extent to reduce the memory requirements of users, while in control the whole system to achieve the same style. Platform is based on the farmers in rural areas, so it should be ordinary, simple and practical style, and clearly refined. (2) symmetry of interface development. A good interface design is that user interface will be able to find a system that can provide the functionality and system boundaries. Make people from whole have a very comfortable feeling, not haphazard, giving a strange psychological effect, which would at the interface greatly discount the effectiveness of the system. (3) easy development of the interface. Interface design can easily locate sometimes, but for developers is not so simple, this project is based on B/S structure, it can take full advantage of HTML and CSS, better positioning the United States and excellent layout.In the interface development, also to simplify the interface operation as far as possible following the 3 clicks [3] principle. (4) Platform interface design ideas. Home page is a system face, from a whole reflects the characteristics of the platform. This project is based on information technology in rural areas, rural networking, home interface, combinating the "@" with green grass; this will hope the magic combination of foam and spring sunlight; combining the rural culture with rural information and to bring out the beautiful countryside prospects, also expressed the good wishes in information construction in rural areas. Using commonly Three word layout shown in Figure 2 that it can grasp the whole system a good style, and easy to be transplanted in the JSP, to reduce development workload.

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Fig. 2. Home page

Rural culture is independent of other pages, it combined the overall style of home page, coupled with some unique elements, a new rural show in your eyes. Combined with T type and three type development shown in Figure 3, where joining a number of new elements, such as big hand holding up a large grass is sprouting, representing under the leadership of the Chinese Communist Party, China's rural development is booming, such as strong as strong grass; Beetle quietly climbed up the green of the grass, points to the unique atmosphere of rural villages, China's unique culture, which contains; deep green in the rise over the from a beautiful rainbow, plus up to the field from the bubble mirroring the totally natural scenery. All the performance features of rural culture and rural head.

Fig. 3. The interface of rural culture

Video instruction interface is a unique feature shows of this project, but this interface is a common interface, can be used for other modules shown in Figure 4. On the basis of grasping the overall rural characteristics, to add the characteristics of each function, combining the quality education with rural technology education, through

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interactions to pull the users closely, to complete resource sharing and experience sharing. Interface using the latest friendly JQuery products and specific information to prompt the user to simplify operation. Plain interface showed the unique rural style.

Fig. 4. Video Instruction Interface

4

Summary

Rural information platform building is the necessary road for China information construction of socialist new countryside, but developed one kind of service in frmers versatility which not mentioned functions, because it should apply in farmers . Among these, the role of interface design holds immeasurable. This paper summarized the item of actual combat experience to develop suitable multi-user type of rural platform templates, these templates not only in functionality to meet the needs of users so that the whole design more standard, but also a platform to speed up the development efficiency.

References [1] Vredenburg, K., Isensee, S., Righi, C.: User-Centered Design: An Integrated Approach. PrenticeHall, NewJersey (2001) [2] Huang, Y.: Design of human-machine interface. Beijing Institute of Technology Press, Beijing (2007) [3] Zhou, C., Wang, C.: Web application interface design. Computer Engineering and Design 27(7), 12–55 (2006) [4] Zhang, J., Nan, Z., Zhao, W.: Based on B/S mode of enterprise-class application system interface design. Practice and Experience (08), 106–108 (2009) [5] Yang, J.: User interface design. Art and Design (10), 268–270 (2009)

Research on Fluorescence Detection System of Ca2+ Bao Liu, Sixiang Zhang, Wei Zhou, and Yinxia Chang Research Institute of Modern Measurement & Control Technology, Hebei University of Technology, Tianjin, China [email protected]

Abstract. A new fluorescence-micro-imaging system has been built using the microfluidic chip. By using this system fluorescence ratio of calcium ion (Ca2+) in cells were measured. Cell fostering, staining and driving can be implemented on the designed microfluidic chip. Then we make use of 340nm or 380nm laser to excite the calcium ion（Ca2+）and enable it emit fluorescence. The image of fluorescence was collected and analyzed with fluorescence-microimaging system. The concentration of calcium ion in cells can be calculated by using the fluorescence ratio method. Based on this new fluorescence-microimaging system, a new application domain was opened for microfluidic chip and a new detection means was developed for measuring calcium ion（Ca2+） in cells. Keywords: microfluidic chip, signal analysis, the concentration of calcium ion, cell detection, microscope, fluorescence ratio.

1 Introduction Organism cells are the basic functional unit of life activities [1]. Contact and control information of cells is essential to the occurrence of the organism, development and survival of basic life activities. Calcium as a second messenger in cell signal conduction play an important role, which is a major regulator in various life activities of cells [2,3,4]. Microfluidic chip is a device that integrates one or several laboratory functions on a single chip of a few square centimeters in size. It deals with extremely small fluid volumes. It replaces the conventional chemical or biological laboratory functions as a technology platform [5]. It is widely applicable for analysis purposes. According to principle of the measurement of calcium concentration by fluorescence ratio method, that fluorescence intensity are detected is the most important means of measuring the concentration of calcium ion in the cells. On the basis of technology studies of microfluidic chip, we have designed and manufactured some microfluidic chips for the detection of fluorescence intensity. According to the method of detecting the fluorescence intensity of calcium ion in the cells, microfluidic chip was presented and the fluorescence-microscopic imaging system was built. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 427–432, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Measuring Principle of Fluorescence Intensity

The living cells are complex objects. The optical pathlength varies from cell to cell, from one position to another within the cells, and also changes due to different time. Fluorescence ratio method is a better approach. Staining reagents is Fura-2 [6,7], which binding calcium ions. In the excitation light of the two different wavelengths, calcium ions emit fluorescence. The ratio of fluorescence intensity of calcium ions in the two different wavelengths excitation light is used to calculate calcium concentration. Fura-2/AM is a fluorescent dye which can penetrate the cell membrane. Calcium ions with Fura-2 in the 330-350nm excitation light can emit strong fluorescence. In the 380nm excitation light, fluorescence decreased. Quantitation of the fluorescence emission of a probe is difficult due to factors involving instrumentation, sample geometry, and probe chemistry. The probe concentration can also vary from cell to cell and over time due to labeling efficiency, photo bleaching, dye leakage, and compartmentalization. By detecting the fluorescence emission intensity in two different excitation light wavelengths, the calcium ions concentration can be quantitatively calculated with the ratio of fluorescence intensity. The formula is as follows:

[Ca2+ ]i = Keff ( R − Rmin ) ( Rmax − R)

(1)

Where, R is the ratio of fluorescence intensity in the 340nm and 380nm. Where, Rmin is R where calcium ions don’t bind Fura-2. Where, Rmax is R where calcium ions bind Fura-2 completely. The formula indicates that the fluorescence is related to the calcium concentration. It illustrates the measuring principle of fluorescent analysis, which means the quantitative concentration information (and its change) can be measured with suitable optical technique. When calcium ions concentration was calculated, the key is fluorescence ratio of the excited calcium ions. Namely, values of R were measured. The design of the fluorescence detection system in the paper completed the measurement of fluorescence intensity ratio.

3

The Construction of System Design

A. The Structural Design of Detection System for Cell Fluorescence The design schemes of fluorescence detection system consist of the two subsystems, including microfluidic chip subsystem and microscopic imaging subsystem. Figure 1 is the system structure diagram. Microfluidic chip system is used in cells fostering, staining and transportation etc. Microscopic imaging system is responsible for the clear cells images acquisition and processing when switching is in progress between in 340nm and 380nm wavelengths automatically.

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Fig. 1. Schematic diagram of fluorescence detection system

B. The Microscopic Imaging System In fluorescence detection system design scheme, Olympus IX51 microscope is used in microscopic imaging system. It is the latest UIS2 optical system, which gives the microscopic image better contrast and makes more excellent image. In the experiment, 340nm and 380nm wavelengths of light source are requisite. The most important device in microscopic imaging system includes the fast switching device between 340nm and 380nm wavelengths. The length of switching time directly influences the results. The functional requirement include that switching interval between 55 milliseconds -1.2 seconds is adjustable, several optical filters are required, and luminousness is more than 75 percent. The switching operation can be completed through the switch software. In the image acquisition device, the CoolSNAP colored CCDs are applicable. Analog to digital conversion can achieve 30bit. Fluorescent specimens can access to high quality images. Output images from CCD are sent into a computer directly. With a set of suitably designed software, supported by the Windows system, a lot of functions for measuring and processing can be carried out. C. The Microfluidic Chip System According to the requirements of experiments, microfluidic chips were produced with PDMS [8,9] materials. In the experiment, the cells loading, culturing, staining and so on had been completed by microfluidic chips. Chips were designed as follows. A, B, C, D respectively are four interfaces. Cells solution were injected in interface A. Dye reagents were injected in interface B. Different stimulating reagents were injected in interface C. Waste liquor were out

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from interface D . Each of Four channels has width of 200μm, depth of 150μm, length of 2mm. This design can make reagents flow smoothly and can minimize the waste of the reagents. This design can give a better environment for the growth of cells, and be conducive to cells fluorescence detection.

4

Detection and Analysis in Practice of Fluorescence Detection System

D. The Process of Experiment Firstly, the cells were loaded. Cells with a certain concentration of the solution were injected into the microfluidic chip channel. A few minutes after cells had been loaded into the chip channel, the liquid pressure inside the chip pipeline achieved balance and cells were close to rest state. In the environment of 37 and 5% CO2, after cells had been cultured for 12 hours, the cells adhered. Overnight, Fura-2/AM solution was injected into the chip. In the environment of 37 dye reagents were combined with calcium fully [10]. After Above steps had been completed, the xenon light source opened and the fast wavelength switching device, CCD and imaging software switched on. Firstly, cells were irradiated with 340nm wavelength under the control of the software. Then focal length of the objective lens were regulated after clear pictures of the cells were in the eyepiece. Cells in relatively good condition for testing were found in the field of vision. At this moment, the fast wavelength switching device switched on by imaging software. When light source switched fast between 340nm and 380nm, the fluorescence intensity were collected at two wavelengths. And then the ratio of fluorescence intensity were calculated by using software running in the computer.

℃ ℃

E. The Experimental Results and Analysis Three groups of detection experiments were designed. Three groups of marked concentration of Fura-2/AM were 1μmol/L, 3μmol/L and 5μmol/L respective. Marked time was set to 30 minutes. The experimental results of the fluorescence intensity were shown in Figure 2. Figure 2 illustrates that in the same of marked time, fluorescence intensity stimulated by the 340nm light source is the strongest in Fura-2/AM concentration of 5μmol /L. At the same condition, difference value of fluorescence intensity stimulated by between the 340nm light source and the 380nm light source is the maximum. That means R is the maximum and result of detection is the best at this condition. Based on the above analysis of measured values, best result of measured fluorescence ratio can be achieved in the condition of that marked time is set to 30 minutes and the marked concentration is 5μmol /L.

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Fig. 2. The experimental results of the fluorescence intensity in cells

Figure 3 illustrates fluorescence ratio curve of two cells. y 0.3 t i s n e 0.25 t n I 0.2 e c n e 0.15 c s e r o 0.1 u l F 0.05 f o o i 0 t a R me 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 76 ti 15. 21. 27. 33. 39. 45. 51. 57. 63. 69. 75. 81. 87. 93. 99. 05. 11. 17. 1 1 1

r1 r2

Time(s)

Fig. 3. Graph of fluorescence ratio for two cells

Measuring the ratio of fluorescence intensity of calcium ions that is means R is the key. The fluorescence detection system described in the paper completed the measurement task of R well, where R is the ratio of fluorescence intensity in the 340nm and 380nm. Measurement of R met the basic requirements for calculation and analysis of calcium ion.

5

Conclusions

Calcium as a second messenger in cell signal conduction play an important role, which is always the key of research in the field of biology and biophysics. By research on measuring principle of fluorescence intensity, design scheme of fluorescence detection system was implemented. With reliable fluorescence microscope, CCD and computer aids, we designed and built the fluorescence detection system. By using this system we measured fluorescence intensity in synovial cells. Experimental results proved the feasibility of measuring fluorescence intensity of calcium ion in cells with the microfluidic chip.

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Measuring the ratio of fluorescence intensity of calcium ions was successful finally. It is the key to that the calcium concentration can be quantitatively calculated. Through the measurements of fluorescence ratio in the intracellular calcium, we can summarize the distribution and change of them in order to laid a favorable foundation for improving the accuracy of detection of calcium ion concentration. Acknowledgment. We are grateful to Tianjin Natural Science Foundation (No. 08JCYBJC03100).

References [1] Kiedrowski, L., Brooker, G., Coota, E., et al.: Glutamate impairs neuronal calcium extraction while reducing sodium gradient. Neuron 12, 295–300 (1999) [2] Ei-Ali, Sorger,P.K., Jensen, K.F.: Cells on chip. Nature 442(7101), 403–411 (2006) [3] Grynkiewicz, G., Poenie, M., Tsien, R.Y.: A new generation of fluorescent calcium indicators with greatly improved fluorescence properties. J. Biol. Chem. (260), 3440– 3450 (1985) [4] Yoshihisa, K., Kazuho, O.: Monitoring of intracellular [Ca2+]i elevation in single neural cell using a fluorescence microscope/video-camera system. Japan J. Pharmacol. 41, 345– 351 (1996) [5] Mznz, A., Graber, N., Widmer, H.M.: Miniaturized total chemical analysis systems: a novel concept for chemical sensing. Sens Actuators B, B1:244 (1990) [6] Berridge, M.J.: Elementary and global aspects of calcium signaling. Exp. Biol. (200), 215–319 (1997) [7] Grynkiewicz, G., Poenie, M., Tsien: A new generation of Ca2+ indicators with greatly improved fluorescence properties. Biol. Chem. (260), 3440–3450 (1985) [8] Sui, G.D., Wang, J.Y., Lee, C.C., et al.: Solution-phase surface modification in intact poly microfluidic channels. Analytical Chemistry 78(15), 5543–5551 (2006) [9] Yin, X., Shen, H., Fang, Z.: A Simplified Microfabrication Technology for Production of Glass Microfluidic Chips. Chinese Journal Of Analytical Chemistry 31(1) (2003) [10] Andersson, H.: Micro technologies and nanotechnologies for single-cell analysis. Current Opinion Biotechnology 15(1), 44–49 (2004)

Channel Estimation for OFDM Systems with Total Least-Squares Solution Tongliang Fan and Dan Wang Chongqing University, College of Communication Engineering, Chongqing, China [email protected]

Abstract. Orthogonal frequency-division multiplexing (OFDM) combines the advantages of high performance and relatively low implementation complexity. However, for reliable coherent detection of the input signal, the OFDM receiver needs accurate channel information. The Doppler shift of fast-fading channels will generate intercarrier interference (ICI) and, hence, degrade the performance of orthogonal frequency-division multiplexing (OFDM) systems. In this paper, the ICI is considered disturbance of channel information.We present total leastsquares (TLS) scheme to eliminate the ICI and noise. Keywords: OFDM, Channel Estimation, Total Least-squares, ICI.

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Introduction

OFDM divides the available spectrum into a number of overlapping but orthogonal narrowband subchannels, and hence converts a frequency selective channel into a non-frequency selective channel [1]. Moreover, ISI is avoided by the use of CP, which is achieved by extending an OFDM symbol with some portion of its head or tail [2]. With these vital advantages, OFDM has been adopted by many wireless standards such as DAB, DVB, WLAN, and WMAN [3,4]. Usually, the channel estimation is performed by inserting pilot symbols into the transmitted signals. In slow fading channels or time invariant channels, reference [5] proposed an LS channel estimation technique based on the comb-type pilot arrangement. In [6], an MMSE channel estimator has been proposed, which makes full use of the channel correlations both in time and frequency domains and can significantly improve the OFDM system performance. To reduce the computational complexity of the MMSE estimator, reference [7] proposed a linear MMSE (LMMSE) channel estimation method based on a channel autocorrelation matrix in the frequency domain. However, in fast fading channels, inter-carrier interference (ICI) degrades the performance of estimator. To alleviate this, in [8] proposed a new pilot pattern that is the grouped and equispaced pilot pattern and the corresponding channel estimation and signal detection to combat the ICI. In [10], a new distributive training sequence based on modified m-sequences was proposed to perform the ICI matrix estimation. The new training sequence can deal with noncirculant ICI matrices, Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 433–440, 2011. © Springer-Verlag Berlin Heidelberg 2011

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whereas most existing schemes assume that the ICI matrix is circulant. These methods have high complexity since it requires the joint estimation of several adjacent OFDM symbols. In this paper, it is assumed that the fading channel varies very fast so that it cannot be deemed to be stationary over the OFDM symbol duration. Therefore, the ICI effect due to the high Doppler shift has to be considered. To mitigate the BER degradation due to the ICI, the ICI is considered disturbance of channel information and we propose that the estimation of channel response can be obtained by the total least squares (TLS) method. A closed-form mathematical expression has been derived to express the channel estimation. It has been shown that the proposed channel estimation and data detect can effectively eliminate the ICI effect. In comparison with the LS algorithm, the proposed algorithm, achieves superior performance in terms of the SER and the BER.

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System Model

We adopt the OFDM system model in [9] without loss ofgenerality. The basic idea of OFDM modulation is to partition a wideband signal bandwidth into a set of orthogonal subcarriers. To maintain orthogonality among subcarriers, it is necessary to add cyclic prefix (CP) in every symbol. The length of cyclic prefix should cover the duration of channel response. From the hardware implementation point of view, an inverse fast Fourier transform (IFFT) can be used as the modulator while a fast Fourier transform (FFT) is used as the demodulator in the OFDM system. Fig. 1 shows a functional block diagram of the OFDM system with channel estimation circuit.

Fig. 1. Simplified block diagram of OFDM system

Let

x be the transmitted OFDM symbol before CP is added. x = [ x(1) x(2) L x( N − 1)]T

(1)

Channel Estimation for OFDM Systems with Total Least-Squares Solution

In which

N

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is the number of subcarriers, that is, the size of FFT/IFFT. The

transmitted symbol will then be passed through a multi-path time-varying channel

g (t ,τ ) .A tapped delay line multipath fading channel model is adopted [13]. L −1

g (t ,τ ) = ∑ α lTlδ (τ − τ l )

(2)

l =0

In which, α l ,l= 1, 2, …, L, are used to control the energy of l-th paths, and L is the total number of paths; Tl (t ) indicates the time-varying gain of the l-th path with normalized power;

τl

is the l-th path delay. For a discretetime channel model,

assuming the magnitude of the l-th path of channel in k-th sample time is denoted as

g ( k , l ) , then the received signal can be represented as: L −1

y (k ) = ∑ g ( k , l ) x(k − l ) + n( k ), k = 0,1,L, N − 1

(3)

l =0

Where, n (k ) is the additive white Gaussian noise at the k-th sample time during a symbol period. For convenient representation (3) can be represented in matrix form as follows:

Y = GX + W . Where

G

is a

N

by

N

(4)

matrix constructed by g ( k , l ) :

0 L 0 g(0, L −1) g(0, L − 2) L g(0,1) ⎤ ⎡g(0,0) ⎢ g(1,1) g(1,0) 0 0 g(1, L −1) L g(1,2) ⎥⎥ (5) L G=⎢ ⎢ M ⎥ O O M ⎢ ⎥ L 0 g(N −1, L −1) g(N − 2, L − 2) L L g(N −1,0)⎦ ⎣ 0 The received signal can then be obtained in the frequency domain as:

Y = FGF H + W = HX + W . In which

(6)

F H is the Fourier transform in matrix form; H is referred to as the

frequency domain channel matrix. If the channel response is time invariant within an OFDM symbol, H will be a diagonal matrix. When the CIR is time-varying within an OFDM symbol, matrix H will no longer be a diagonal matrix and ICI will occur. In practice, obtaining the matrix H in a time-varying environment is very difficult.

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Channel Estimation

In order to perform coherent demodulation at the receiver, reference signals known at the transmitter and the receiver need to know Channel impulse response. First of all, we give the LS estimation technique as it is needed by many estimation techniques as an initial estimation. A. Least-Squares Estimation The received signal Y can be expressed as :

Y = GH + N

.

(7)

LS channel estimation algorithm is to make the following squared error minimum:

Hˆ = arg min Y − GH .

(8)

If there is only white Gaussian noise channel, then the equation (8) can be written as:

Simplified:

Hˆ LS = (G H G ) −1 G H Y .

(9)

Hˆ LS = G −1Y + G −1 N .

(10)

The greatest advantage of LS estimation algorithm is its simple structure, complexity is low, channel feature on the pilot sub-carrier can be obtained through an inverse operations and multiplication. However, LS estimation algorithm ignores the effect of noise, so the accuracy of this algorithm is limited; LS algorithm is useful when channel noise is small. B. Total Least-Squares The channel isn’t constant over an OFDM symbol duration. Therefore, orthogonality among subcarriers is destroyed, and inter-carrier interference (ICI) occurs, which degrades the channel estimation performance. External interfering sources also affect the performance of channel estimation. Although most systems treat ICI and external interference as part of noise, better channel estimation performance can be obtained by more accurate modeling. In this section we study the all effect on the channel estimation of OFDM systems. The received signal Y can be expressed as :

Y = GH + GI H + N . Where,

(11)

GI is the interference matrix. The expression in (11) can be expressed as: (G + GI ) H = (Y + N ) .

(12)

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The TLS approach to this problem is reduced to the following problem.

min GI + N , 2

2

GI , N

subject to:

(Y + N ) ∈ Range(G + GI ) .

(13)

The TLS solution (10) can be expressed as:

Hˆ LS = (G H G − γI ) −1 G H Y .

(14).

For the equation (14), the Major problem is how to get precise parameter γ . C. Total Least-Squares Estimation The expression in (12) can be rewritten as:

( A + E ) X = (Y + r )

(15)

We consider the problem:

min E + r , 2

2

E ,r , X

subject to (Y + r ) ∈ Range ( A + E ) .

(16)

The Lagrangian of problem (16) is given by

Φ( E , r , λ ) = E + r + 2λT (( A + E ) − Y − r ) . 2

2

(17)

Note that problem (14) is a linearly constrained convex problem with respect to the disturbance variables E and r .we conclude that ( E , r ) is an optimal solution of (15) if and only if there exists

λ such that

∂Φ ( E , r , λ ) = 2 E + 2λ X T = 0 . ∂E

(18)

∂Φ ( E , r , λ ) = 2 r − 2λ = 0 . ∂r

(19)

From (18) and (19) we have

r =λ . E = −λX T .

(20) (21)

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Combining (20) and (21) we obtain:

( A − λX T ) X = (Y + r ) ,

(22)

So,

r=

AX − Y 1+ X

2

.

(23)

From (20), (22) and (23) we have

E = −λ

( AX − Y )T X T 1+ X

2

(24)

For a given optimal solution X to the simplified TLS problem (16), the optimal pair ( E , r ) to the original TLS problem is given by (23) and (24).

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Simulation Result

In order to verify the performance of this algorithm, we use matlab to do simulation and compare it to LS algorithm. Let us briefly refer to main parameters in the OFDM system that we consider. The system uses 16 QAM modulation mode, channel model is the Rayleigh fading channel with Doppler frequency shift, maximum Doppler frequency shift is 132Hz, the number of sub-carriers is 128, the CP length is 16, and guard interval is 7.8125 kHz. The simulations assume that the channel in an OFDM fame is changes slowly. Furthermore, we have assumed the 5-tap channel model and having path delays of 0, 2, 4, 8 and 12μs, respectively. To compare the original LS channel estimation with the improved channel estimation algorithm by simulation to verify the bit error rate (BER) and symbol error rate (SER). The simulation results are shown in figure 2 and 3. Fig.2 compares bit error rate (BER) versus the SNR or two channel estimation schemes. LS channel estimator presents extremely poor performance in low SNRs. Also, as is illustrated in this figure, in low SNRs, where noise is the dominant factor degrading channel estimation performance, the proposed scheme can eliminate noise much more effectively because it reduces the effect of noise. With the increased SNR, external interference and noise will be degraded, so both of estimation schemes have a approximately performance. Fig. 3 shows the SER of the channel estimation for the proposed algorithm and the LS algorithm. It can be seen that the SER of TLS scheme can work even better than the SER of LS scheme, because the TLS is susceptible to the presence of noise and interference.

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Fig. 2. Bit error rate comparisons between LS and TLS

Fig. 3. SER of the channel estimation between LS and TLS

5 Conclusion In this paper, we have considered the improvement of channel estimation with Interference cancellation. We have proposed an effective TLS algorithm for channel estimation of OFDM systems over time varying and frequency-selective channels. By applying TLS criterion, we successfully avoid the effect of error and interference. The major advantage of using TLS algorithm is that noise and interference can be reduced together. The major disadvantage of the presented channel method is difficultly to guarantee the existence of solutions. Through the simulations, we show that the proposed scheme is better compared to the LS scheme.

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References [1] Li, Y., Winters, J.H., Sollenberger, N.R.: Mimo-Ofdm For Wireless Communications, Signal Detection With Enhanced Channel Estimation. IEEE Trans. Commun. 50(9), 1471–1477 (2002) [2] Engels, M.: Wireless OFDM Systems: How To Make Them Work? Kluwer Academic Publishers, Dordrecht (2002) [3] Koffman, I., Roman, V.: Broadband Wireless Access Solutions Based on OFDM Access in IEEE 802.16. IEEE Commun. Mag. 40(4), 96–103 (2002) [4] Barhumi, I., Leus, G., Moonen, M.: Optimal Training Design For Mimo–Ofdm Systems in Mobile Wireless Channels. IEEE Trans. Signal Processing 51(6), 1615–1624 (2003) [5] Lin, J.-C.: Least-square channel estimation for mobile OFDM communication on timevarying frequency-selective fading channels. IEEE Trans. Veh. Technol. 57(6), 3538– 3550 (2008) [6] Li, Y., Cimini, L.J., Sollenberger, N.R.: Robust channel estimation for OFDM systems with rapid dispersive fading channels. IEEE Trans. Commun. 46(7), 902–915 (1998) [7] van de Beek, J.-J., Edfors, O., Sandell, M., Wilson, S.K., Börjesson, P.O.: On channel estimation in OFDM systems. In: Proc. IEEE Veh. Technol. Conf., pp. 815–819 (July 1995) [8] Song, W.-G., Lim, J.-T.: Channel estimation and signal detection for MIMO-OFDM with time varying channels. IEEE Commun. Lett. 10(7), 540–542 (2006) [9] Chen, C.-W., Wei, S.-W.: Channel Estimation for OFDM Systems with Asymmetric Pilot Symbols. In: IEEE Communications Society Subject Matter Experts for Publication in the WCNC 2010 Proceedings (2010) [10] Chandrasekaran, S., Gu, M., Sayed, A., Schubert, K.E.: The degenerate bounded errorsin-variables model. SIAM J. Mater. Anal. Appl. 23(1), 138–166 (2001) [11] Beck, A., Ben-Tal, A.: On the Solution of the Tikhonov Regularization of the Total Least Squares Problem. SIAM J. OPTIM. 17(1), 98–118 (2006) [12] Wu, H.-C., Wu, Y.: A new ICI matrices estimation scheme using Hadamard sequences for OFDM systems. IEEE Trans. Broadcast 51(3), 305–314 (2005)

Detection of Double-Compression in JPEG2000 by Using Markov Features Zhao Fan, Shilin Wang, Shenghong Li, and Yujin Zhang Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China Department of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, China [email protected] [email protected]

Abstract. The detection of double compression in JPEG2000 can make crucial contribution to the steganalysis. We propose an effective approach based on 2D Markov chain of thresholded prediction-error image to detect the double compression in JPEG2000. The thresholded prediction-error images are generated by subtracting horizontal, vertical, main diagonal and minor diagonal pixel values from current pixel values respectively and then thresholding with a predefined threshold T. Then we use the Markov random process to model the thresholded prediction-error in order to utilize the second-order statistics. Support vector machine is used as the classifier. Experimental results have shown that our proposed approach has outperformed the prior arts. Keywords: Double JPEG2000 Compression, Discrete Wavelet Transform, Prediction Error Image, Markov, Support vector machine.

1

Introduction

Discrete Wavelet Transform based JPEG2000 compression was developed to improve on the performance of JPEG compression. JPEG2000 compression has eliminated the Block Effect in JPEG. It has been used widely in the daily life. Therefore, it is increasingly significant to identify the authenticity and integrity of the images stored in JPEG2000. Double JPEG and JPEG2000 compression indicates that an image was originally a JPEG or JPEG2000 and has been compressed once again by JPEG or JPEG2000. Before the compression for the second time, there may be image manipulation included. Therefore, identifying the double compression history of an image may be an initial step to detect image forgeries. We have great interest in the detection of double-compression in JPEG or JPEG2000 for it tells a given image’s history. We have one main concern with the issue of the double JPEG2000 compression detection, i.e. the detection aims to differentiate whether a given JPEG2000 image is Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 441–449, 2011. © Springer-Verlag Berlin Heidelberg 2011

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singly compressed or doubly compressed. Recently, several approaches for detecting the double JPEG compression were proposed. However, there are few approaches to double JPEG2000 compression detection. In [1], an approach for detecting double JPEG2000 compression is proposed. This technique exploits the fact that double JPEG2000 compression amounts to particular double quantization of the sub-band DWT coefficients, which introduces specific artifacts visible in the histograms of these coefficients. The authors have devised a quantitative measure for these artifacts in four sub-bands (5HL, 5LH, 4HL, 4LH), and employed this measure to discriminate between single and double JPEG2000 compressed images. The detection accuracy is favorable when the measure was applied to 18 standard test images but the accuracy drops to approximately 50% when the measure was applied to our image dataset which contains 933 images. In [2], Popescu found that the histogram of a JPEG mode of a double JPEG compressed image contains some periodic artifacts, which could thus be utilized to distinguish between double and single JPEG compressed image. In [3], Chen et al. used Markov based transition probability matrix as the feature and employed the support vector machine as the classifier. The authors claimed that their algorithm outperformed the prior work in [2]. This algorithm is based on the block discrete cosine transform. In this paper, a JPEG2000 double-compression detection approach based on 2-D Markov chain of thresholded prediction-error image is proposed. The prediction-error images are generated by subtracting horizontal, vertical, main diagonal and minor diagonal pixel values from current pixel values respectively. Considering the large values in the prediction-error image may mainly be caused by the image content rather than by the double compression of JPEG2000, a certain threshold scheme is applied to the prediction error images to remove the large values in the prediction error images for the detection of the double compression in JPEG2000. This threshold scheme can limit the dynamic range of the prediction-error image and reduce the computational cost at the same time. At last, the Markov random process is used to model these prediction-error images in order to utilize the second-order statistics, which will generate a transition probability matrix to characterize the Markov process. All elements of these matrices will be used as features for the support vector machine which is employed as the classifier. The rest of this paper is organized as follows: Section 2 presents the proposed features extraction approach and the experimental results are given in section 3. Section 4 makes discussions. Conclusions are drawn in section 5.

Fig. 1. JPEG2000 compression procedure

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Proposed Approach for Feature Extraction

JPEG2000 compression procedure is shown in Fig.1. In the JPEG2000, the original image is first preprocessed. Two-dimensional DWT is then applied to the image. After that, the DWT coefficients are quantized, and arithmetic entropy coding is used to encode the quantized coefficients. Once the entire image has been compressed, a data-ordering operation is used to generate the bit stream. The double JPEG2000 compression leaves statistical artifacts among elements of the prediction-error image. They are generated by the rounding errors in the double JPEG2000 compression. The elements in the prediction-error image of a single JPEG2000 compressed image are often correlated. The double JPEG2000 compression artifacts disturb the prediction-error image, weakening the correlation among elements of the prediction-error image of a double JPEG2000 compressed image. Therefore, the Markov random process can be used to model these changes in the prediction-error image and the proposed features are formed by transition probability matrices which characterize the Markov process. The transition probability matrices can be used to discriminate the double JPEG2000 compressed image from the single JPEG2000 compressed image. Fig.2 shows the framework of our features extraction method. In general, a color JPEG2000 image has Y, Cb, and Cr components. For simplicity, we only use the Y component.

Fig. 2. General block diagram of the proposed feature extraction procedure

A. Magnitude of the Given Image For a given image, we only consider the magnitude of the pixel value. The reason is that double JPEG2000 compression seldom changes the sign of a pixel value after preprocessing. Furthermore, our proposed approach utilizes the difference between an element and its neighbors in the magnitude array of an image. Difference between two non-negative numbers is less likely to be truncated by the thresholding operation introduced later.

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B. Prediction-Error Image It is expected that single JPEG2000 compressed image tends to have a high correlation between neighboring pixels. The double JPEG2000 compression may reduce the correlation among adjacent pixels. In the detection of double JPEG2000 compression, the artifacts caused by double JPEG2000 compressed should be enhanced. We propose to use neighboring pixels to predict the current pixel. The predictions are made in four directions, i.e. horizontal, vertical, main diagonal and minor diagonal. For each prediction we made, the prediction error can be obtained by subtracting the neighboring pixel value from the original pixel value as shown in (1) , (2), (3) and (4).

eh (i, j ) = x(i, j ) − x(i + 1, j )

(1)

ev (i, j ) = x(i, j ) − x(i, j + 1)

(2)

ed (i, j ) = x(i, j ) − x(i + 1, j + 1)

(3)

em (i, j ) = x(i + 1, j ) − x(i, j + 1)

(4)

Where x (i , j ) indicates the magnitude of the pixel (i, j) after preprocessing. The symbol eh (i, j ) indicates the prediction error for pixel (i, j) along horizontal direction while ev (i, j ) , ed (i, j ) and em (i, j ) the predictions error for pixel (i, j) on vertical, main diagonal and minor diagonal directions, respectively. C. Thresholded Prediction-Error Image The artifacts incurred by the double JPEG2000 compression are usually small compared to the difference along pixels due to the presence of different objects in an image. Moreover, the artifacts itself will raise alarm when inspected by human eyes, thus breaking the very purpose of the JPEG2000 compression. Hence, we think that large prediction errors reflect more on the image content itself rather than double JPEG2000 compression. A predefined threshold T is used to deal with the predictionerror in (5).

⎧e(i, j ) e ( i, j ) = ⎨ ⎩T

|e(i,j)| ≤ T |e(i,j)|>T

(5)

In addition, the technique has further reduced the computational cost. By this way, the value range of the prediction-error image is limited to [-T, T], with only 2*T+1 possible values. Based on our statistical study on a large natural image dataset, this threshold is selected as 4 in our work.

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D. Markov Transition Probability Matrix of Thresholded Prediction-Error Image We model the prediction error images defined above by using one-step Markov random process. According to the random process theory, a transition probability matrix (TPM) can be used to characterize a Markov process. Equation (6) shows the transition model for horizontal prediction-error image Eh and Equation (7) shows the transition model for vertical prediction-error image Ev, while (8) and (9) show the transition model for main diagonal and minor diagonal prediction error images Ed and Em, respectively. The elements of the transition probability matrices for Eh, Ev, Ed and Em are served as features for the detection of double-compression in JPEG2000. We have 9*9 elements for the TPM along each direction. As a result, there are 324 elements in the feature vector. M and N are the size of the prediction error image along the horizontal and vertical direction, respectively. M −2 N −2

{

∑ ∑ δ ( e ( i, j ) = m, e ( i + 1, j ) = n )

}

p eh ( i + 1, j ) = n eh ( i, j ) = m =

i =0

h

j=0

h

M −2 N −2

∑ ∑ δ ( e ( i, j ) = m )

(6)

h

i =0 j =0

M −2 N −2

{

}

p ev ( i, j + 1) = n ev ( i, j ) = m =

∑ ∑ δ ( e ( i, j ) = m, e ( i, j + 1) = n ) v

i =0 j =0

v

M −2 N −2

∑ ∑ δ ( e ( i, j ) = m) i = 0 j =0

(7)

v

M −2 N −2

{

}

p ed (i + 1, j + 1) = n ed ( i , j ) = m =

∑ ∑ δ ( e ( i, j ) = m, e ( i + 1, j + 1) = n ) i =0

j =0

d

d

M −2 N −2

∑ i =0

∑ δ ( ed ( i, j ) = m )

(8)

j =0

M −2 N −2

{

}

p em ( i, j + 1) = n em ( i + 1, j ) = m =

∑ ∑ δ ( e ( i + 1, j ) = m, e ( i, j + 1) = n ) i =0 j =0

m

m

M −2 N −2

∑ ∑ δ ( em ( i + 1, j ) = m )

(9)

i =0 j =0

where, m, n ∈ Z , -T ≤ m, n ≤ T , and

A m, B n

⎧ 1, A mDQGB ⎨ ⎩ 0, Otherwise

n

(10)

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Experiments and Results

E. Classifier Selection The support vector machine (SVM) is used as the classifier in our experiments. The SVM is a kind of supervised machine leaning method and widely used in pattern recognition applications. In our work, the quadratic polynomial kernel is used and the cross validation method is applied to determine the proper parameters for the polynomial kernel. The SVM toolbox in Matlab code in [6] is adopted in our experiments. F. Training/Testing Strategy In our experiments, 933 uncompressed images from the Columbia Image Dataset in [4] are used as our source images. The 933 uncompressed images are first JPEG2000 compressed with bit rate b1 ranging from 0.5bpp to 1.0bpp with a step size of 0.1bpp, which form 6 groups of single compressed JPEG2000 images. Then these single JPEG2000 compressed images are decoded and second JPEG2000 compressed with bit rate b2 ranging from 0.5bpp to 1.0bpp with a step size of 0.1bpp, which form 36 groups of double compressed JPEG2000 images. During the experiments, 5/6 of the single JPEG2000 compressed images with bit rate b2 and 5/6 of the double JPEG2000 compressed images with bit rate b1 followed by bit rate b2 were selected to train the SVM classifier and the remaining 1/6 single and double JPEG2000 compressed images were used to test the trained classifier. G. Experimental Results The approach proposed in [1] has been implemented on the 933 images. The detection accuracies are reported in table 1. The detection accuracies of our proposed approach are reported in table 3. We can see from table 1 and table 3 that our proposed approach outperforms the approach proposed in [1]. The approach proposed in [3] was also implemented and the results are shown in table 2. From this point, we can see that out approach is more effective than the approach proposed in [3] when used to detect double JPEG2000 compression.

4

Discussion

Comparing our proposed approach with the approaches from [1] and [3], we may have noticed the following: 1) In the double JPEG2000 compression, there are rounding errors in the DWT, quantization, dequantization, inverse DWT, therefore, the artifacts caused by the double JPEG2000 compression is extensive. This is one reason why our proposed approach can achieve good performance.

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2) The approach in [2] focuses on the histogram of DWT sub-band, which is of the first order statistics. In contrast, our proposed scheme relies on the Markov process, and TPM applied to the prediction-error image, which are of the second order statistics. 3) In order to effectively catch the artifacts left by the double JPEG2000 compression, our proposed approach generates prediction-error images along various directions so as to utilize the information of the whole image, but the approach in [1] just makes use of four sub-bands ( 5HL, 5LH, 4HL, 4LH ) of the DWT, it is hard to catch the information of the whole image. This is the reason why the detection accuracies of the approach proposed in [1] are not favorable. 4) The approach in [3] is proposed to detect the double compression of the JPEG image, therefore the approach is based on the block discrete cosine transform (BDCT). From table 2 and table 3 we can see that the approach based on BDCT can not have good performance as the proposed approach which is not based on BDCT when they are used to detect the double JPEG2000 compressed image. The reason is that the compression of JPEG2000 is based on discrete wavelet transform rather than BDCT. Table 1. Detection accuracy of approach proposed in [1](%) b2 b1 0.5bpp

0.6bpp

0.7bpp

0.8bpp

0.9bpp

1.0bpp

0.5bpp

52.632

52.632

55.639

59.023

55.639

57.519

0.6bpp

52.632

53.759

53.759

60.15

53.383

54.135

0.7bpp

52.632

53.383

52.256

54.511

54.511

55.263

0.8bpp

53.759

55.639

53.759

52.632

54.135

52.256

0.9bpp

51.88

55.263

53.759

53.383

52.256

54.511

1.0bpp

52.256

55.639

54.511

53.383

53.008

52.256

Table 2. Detection accuracy of approach proposed in [3](%) b2 b1 0.5bpp

0.6bpp

0.7bpp

0.8bpp

0.9bpp

1.0bpp

0.5bpp

50.188

80.977

92.049

95.62

97.575

98.365

0.6bpp

52.594

50.432

73.91

90.207

94.699

96.88

0.7bpp

53.308

53.026

49.944

68.44

85.564

91.823

0.8bpp

51.805

52.425

54.474

50.188

66.635

79.305

0.9bpp

50.207

51.635

55.244

50.677

50.019

65.357

1.0bpp

49.962

51.673

53.346

50.244

50.207

50.075

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Table 3. Detection accuracy of the proposed approach(%) b2 b1

5

0.5bpp

0.6bpp

0.7bpp

0.8bpp

0.9bpp

1.0bpp

0.5bpp

50.752

86.466

95.489

97.744

99.248

99.624

0.6bpp

52.256

51.128

78.947

94.737

97.368

98.872

0.7bpp

52.256

51.88

51.504

77.068

90.977

96.617

0.8bpp

53.008

52.256

53.008

51.128

74.436

91.729

0.9bpp

52.632

52.256

53.383

51.88

50.376

72.18

1.0bpp

53.759

54.135

53.008

53.759

52.632

51.128

Conclutions

In this paper, a JPEG2000 double-compression detection approach based on 2-D Markov chain of thresholded prediction-error image is proposed. The support vector machine (SVM) is used as the classifier. The experimental results have proved that the proposed approach is more effective than the approaches proposed in [1] and [3]. Our proposed approach is an initial step to detect image forgeries, and the experimental results have demonstrated the effectiveness of our method. We may extend out proposed method to the image forgery based on the detection of the double compression of the JPEG2000 image. Acknowledgment. This research work is funded by the National Natural Science Foundation of China (Grant No. 61071152, 60772098, 60702043), National Basic Research 973 Program of China (Grant No. 2010CB731403), and Shanghai Educational Development Foundation. Credits for the use of the Columbia Image Splicing Detection Evaluation Dataset are given to the DVMM Laboratory of Columbia University, CalPhotos Digital Library, and the photographers, whose names are listed in [9].

References [1] Zhang, J., Wang, H., Su, Y.: Detection of Double-Compression in JPEG2000 Images. In: Proceedings of 2nd International Symposium on Intelligent Information Technology Application, Shanghai, vol. 1, pp. 418–421 (2008) [2] Popescu, A.C.: Statistical tools for digital image forensics, PhD thesis, Dartmouth College, Hanover, NH, USA (advised by H. Farid) (December 2004) [3] Chen, C., Shi, Y.Q., Su, W.: A machine learning based scheme for double JPEG compression detection. In: ICPR 2008, Tampa, FL, pp. 1–4 (2008)

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[4] Columbia DVMM Research Lab,Columbia Image Splicing Detection Evaluation Dataset (2004), http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedData Set/AuthSplicedDataSet.htm [5] Pevny, T., Fridrich, J.: Detection of double compression in JPEG images for applications in steganography. IEEE Transactions on Information Forensics and Security 3(2), 247– 258 (2008) [6] Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/cjlin/libsvm [7] Fu, D., Shi, Y.Q., Su, Q.: A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In: Proceeedings of SPIE Electronic Imaging, Security and Watermarking of Multimedia Contents IX, San Jose, USA, vol. 6505, pp. 1L1–1L11 (2007) [8] fcd15444-1, http://www.jpeg.org/jpeg2000 [9] http://www.ee.columbia.edu/dvmm/publications/04/TR_splicingD ataSet_ttng.pdf

High Speed Imaging Control System Based on Custom Ethernet Frame Dawei Xu, Yuanyuan Shang, Xinhua Yang, and Baoyuan Han Information Engineering College, Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Haidian Dist., Beijing, China, 100048 [email protected]

Abstract. For disadvantage of lower transmission speed which exist in embedded imaging system using TCP/IP protocol stack, this paper proposed a new communication method which based on custom Ethernet frame. This paper defined several kinds of frames and designed a control protocol based on state machine for image data transmission and imaging system control. This paper achieved this method on the hardware platform and test the network communication speed and make the communication speed compare with the speed of the system based on TCP/IP protocol stack, found that the increase of the speed is more than 4 times. Keywords: Ethernet, Embedded System, Imaging System.

1

Introduction

For its flexibility, scalability, and many other advantages, the embedded network imaging system is increasingly being used in aerospace, military agriculture and other fields. With the increasing quality and resolution of the image, it’s required to improve the data transmission speed of the image forming system. In such of these existing imaging systems, the image data and the control information are transmitted through the TCP/IP protocol stack[1][2][3]. For the purpose of the limited resources of the embedded system, the overhead generated by the protocol stack will be the bottleneck of the data transmission, which led to lower network bandwidth utilization[4]. This paper propose a new kind of method to transmit the image data: packaging the image data in custom Ethernet frame, and we design the NIC driver to send and receive the Ethernet frame. On this basis, we propose a control protocol that makes the camera working under control, it contains the following features: exposure control, image data transmission, the sensor and the circuit parameters setting, etc. This method have some advantages such as it’s more targeted, more flexibility, low overhead and faster than before. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 450–455, 2011. © Springer-Verlag Berlin Heidelberg 2011

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System Design

The image forming system is composed by two different parts: the PC and the lower machine. The lower machine is designed based on the Altera’s FPGA, the system block diagram is shown as the Figure 1. The dashed box in the diagram is the hardware system which constructed by the SOPC Builder. The hardware system composed of the NiosII® soft core processor and the custom CMOS-controller which met the Avalon bus standard. The CMOS-controller generated the waveforms to drive the CMOS image sensor. The NIC-controller is used to generate the waveforms to drive the Lan91c111, it maps the Lan91c111’s internal registers to the system memory area so that we can access to the registers facilitative.

Fig. 1. System Block Diagram of the Lower Machine

Cause the PC need to send and receive the custom Ethernet frame and the existed protocol stack do not provide this functionality, we used the ‘PCAP’ as the packet capture tool. It was a packet capture and analysis tool originally in Unix environment. We used the edition in Win32 environment named ‘Winpcap’[5], it can capture the Ethernet frame and fill the custom frame into the link. The PC and lower-machine are both working on the 100Mbps Ethernet and the communication data between each other are packaged in custom frames for transmission over Ethernet.

3

Design of Transmission Mode

The PC and the lower computer communicate with each other via the Ethernet. The communication data transfer through only two layers instead of whole four layers of the TCP/IP protocol stack. This design is based on the following reasons: The lower machine used the NiosII® soft-core processor as the CPU what designed by Altera Corporation for their FPGA, its processing power is limited. This conclusion was based on the following experiment: We put the CPU run under the 150MHz frequency, using the Lwip(light weight IP) as the TCP/IP protocol stack and uCosII as

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the operation system, the data transmission speed we measured at the PC end is about 19Mbps. In addition, we can’t increase the data transmission speed by upgrading the system frequency, because the frequency limitation of the SDRAM we used is 166MHz. In the other hand, if we package the data manually in UPD packet without LwIP and send them to the PC, the data transmission speed we measured at the PC end is about 72Mbps. Actually the LwIP costed so much resources of whole system, So finally the communication mode we decided is using custom Ethernet frame. Table 1. Format of the custom Ethernet Frame

Destiny Address

Mac

6Byte

Source Address

Mac

6Byte

Frame type

Command type

Reques/Ack

Data

2Byte

1Byte

1Byte

NByte

Table 2. Format of the Image Transmission Related Frames Exposure Command

Image No. 2Bytes

Padding 42Byte

Image Data

Image No. 2Bytes

Frame No. 2Bytes

Length 2Bytes

Retransmission Request

Image No. 2Bytes

Retransmission Frames 2Bytes

Retransmission Frame No. List nBytes

Data NBytes

A. Custom Ethernet Frame The format of the frame transmitted between the upper and lower machine is shown as the TABLE 1. Three fields before the agreement is the Ethernet frame format, we used a special word ‘0x0A0A’ as the ‘frame type’ field[6]. This word should to avoid all Ethernet frame type code which are being used, so that to make sure the transmission of such custom Ethernet frame will not affect the normal transmission of existing protocol frames. The ‘command type’ field after the ‘frame type’ field is used to indicate the feature of the frame. We define 6 different kinds of command code which correspond to 6 kinds of operations, they are ‘device detect’, ‘device connect’, ‘device disconnect’, ‘exposure parameters set’, ‘exposure’, ‘connection keep alive’. ‘request/ack’ field is used to indicate who send this frame, this field will be ‘request’ code when the upper machine send the frame, or it will be ‘ack’ code when the lower machine send the frame. The format of the ‘Data’ field is determined by the ‘command type’ field. The storage of all protocol data sequence is used Little-Ending mode. Due to limited space, we will just introduce the frame format about image transmission.

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B. Image Transmission Frame Format The upper machine start the exposure by sending the exposure command frame, the ‘Data’ field of the exposure command frame is shown as the TABLE 2. The ‘Image No.’ field is used to distinguish the pictures generated by several exposure operations, the upper machine maintains a variable to save it. The lower machine will start the exposure as soon as receive the ‘Exposure Command’ frame, after that it’ll device the image sensor to take one picture, the image data will package into the ‘Image Data’ frame which will send to the upper machine. As one image is much larger than the amount of data of an Ethernet frame, we take the whole image data into fragments to transmit. The ‘Image No.’ field of the ‘Image Data’ frame has the same value to the such field of the corresponding ‘Exposure Command’ frame. The ‘Frame No.’ field of the ‘Image Data’ frame is used to index the fragment frame of one picture. The ‘Length’ field of the of the ‘Image Data’ frame is used to identify the length of the ‘Data’ field after. If frame loss phenomenon occurs among the image data transmission, the upper machine will send the ‘Retransmission Request’ frame to start the data retransmission operation. The ‘Image No.’ field of the ‘Retransmission Request’ frame is used to identify a special exposure. The ‘Retransmission Frames’ field is used to identify the number of the retransmission frames. The ‘Retransmission Frame No. List’ field is the list of the retransmission frame number.

4

Design and Implementation of the Control Protocol

The upper machine control the lower machine using the control protocol through the Ethernet. The mechanism of the protocol is a kind of ‘request/acknowledgement’. All the operation of the lower machine started by a ‘request’ frame from the upper machine, and it turn into a waiting state while it start a timeout timer. The lower machine must reply a ‘acknowledgement’ frame when it has completed the work specified. The upper machine used the state machine to realize the control protocol. The state transition diagram is shown as the Figure 2. We totally define 7 different states, the state machine will be the ‘Disconnect Idle’ state after initialization. The upper machine try to connect the lower machine what’s Mac address specified by sending the ‘Connect Request’ frame. The state machine will turn to the ‘Connected Idle’ state when the upper machine received the request of the ‘Connect Request’ frame. The ‘Parameters Setting’ state and the ‘Parameters Getting’ state are used for setting and getting the lower machine’s working parameters. We will detail the data communication next. when the state machine is “Connected Idle” state, the upper machine can start an exposure and image transmission by issuing the ‘exposure command’ frame, then the state machine will be set to “Image Data Receiving” state. At the same time the upper machine start a periodic timer which named ‘CycleTimer’, then set a BOOL type variable to FALSE which named ‘NewFrameFlag’. Until now the upper machine is ready to receive the image data. When the upper machine receives an image data

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frame, the variable ‘NewFrameFlag’ is set to TRUE, it means that the recent arrival of new data frame. The variable ‘NewFrameFlag’ will be detected each timeout of the timer ‘CycleTimer’, if the variable is TRUE then set it to FALSE; if the variable is FALSE it means there is no new image data arrived between the last two timeout of the timer. Therefore, the data transfer has ended, and the state machine will turn to ”Image Data Integrity Checking”. In this state, the upper machine check the integrity of the received image data. If there is no loss of image data frame, the state machine will directly turn to “Connected Idle” and stop the timeout timer to end the data transfer process; otherwise the upper machine will construct the ‘retransmission command’ and send it to lower machine to start the image data retransmission process, at same time the state machine will turn to the “Image Data Receiving” state.

Fig. 2. The State Transition Diagram

5

System Running Result

We do the speed test of the image forming system, the hardware and the software of the lower machine is: the CPU frequency is 150Mhz, the size of the system RAM is 32M*8Bit. We used the uCosII as the embedded operating system, and there are totally two tasks running on it. The upper and lower machine work at the 100Mbps Ethernet environment. We use the software named wireshark as the speed test utility. We started one exposure and capture the communication data by the wireshark and analyzed them, finally we got the following conclusions: the time interval between the

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first and last image data frame is 833ms, during this period the lower machine has transferred image data which size is totally 8192*1024*8 bits, so the image data transmission speed is about 80.56Mbps. For verifying the accuracy of the test, we used another Ethernet packet capturer which named Sniffer Pro to get and analyze the communication frames. At last we get the similar test conclusion.

6

Conclusion

In this paper, for solving low transmission speed of the embedded imaging system which using the TCP/IP protocol stack, we proposed a new kind of transmission method based on custom Ethernet frame. The transmission speed of the hardware system using this method reaches to 80Mbps in the 100M Ethernet environment, it’s about 4 times speed increase compared to the transmission method using the protocol stack. This method also suit the 10M and 1000M Ethernet environment. At the same time it doesn’t only apply to the imaging system, it can be also used in other kind of embedded systems which need to transfer large amounts of data in time.

References [1] Zhao, W., Liu, L.-b., Zhang, L., Wang, Z.-h., Xie, S.-g.: High Resolution Digital Image Surveillance System Based on JPEG2000 CODEC Chip. Microelectronics & Computer 22(6) (2005) [2] Wu, L.-z., Hao, X.-h.: Research and Design for Integration of Control Networks Systems Based on Ethernet. Application Research of Computer 23(9) (2006) [3] Li, W.-m., Liang, J.-r., Wei, W.-x.: MCU based image capture and transmission on LAN. Control & Automation (32) (2005) [4] Huang, H.K., Lou, S.L., Cho, P.S., Valentino, D.J., Wong, A.W.K., Chan, K.K., Stewart, B.K.: Radiologic Image Communication Methods. Radiologic Image Communication 7, 183–186 (1990) [5] Politecnico di Torino, CACE Technologies. WinPcap documentation, http://www.winpcap.org [6] Richard Stevens, W.: TCP/IP Illustrated. The Protocols, vol. 1. Addison Wesley/Pearson

Design and Implement of Pocket PC Game Based on Brain-Computer Interface Jinghai Yin and Jianfeng Hu Institute of Information Technology, Jiangxi Bluesky University, Nanchang 330098, China [email protected]

Abstract. To enhance human interaction with machines, research interest is growing to develop a ’Brain-Computer Interface’ (BCI), which allows communication of a human with a machine only by use of brain signals. In this paper, one type of pocket PC game was designed for application of brain computer interfaces. In this system, The cerebral cortex EEG based on motor imagery were fed into the input of signal processing module, and then classification algorithm module of motor imagery deal with this signal. Output results for classification of motor imagery were converted to control the role in the games. The result of experiment shows that BCI technology not only can be used for rehabilitation, but also can be used for general public entertainment. Keywords: Brain-Computer Interface, Pocket PC Game, EEG.

1 Introduction EEG based brain computer interface (BCI) systems can be used for people with disabilities to improve their quality of life. Applications of BCI systems comprise the restoration of movements, communication and environmental control [1]. The most interesting thing in these applications is to control the roles in game by player’s EEG. A volume of research has been performed on create information chain from brain to PC game by BCI system. Anton Nijholt(2008) Reported that Prototype BCI applications now appear in the domain of games and entertainment that aim at adapting and controlling a game using brain signals in addition to traditional physical and mental abilities[2]. Although some progress has been made in this area, at least two major obstacles must be overcome before BCI technology has begun to develop commercial applications. Firstly, most of BCI systems were achieved under laboratory conditions, lack of flexibility, scalability, and availability. Secondly, full set of BCI system equipment was both complex and expensive, and the related applications were difficult to promotion. The above difficulties are real challenges faced by researchers attempting to develop. In recent years, with improvement of pocket PC processing power, Many PC games were ported to pocket PC platform. To play pocket PC game by BCI system maybe becomes a Very interesting entertainment in the near future. In order to achieve these objectives, a BCI-PPC game system must meet the following Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 456–463, 2011. © Springer-Verlag Berlin Heidelberg 2011

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requirements: It must also be able to operate without expert oversight, Equipment prices must have fallen to an acceptable standard, and people can play game by EEG without long-term training. The goal of this paper is to design a Pocket PC game on real-time BCI system that includes physiological signal acquisition, EEG transmission, and a Pocket PC which be used to analyze and process EEG and then control games.

2 System Architecture The Architecture diagram of the Pocket PC game based BCI system is shown in Fig. 1, which includes four units: Dedicated EEG Cap; signal acquisition and amplification unit; Signal Processing module and Video game controlled by EEG. The four units and game player constitute a complete data chain.

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Fig. 1. Architecture of mobile BCI System

There are five steps in this data cycle. 1) Human’s thinking activity will make the changes in cortical potentials. This change can be acquisition by The EEG cap can acquire this change by high sensitivity electrode. 2) This signal is send to Signal acquisition and amplification unit by data cable of EEG cap. The device supports 8 analog input channels digitized at 16 bit resolution and sampled at a fixed 256 Hz sampling rate. 3) By Signal acquisition and amplification unit, the original signal is transform to formatted data like a huge data matrix. 4) The main function of this unit is to transform pretreatment EEG signal into flag signal like left, right, up, down, stop etc. 5) Through the built-in Video games in the PDA, flag signal be used to control the role in the games. The player will get feedback by the role’s movement.

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3 Design of Pocket PC Game Pocket PC Game is a special type of game; it usually has a relatively simple interface because of limited display. Moreover, the most important feature of PPC Game is simply operation mode. Some of them can be played by only two buttons (left button and right button), at the same time, the count of output we can achieved from the player’s EEG by Feature Recognition Program is two. Therefore, some simple PPC game can be controlled by BCI technology. A. Racing Games Racing Games is a type of popular game. This type of game usually includes some elements of the following: First of all, a role is controlled by player, such as a car, a bicycle, a motor car etc. The second, a track for the race, it may be a straight or a closed loop with some types of corners. The third, some operating rules, for example, how to control the movement of the player's role by the keyboard or mouse, how to Initialize the moving direction and speed of players and control the Changes in the background with game play , How to deal with the role of the collision and the boundaries of the environment. In this type of game, player controlled the car move to left or right by his EEG to avoid the other cars and obstacles. If player failed to accomplished game mission, the system can reduce the mission’s difficulty and restart this mission. In the new mission, the movement speed of these objects in the game will be slower. B. Pool Games Pool Games is another type of popular game. This type of game always has a Horizontal or vertical baffle and a constant moving ball, sometimes it also has a wall which can rebound the moving ball. In the beginning of the game, this ball will be launch by the baffle, then the ball start moving along with the default path and speed. When the ball hit the wall or the border, it will be rebound to the opposite direction. The player controlled the baffle move to left and right, the purpose of this game is to prevent the ball falling off. When it falls into the baffle, the little ball can be rebound to the opposite direction.

4 Methods of Signal Processing In this paper, we use short-term Fourier transform for the adaptive time–frequency analysis and energy entropy have been applied to feature extraction. A. Shannon Entropy The conventional definition of the Shannon entropy was described in terms of the temporal distribution of signal energy in a given time window. The distribution of energy in a specified number of data values intervals was described in terms of the probabilities in signal space { pi } where pi was the probability that X = a i , so entropy for discrete random variable

X was defined as

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H ( X ) = −∑ pi log( pi )

(1)

i

The entropy of a random variable reflected the degree of disorder that the variable possessed. The more uncertain the variable was, the greater its entropy. B. Energy Entropy Short-term Fourier transform was a time-frequency analysis, which could analyze no stationary time-varying signals on frequency domain and time domain at the same time, so the dynamic change of signal energy with time could be observed. Energy Entropy defined on the basis of this could characterize signal complexity with the changes in time, and also many of the characteristics in frequency domain, which had a good time-frequency local capabilities. Let E1 , E 2 , ∧ E m represented energy distribution on the m frequency bands. m

Then signal energy

E equaled to the sum of E j in a time window E = ∑ E j , j =1

where

E j = ∑ | D j (k ) | , where D j was spectrum on the jth frequency band. Let 2

k

pj = Ej / E then

∑p

j

(2)

= 1 , The definition of energy entropy was corresponding to: We = −∑ p j log p j

(3)

j

C. Fisher Distance The Fisher class separability criterion [3] was used preparatory to extract features. The Fisher distance of two classes was calculated as

F=

( μ1 − μ 2 ) 2 σ 12 + σ 22

(4)

where μ was equalizing value and σ was variance. The Fisher distance was often used to denote differences between classes in classification research. The difference will more notable with the larger fisher distance. The difference between serveral types of motor imagery EEG signals was not obvious because of instability of EEG, coupled with the impact of noise, the features with large fisher distance might be features of motor imagery, and the features with small fisher distance could not distinguish different types of motor imagery EEG signals.

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In order to make the selected features included the essential character of motor imagery as much as possible; the concept of the contribution was introduced in this paper, which was calculated as

tn =

Sn −1 S n −1

Where t n was the contribution, and distances.[4]

(5)

S n was summation of first n larger fisher

5 Implement of PPC Game The complete process of the game development is shown in Figure 2.

transplant

Mission A

Mission D Integrated Integrated

Mission B

Mission E transplant

Mission C

Integrated

Mission F

Fig. 2. Process of PPC Game development

A. Develop Feature Extraction Module in Matlab 2009 MATLAB is a professional tool for signal analysis and processing. As the fast Fourier transform and wavelet analysis functions, MATLAB provides a powerful package to achieve these Functions. In accordance with previously described the theory of energy entropy, we signal analysis and feature recognition, and some auxiliary procedure are encapsulated into a MATLAB function. The input parameters of this function is EEG data with matrix format, and the output parameters is a flag which can be values with left, right, null. B. Develop Game’s Basic Framework by Virtual Machine in vs.Net Development Platform The complete framework of PPC game is shown in Figure 3. The software development platform is vs.net 2005 and windows mobile 5.0 SDK. The basic framework mainly includes the system configuration, data input/output manage, data interface and other modules.

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Platform: vs.net 2005+windows mobile 5.0 SDK Basic framework

Signal Acquisition

Signal Processing

user interface

Fig. 3. Software framework of PPC Game

As it can be shown in figure 3, basic framework is the base of this BCI system. On this basis we established three functional modules: Signal Acquisition, Signal Processing, user interface. EEG signal also into the three modules according to the order of data flow. C. Develop Game’s User Interface by Virtual Machine in vs.Net Development Platform It is very easy to develop the user interface of PPC game by Using the virtual machine inside windows mobile 5.0 SDK, The SDK provides a lot of drawing class and I/O class for developers to call it. Figure 4 and Figure 5 are Screenshot of these games interface. The first screen is a car game and the second screen is a pool game.

Fig. 4. Car Racing Game

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Fig. 5. Pool Game

D. Transplant the Feature Extraction Module from MATLAB to Windows Mobile Platform Because MATLAB can’t run in mobile platform, so we must develop the corresponding program in Pocket PC. The most difficult development of this mission is to achieve signal processing functions. In MATLAB 2009, we can easily achieve some functions such as fast Fourier transform and wavelet transform because of its powerful toolbox. But in windows mobile, we must do it by the most basic functions. At the same time, we must consider the efficiency due to the limited CPU and memory. E. Integrated the Whole System by Virtual Machine in vs.Net Development Platform We assembled several modules in a mobile project by vs.net compile tools, then we get a full version and debug it until there is no bug in the project. F. Transplant the Whole System from Virtual Machine to Pocket PC Platform At last, we transplant the project from virtual machine to the PDA by some related tools. The most important purpose is test the performance of the system in the real operating environment.

6 Discussion Further research was proposed to improve the BCI system, such as faster response speed, high recognition rate and short-term training time for subjects. Brain computer

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interface prototypes based on mobile platform were established by building the common platform, several application programs were created (such as games, controller, etc.) in this platform. Achievement of these will be used widely for patients who suffer from severe motor impairments and will perfect technological foundation for future commercial application. Acknowledgment. This work was supported by IT Project of Jiangxi Office of Education [GJJ09621] and Natural Sciences Project of Jiangxi Science and Technology Department [2008GQS0003]. The authors are grateful for the anonymous reviewers who made constructive comments.

References [1] Pfurtscheller, G., Guger, C., Müller, G., Krausz, G., Neuper, C.: Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292, 211–214 (2000) [2] Nijholt, A.: BCI for Games: A ‘State of the Art’ Survey. In: Stevens, S.M., Saldamarco, S.J. (eds.) ICEC 2008. LNCS, vol. 5309, pp. 225–228. Springer, Heidelberg (2008) [3] Ince, N.F., Arica, S., Tewfik, A.: Classification of single trial motor imagery EEG recordings with subject adapted nondyadi arbitrary time-frequency tilings. J. Neural. Eng. 3, 235–244 (2006) [4] Jianfeng, H., Dan, X., Zhendong, M.: Application of Energy Entropy in Motor Imagery EEG Classification. International Journal of Digital Content Technology and its Applications 3(2), 83–90 (2009) [5] Nicole, R.: Title of paper with only first word capitalized. J. Name Stand. Abbrev. (in press) [6] McFarland, D.J., Wolpaw, J.R.: Sensorimotor rhythm-based brain–computer interface (BCI): model order selection for autoregressive spectral analysis. J. Neural. Eng. 5, 155– 162 (2008) [7] Ramoser, H., Müller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans. Rehabil Eng. 8, 441–446 (2000) [8] Müller-Gerking, J., Pfurtscheller, G., Flyvbjerg, H.: Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol. 110, 787–798 (1999) [9] Hoffmann, U., Vesin, J.M., Ebrahimi, T.: Spatial filters for the classification of eventrelated potentials. In: Proc. 14th Eur. Symp. Artif. Neural Networks, pp. 47–52 (2006) [10] Liao, X., Yao, D.Z., Wu, D., et al.: Combining Spatial Filters for the Classification of Single-Trial EEG in a Finger Movement Task. IEEE Trans. Biomed. Eng. 54, 821–831 (2007) [11] Hinterberger, T., Kubler, A., Kaiser, J.: A brain-computer interface (BCI) for the lockedin: comparison of different EEG classifications for the thought translation device. Clin Neurophys 114, 416–425 (2003) [12] Bostanov, V.: BCI Competition 2003 - data sets Ib and IIb: feature extraction from eventrelated brain potentials with the continuous wavelet transform and the t-value scalogram. IEEE Trans. Biomed. Eng. Special Issue on Brain Machine Interfaces 51, 1057–1061 (2004)

Authentication Service for Tactical Ad-Hoc Networks with UAV Dong Han, Shijun Wang, and Laishun Zhang Zhengzhou Information Science and Technology Institute, Zhengzhou, China [email protected]

Abstract. A tactical ad-hoc network is a self-organizing, infrastructureless, multi-hop network. The wireless and distributed nature of tactical ad-hoc networks and the very bad security environment in battlefield bring a great challenge to securing tactical ad-hoc networks. Unmanned aerial vehicles (UAVs) play more and more important roles in digital battlefields, such as repeaters, routers and detectors. In fact, UAVS can also help improve the security of the system if their shortages can be overcome. In this paper, we propose a scheme of authentication service for tactical ad-hoc networks, in which UAVs can play a important role as certificate authority. When UAVs can not work again or are captured, their functions can be distributed to some selected units on the ground. Keywords: Authentication Service, tactical, ad-hoc networks, UAV.

1 Introduction Today’s warfare is network centric. Communications on the move are essential for successful mission operations. Dismounted tactical soldiers often communicate at short range and peer-to-peer with mobility and task coordination. Tactical ad-hoc networks are self-organizing wireless systems capable of forming networks on the fly, without fixed relays or repeaters. Such a network is highly deployable and exceptionally well suited to handle Lower Tactical Internet communications at brigade and below. Unmanned aerial vehicles (UAVs) are very important equipments for digital army. They can play as flying repeaters or routers in order to improve efficiency of communication. They can play detectors to find enemy in good time. They can also be attackers by bearing arms. Up to now, some works on securing tactical ad-hoc networks have been proposed. Most of them consider novel security schemes which are deployed among nodes of tactical ad-hoc network. Distributed security scheme is complicated and low efficient. We can make use of UAVs to provide more efficiency security services for the networks. When UAVs are destroyed by enemy, their security services need to be distributed to nodes on the ground. In this paper, we propose a scheme of authentication based on CA (certificate authority) carried by UAV or distributed to some selected nodes on the ground. In the Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 464–469, 2011. © Springer-Verlag Berlin Heidelberg 2011

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following, we first analyze the challenge and goals to the security of tactical ad-hoc networks. In section 3, the centralized CA and distributed CA are analyzed, which are applied in tactical ad-hoc networks. The main authentication scheme based on CA carried on UAVs or distributed to some selected node on the ground is introduced is section 4. The relationship between centralized CA and distributed CA is analyzed too. Lastly conclusion is made in section 5.

2 Security Challenges and Goals of Tactical Ad-Hoc Networks Tactical ad-hoc networks are subject to various kinds of attacks due to their nature. Firstly, the wireless network is more susceptible to attacks ranging from passive eavesdropping to active interfering. Wireless communication links can be eavesdropped on without noticeable effort and communication protocols on all layers are vulnerable to specific attacks. In contrast to wire-line networks, known attacks like masquerading, man-in-the-middle, and replaying of messages can easily be carried out. Secondly, deploying security mechanisms is difficult due to inherent properties of ad-hoc networks, such as the high dynamics of their topology (due to mobility and joining/leaving devices), limited resources of end systems, or bandwidth-restricted and possibly asymmetrical communication links. Thirdly, mobile devices tend to have limited power consumption and computation capabilities which make it more vulnerable to Denial of Service attacks and incapable to execute computation-heavy algorithms like public key algorithms. Finally, node mobility enforces frequent networking reconfiguration which creates more chances for attacks, for example, it is difficult to distinguish between stale routing information and faked routing information. For mission-critical applications such as a military application in a hostile environment there are more stringent security requirements than in ad-hoc networks for commercial or personal uses. In tactical ad-hoc networks, there are more probabilities for trusted node being compromised and then being used by adversary to launch attacks on networks, in another word, we need to consider both insider attacks and outsider attacks in ad-hoc networks, in which insider attacks are more difficult to deal with. There are five main security services for ad-hoc networks: authentication, confidentiality, integrity, non-repudiation, availability[1]. Authentication means that correct identity is known to communicating partner; Confidentiality means certain message information is kept secure from unauthorized party; integrity means message is unaltered during the communications; nonrepudiation means the origin of a message cannot deny having sent the message; availability means the normal service provision in face of all kinds of attacks. Among all the security services, authentication is probably the most complex and important issue in tactical ad-hoc networks since it is the bootstrap of the whole security system. Without knowing exactly who you are talking with, it is worthless to protect your data from being read or altered. Note that these security services may be provided singly or in combination.

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3 Public Key Infrastructure Applied in Ad-Hoc Networks Of the security scheme in the ad-hoc networks, the most important approach is to create a secure communication channel between two nodes, because it forms the basis of all secure protocols in a distributed system. A secure channel has the following properties. Secrecy: the channel is immune to eavesdropping; Integrity: the messages passed can not be altered without being noticed by the respective receivers, and the communication parties are mutually authenticated; Availability: the operations of the channel can not be disrupted by a malicious entity. Because of it’s superiority in distributing keys and in achieving integrity and nonrepudiation, the PKI (Public Key Infrastructure) is always adopted to achieve those secure goals. Public key cryptography has been recognized as one of the most effective mechanisms for providing fundamental security services including authentication, digital signatures and encryption. The most important component of PKI is the CA, the trusted entity in the system that vouches for the validity of digital certificates. PKI has been deployed for wired networks and some infrastructure-based wireless networks. Since good connectivity can be assumed in these networks, the main thrust of research in such environments has focused on the security of the CA and the scalability of the CA to handle a large number of requests. Distributed CA is such a scheme, it divides the master secret key keym of Certificate Authority, which is the core of the PKI, into several shares and distributes them to several chosen entities, and each has one and only one part (as shown in Figure 1)[2]. These chosen entities are called servers.

Fig. 1. Distribution of shares of the master secret key

Distributed CA is accomplished using threshold cryptography[3, 4]. A (k, n) threshold cryptography scheme allows n parties to share the ability to perform a cryptographic operation (e.g. creating a digital signature), so that any k parties can perform this operation jointly, whereas it is infeasible for at most k-1 parties to do so, even by collusion. In our case, any k servers can reconstruct the master secret key and less than k ones can’t reconstruct the master secret key. For example, for the secure service to sign a message M in a distributed CA using a (2, 3) threshold cryptography

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Fig. 2.Threshold signature

scheme, servers generate partial signatures for M using its master secret key keym share respectively. With 2 correct partial signatures, the signature for M can be accomplished (as shown in Figure 2). Distributed CA can classify into two kinds: partial distributed CA[5] and fully distributed CA[6]. The process of one application of common node for secure service to k servers of distributed CA is shown in Figure 3. When a common node v needs some secure service provided by the distributed CA of MANETs (e.g. signing certificate or validating other’s certificate), it broadcasts an application message firstly. The servers who receive this message reply an echo message. Then node v selects k servers ( s1 , s 2 , L , s k ) from them and broadcasts certificate requests to them. Upon receiving k partial certificates from coalition ( v1 , v 2 , L , v k ) , node v multiplies them together to gain the secure service. Compared to traditional CA, distributed CA is more fault tolerance and secure. But it is also more complicated and lower efficient.

Fig. 3. Application for secure service and the reply

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4 Novel Authentication Scheme Based on CA Carried on UAV or Distributed to Nodes In traditional wired networks, central servers are available to provide security services for the users inside the network system. In ad-hoc networks, UAV can play the same role to provide security services as CA. In fact, UAVs are very important equipment for digital army to improve efficiency of communication and reconnaissance. During combat, any node, including the ground nodes and the UAVs, may be destroyed at any time. When the UAV is destroyed, which carried CA server, its security services should be substituted by other nodes on the ground. These nodes act as a single CA to provide security services. So, there are two CA in our architecture. The DCA (Distributed CA) is primary for the reason that it is safer than centralized CA. But it is less efficient, so it act as the backup CA. It works only when the CA carried on UAV failed. The public key of the DCA is known by all nodes in the networks and the private key of it is distributed to some selected nodes. So any node who wants some security services will apply to these selected nodes. These nodes will use parts of private key of DCA to sign parts of certificate, and then send it to the applier. The process is complicated and will fail if too many nodes make mistakes. We can use a UAV to simply the process and improve its efficiency. Firstly, the CA, carried on the UAV, produce a pair of public and private key. Secondly, it applies to nodes of DCA to get the certificate. After it get the right certificate, it can be approved by other nodes and provide security services for other nodes. Thirdly, it broadcast its certificate to other nodes. CA carried on UAV can provide on-line access to nodes on the ground. So it can improve the efficient of secure system.

5 Conclusion Security challenges and goals of tactical ad-hoc networks are analyzed and new authentication scheme is presented in this paper. Different from other schemes, our scheme is based on centralized CA which is carried on UAVs or distributed CA which is distributed to some selected nodes on the ground.

References [1] Yu, S., Zhang, Y., Song, C., Chen, K.: A security architecture for Mobile Ad Hoc Networks (March 26, 2005) [2] Lidong, Z., Haas, Z.J.: Securing ad hoc networks. IEEE Network Magazine 13, 24–30 (1999) [3] Desmedt, Y.: Threshold cryptography. European Transactions on Telecommunications 5, 449–457 (1994) [4] Desmedt, Y.F.Y.: Threshold cryptosystems. In: The 9th Annual International Cryptology Conference, Santa Barbara, CA, USA (1989)

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[5] Yi, S., Kravets, R.: MOCA:Mobile Certificate Authority for Wireless Ad Hoc Networks. In: IEEE Proc. of 2nd Annual PKI Research Workshop Program (PKI 2003), Gaithersburg, Maryland, pp. 65–79 (2003) [6] Kong, J., Zerfos, P., Luo, H., Lu, S., Zhang, L.: Providing Robust and Ubiquitous Security Support for Mobile Ad-Hoc Networks. In: IEEE ICNP 2001, Riverside, California, USA (2001)

An Adjustable Entropy Interval Newton Method for Linear Complementarity Problem Hu Sha and Yanqiang Wu School of Sciences, China University of Mining and Technology Xuzhou, Jiangsu, 221116, P.R. China [email protected]

Abstract. This paper presents an adjustbale entropy method for complementarity problem. Firstly, the linear complementarity problem is formulated as an equivalent fixed point problem. So, a interval maximum entropy function is adopted in the fixed point problem. As such, an interval extension of the entropy function is proposed. The corresponding solution algorithm is named as adjustable entropy interval Newton method. The algorithm is given together with some numerical examples . Keywords: complementarity, matrix, adjustable, entropy, interval, Newton method.

1 Introduction Complementarity problem has many applications in the field of economics, management sciences, transportation engineering as well as in mathematics. Therefore, the complementarity problem has achieved more and more attentions during the last several decades [1,2]. One of the significant applications of complementarity problem in transportation planning engineering is to traffic assignment model. Traffic assignment is an important step in transportation planning analysis. The fundamental aim of traffic assignment is to find link or path flow patterns under given travel demand, network topology, and link performance functions [3]. Some classical traffic assignment models (e.g. User Equilibrium and Stochastic User Equilibrium models [3]) and some modified traffic assignment models are also formulated as the complementarity problems [4]. Therefore, the study on the solution method for the complementarity problem is of importance not only in the field of transportation engineering but also in that of mathematics. There are a plenty of research works regarding the solution algorithms of complementarity problem (see [5,6] for some complete reviews on the recent developments). Different from the conventional solution algorithms for complementarity problem, a new adjustable entropy interval Newton method for linear complementarity problem (LCP) is proposed in this paper. The proposed method takes advantage of both good property of adjustable entropy function and technique of interval Newton method in order to improve the efficiency of the solution algorithm. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 470–475, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Let M be a n × n matrix and x ∈ R n . The linear complementarity problem, denoted by LCP( M , q) , is to find a vector x such that

x ≥ 0, ( Mx + q) ≥ 0, xT ( Mx + q) = 0 .

(1)

The following notations are used throughout this paper. The absolution value of a vector x is defined as: x = ( x1 , x2 ,L , xn )T .

(2)

Following the principle of interval mathematics[7,8], X = ( X 1 , X 2 ,L , X n )T is denoted as a n-dimensional interval vector, where X i = [ xi , xi ] is a closed bounded interval. Let I ( R+n ) be the set of all interval vectors in R+n . Let f be a real valued function of n real variables x1 , x2 ,L , xn . By an interval extension of f , an interval valued function F with respect to n interval variables X 1 , X 2 , L , X n , is defined with the following property: F ( x1 , x2 ,L , xn ) = f ( x1 , x2 ,L , xn ) for real variables.

2 Interval Newton Method The problem (1) can be formulated as a fixed point problem[7] as follows: Lemma 2.1[7] x* is the solution of (1) if and only if x* is a fixed point of the mapping: h( x) = max{0, x − ( Mx + q)} .

(3)

Let N = I − M , r = q , so (3) can be transforted as following: g ( x) = max{0, Nx − r} .

(4)

Let F ( x, p, u ) be adjustbale maximum entropy function [9] of g ( x) , it follows that: F ( x, p, u ) = ( F1 ( x, p, u ),L , Fn ( x, p, u ))T , Fi ( x, p, u ) =

(5)

n 1 ln{ui1 + ui 2 exp( p( ∑ nij x j − ri )))} , p j =1

( i = 1, 2,L , n ).

(6)

The following equation is considered: F ( x, p , u ) − x = 0 .

(7)

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Let x* be a solution of (7). According to the Taylor expansion of F ( x, p, u ) − x at x* , it follows that F ( x, p, u ) − x = F ( x* , p, u ) − x* + ( F ′(ξ , p, u ) − I )( x − x* ) = (V (ξ ) N − I )( x − x* ) ,

(8)

where V ( x ) is a diagonal matrix with the diagonal entries: n

vi ( x) =

exp( p( ∑ nij x j − ri ) j =1

n

ui1 + ui 2 exp( p( ∑ nij x j − ri )

, (i = 1, 2,L , n)

(9)

j =1

and ξ = (ξ1 ,L , ξ n ) such that ξi is between x and x* . T

So

x* = x − (V (ξ ) N − I )−1 ( F ( x, p ) − x) .

(10)

Define V ( X ) N − I as an interval extension of V ( x) N − I . When x, x* ∈ X and ξ ∈ X , we can get (V (ξ ) N − I ) ∈ (V ( X ) N − I ) ,

(11)

so

0 ⎞ ⎛ V1 ( x) 0 ⎜ ⎟ V ( x) = ⎜ 0 0 ⎟, O ⎜ 0 0 Vn ( x) ⎟⎠ ⎝

(12)

x* = x − (V (ξ ) N − I )−1 ( F ( x, p, u ) − x) ∈ x − (V ( X ) − I ) −1 ( F ( x, p, u ) − x) .

(13)

Define interval Newton operator as:

N ( X ) = m( X ) − (V ( X ) N − I )−1 (F (m( X ), p, u) − m( X )) .

(14)

Theorem 1. (a) If x* ∈ X is a solution of (7), it follows that x* ∈ N ( X ) ; (b) If

N ( X ) I X = ∅, there does not exist the solution of (7) in X ; (c) If N ( X ) ⊂ X , there is a solution of (7) in X . Proof: (a) From the definition of interval Newton operator, if x* ∈ X , it follows that x* ∈ m( X ) − (V ( X ) N − I )−1 ( F ( m( X ), p, u ) − m( X )) = N ( X ) ;

(b) By the statement of (a), we know that if x* ∈ X is a solution of (7), it holds that x* ∈ ( N ( X ) I X ) ≠ ∅ , which is a contradiction with the assumption. Therefore, there does not exist the solution of (7) in X ;

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(c) Denote n( x) = x − (V ( x) N − I ) −1 ( F ( x, p, u ) − x) .We know n( x) is continuous in X and X is a bounded convex set. Denote N ( X ) be an interval extension of n( x) . When N ( X ) ⊂ X , it follows that n( x) ∈ N ( X ) ⊂ X , for ∀x ∈ X . According to Brouwer theorem, we know there is a fixed point x ∈ X such that x = n( x) , i.e. F ( x, p, u ) − x = 0 .

3 Iterative Method and Convergence From theorem 1, we get the following algorithm ⎧ N ( X ( k ) ) = m( X ( k ) ) − (V ( X ) − I )−1 ⎪ × ( F (m( X ( k ) ), p, u ) − m( X ( k ) )) . ⎨ ⎪ ( k +1) = X ( k ) I N ( X ( k ) ) (k = 0,1,L) ⎩X

(15)

In practice, because of the difficulty of computing (V ( X ) N − I ) −1 , we consider the following Krawczyk operator: K ( X ) = m( X ) − Y ( F (m( X ), p, u ) − m( X )) +{I − Y (V ( X ) N − I )}( X − m( X )) ,

(16)

in which Y is a nonsingular matrix. We can also get the similar conclusion. Theorem 2. (a) If x* ∈ X is a solution of (7), x* ∈ K ( X ) ; (b) If K ( X ) I X = ∅,

there does not exist the solution of (7) in X ; (c) If K ( X ) ⊂ X , there is a solution of (7) in X . From theorem 2, we get the following iteration sequence: ⎧ K ( X ( k ) ) = m( X ( k ) ) − Y ( F ( m( X ( k ) ), p, u ) − m( X ( k ) )) ⎪ (k ) (k ) (k ) ⎨ + {I − Y (V ( X ) N − I )}( X − m( X )) ⎪ ( k +1) = X ( k ) I K ( X ( k ) ) (k = 0,1,L) ⎩X Theorem 3. If I − Y (V ( X ) N − I )) convergent.

∞

< 1 , the iteration sequence defined by (17) is

Proof:

W ( X ( k +1) ) ≤ W (m( X ( k ) ) − Y ( F (m( X ( k ) ), p, u ) − m( X )) + {I − Y (V ( X ( k ) ) N − I )}( X − m( X ))) ≤ I − Y (V ( X ) N − I )) ∞ W ( X ( k ) ) < W ( X (k ) ) . So (17) is convergent.

. (17)

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4 Numerical Exampels The detailed steps of the algorithm are listed as follows. Step 1. Set X = X (0) , let p be sufficiently large. Step 2. Compute K ( X ) = m( X ) − Y ( F (m( X ), p, u ) − m( X )) + . {I − Y (k ( X ) N − I )}( X − m( X )) Step 3. Let X ( k +1) = T ( X ( k ) , p ) I X ( k ) . Step 4. If X ( k +1) = ∅ , we terminate the algorithm; otherwise proceed with step 2. Step 5. If W ( X ( k +1) ) < ε , we obtain the solution X ( k +1) , and terminate the ∞

algorithm. Otherwise proceed with step 2. The algorithm has been implemented using Turbo C2.0 on a computer. X (0) is the tentative interval, x* and X are the exact and numerical solution, respectively, K is the number of iteration, the precision is ε = 10−5 , and the entropy factor p = 300 . Example 1:

⎛ 4 −1 0 0 ⎞ ⎛ −4 ⎞ ⎜ ⎟ ⎜ ⎟ −1 4 −1 0 ⎟ 3 ⎜ M = , q = ⎜ ⎟ , x* = (1, 0,1, 0)T , ⎜ 0 −1 4 −1 ⎟ ⎜ −4 ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ 0 0 −1 4 ⎠ ⎝ 2⎠ ⎛ [0, 2] ⎞ ⎛ [0.9999989, 0.9999997] ⎞ ⎜ ⎟ ⎜ ⎟ [0, 2] ⎟ [0.0000012, 0.0000022] ⎟ ,X =⎜ , X (0) = ⎜ ⎜ [0, 2] ⎟ ⎜ [0.9999998,1.0000003] ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ [0, 2] ⎠ ⎝ [0.0000023, 0.0000033] ⎠ K = 23 .

5 Conclusions This paper gives an adjustable entropy interval method for linear complementarity problem. This method differs from the conventional methods for solving the linear complementarity problem. In the time, a interval maximum entropy function is established by the interval Newton operator. The numerical examples indicate that the proposed solution algorithm works efficiently. Further studies could be carried out to apply the proposed method to some large scale problems or ill-conditional problems in reality. Acknowledgement. This research was supported by grants Xuzhou Science and Technology Plan Project (Stochastic travel behavior and network design problems for urban transportation networks with uncertainties in demand and supply), Foundation of Cafuc (Q2007-34).

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References [1] He, B.S.: A Modified Projection and Contraction Methods for A Class of Linear Complementarity Problems. Journal of Computational Mathematics 14, 54–63 (1996) [2] Ferris, M.C., Mangasarian, O.L., Pang, J.S.: Complementarity: Applications, Algorithms and Extensions. Kluwer Academic Publishers, Dordrecht (2001) [3] Patriksson, M.: The Traffic Assignment Problem - Models and Methods. VSP, Utrecht (1994) [4] Lo, H.K., Tung, Y.K.: Network with Degradable Links: Capacity Analysis and Design. Transportation Research Part B 37, 345–363 (2003) [5] Billups, S.C., Murty, K.G.: Complementarity problems. Journal of Computational and Applied Mathematics 124, 303–318 (2000) [6] Facchinei, F., Pang, J.S.: Finite-Dimensional Variational Inequalities and Complementarity Problems. Springer, New York (2003) [7] Eaves, B.C.: On the Basic Theorem of Complementarity. Mathematical Programming 1, 68–75 (1978) [8] Moore, R.E.: Methods and Applications of Interval Analysis. SIAM, Philadelphia (1979) [9] Li, X.S.: On the Entropic Regularization Method for Solving Min-Max Problems with Applications. Mathematical Methods of Operations Research 46, 119–130 (1997)

Applied Research of Cooperating Manipulators Assignments Based on Virtual Assembly Technology Shizong Nan and Lianhe Yang School of Computer Science and Software, Tianjin Polytechnic University, Tianjin, China [email protected]

Abstract. With the progress of science and technology, manipulator is widely used in all kinds of industries. Based on the manipulator modeling by CATIA software, virtual assembly technology is introduced in this paper using the virtual assembly modeling technique to research the cooperative manipulators assignments, using the coordinate transformation technique and macro commands to achieve precise control of the product. Simulation experiment proves that the virtual assembly technology can make the enterprises arrange layout reasonably, compress production process and improve the production efficiency. Keywords: virtual assembly, manipulator, CATIA.

1 Introduction With the continuous development of science, fierce market has tended to become more competitive. If an enterprise wants to grasp the opportunities for survival, saving time and improving production efficiency are indispensable. In the modern enterprises, especially in the automobile, computer, chemical industry, metallurgy and other types of enterprise, all kinds of high-precision cooperating manipulators assignments have become the mainstream. Meanwhile, the enterprise is also facing on how to arrange time sequence manipulator. Through the use of the procession plan centered of virtual assembly, we can simulate the manufacturing, assembly factory environment and process, solve the problems occurred in cooperating manipulators work. On this basis, the enterprise can reasonably arrange the manipulator’s position and time, resolve mutual interference between the manipulators, shorten the production cycle and improve operation efficiency.

2 Related Work A. Virtual Assembly Technology Virtual assembly which using computer simulation and virtual reality technology is the essence of the actual assembly process on the computer, according to the Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 476–483, 2011. © Springer-Verlag Berlin Heidelberg 2011

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simulation model to simulate the whole assembly process and realize planning, manufacturing, assembling and commissioning on the computer[1]. Through the establishment of digital assembly model, virtual assembly technology creates a virtual environment in the computer. In this environment, the virtual products can be used to replace the traditional design of physical prototype, simulate and analyze product assembly process conveniently, estimate product performance, detect potential conflict and defect as early as possible and return feedback to designers. B. Design According to the difference of function and the purpose, virtual assembly can be divided into production design centered, procession control centered and virtual simulation centered categories [2]. In this paper, we focus on procession control as a center of virtual assembly and research the cooperation between the intelligent mechanicals in the production process. So-called procession control as a center of virtual assembly consists mainly in the following two aspects: z

z

Realization controls the overall design process of product. In the process of digital product definition, combined with the product development characteristics, virtual assembly technology product design process is divided into three stages: the overall design phase, assembly design phase, the detailed design stage. The overall design process of product can be realized through controlling three design stages and virtual assembly design process. Process control and management. Process model includes the description of production development process, the internal relationship and the cooperation between processes, etc. We hope that we manage engineering development process of product design and related information, so as to realize the optimization of the product development process through the effective management.

In order to study the virtual assembly technology application in cooperating manipulators assignments, we use X welding gun and C welding gun weld body of car synergistically as an example and use CATIA software modeling module and assembly modules, to achieve collaborative process simulation of welding car through the PPR module.

3 Model Design and Assembly A. Virtual Assembly Technology Design Process In this paper, use CATIA software to model product modeling and assembly. CATIA is French Dassault Company for the development of virtual product flagship software. CATIA provides a powerful 3D geometrical graphics design, three-dimensional computer aided manufacturing, processing trajectory simulation and so on, in this paper, we use its parts design and assembly function primarily.

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While a part in the assembly parts, assembly parts will connect this part document. Assembly file cannot exist alone and any change to part documents is reflected to the assembly. Therefore, the assembly process of the product is planned before assembling, which represents need to consider the hierarchical relationships between components, the assembly relation and assembly constraint. Then judge the possibility and unobstructed of assembly, define each object of tolerance allocation, choose good assembly sequence and assembly path. At the same time, in the virtual assembly process, there is needed to judge that parts can be assembled or not and whether assembly is matched by interference technique. The virtual assembly semantics of recognition and virtual assembly geometric constraint identification are also needed to consider. The virtual assembly technology design flow chart is shown in figure 1 [3]. Software interface

Display

Design modification analysis report

Assembly complete Assembly animation Simulation process

Design Design Three dimensional parts

Assembly analysis Assembly Relations Assembly Gain Assembly complete Interference inspection Assembly sequence

Fig. 1. Virtual assembly technology design flow chart

B. Model Design and Assembly Because of taking X and C welding guns for welding car’s body, so guns and body to the model design and assembly at first. By using CATIA software component design functions, firstly, three parts of the car’s body and welding spot are designed and assembled. In the assembly process, we program assembly file through macro script programming in order to realized assembly accurately and realize the effect of batch processing and precise positioning. Each Component is positioned in 3d coordinate system of three-dimensional localization which location of information is determined by Position attribute and can be changed by Move attribute in the assembly. Location information by the array to define [4]:

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A (0) A (1) A (2) A (3) A (4) A (5) A (6) A (7) A (8)

Ux Vx Wx

A (9) A (10) A (11)

Tx

479

Uy Uz Vy Vz Wy Wz Ty

Tz

Meaning: Ux , Uy and Uz, describes each component of the U axis in (O, x, y, z) coordinates system (A unit). Vx , Vy and Vz, describes each component of the V axis in (O, x, y, z) coordinate system (A unit). Wx , Wy and Wz describes each component of the W axis in (O, x, y, z) coordinate system (A unit). Tx ,Ty and Tz is the position of the T point in (O , x, y, z) coordinate system. A (0) -- A (8) compose a 3X3 matrix to describe the rotation of information and A (9) -- A (11) describes the origin position. So, by A (0) -- A (11) consisting of a length of 12 array will describe a relative coordinate system to the absolutely 3d coordinate system (T, U, V, W), as shown in figure 2 and figure 3. Z O

Y

X Fig. 2. Benchmark coordinates system

W T U

V

Fig. 3. After transformation of coordinates system

Through transformation, mass, sequence, regular parts assembly can be realized by programming. The following code will object position setting 45 ° for the X axis, and moved to (10, 0,10). Dim Array (11) ‘x axis components Array (0) = 1, Array (1) = 0, Array (2) = 0 ‘y axis components Array (3) = 0, Array (4) = 0.707, Array (5) = 0.707

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‘z axis components Array (6) = 0, Array (7) = —.707, Array (8) =0.707 ‘origin point coordinates Array (9) =10 Array (10) = 0 Array (11) = 10 MyObejct.Position.Setcomponents Array By using CATIA software component design functions and combining parts coordinate transform technique that the door on the vehicle was assembled. Figure 4 shows the car’s body after assembly.

Fig. 4. Body assembly

X and C welding guns by multiple parts assembly, modeling and assembly process is relatively complex, because of limited space, no longer related. Result shows in figure 5.

Fig. 5. X welding gun (left) and C welding gun (right) assembly

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4 Virtual Simulation of Cooperating Assignments A. The Simulation Method of Cooperating Assignments Front has introduced three types of virtual assembly, this paper mainly used in procession control as a center of virtual assembly. When we simulate cooperating manipulators assignments, we need the digital process of manufacturing (D P M) module of CATIA software, especially process and resource defined workshop (P R D). Support to basic parallel operation of the products, process, resource (PPR), and users can also through a series of internal and external analysis tools to verify the accuracy and operability of the defined process. First open the CATIA software (Assembly Process Simulation). Then X and C welding guns as resources to be loaded to the project, the body as a product to be loaded to the project. Then we can virtual simulate cooperation assignment. By adding Move Process, we can simulate the movement and whirl of guns. Through the recording toolbar to record the track of guns, because of the heavy and dense body spots, so we can properly use the movement and revolution of the component method of section 2.2 to accurate positioning welding guns every move coordinates and record its track. Limited space so that no longer description. When two guns moving and spot welding, we must consider the condition of both welding gun appear crossover and collision, then will need to define PERT chart to constraint. As shown in figure 6.

Fig. 6. PERT chart

Figure 6, we defined the moving process of two guns, and set up a priority one which with the action constraint, namely that when two welding guns might send conflict, figure with plus icon

complete its priority.

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B. Analysis of Experimental Result When track record and constraint definition was completed, basic simulation of cooperation assignment was finished. There is a simple simulation experiment which described in this paper, virtual realized cooperation process of two welding guns to weld car’s body so that proving the virtual assembly technology is able to realize the simulation assignment for collaborative. Under the precondition of without manufacture environment and practical operation personnel, virtual assembly technology simulates assembly process and verifies the process of assembling complete. There will be important practical significance to apply it to shrink product development cycle, reduce production cost, and improve work efficiency. Figure 7 is a frame that finished recording tracks.

Fig. 7. Rendering

5 Conclusion The virtual assembly technique has changed the traditional product development and manufacturing process. Using CATIA software and virtual assembly technology we can achieve successful simulation of cooperating manipulators assignments in producing manufacturing process, which make vender to design production reasonably, improve manufacturing efficiency, make full use of time and space and simplify the process of production. Using reasonable virtual assembly to manage the manufacturing process effectively will play an increasingly important role in the future product design and manufacturing.

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References [1] Sui, A.N., Wu, W.: The virtual assembly and virtual prototype theory and technical analysis. Journal of System Simulation 12(4), 386–388 (2000) [2] Xiao, T.Y.: Virtual manufacturing. Tsinghua University Press, Peking (2004) [3] Zhu, L., Kong, F.R., Yi, C.L.: Virtual assembly technology application in housings. Journal of Agricultural Machinery 37(10), 165–168 (2006) [4] Hu, T., Wu, L.J.: Second development technology of CATIA. Publishing House of Electronics Industry, Peking (2006) [5] Hu, X.M., Zhu, W.H., Yu, T., Xiong, Z.H.: A Script-driven Virtual Assembly Simulation System based on Assembly Sequence Concurrent Planning. In: International Conference on Mechatronics and Automation, pp. 2478–2483 (2009) [6] Yang, B., Huang, K.Z., Wang, H., Chen, H.W.: Concurrent optimization of assembly sequence based on constraint release strategy. Computer Intergraded Manufacturing Systems 10(7), 832–837 (2004) [7] Yin, Z.P., Ding, H., Li, H.X., Xiong, Y.L.: A connector-based hierarchical approach to assembly sequence planning for mechanical assemblies. Computer Aided Design 35(1), 38–56 (2003)

Kusu Cluster Computing Introduction and Deployment of Applications Liang Zhang and Zhenkai Wan School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin, China [email protected]

Abstract. Cluster computing is a distributed parallel computing system. As a small cloud, cluster is mainly used in high availability, reliability or high performance computing. Kusu, which is the first open-source software developed by Platform Computing, is a tool for the deployment of cluster computing. Kusu has the advantages of operating and managing the cluster with easy, deploying cluster nodes rapidly and supporting multiple Linux operating systems. This paper describes the Kusu cluster concepts, benefits, structure and how to deploy nodes in details. Keywords: Kusu Cluster, Cluster Deployment, Linux.

1 Introduction Cluster computing (short for cluster) is the core of the distributed parallel computing system with the advantages of high performance, high scalability, high availability and high rate of costs. The IBM engineer Gene Amdahl defined the standard of cluster for the first time in his published paper on parallel processing in 1976: doing parallel work of any sort in the computer group [1]. With the development of network, the Unix and Linux system, cluster as a small-scale cloud developed unprecedentedly, now plays an irreplaceable role in aviation, finance, automobile, education and other fields. Kusu known as an island of south Singapore-Kusu island is also the first opensource project developed by Platform Computing [2]. Kusu is composed of Python, Shell script and database, and has the characteristics of operating and managing the cluster with easy, deploying cluster nodes rapidly and supporting multiple Linux operating systems, such as Red Hat, CentOS and SLES, may support Windows system in future.

2 Kusu Cluster Profile A. Kusu Cluster Related Concepts Cluster: through software and network, a number of independent computers are combined into a unified distributed system which takes parallel computing as the core. Kusu is just the cluster software that integrates the compute nodes. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 484–490, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Clusters can be divided into high availability cluster, load balancing cluster, high performance computing clusters and grid computing in terms of function and architecture [1]. Node is defined as a single independent computer linked by highspeed networks (such as Gigabit Ethernet, high-performance Myrinet [3], Infiniband [4]). Each cluster node is an independent server running its own process. Processes can communicate with each other. The nodes appear as a single system image from external and cooperate to provide users with applications and data resources. Cluster also has the ability to recover a server-level failure and provides greater computational power than a single computer can provide. The deployment work of cluster system is a very important task, which is the prerequisite before applying the cluster system. Manually deployment of hundreds of nodes with large-scale cluster system is not only prone to error, but also affects and restricts the practical application of the cluster system because of the big task, the slow speed of deployment and other factors. For cluster with a mass of nodes, it is necessary to select an appropriate method to install the operating system automatically and deploy application softwares, while Kusu is the exact cluster tool that automatically deploys node operating system. The well-known Google cloud is a cluster just for internal use at the beginning. After Google’s packaging, it becomes today's cloud platform for outside use [5]. Installer node: used to install the cluster nodes, provides a variety of installation packages and management of configuration files, is the core of the whole system and is responsible for task allocation, load monitoring and balancing, session management, resource management and so on; Repository (short for repo): used to store kits and configuration files, the same installer node can manage multiple repositories; Kit: pre-packaged applications or services that once added to the cluster can be automatically installed and configured for the nodes. Node Group: a template that defines cluster nodes’ attributes containing the repo, packages and network configuration; Components: composed of a number of rpm packages. Several components constitute a kit. B. Kusu Basic Commands Addhost: add, replace or remove nodes from cluster; Boothost: create PXE (Prebooting eXecute Environment) configuration file for booting, query the configuration information of nodes; Netedit: create and edit the cluster networks; Ngedit: create and edit the nodes with the same characteristic; Nghosts: view and manage nodes in node groups including moving nodes from one node group to another; Kitops: query, add and remove kits; Repoman: manage the repo, add or delete kits from repo. For more details of commands, please refer to the command manual [6]. C. Kusu Cluster Architecture Kusu cluster architecture is built based on common cluster architecture. Figure 1 shows the architecture of Kusu cluster:

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Fig. 1. Kusu cluster architecture

Nodes are connected by high-speed transmission network. Through Kusu, each node can install Linux operating system and services. You can also add middleware on top of Kusu or a third-party software to scale the cluster function if needed. D. Kusu Cluster Features Kusu is similar to IBM's xCat toolkit. That using Kusu to deploy cluster nodes has the following features: 1) Simplify cluster operation, deployment and maintenance; 2) Manage the repo and files of cluster with easy; 3) Support mixed cluster: Windows + Linux (future features); 4) Use image file to support diskless cluster; 5) Update compute nodes without re-installing, install patches or packages directly without rebooting; 6) Synchronize critical files to all nodes; 7) Self-monitoring and reporting problems; 8) Scale to thousands of nodes, multiple clusters.

3 Deployment of Cluster Nodes Deployment preparation: Kusu-RHEL2.01 (available to download from Platform Computing website) and RHEL (Red Hat Enterprise Linux) 5.3 DVDs. For simulating the installer node and compute nodes, it needs at least two computers with minimal hardware requirements: 500MB RAM and 40GB hard disk (one DVD drive for installer node).

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Fig. 2. Kusu installer node Anaconda flowchart

A. Installer Node Used to deploy, manage and configure cluster compute node. Two network cards are needed: one for private network card, used to allocate the internal network IP address of the computing nodes; the other for public network card, used to assign the IP address of installer node for connecting external network. It ensures that any cluster node can communicate with the outside through installer node. B. Compute Node A real node for task handling and computing in the entire cluster. It returns the processing results to installer node. Compared with the installer node configuration, compute node needs only one network card and it must be configured to PXE booting mode on the BIOS. C. Node Deployment Installer node is installed by Kusu-RHEL DVD disk. The initial installation process is the same as Red Hat’s. Kusu applies RedHat’s Anaconda [7] program to perform the installation for package based installation nodes. Anaconda uses a kickstart file to direct its operation. The kickstart file is a single file containing the answers to all the questions that would normally be asked during a typical Red Hat Linux installation [8]. Kusu will create its own kickstart file and a fake Anaconda to load the system. Then the installation will jump to the real anaconda program to go on installing automatically. Kusu installer node’s flowchart is shown in Figure 2. Figure 3 displays the installation process screen including language, keyboard, time zone, network, disk partition, kits settings. The key parts are network and kits settings. Kusu takes eth0 as private network and eth1 as public network by default. You should configure IP address, subnet mask and default router both of eth0’s and

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Fig. 3. Installer node installation process screen view

eth1’s. Kits setting provides information of kits supported by Kusu. You can add kits by CDs or delete kits directly as you like. Once basic kits are added, the last and most crucial step is to add the OS kit. As RHEL5.3 is too large, it may take some time to add this kit completely. After this, Kusu begins to copy kits, create repo, build its own kickstart file based on the values specified on installation screen, and then finish the installation automatically. The whole installation process may take about an hour. After deploying the installer node successfully, you can deploy cluster compute nodes according to the installer node. Compute nodes should always boot up via PXE mode. PXE is a network boot technology that is developed by Intel and works at C\S mode. It supports multiple network protocols, such as DHCP, TFTP. After starting up, compute node sends DHCP request to DHCP server. The DHCP server will respond to the request with IP address of compute node and the mirror server. Compute node downloads bootloader and run it via TFTP protocol, then the bootloader downloads the required Linux boot kernel and initrd file from the mirror server and starts the installation [9]. The flowchart is shown in Figure 4. The basic command of deploying compute node is ‘addhost’. After selecting node group, deployment network card and monitoring the MAC and IP addresses of compute node on installer node, compute node starts downloading Linux kernel and initrd files through PXE and installing automatically. Kusu cluster deployment is successful after deploying the installer node and compute nodes, but this is only a cluster framework without practical effect. For solving specific computational tasks, it requires specific kits. Kusu supports the feature of dynamically adding and installing related kits. All you have to do is running ‘kitops’ and ‘repoman’ [6] commands to add kit to installer node and make it relate with the repo, then synchronize to all compute nodes through ‘cfmsync’ [6] command. With the new kit installed at each node, users can achieve complex computing tasks by the kit’s function. For example Nagios(the powerful open source host, service and network monitoring equipment) [10], Abaqus (a powerful finite element software for engineering simulation) [11], Matlab [12]. The most important issue for cluster is to solve parallel computing load balancing and task scheduling. You can design a kit about this kind according to your interest.

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Fig. 4. PXE network boot process

4 Kusu Cluster Application We can apply Kusu cluster to reality based on its feature. Figure 5 shows the idea of Kusu cluster architecture for the spinning factory. The installer node is loacated in root spinning plant and compute nodes (upper computers) are located in branch plants. The entire flow is: Installed with task scheduling, load balancing, and service equipment monitoring related kits, all nodes are deployed through Kusu. Via lower computers and sensors, the datas of spinning machines (drawing frame, roving frame, spinning frame) are transmitted to upper computers, then to the installer node. From the datas’ analysis, administrator can get the status and performance of spinning machines at each branch, which makes monitor and schedule tasks easier and accomplish the spinning tasks more efficiently.

Fig. 5. Kusu cluster used in spinning factory

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5 Summary The above contents are simple introduction about Kusu cluster and its deployment of applications. Kusu is a cluster deployment tool, and you can just use it to deploy cluster nodes without other functions. To realize Kusu cluster of fulfilling complex computing tasks, you need related existing kits. Since the available softwares for cluster are too few, it needs to develop them specifically and professionally, which makes it is difficult to expand the application of cluster computing. The most usable field for cluster is in large-scale scientific computing. Acknowledgment. The authors would like to thank Platform Computing company in Kusu open source project which aims to deploy and manage cluster nodes with easy.

References [1] Computer cluster, http://en.wikipedia.org/wiki/Cluster_(computing) [2] Kusu, http://www.platform.com/cluster-computing/clustermanagement/project-kusu [3] Gui, H., Chen, J.-E., Chen, S.: Parallel-job Scheduling on Cluster Computing Systems. Chinese Journal of Computers 27(6), 765–771 (2004) [4] Lee, M., Kim, E.J., Yum, K.H., Yousif, M.: An Overview of Security Issues in Cluster Interconnects. In: Sixth IEEE International Symposium on Cluster Computing and the Grid Workshops, CCGRIDW 2006 (2006) [5] Barroso, L.A., Dean, J., Holzle, U.: Web search for a planet: The Google cluster architecture. IEEE Micro 23(2), 22–28 (2003) [6] Kusu Commands, http://www.hpccommunity.org/exdata/Kusu/doc/Kusu-doc-1.1 [7] How to use Kickstart (Anaconda’s remote control), http://www.redhat.com/magazine/024oct06/features/kickstart/? intcmp=bcm_edmsept_007 [8] Red Hat Linux 7.3: The Official Red Hat Linux Customization Guide, http://www.redhat.com/docs/manuals/linux/RHL-7.3Manual/custom-guide/ch-kick [9] Gu, M., Xu, W.: A Deployment System for Clusters Based On Linux. Computer Applications and Software 25(1), 102–104 (2008) [10] Nagios, http://www.nagios.org [11] Abaqus, http://www.abaqus.com [12] Matlab, http://www.mathworks.com

Protein Function Prediction Using Kernal Logistic Regresssion with ROC Curves Jingwei Liu1 and Minping Qian2 1

School of Mathematics and System Sciences, Beihang University, LMIB of the Ministry of Education, Beijing, 100191, P.R.China 2 LMAM, School of Mathematical Sciences & Center for Theoretical Biology, Peking University, Beijing, 100871, P.R. China [email protected]

Abstract. To avoid the “over-fitting” problem in protein function prediction based on protein-protein interactions (PPI), we propose a pattern recognition strategy that all the features of PPI observation data are divided into three sets, training set, learning set and testing set. The employed classifiers are trained on training sets, the receiver operating characteristic (ROC) curve and optimal operating point (OOP) is calculated on learning set, and the accuracy rate is reported on the testing set with OOP. Under this framework, we compare the performances of logistic regression (LR) model with kernel logistic regression (KLR) model on two different feature selection sets, 1-order feature and 2-order feature according to PPI data. The experiment results on a standard PPI data show that KLR model performs better than LR model on training sets of both 1order feature set and 2-order feature set, and the 2-order feature outperforms 1order feature set with KLR model on training set . The predictive rates on testing set of both 1-order feature and 2-order feature with LR and KLR can achieve 95%. Keywords: protein-protein interaction, logistic regression, kernel logistic regression, receiver operating characteristic, optimal operating point.

1 Introduction Protein function prediction based on protein-protein interactions is essential for understanding the biological process and molecular machinery of living organisms. It can help to decipher the molecular pathways by identifying the proteins function according to their interaction partners. Furthermore It is important for human beings to explain the diseases machinery based on genomic information and provide the treatment strategy for gene therapy. In recent decade, PPI function prediction attracts more research attentions of both biologics and biostatistics [1-15]. For a given function, the PPI based protein prediction is a typical binary classification problem, many statistical models are employed in the classification task,

，

Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 491–502, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Markov random field (MRF) [3,13], support vector machines (SVM) [14], hidden vector state model (HVS) [15], logistic regression (LR) model and diffusion kernel logistic regression (DKLR) [6], etc. In [3], Deng et al transformed the MRF to the form of logistic regression, and [6] extended the logistic regression to diffusion kernel logistic regression. And, ROC [16,17] and average percentage all [3,6] are taken as evaluation indexes at the same time. As for the PPI feature extraction, many feature extraction methods are investigated, [15] gave a rough division of three categories: manual pattern engineering approaches, grammar engineering approaches and machine learning approaches. One important method is proposed by Schwikowski et al. in [1] that a function was assigned to a protein based on the majority of functional labels of its interacting partners. [3] [6] extended this idea and embedded the numbers of one protein having or not having the function as the coefficients of logistic model or diffusion kernel logistic model (see Section 2), if we take the two numbers as components of a vector that will be wedged into logistic model, we call this feature extraction method as 1order feature extraction. In this paper, we will expand the 1-order feature extraction and take into account the protein numbers that indirectly connect to the considered protein as a generalization method of feature extraction method, called 2-order feature extraction. In PPI function prediction, after the feature extraction, the classification procedure is a typical machine learning problem in despite of which classifier is involved in, one inevitable problem is “over-fitting” problem[18] that a well –trained model on training set has poor performance in testing set. To avoid the well-known “overfitting” problem, we propose a machine learning strategy that all of the feature data are divided into three sets: training set, learning set and testing set. The classifier is trained on training set, and the ROC curves and OOP are calculated on learning set and the final accuracy rate is evaluated on test data with the OOP. Under the framework, we will evaluate the 1-order feature extraction and 2-order feature extraction with LR and radical basis function kernel logistic regression (RBF-KLR). The rest of the paper is organized as follows. In section 2, the logistic regression is reviewed and RBF kernel logistic regression with gradient descent updating algorithm is developed, and 2-order neighbor feature is introduced. In section 3 the ROC curve and accuracy rate are discussed. In section 4, the strategy of “over--fitting” is outlined. The performance of LR and KLR on 1-order feature sets and 2-order feature set are shown in Section 5, and the conclusion and discussion are given in Section 6.

2 Review of Logistic Regrssion and Kernel Logistic Regrssion A. Logistic Regrssion Suppose N iid samples

{( xi , yi )}iN=1 , yi ∈ {0,1} , satisfy unknown distribution

p ( x, y ) . Define distinguish function g ( x ) = ln

P ( y = 1 | x) 1 − P ( y = 1 | x)

(1)

Protein Function Prediction Using Kernal Logistic Regresssion with ROC Curves

and the linear function g w ( x ) =

w0T x + b is used to estimate g ( x) . This method is

called logistic regression. Denote w = [w0 b] , x T

The posterior probability of “ y

T

*

=[xT 1], we obtain gw (x) = wT x* .

= 1 | x ” is denoted as

π w (x) = p( y = 1 | x) = where

493

1 1 + ex p ( − w T x )

(2)

w is estimated by maximizing the maximum likelihood function N

l ( w) = ∑ [ yi ln π w ( xi ) + (1 − yi ) ln(1 − π w ( xi ))] . =

i =1 N

∑ [ y (w i =1

T

i

x i ) − ln (1 + ex p ( w T x i ))]

(3)

In [3], Deng, et al proposed a logistic model for one function protein- protein interaction as,

log

Pr( X i = 1| X[−i ] ,θ ) 1 − Pr( X i = 1| X [−i ] ,θ )

= γ + δ M 0 (i) +η M1 (i)

(4)

where

X[−i ] = ( X1,L, Xi−1, Xi+1,LX N ) , X i = 1 If protein i has the function, X i = 0 If protein i doesn’t have the function, M 0 (i) = M 1 (i ) =

∑

K (i, j ) I{x j = 0}

∑

K (i, j ) I {x j = 1}

j ≠i , x j known

j ≠ i , x j known

,

K(i, j) =1 If protein i interacts with protein j , K(i, j) =0 If protein i doesn’t interact with protein j We adopt the ridge regression [16] model as N

H ( w) = −∑[ yi ( wT xi ) − ln(1 + exp( wT xi ))] + i =1

And

λ 2

|| w ||2

(5)

w is updated by minimizing H ( w) , w new = ( X T WX + λ I ) −1 X T W ( Xw old + W −1 ( y − p ))

(6)

X is the data matrix, W is the diagonal matrix with entries p ( w , xi )(1 − p ( wold , xi )) .

where

old

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B. Kernel Logistic Regrssion

Kernel logistic regression uses kernel trick to generalize logistic regression to high dimension feature space. Suppose k ( x, y ) is any kernel function satisfying Mercer condition, it defines a non–linear mapping from sample space to reproducing kernel Hilbert space (RKHS) Φ : x → Φ ( x ) . We define the discriminative function in the RKHS space as g w ( x ) = w Φ ( x) T

.

Theoretically, we can get N

w = ∑ α i Φ ( xi ) i =1

. N

N

gα ( x) = gw ( x) = (∑αi Φ( xi ))Φ( x) = ∑αi k ( xi , x) i =1

Hence,

Denote the posterior probability,

π α ( x) =

i =1

1 N

1 + exp( − ∑ α i k ( xi , x )) i =1

The objective function of the ridge kernel logistic regression is defined as, N

{

}

H (α ) = −∑ yi gα ( xi ) − ln (1 + exp { gα ( xi )} ) + i =1 N

{

}

λ 2

gα

2

λ

= − ∑ yi Kiα − ln (1 + exp { Kiα} ) + α T Kα 2 i =1 where

K = ⎡⎣ k ( xi , x j ) ⎤⎦ is kernel matrix, K i is the i -th line of kernel matrix N×N

K . To reduce the complexity and accelerate the computation speed, we propose a sub-optimization strategy with gradient descent method based updating algorithm as follows, ∂ H (α ) α new = α old − δ ∂α where δ > 0 is the step factor. The RBF kernel function is adopted in this paper, where

k ( xi , x j ) = e

− γ | xi − x j |2

,

γ > 0.

C. n-Order Neigborhood Feature

In formula (4),

M 1 (i ) and M 0 (i) denote the numbers of proteins linked directly to

protein i having or not having the labeled function, we call it as 1-order neighbor feature. Obviously, the more detail description of the network of protein-protein interactions can be extended to the indirectly linked proteins having or not having the functions, we call it as 2-order neighbor feature.

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Denote the proteins that directly linked to protein i as

Ιi = {x j | x j interacts with xi , j ≠ i} , we denote

M 2 (i) =

M 3 (i ) =

∑

∑

x j ∈Ii j ≠i , k ≠i , xk known

∑

K ( j, k ) I{xk = 0} ,

∑

x j ∈Ii j ≠ i , k ≠ i , xk known

K ( j , k ) I {xk = 1}

and call [1 M0 (i) M1 (i)] as 1-order feature, and call [1 M0 (i) M1 (i) M2 (i) M3 (i)] as 2-order feature. If the different feature sets are input to LR and KLR, we will obtain different statistical model. In addition, we illustrate an example to show the merit of 2-order feature.

Fig. 1. The neighbor information of 6 protein-protein interaction data. Red protein denotes having the function while green protein denotes not having the function. Table 1. Feature extraction of Neighbor information

Protein 1 2 3 4 5 6

M0 (i)

M1 (i)

M 2 (i)

M3(i)

2 2 2 0 2 2

1 1 1 2 1 0

3 4 3 2 4 4

2 1 2 2 1 2

Function 0 1 0 0 1 0

From Table 1, we can see that protein 1, 2, 3, 5 are not distinguished by 1-order feature [ M0 (i) M1 (i)] , however, under 2-order feature [ M0 (i) M1(i) M2 (i) M3 (i)] , they are distinctly discriminated. And, protein 4, 6 are still different under 2-order feature [ M0 (i) M1(i) M2 (i) M3 (i)] . Therefore, we believe that 2-order feature extraction method can improve the PPI prediction rate.

3 ROC Curves and Accuracy Rate A. Sensitivity and False-Positive

An important statistical criterion is based on the true positive, true negative, false positive, and false negative, which are list in the following table[3,6].

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Real positive Real negative

Predicted positive True positive, TP False positive, FP

Predicted negative False Negative, FN True negative, TN

The standard performance measures for the classification problem based on the four values are sensitivity (SN) and false-positive rate (FPR) defined as follows:

SN =

TP , TP + FN

FPR =

FP TN + FP

where 1- FPR is called as specificity. Conventionally, ROC curve is applied to analyze the data from the tests [17,18], and ROC curve takes FPR and SN as x-axis and y-axis respectively in graph. B. OOP Selection

OOP is an important index to affect the accuracy rate, several methods are discussed in [18,19]. We discuss three OOP determination methods: The first one is discussed in [19] that the OOP is the cutoff that maximizes the sensitivity with the minimum threshold for specificity simultaneously, we call it the “Ratio OOP”, it is the left-top point at ROC curve. The second OOP is determined by the intersection of sensitivity and specificity. We call it “Inter OOP” Since accuracy rate is an important index in pattern recognition, we propose an accuracy rate OOP which maximizes the accuracy rate in pattern recognition, we call it “Rate OOP”. C. Accuracy Rate

Given any cutoff oop ( 0 ≤ oop ≤1)of ROC, we can calculate the accuracy rate for testing set as follows: If P( X i = 1) ≥ oop , protein i is predicted to have the function; If P( X i = 1) < oop , protein i is predicted as not having the function. This accuracy rate is in some sense the absolute prediction rate, since the testing set is not involved in training and modeling. If oop = 0.5 , it is the traditional cutoff. Generally, given any cutoff oop , we can calculate all the accuracy rates on training set, learning set and testing set.

4 Strategy for “Over-Fitting” “Over-fitting” is an important concept in machine learning [20]. A learning algorithm is usually trained on training examples with the output is known. We assume the

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learner can be able to predict the correct output for other examples. However, especially in cases that learning time is performed too long or training examples are rare, the learner may only adjust to features of the training data, and have no relationship to the target function. The performance on unseen data will perform worse. Generally speaking, over-fitting means the learning algorithm is more accurate in fitting known data but less accurate in predicting new data. To avoid this phenomenon, we propose a strategy that is widely used in machine learning that all the feature set is randomly divided into three sets, training set, learning set and testing set. The classifier is trained on training data, the ROC curve is determined by learning set and the OOP is also be calculated, finally the accuracy rate is reported on testing set. Under this framework, we believe that we can reduce the “over-fitting” as possible as we can.

5 Performance of Evaluation A. Datasets and Feature Extraction

To predict the PPI function, the yeast cellar data from Yeast Proteome database (YPD, http://www.incyte.com/) and the PPI data from the Munich Information Center for Protein Sequences (MIPS, http://mips.gfs.de/) are involved in the experiments. Both ``YPD function category--cellular role'' and ``MIPS Physical interactions'' data files are downloaded from http://www.cmb.usc.edu/msms/ Function Prediction/. There are 43 known cellular functions ( including ``other '') in YPD, and 2559 MIPS interaction pairs with names in YPD. We first delete the self--interaction data and the symmetric--interaction data in MIPS data, which means that the “A-A” type data will not appear in the feature extraction procedure and the “A-B”, “B-A” type data will be calculate once. Then the proteins on the final MIPS data can fit a simple undirected graph--no loop and no multiple edges. Finally, there are 43 function categories and each function has 1282 different proteins with binary class property in our sample space. Then, each protein's 1-order and 2-order neighbor features are calculated according to the MIPS data , and its function is labeled according to the YPD function tabular. • •

3-dimension feature [1 M0 (i) M1(i) ]

4- dimension feature [1 M0 (i) M1 (i) M3 (i)]

5-dimension feature [1 M 0 (i ) M 1 (i ) M 2 (i ) M 3 (i ) ] . All of the features of 1-order neighbor information and 2--order neighbor information are randomly divided into 3 sets, training set, learning set and testing set, which take percents of (60%,20%,20%) respectively. •

B. Experiment Results

Since, there are more parameters to be determined in the LR and KLR model. We first calculate the optimal ridge λ∈{10−5,10−4,10−3,10−2,10−1,1,10} for LR by training the classifier on training set and determining the accuracy rates on learning set. The experimental

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results with OOP selection methods on learning sets of three kinds of feature sets with LR are shown in Fig1., Fig2 and Fig3. The accuracy rates based on the third OOP selection method with LR are shown in Fig.4-Fig.6. From Fig.1 to Fig.6, we can conclude that, the optimal ridge can be set λ = 1 0 − 5 . And we set optimal OOP = 0.5 according to Fig4, Fig5 and Fig6.

Fig. 1. Accuracy rate with Ratio and Inter OOP selection on 3-dim feature set of LR

Fig. 2. Accuracy rate with Ratio and Inter OOP selection on 4-dim feature set of LR

Fig. 3. Accuracy rate with Ratio and Inter OOP selection on 4-dim feature set of LR

Fig. 4. Accuracy rate with ridge and OOP on 3-dim feature set of LR

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Fig. 5. Accuracy rate with ridge and OOP on 4-dim feature set of LR.

Fig. 6. Accuracy rate with ridge and OOP on 5-dim feature set of LR

To avoid burden of computation, we set λ =10−5 for KLR and search the optimal γ ∈{10−3 ,10−2 ,10−1,1,10,102 ,103} and OOP by training the KLR classifier on training set and determining the accuracy rates on learning set. The results are shown in Fig.7-Fig.9 , where the “Ratio OOP” is 0.5.

Fig. 7. Accuracy rate with OOP on 3-dim feature set of KLR

Fig. 8. Accuracy rate with OOP on 4-dim feature set of KLR

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Fig. 9. Accuracy rate with OOP on 5-dim feature set of KLR

From Fig7 to Fig 9, we can conclude that “Rate OOP” selection method is better than “Ratio OOP” and “Inter_ OOP” methods, and the performance of “Inter_OOP” determining method is the worst among these three optimal OOP methods. Hence, we set γ =1 and OOP = 0.5 . We finally calculate the accuracy rate on testing set with LR and KLR on three kinds of feature sets. The experimental results are shown in Table 3. Table 3. Protein prediction rate on training set, learning set and testing set with LR and KLR respectively.(%)

Feature 3-dim 4-dim 5- dim

LR Train 96.46 96.56 96.60

Learn 96.20 96.21 96.17

Test 96.15 96.19 96.14

KLR Train 96.76 97.23 97.78

Learn 96.06 95.94 95.80

Test 95.94 95.27 95.20

From Table 3, we can conclude that KLR outperforms LR on training set of three feature sets, and with the same classifier, 4-dim and 5-dim features outperform 3-dim features , which demonstrates that 2-order graphic neighbor features can describe the difference between the function of proteins, however its discrimination slightly abates in absolute sense of prediction. How to find the feature of PPI data that has high description capability on both training set and testing set of PPI data is a profound task in biostatistics.

6 Conclusion To predict the protein function from PPI data, a 2-order graphic neighbor information extraction method is proposed for PPI prediction, an example is given to illustrate its merit. Theoretically, we can extend to the n− neighbor information case ( n>2), however, it will be more complicated, we will discuss this problem in the future work. To demonstrate the effectiveness of 1-order feature and 2-order feature in one function PPI prediction, LR, RBF-KLR are involved in real PPI protein function prediction and the OOP selection of ROC is also discussed to avoid “over-fitting” problem. The experimental results show that RBF-KLR can achieve high accuracy rate on training set of 2-order graphic neighbor feature. And we obtain 95% predictive

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rate on both training set and testing set. The future work will focus on applying the graphic features and KLR model to unknown protein function prediction with microRNA-regulated PPI data. Acknowledgment. This project was supported by 863 Project of China (2008AA02Z306).

References [1] Schwikowski, B., Uetz, P., Fields, S.: A network of protein-protein interactions in yeast. Nature America 18, 1257–1261 (2000) [2] Letovsky, S., Kasif, S.: Predicting protein function from protein/ protein interaction data: a probabilistic approach. Bioinformatics 19, i197–i204 (2003) [3] Deng, M.H., Zhang, K., Mehta, S., Chen, T., Sun, F.Z.: Prediction of protein function using protein-protein interaction data. Journal of Computational Biology 10, 947–960 (2003) [4] Mewes, H.W., Amid, C., Arnold, R., Frishman, D., Guldener, U., Mannhaupt, G., Munsterkotter, M., Pagel, P., Strack, N., Stumpen, V., Warfsmann, J., Ruepp, A.: MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Research 32, D41–D44 (2004) [5] Scott, J., Ideker, T., Karp, R.M., Sharan, R.: Efficient algorithms for detecting signaling pathways in protein interaction networks. In: Miyano, S., Mesirov, J., Kasif, S., Istrail, S., Pevzner, P.A., Waterman, M. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3500, pp. 1–13. Springer, Heidelberg (2005) [6] Lee, H., Tu, Z., Deng, M.H., Sun, F.Z., Chen, Y.: Diffusion kernel-based logistic models for protein function prediction. OMICS: A Journal of Integrative Biology 10(1), 40–55 (2006) [7] Singh, R., Xu, J.B., Berger, B.: Struct2Net: Integrating Structure into Protein-Protein Interaction Prediction. In: Pacific Symposium on Biocomputing, vol. 11, pp. 403–414 (2006) [8] Reyes, J.A., Gilbert, D.: Prediction of protein-protein interactions using one-class classification methods and integrating diverse bio-logical data. Journal of Integrative Bioinformatics 4(3), 1–17 (2007) [9] Huang, C.B., Morcos, F., Kanaan, S.P., Wuchty, S., Chen, D.Z., Izaguirre, J.A.: Predicting Protein-Protein Interactions from Protein Domains Using a Set Cover Approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4(1), 78–87 (2007) [10] Sharan, R., Ulitsky, I., Shamir, R.: Network-based prediction of protein function. Molecular Systems Biology 3, 1–13 (2007) [11] Hu, C.W., Juan, H.F., Huang, H.C.: Characterization of microRNA-regulated proteinprotein interaction network. Proteomics 8(10), 1975–1979 (2008) [12] Bagchi, A., Powell, C., Mooney, S.D., Mort, M., Cooper, D.N., Youn, E., Xin, F.X., Radivojac, P.: Machine Learning Approaches On Prediction Of Protein-Protein Interactions And Feature Selection With An Eye To Mutational Analyses. In: 9th Annual International Conference on Computational Systems Bioinformatics, Stanford, California, August 16-18 (2010) [13] Kourmpetis, Y.A.I., van Dijk, A.D.J., Bink, M.C.A.M., van Ham, R.C.H.J., ter Braak, C.J.F.: Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data. PLoS ONE 5(2), e9293:1-17 (2010)

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[14] Yang, Z.H., Lina, H.F., Lia, Y.P.: BioPPISVMExtractor: A protein–protein interaction extractor for biomedical literature using SVM and rich feature sets 43(1), 88–96 (2010) [15] Zhou, D.Y., He, Y.L.: Extracting Protein-Protein Interaction based on Discriminative Training of the Hidden Vector State Model. In: BioNLP 2008: Current Trends in Biomedical Natural Language Processing, Columbus, Ohio, USA, pp. 98–99 (June 2008) [16] Katz, M., SchaffÖner, M., Andelic, E., Anderlic, E., Krüger, S., Wendemuth, A.: Sparse kernel Logistic regression for phoneme classification. In: Proc. of 10th Int. Conf. on Speech and Computer. Patras, vol. 2, pp. 523–526 (2005) [17] Metz, C.: Basic principles of ROC-analysis. Sem. Nucl. Med. 8(1), 283–298 (1978) [18] Hanley, J., McNeil, B.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982) [19] Gallop, R.J.: Determination and Interpretation of the OOP for ROC’s with PROC LOGISTIC. In: Proceeding of NESUG 2001, Baltimore, MD, pp. 777–782 (2001) [20] Tetko, I.V., Livingstone, D.J., Luik, A.I.: Neural network studies. 1. Comparison of Overfitting and Overtraining. J. Chem. Inf. Comput. Sci. 35, 826–833 (1995)

A Novel Algorithm of Detecting Martial Arts Shots Zhai Guangyu and Cao Jianwen Department of Software Engineering, Lanzhou Polytechnical College, Lanzhou Gansu [email protected]

Abstract. Method template matching and histogram based on gray images are based two methods about video boundary detection. But there is phenomenon of undetected about the two methods. By analyzing the advantages and disadvantages of the two algorithms, it can be combination of the two algorithms, discussing the boundary detection algorithm for martial arts video. Experimental results proved using the method can be better detected the edge of martial arts, so the video can be segmented. Keywords: histogram, template matching, Video edge detection.

1 Introduction Shot is the time continuous and content of similar image sequences which shooting using DV or DC. These elements can be the basis of structural in the further. For the vast martial arts video, processed frame by frame in terms of time and space are not allowed. So the shot is basic unit with video retrieval, the video will be divided into independent lens combination. Key frame will be selected in each shot. these are the shot’s description. There are a number of frames in a shot. But the key frame is a image which represents the main content. Extracting movement feature, removing background and video retrieval, there are done before key frame can be extracted. Therefore shot detection in video retrieval is a crucial step. About shot boundary detection has been studied by lot of people. for, It has been proposed that the video edge detection method about the football video in literature [1]. In this method, using the shape and color of football pitch that different in general video detected the video edge. In this paper, learning from this approach, martial arts video has been separated that based on the characteristics.

2 Shots Conversion Method and Detection Algorithm Shots’ conversion included shear and gradual change, Shear which is also known as mutation refers to a scene before the last frame which different means about a scene after the first frame. Figure 1, effecting of shear shots. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 503–507, 2011. © Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. Shear change

But gradual change also known as sliding variable which is a procedure that converted between the shot and the next shot. Before and after two shots did not change significantly between the first and last frame, this changing is very low. The analysis of gradual change is very complexity. This effect included the fade in, fade out, turning, fly in and slide in and so on[2].

Fig. 2. Gradual change

Some one used the method of physics and statistics detecting the converted of shots. The method of optical flow in physics[3] used in all kinds of video of analysis. But in this paper it has been put forward that is the method of shortage. Next some commonly detecting methods will be described. A. Method of Histogram Based on Gray Image G =1

D ( I1 , I 2 ) =

∑ min( Z [ I ( x, y, t ), i], Z [ I ( x, y, t + 1), i ]) i=0

G =1

(1)

∑ Z [ I ( x, y, t + 1), i] i =0

This formula defines the difference between the two image frames. The function Z() expressed the image frame’s pixel histogram. G expressed gray series of pixels. It is a condition algorithm: Firstly the Image converted to grayscale image, and when background and objectives of the two images are the same, pixel histogram is not very different performance. Histogram method is the number of image pixels that have been distributed in each gray level. So interference in general does not have much impact, Histogram can also be the largest and most widely used. Its weeds, there is a important general knowledge that is although not the same as the contents of two images, but indeed similar to the gray distribution. This method has a phenomenon of undetected [5].

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B. The Method Based on Template Matching ⎧ P(I1, i, j) − P(I2 , i, j) gray ⎪ D(I1, I2 , i, j) = ⎨ 3 ⎪∑ P(I1, Cn , i, j) − P(I2 , Cn , i, j) color ⎩ n=1

(2)

The algorithm included two kinds calculation which are color image and gray image. It used the difference which is corresponding of pixels in two frames to determine the detection. D of the formula is the similarity of two frames. P(I,I,j) is the gray value of pixel which coordinate is (i, j). The same P(I,C,I,j) is the value of RGB about corresponding pixel point. Z

W

T ( I1 , I 2 ) = ∑ ∑ D ( I 1 , I 2 , i , j )

(3)

i =1 j =1

Formula (3) is the sum of pixels which are frames similarity of each point. If T > t (t is a threshold ), gradual change is coming which are determined by the algorithm. But there is undetected phenomenon in the algorithm. When there is the rapidly moving target, it influenced the difference between images to a large extent.

3 The Detection Algorithm Suitable for Martial Arts Video According to the characteristic of martial arts video, the difference can be discovered between martial arts video and other video. So a particular method can be used in the shot boundary detection. In the study, martial arts video that have been shoot have some modes. Based on the above characteristics, these can be discovery that shot boundary detection is more complex. In the video, not only the shear but also contains a variety of ways gradual change. So if we used the algorithm that put forward above, this can not completely solve the problem of martial arts video segments. In this paper, it can be propose that suited for martial arts video boundary detection algorithm. The algorithm is the combination of two algorithms. It can be call the method of histogram and domain matching. The value can be calculated of component value of frame, so it can be calculated in this domain using histogram matching method. If the value is greater than the upper limit, shear is considered to have taken place. If it has been in the definition domain, gradual change can be considered to have taken place. If it is less than this value, gradual change is over. Description of the algorithm:

，

Step1 Traverse all the frames in the video, calculated D(Ii) the definition domain is [T1 ,T2]. Step2 The difference between two adjacent frames. if D( Ii)- D( Ii-1)>T2 then Ii is the end of the frame at shot in shear else this is the end of shot. end if

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Step3 Do comparison of the follow frame with this frame for j=1 { if D( Ii+j)- if D( Ii)> T1 then gradual change is occur at frame Ii+j else change is over End if Step4 Determine where is shear and where is gradual change shots. If D( Ii+j)- D( Ii)< T1 Then image sequence of gradual change form frame I to i+j End program Else other frame or shot shear End if Next

｝

In this algorithm, function D and T of the threshold which all can be used in equation (1) and (2). The important problem is selected the threshold of the definition domain. In general, the value can be selected: firstly, getting the total of all frames in the video called N, next selected the 0.5 % of total frames at the head of video. At seem time, getting 0.5 % of total frames at the end of video. In this frames, the difference can be obtained between two adjacent frames. The value of less is lower limited value, the higher is upper limited value.

4 Experimental Results In this paper, the experimental video is selected which has been performed in Hong Kong martial arts match 2005 by player coming from GanSu. The time of video has 3 minutes 24 second, it has 11 shots. In this video included shear and gradual change shots. Without loss of generality in experiment, threshold T which has been calculated has been made the deal. The coefficient in the [0,1] has been used to multiply T. Finally, we compared with the numbers of the shots which have been deal, obtained the results. Table 1. The results

LV T1

UL T2

Using algorithm the segments of video

the segments of video

T1

T2

7

11

0.5*T1

0.8*T2

12

11

0.8*T1

0.9*T2

11

11

0.9*T1

0.5*T2

6

11

0.6*T1

0.8*T2

10

11

The figure 3 is the result which is frame of the segments of video using algorithm.

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Fig. 3. Using algorithm the segments of video

References [1] Zhang, F.: Research on semantic events of soccer video. Nanjing University of Science and Technology (2006) [2] Bezerra, F.N.: A Longest Common Subsequence Approach to Detect Cut and Wipe Video Transitions. In: Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium on (SIBGRAPI 2004) (October 2004) [3] Barron, J., Fleet, D., Beauehemin, S.: Performance of optical flow techniques. International Journal of Computer Vision 12(l), 42–77 (1994) [4] Cao, J.-r., Cai, A.-n.: Semantic Video Summarization Based on Support Vector Machine. Journal of Beijing University of Posts and Telecommunications 29(4), 94–98 (2006) [5] Jie, G.: Research on Some Techniques of sports video retrieval. Nanjing University of Science and Technology (2007)

Method of Bayesian Network Parameter Learning Base on Improved Artificial Fish Swarm Algorithm Yan Wang1 and Liguo Zhang2 1 Dept. of Computer, North China Electric Power University, Baoding, China 2 College of Information Science & Technology, Agricultural University of Hebei, Baoding, China

Abstract. Bayesian network is an effective model to solve uncertainty problem. Parameter learning is an important step for building a Bayesian network, and its performance directly affects the network’s accuracy. In this paper, artificial fish swarm algorithm is introduced into the parameter learning of Bayesian network composed of Noisy-Or and Noisy-And nodes, and the global search capability is also improved by genetic algorithm. The experimental results show that the improved artificial fish swarm algorithm can learn the parameter better, with the characteristic of rapid optimization speed, good global convergence and insensitivity to initial value. Keywords: Bayesian network, parameter learning, Artificial fish swarm algorithm, genetic algorithm, Noisy-And, Noisy-Or.

1 Introduction Bayesian network is an effective method for uncertain knowledge representation and reasoning. With solid theoretical foundation, natural knowledge structure representation and flexible reasoning ability, it becomes one of the research focuses in artificial intelligence in recent years. In many application fields, Bayesian network’s structure can be obtained through expert knowledge, while the parameter must be obtained by learning. The performance of parameter learning directly affects the prospects of Bayesian Network’s application. Artificial fish swarm algorithm (AFSA) is an optimization algorithm by simulating fish behavior. The process of learning Bayesian network’s parameter is essentially an optimization process of parameter, so AFSA can be applied to learn Bayesian network’s parameter. Meanwhile, in order to improve the stability and the ability to obtain global optimal solution of AFSA, we adopt genetic algorithm’s variation factor to modify the parameter of artificial fish, which can make artificial fish jump out of local optimal solution. Experimental results show that genetic algorithm based AFSA can better complete the Bayesian network’s parameter learning. Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 508–513, 2011. © Springer-Verlag Berlin Heidelberg 2011

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2 Related Knowledge A. Bayesian Network Model When the causes of node in Bayesian network are independent, we can simplify the network by the model of independent cause. Reference [1] points out that this simplifying can reduce the number of required parameters, and it will not lose information in the original conditional probability table. Base on above, this paper uses the model of independent cause, including Noisy-Or, Noisy-And and etc., as research object, and then the method will be extended to the general Bayesian network. The model is shown in Fig.1. The relationship between Noisy-Or node and its precondition nodes (also called parent nodes or cause nodes) is shown in (1). Bel( N j = True) = 1 − ∏ (1 − cij Bel( N i = True)) i

Here, Nj denotes j-th Noisy-Or node in the network. Ni denotes i-th direct precondition of Nj, cij denotes the conditional probability from Ni to Nj, Bel (Nj =True) denotes the probability that node Nj is true. The relationship between Noisy-And node and its precondition nodes as shown in (2). Bel( N j = True) = ∏ (1 − cij (1 − Bel( N i = True))) i

B. Artificial Fish Swarm Algorithm In water, fish often find the place with more nutrients by itself or following other fish, and thus where the largest number of fish in the waters, where the most nutrients. Base on this feature, AFSA firstly constructs artificial fish and then imitates fish behavior to achieve optimization. The following is several typical fish behaviors given in [3]. 1) Foraging behavior: Under normal circumstances, fish swim at random in the water. When finding food, it will swim quickly towards the direction that food gradually increases. 2) Cluster behavior: In the swimming process, in order to ensure their own survival and avoid harm, Fish will be naturally clustered. When clustering, fish follows three rules: the first is separate rule that fish will try to avoid overcrowding. Second is that fish will swim in same average direction with its close partners as much as possible. Last is cohesive rule that fish will move to the center of the close partners. 3) Rear-end behavior: When one fish find food, the close partners will follow it to the food position.

Fig. 1. Bayesian network model

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3 Parameters Learning by Afsa A. Algorithm Structure and Related Definitions Using AFSA to learn Bayesian network parameter, the key is the construction of AF individual model. The process of learning Bayesian network parameter is essentially the process of optimization of conditional probability cij. Thus the status of AF individual can be represented by all the conditional probability cij in Bayesian network and then each AF represents one Bayesian network. In addition, we define AF’s three behaviors: Foraging behavior, Clusters behavior and Rear-end behavior. The distance between two AF Xp and Xq is defined as (3). d pq = ∑∑ (cij ( p) − c ij (q )) 2 i

j

The food concentration (FC) of AF is defined as (4). Base on this definition, the lower the food concentration, the better the parameter of Bayesian network. FC =

1 m n ∑∑ (ζ ( N i = True) − P( N i = True)) 2 2 k =1 i =1

(4)

Here, ζ ( N i = True) denotes the actual belief that i-th target variable Ni is true. P(Ni=True) denotes the calculated value of this node obtained by Bayesian network inference. k denotes k-th training sample in training samples. We also define AF’s parameter as follows: VISUAL denotes visible distance, Step denotes the largest move step, δ denotes crowding factor. We define a class in C# as follows and each AF is an object of the class. public class AFSC { public double[ ][ ] Cij; // AF’s parameter (i.e. AF’s place) public double FC; private calculateFC(){...}; //calculate the current AF’s FC private foragingBehavior() {...};// execute foraging Behavior private clusterBehavior() {...};// execute cluster Behavior private rearendBehavior(){...};//execute rear-end Behavior } The process of parameter learning by AFSA is shown as Fig. 2. B. Behavior Description 1) Foraging behavior Given Xp is the current AF. It chooses a random place q in its visible region (that is dpq ≤ VISUAL), if FC of Xq is better, it moves to this direction. i.e. cij ( p +1) = cij ( p) + Random(Step)(cij (q) − cij ( p)) d pq

. Otherwise, re-select the random place and judge whether meet the demand for go forward. After failing for several times, it will randomly move one step, that is cij ( p + 1) = cij ( p) + Random( Step) . Here, Random(Step) denotes a random number between 0 and Step.

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2) Cluster behavior

KJ p = {X q | d pq ≤ VISUAL}

is the collection of AF in Xp ’s visible region. If KJp is null, Xp will execute foraging behavior. Otherwise, we get the center place of KJp , that is nf

cij (c ) = ∑ c ij (q ) n f q =1

(Here, nf is the number of AF in KJp). Then if FC in the center place is

better and these is not too crowded (that is

FC c n f < δ FC p

(δ>1)), Xp will move one step

cij ( p + 1) = cij ( p) + Random( Step)(cij (c) − cij ( p)) d pq

toward this center direction (i.e. otherwise, it will also execute foraging behavior.

),

Initialize the parameter of AFSA( VISUAL, δ,Step, NumofAF) Initialize the parameter of AF at random Calculate FC of each AF and Record the smallest FC (FCmin) and the related AF in bulletin board Set the number of iterations:N Iteration counter n=0

Each AF executes cluster behavior and default action is Foraging behavior

Each AF executes Rear-end behavior and default action is Foraging behavior

’

Compare FC of each AF s two behaviors separately and actually execute the behavior with smaller FC Calculate FC of each AF; Update the bulletin board with the smallest FC and the related AF

n++; n>N

N

Y End iteration and output results

Fig. 2. The process of parameter learning by AFSA

3) Rear-end behavior: Xp explores its partner Xmin with smallest FC in its visible region. If Xmin’s FC meet ( FCmin n f < δFC p (δ>1)), then it moves one step toward Xmin direction. Otherwise, it carries out foraging behavior. If there are not partners in Xp’s visible area, it carries out foraging behavior too.

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4 Improve AFSA by Genetic Algorithm AFSA has the advantage of fast convergence, but it is easy to fall into local optimal solution. Therefore, the variation factor of genetic algorithm is introduced to AFSA. We do these as follows: When (recordFC-FC < eps) has appeared for several times continuously (here, FC is the current food concentration, recordFC is the FC in bulletin board, eps is a positive number), we will execute variation factor on each AF’s parameter with probability p. However, AF with the smallest FC is an exception and it doesn’t need this operation. The step of using Genetic algorithm to improve AFSA is shown in Fig.3. K=0; maxK

recordFC-FC<eps N K=0; Record this FC in bulletin board

Y K++

K>=maxK

N

Y execute variation factor; K=0

Fig. 3. Step of using Genetic algorithm to improve AFSA

5 Experimental Results In this paper, we respectively use AFSA and improved AFSA to learn Bayesian network. There give a simple Bayesian network’s parameter learning results. Bayesian network is shown in Fig. 1, in which the target node is Noisy-And node and the parameters are 0.75 and 0.86, respectively. The experiment process includes two steps. Firstly, we randomly generate 100 samples base on the network structure and parameter. Then, according to the structure and 100 samples, we learn the network’s parameter. Fig.4 gives the mean squared error between the parameters’ actual value and calculated value by AFSA and improved AFSA (IAFSA) respectively. The horizontal axis in Fig.4 represents the number of iterations; the vertical axis represents mean squared error. Experimental results show that AFSA can apply to learn Bayesian network’s parameter, and it has the advantage of fast convergence. Further, improved AFSA has the better Convergence. For simple network, the parameter learning method presented in this paper can obtain the parameter quickly. For complex network, we can also get better result after more iteration.

Method of Bayesian Network Parameter Learning Base on Improved AFSA

r 0.3 o r r 0.25 e d 0.2 e r a 0.15 u q 0.1 s n 0.05 a e 0 M

513

AFSA IAFSA

1

5

10

15

20

25

30

35

50

The number of iterations

Fig. 4. Experimental results

6 Conclusions This paper introduces artificial fish swarm algorithm into learning Bayesian network’s parameter, and experiment shows it has a good ability. Then the method is improved by variability factor of genetic algorithm, which can overcome the shortcoming of result falling into local optimal solution. Improved AFSA based parameter learning method is feasible and superior for Bayesian network. Acknowledgment. This research is supported by the youth research fund of North China Electric Power University (200911021).

References [1] Herkerman, D., Breese, J.: A new look at causal independence. In: Proc. of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 286–292 (1994) [2] Sowmya Ramachandran, J.: Theory Refinement of Bayesian Networks with Hidden Variables. The University of Texas at Austin, Austin (1998) [3] Li, X.-l., Shao, Z.-j., Qian, J.-x.: An Optimizing Method Based on Autonomous Animates: Fish-swarm Algorithm. Systems Engineering-Theory & Practice 22(11), 32–38 (2002) [4] Li, X.-l., Qian, J.-x.: Studies on Artificial Fish Swarm Optimization Algorithm based on Decomposition and Coordination Techniques. Journal of Circuits and Systems 8(1), 1–6 (2003) [5] Li, X.-l., Lu, F., Tian, G.-h., Qian, J.-x.: Applications of artificial fish school algorithm in combinatorial optimixation problems. Journal of Shandong University (Engineering Science) 34(10), 64–67 (2004) [6] Wang, C.-R., Zhou, C.-L., Ma, J.-W.: An improved artificial fish-swarm algorithm and its application in feed-forward neural networks. Machine Learning and Cybernetics 5, (August 18-21, 2005)

A Research of the Mine Fiber Communication System Based on GA ZuoMing School of Computer Science and Technolog of China, University of Mining and Tehnology, 221008 [email protected]

Abstract. Mine system of the fiber communications is a technique implementing scheme that is indispensible for the mine industries to do secure exploitation and green exploitation. In this paper we present a genetic algorithm for multicast routing. From the computer simulation we can conclude that our algorithm search’s search speed is faster and its efficiency is higher than other algorithms. Besides, it could transmit the real time and effective data information to the security, monitor and auto-control system. Keywords: CA, fiber communication, multicast routing.

1 Introduction Now, the trend of economic globalization is becoming clearer, the international competition is becoming fiercer, and the relationships between technology and economic are becoming closer, energy security, environmental security, resource security are becoming more and more important, and energy resources remains an important material foundation of human existence and development. In China, coal is the main energy, and the coal industries are the important basic industries that are important to the economic. Latest results shows that more than 50% of China’s primary energy still comes from coal, while more than 95%of the coal production come from the exploitation of mine workers. Mining workers can currently reach an average depth of 800 meters. To promote the research and development of energy-efficient low-carbon, the building of modern high-yielding and efficient mine well is still the mainstream of world’s coal production and development. With advanced network technology, multimedia technology, digital video technology, communication technology, software technology, and so on, the mine automation platform is based on coal mine optical fiber communication system, and it can achieve data, images, voice triple play, sharing of resources. The system can do well to upgrade the technologies of safety mining, green mining services.

2 The Mine Fibel Communication System In order to build 1000 MB Industrial Ethernet Ring that regards optical fiber communication as the main body, we integrate the ground information management Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 514–520, 2011. © Springer-Verlag Berlin Heidelberg 2011

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system, mine environmental monitoring subsystem, the production chain automation subsystem, communication command and control subsystem through a high-speed Gigabit Industrial Ethernet Ring and automation platform software. This system could offer high-speed physical channel to the real-time data of site security, production monitor and automatic control system, and through this channel real-time safety and production data information will be dispatched to the ground mine command center. A. Digital Optical Fiber Transmission The geological conditions of coal mine are complex, not only the environment is bad, natural disasters are very common. To effectively avoid the gas and dust explosion accidents, the coal mine industries install advanced surface-underground coal mine production digital monitor system and industrial TV digital monitor system to do real time monitoring of the monitoring control centre, remote image and voice, and other sensitive data. Because digital videos transmit image data over a computer network is basically distance insensitivity, and the signal is less susceptible to interference, it can greatly enhance image quality and stability. On the other hand, the digital video can use a computer to connect to the Internet, thus network bandwidth can be re-used without repeat routing. In addition, the digital storage make it possible to store the compressed video data in the disk array or stored CD-ROM, besides, through digital compression, encoding video, audio, and data can be transmitted to the monitoring center, the center’s computer will decompress and decode the various kinds of data, and at the same time it could play back video and audio to alarm system so that the system can do the alarm handling. In order to make mine safety and production monitoring system do better, the best way is to choose a channel to transmit. Surface-underground coal mine optical fiber ring network system uses optical fiber as the transmission medium, such technology has many advantages: high information content, no electromagnetic interference, frequency bandwidth, intrinsically safe, light weight, water-fire, high tensile strength, non-relay long-distance transmission. It can transmit the real time data of surface-underground coal mine’s main production areas, important equipment and key occasions to mine scheduling and mine command center and the mine leadership offices, thus the command workers and mining leaders can intuitively and quickly understand the production line situation and the real-time working conditions of surface-underground coal mine key equipment. B. Fiber-Optic Communication Systems The coal mine dispatching communication system applies optical fiber to use a single channel to transmit the integrated scheduling information of underground coal mine safety, production monitoring, which completely changes the means of surface-underground coal mine communication. Taking the factors such as the special circumstances coal mine, multiplex technology and price performance ratio into account, in the current coal mine, the base-band transmission method is more practical, because a variety of numeric text type data of coal mine can be processed directly through the computer network. Underground coal mine optical fiber communication uses mine flame-retardant optical fiber as the channel for information transmission.

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ZuoMing

Underground low-rate data signals multiply connect optical fiber transmission to monitor the security-related simulation amount such as gas, temperature, air pressure of mining face, while underground real-time production processes, equipment and image use underground industrial television image signals transmitted by optical fiber. Aboveground image data’s transmission uses the video server surveillance composed of video surveillance modules to do digital processing and compression coding for multi-channel simulation monitoring video signals, so that the processed image data can use computer network for transmission. The part above the ground use multi-media computer network system composed of a variety of servers and browsers consisting to achieve safe production monitoring and scheduling data browsing, monitoring the image of the network transmission and control. Through the coal mine safety, production-monitoring system for data acquisition and processing to store the corresponding data, and then integrate a variety of sub-database systems of production, marketing, scheduling, management to form a comprehensive database system to achieve a variety of information resources sharing.

Fig. 1. The diagram of mine optical fiber communication system

3 The Determination of the Best Route A. MulticastRouting A modern coal industry should have its own basic information network platform, coal production synthesis automation technology, management information systems, internal/external sites, and so on. In China, many energy-based important companies have more than one coal production enterprises, dozens of coal mines, and mines. Regarding to the basic construction of information networks, they have also built up all-optical digital communications networks, which realize fiber-optic digital information transmission. All the network equipments have used SDH optical-digital synchronous transmission, program-controlled telephone switches, routers, Gigabit, fast network switches, computer servers, and so on. In other words, the integration of voice, video and data networks have been realized. With the advancement of information technology and the implementation and promotion of coal fiber-optic networks, more and more hosts used for information distribution will appear, which could be hundreds of thousands of units, thus sending messages will need a higher bandwidth. The traditional point-to-point unicast or broadcast mode of communication can not meet the requirements of information transmission, which not only wastes a lot of network bandwidth, but also is not efficient. The application of multicast technology can change the original data transmission mode between data. Through multicast

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routing to find a series of best path from the source node and eventually reach the destination node the best path, it reduces network bandwidth and improves efficiency of communication. Communication networks could achieve multicast communication by constructing a multicast routing tree to cover the source node and destination node. The source node only needs to send data once, and then the data will be transmitted through the multicast tree. The data will be replicated at tree crotch until each destination node. This approach reduces the latency of information delivery, saves network bandwidth resources, reduces congestion and reduces network load. Generally there are two ways for finding the minimal spanning tree of constructing multicast route tree. The minimum spanning tree is to find a minimum spanning tree covering all nodes from the graph, which makes the smallest cost of the tree (based on distance, channel bandwidth, average traffic, communication overhead, the average queue length and other factors). That is to say, this method makes the connection-specific plans for specific members of the group need the least number of links to reach the end point (the shortest path). B. The Application of GA In GA, the solution of the problem is expressed as “chromosome”, and in the algorithm it is called a binary-coded “strings”. Before the implementation of genetic algorithms, a group of “chromosomes” (assuming solution) will be given. Then, these supposed solutions will be put into the “environment” of the question. In accordance with the principle of survival of the fittest, we choose the "chromosome" that is more suitable to the environment of to copy. After that, through crossover, the mutation processes will generate the new generation of "chromosome" group which is more suitable to the environment. In this way, the evolution process will do from one generation to another, and finally get a “chromosome” that is the most suitable to the environment. This is the optimal solution of the problem. The key application of GA is the encoding method of the string and three problems of the fitness function. The built route model could be expressed by undirected weighted graph G = (V, E), where V = {V1, V2, …, Vn}, which is the set of all the switching node in the network, E = {e1, e2, …, em}, which is the set of communication link of two random adjacent nodes. Each line e = [vi, vj] of G has two positive real number weights (De, Ce). Assume De is the information transfer delay of e E, Ce is the cost of e E, its value is related to the resource utilization of e. Considering the affection of delay and delay

∈

∈

jitter to the routing selection, Multicast routing can be expressed by min( The limitation is

∑ D(e) ≤ Δ , where Δ is the maximum delay, P

e∈PT ( u , v )

∑ c(e) ).

e∈ET

T ( u , v) is

the path

from source node u to destination node v, M is the set of multicast group. When constructing the minimal spanning tree, first suppose that the known source node has all the topology of the whole network, and then apply the shortest path algorithm of Dijkstra and we can conclude that every pair of node (u, vi) (where vi M) could satisfy the condition.

∈

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ZuoMing

C. The Model Construction of GA 1) Encoding of String Suppose that there are m nodes (m = |M|) in the network have multicast requirement, every node vi M is corresponding to the active set Ψvi, and every set Ψvi has n′vi alternative paths. For the path of every Ψvi, do non-negative integer encoding expansion respectively according to the oder and get the code (0, 1, 2, …, nvm), nvm Z+ ( Z+ is the set of positive integer). The selected path sets of vi can use the individual of multi-dimensional non-negative integer encoding to express, and the encoding method is (av1, av2, , avm), where m is the length of code, and the value of each code is

∈

∈

⋯

⎧0 , here 0 is to say that the alternative paths of Ψvi are not selected, nvi expresses ⎩nvi

avi= ⎨

the n″vith path of Ψvi is selected. 2) the determination of fitness function

⎧G ( x ) : if f(x) =G(x), av1 + av2 + av3 + …+ avm ≠0, if f(x) = Cmin, av1 = av2 = av3 = ⎩C min

f(x) = ⎨

…= avm = 0. Here G( x) = -

∑ C ( e) - ∑ C ( e) e∈E

∑ C ( e) e∈E

- [

∑ C (e) +( m - m′)/m( Σe ∈ EC( e)

e∈ET

) ], m is the number of multicast, m’ is the number of

e∈ET

destination nodes in ET, E is every line of the weighted graph. Cmin = (1 - k)

∑ C ( e) , e∈E

k < 1 is a constant. Through the GA we will get the max value. 3) the setting of Genetic algorithm’s parameter Use roulette wheel selection to choose operator, and the crossover of crossover probability is Pc. The variation and adaptive probability of crossover algorithm Pm = C′( fmax - f ) / ( fmax - fmin), where fmax fmin are the largest fitness value and the minimum fitness value of the group respectively, C′ = 0.003.

与

4 Simulation Verification The simulation performance of the routing algorithm in random network topology is almost the same to the application performance in the practical application [1].

Fig. 2. A random network topology diagram

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Therefore, we usually choose the common multicast routing algorithm simulation platform (MCRSIM). First, we randomly generate a series of nodes, and build up the connection of the link according a certain degree of probability, thus we will get the random network topology diagram as Fig. 2. The network nodes are given randomly, and the values of the lines in the diagram are the real numbers between 0 and 1. Multicast network nodes account for 20% of the total number of nodes. The values of the parameters are given as follows.k = 0.19, Pc = 0.18, C′= 0.003. Table1shows the group size and the change of running algebra as the network nodes become more. In Table 1, the “group size” and “running algebra” are the value of every data node after 10 experiments. Table 1. The group size and the change of running algebra as the network nodes become moreCount the average value. network nodes 20 group size 60 Count 60

20 60 90

30 120 100

40 120 150

50 200 200

The experiment shows that this algorithm will get the optimum solution.Suppose S is the source node, and the set of destination nodes is M = {b, c, d}. Here we have got every alternative path from the source node to destination node, as shows in Fig. 3.

Fig. 3. Alternative path of network in the experiment diagram

The values of every line are randomly given, as shows in Table2Run the algorithm in this paper 1000 times, and all the optimization calculations are finished within 300 times. Simulation results show that the optimal efficiency can reach 76%. Table 2. The alternative path of from the source node to the destination node source node and destination node （s,b）

（s,c) （s,d)

router ｛2｝｛1,7｝｛1,6,4,3｝｛1,6,8,5｝ ｛1,6,10,9,5｝ ｛1,6,8｝｛1,7,5｝｛1,7,3,4,8｝｛2,5｝ ｛1,6,10,9｝｛1,6,4,3,5｝｛1,7,3,4,10,9｝ ｛1,6｝｛2,3,4｝｛1,7,3,4｝｛2,7,6｝ ｛2,5,9,10｝｛2,5,8｝｛1,7,5,8｝

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ZuoMing

5 Conclusions This paper focuses n the first needs of security in the current communications systems of coal industry, which presented in real-time and faster access to the best multicast routing, in order to solve real-time data transmission for the on-site security, production monitoring and automatic control system, provides a network communication methods with a fast searching speed and efficient. It provides the basis for underground services for data, images, sounds triple-playing, sharing of resources in coal mine based optical fiber communication system integrated automation platform for the safe mining, green mining technology. Acknowledgment. This work is supported by Science Foundation of National Science and Technology Support Program--A new generation of trustworthy Internet test network (2008BAH37B05).

References [1] Norolilia, C., Tobagi, F.: Evaluation of Multieast Routing Algorithms for Multimedia Streams [2] Ke, Z., Zhan, C.: Study on mine mobile communication eprotocol the base station mobile phone. Journal of China Coal Society 15, 20–23 (2005) [3] Chu, S., Hu, Y.: The technology of wireless sensor network. Computer Technology and Development 16(4) (April 2006) [4] Zhao, H.: Secure geographical and energy aware routing protocol in wireless sensor network. Information Technology, 1009-2552,09-0044-04 [5] Lin, K., Zhao, H., Yin, Z., Zhang, X.: Energy prediction and routing algorithm in wireless sensor network. Journal on Communications 27(5) (May 2006) [6] In: Proeeedings of the IEEE Intemational Telecommunications Symposium (1994)

Research of Resource Management and Task Scheduling Based on the Mine Safety Grid Xuxiu1,2 1

School of Computer Science and Technology, China University of Mining and Technology 2 State Key Laboratory of Coal Resources and Safe Mining (CUMT) [email protected]

Abstract. Based on the current communication web of mines, the paper puts forward one mine security grid model based on the development of the grid technology. Optimum resource management strategy makes it possible for the grid computing to get higher running capability. Thus, it can provide good service for the date calculation and the evaluation of mine security. Keywords: Grid, Resourcemanagement, Safetyevaluation.

1 Introduction With the development of coal enterprises, a series of safety problems begin to appear. Adopting scientific and perfect measures to change the actuality of coal mine safety and strengthen the study of safety evaluation technology is one urgent problem the coal mines need to solve as soon as possible [1]. A mine safety grid based on the current telecommunication networks is built. With special characteristics of grid, it can provide better service for the calculation and data’s acquirement used for mine safety evaluation. In this way, we can forecast the dangerous situation of the system and tell the technical department and administrative department to adopt proper measures for the aim of safety production. The core of grid is resource management system and task scheduling. Traditional resource management system adopts centralized scheduling producers to control the scheduling and assignment of resources. But this method can’t satisfy the requirement of high-capability calculation and service of grid system. There are two main obstacles: Firstly, heterogeneity of grid resources makes the formalization of consistent assignment algorithm very difficult[3]. Extensive distribution of the resources’ owners makes the assign tactics various [2]. Based on the above problems, one new resource management and scheduling method is urgently needed to realize the best management and scheduling for resources.

2 Model of Mine Safety Grid Coal enterprises locate in many different places. With carving up grid crunodes, every enterprise can join the grid, becoming one distributing crunode. Use calculation Y. Wu (Ed.): ICCIC 2011, Part IV, CCIS 234, pp. 521–526, 2011. © Springer-Verlag Berlin Heidelberg 2011

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resource to solve more complicated problems in one more convenient way. Effective management system of mine safety grid can remove the restriction of calculation ability and the limitation of traditional share and collaboration. Based on the current telecommunication networks and safety monitor supervisory control system, the architecture of mine safety information service grid is put forward in this text, combining current grid technology. Figure1 shows the architecture. The safety supporting platform is made of code supporting system, information service system, safety connecting system and safety defense system. It provides multi-levels’ and multi-orientations’ safeguard service. Application system platform can provide groupware‘s integration, exploitation and running circumstance. It can provide basic service and universal application service function. The third one is the information service supporting platform. For this platform, information data provides required information for correlated data supporting system. Then these data will be processed with the data service supporting system. The data service supporting system is made of data centralization, data unification, data conformity, data analysis and data storage. Now, I will give a detailed description for these parts as following. Data centralization: Its main task is to collect external data and form a centralization database with these data.

Fig. 1. Architecture of mine safety information service grid

Data unification: It mainly labels data got from the centralizing database. Then, according to certain regulations, these labeled data can form a unification database. Data conformity: With this layer, data got from the centralization database can be switched and conformed to form conformity database. Data analysis: It mainly does data analysis. With the analysis, a result database of analysis can be formed, including production management, safety supervisory and emergency command and so on. Thus, the database can provide decision-making supporting service. Data storage: It stores and manages data processed in every layer.

3 Grid Resource Management The core of grid is resource management system, which organizes and manages all kinds of resource to satisfy demands of different applications. For resource

Research of Resource Management and Task Scheduling Based on the Mine Safety Grid

523

management, some pivotal problems need to be solved, such as resource description, organization and management, and so on. Great ability of grid environment is showed with grid’s running function. In order to get higher running function, grid needs optimized strategies of resource scheduling to do proper matching between utilizations and resources. Also, these strategies can dynamicly adjust utilizations and resources during running process. As grid system relates to many different resources and participators, resource scheduling is very complicated. Except for preventing resource scheduling from dead-locking, it still pays attention to resource’s harmony in time and space to get better running effect. For more complicated scheduling algorithm, many other problems need to be considered, such as PRI of different applications and resource reserve [5] [6]. A. Resource Management Resource management is the core of grid computing. It includes resources’ organization, orientation, scheduling, and distribution and so on. According to defined standards, System administrator assures that resources can be distributed and used reasonably with resource management software. Thus, the purpose of resource share can be achieved ultimately. What the picture two shows is the architecture of resource management system. Resource manager is very useful for establishing and managing courses. The method of grid management is shown in Figure2.

Fig. 2. Architecture of resource management

Resource requests are described with one extensible resource description language and transferred among groupwares. The resource broker receives high-layer resource request descriptions submitted by analytical application procedures. Then the resource broker transfers the descriptions to concrete low-layer resource description language and sends it to the resource Co-allocator. With the resource Co-allocator, the task of the low-layer resource description language can be separated to many parts and be sent to the following resource managers. At last, the resource managers received task’s low-layer resource requests from the resource Co-allocator, and translate them to one kind language used by local resource managers. In this way, task can be finished by local resource managers [6].

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B. Task Scheduling With the high-speed internet, grid computing can make many computers located in different places be formed resource integration. For grid computing, a lot of applications are running, sharing every kind of grid resources. How to make these applications get the greatest capability is one important problem the task scheduling needs to solve. In order to finish the task submitted by users and satisfy users’ requests, task scheduling searches all useable resources of grid, such as computing resources, storage resources and web resources [9]. The blueprint of task scheduling is shown as the picture 3.

Fig. 3. Design of task scheduling

Task requests are disassembled to many resource manager requests. Then these resource manager requests are submitted to resource managers. If all of the requests are processed successfully, one synchronization point of task’s start-up comes into being [10]. Thus, this task can be executed successfully. But if the synchronization point of task’s start-up doesn’t exist, we can’t assure all needed resources can be got.

4 Application For the application of mine safety grid, the administrators can manage the resource catalogue. There are many different kinds of resources in the safety grid. At the same time, according to different need and time, resource of grid can be added, deleted or changed. To adapt these resources’ dynamic changes, we need one API and surface to finish these managements. Management jobs mainly include the resource establishing, registering, revising, deleting, searching for and authorizing and so on [11]. Based on the resource description language, users of the safety grid can send requests to correspond procedures of safety grid to process these requests . In actual applications, coal mine safety institutions often need to make a safety evaluation. There are lots of data used in the evaluation, including all kinds of parameters of every laneway and data measured in every locale. Besides, superior management institutions also provide data correlated with mine’s safety evaluation in the near future. Grid computing may use some of all the resources. Thus, the resource management and scheduling become one pivotal problem. In actual applications, research workers or application procedures can get safety information resources needed for application requests. According to the resources, users begin to visit resources

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through one application entrance. The resource broker receives high-layer resource request descriptions submitted by analytic application procedures. Then the resource broker transfers the descriptions to concrete low-layer resource description language and sends it to the resource Co-allocator. Then the resource managers received task’s low-layer resource requests from the resource Co-allocator, and translate them to one kind language used by local resource managers. In this way, task can be finished by local resource managers. Resource distributing of every resource manager is stored in every mine institution such as safety studies institution, management institution and application locales. With the above operations, many parameters and notes of reference files can be got. After that, the evaluation service sends these data to spare computing environments to do computing integration and realize fix quantify evaluation. At last, the result is returned to the application procedures. Thus it can be seen, to realize proper safety evaluation, reasonable resource management and task scheduling are very important. Once there is something wrong with resource management and scheduling, safety evaluation can’t be finished successfully, affecting the forecast of mine safety gravely.

5 Conclusions Mine safety needs modern science and technology to provide against accidents. Mine safety grid can organize many resources related mine safety and provide services for every complicated computing task. As one high-performance computing environment and information service infrastructure, grid can provide reliable data for safety evaluation system. With these data, we can find the main reason which induces system’s danger and make reasonable management strategies to reduce or eliminate its criticality. In this text, based on the current telecommunication networks, one mine safety information service grid is put forward. As an important technology of grid, resource management is described particularly. Also, the correlative practical application is expatiated. Optimized resources scheduling makes the grid computing get higher running capability, providing better service for grid computing and data’s fetching. In this way, safety evaluation gets reliable data, establishing one base for realizing safety production. Acknowledgment. This work is supported by Science Foundation of National Science and Technology Support Program--A new generation of trustworthy Internet test network (2008BAH37B05), and This work is supported by the Fundamental Research Funds for the Central Universities(2010QNB19).

References [1] Shi, R., Fan, S.: Study of Building Mine Safety Appraising Index System. Journal of China’s Security Science 5, 25–30 (1995) [2] Yao, Y., Gao, Y.: Study of Grid Resource Scheduling. Journal of Computer Applications 5, 30–32 (2005)

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[3] Foster, I., Kesselman, C.: Grid Computing, pp. 235–245. Press of electronic industry (2004) [4] Liu, W.: Study of Grid Application Technology Based on Glubos, pp. 36–38 (2004) [5] Xiao, L., Huang, L.: Summarization of Grid Computing. Journal of Computer Engineer 3, 27–29 (2002) [6] Luo, Z., Zhang, Z., Li, J., Xie, C.: Grid Computing and Technology Summarization. Journal of Computer Project and Application 30, 31–33 (2003) [7] Liu, Y., Liu, X.: Mine Safety Valuation and Ascertains Method. Journal of Coal Technology 8, 15–18 [8] Qin, G.: Model of Optimization Scheduling Based on Monitoring Grid Computing. Master’s degree thesis, Hunan Normal University, pp. 56–59 (2003) [9] Yu, Z., Xu, S.: Study of Task Scheduling Algorithm Based on Grid Environment. Journal of Computer Project and Application, 35–37 (2004) [10] Zhao, D.: Rearch of Several Key Technologies Based on Grid Computing. Ph. Degree thesis, University of Electronic Science and Technology in China, pp. 69–71 (2003) [11] Du, Z., Chen, Y., Liu, P.: Grid Computing, pp. 169–172. Tsinghuang University Press, Beijing (2002) [12] Xu, S., Wang, J., Li, X.: Study of Architecture of Mine Safety Application Grid Based on Grid. Journal of Computer Simulation, 24–26 (2005)

Author Index

Ai, Shengli VI-480 Aithal, Himajit IV-351 AliHosseinalipour V-36 Anil kumar, A. IV-351 Aslam, Mohammed Zahid

IV-260

Babaei, Shahram V-36 Bailong, Liu III-39, III-47 Bangjun, Lu VI-124 Bangyong, Hu II-60 Bao, Liwei IV-93 Bi, Guoan IV-224 Bin, Dai II-436, II-443 Bin, Li III-151 Bing, Hu V-476 Bingxue, Han IV-265 Bo, Qi VI-282 Bo, Sun I-526, II-475 Bo, Wu V-74 Bo, Yang IV-1 Bo, Zhou V-461 Bu, Yingyong I-335 Cai, Nengbin IV-402 Cai, Ning II-87 Cai, Xiaonan I-519 Cai, Xiaoqing VI-166 Cao, An-Jie IV-376 Cao, Fengwen IV-189 Cao, Jianbo VI-398 Cao, Jianshu VI-536 Cao, Qiang VI-417 Cao, Yukun III-232 Chang, Henry Ker-Chang VI-500 Chang, Ling-Wei V-483 Chang, Yinxia IV-427 Chang, Zhengwei IV-167 ChangJie, Hu VI-461 Chang-ping, Zhao VI-290 Changxi, Ma VI-282 Changyuan, He V-211 Chao, Hu IV-42 Chao, Yan II-530 Chaoshi, Cai I-395, III-321

Chen, Bin V-170, V-175 Chen, Cheng II-157 Chen, Chuan IV-369 Chen, Chun IV-376 Chen, Haijian III-508 Chen, Haiyuan III-9 Chen, Hong-Ren III-407 Chen, Huiying V-201 Chen, Lingling V-125 Chen, Weiping III-508 Chen, Xiaodong V-175 Chen, Xinglin VI-110 Chen, Yan VI-152 Chen, Yanhui VI-84, VI-89 Chen, Yu-Jui V-10 Cheng, Jiaji IV-280 Cheng, Li V-321 Cheng, Shih-Chuan VI-436 Cheng, Yingjie III-74 Chengcheng, Jiang I-288 Chenguang, Zhao IV-136, IV-144, IV-151 Chi, Xiaoni I-143 Chiang, Yea-Lih III-407 Chong, Guo VI-50 Chuan, Tang I-29 Chuang, Li VI-404 Chujian, Wang I-191, I-366 Chun, Huang I-36 Chun, Yang Chang VI-511 Chunhong, Zhang II-326 ChunJin, Tian III-207 Chunling, Zhang V-381 Chunqin, Zhang III-369 Congdong, Li I-288 Congmei, Wan V-321 Cui, Kang IV-59 Cui, Yanqiu I-550 Cui-lin, Zhang V-461 Da, Zheng V-94 Dai, Minli VI-424, VI-430 Dai, Wei-min V-100 Danxia, Bi V-105

528

Author Index

Dasen, Li II-405 Deng, Fang II-294 Deng, Hui V-201 Deng, Jianping IV-189 Deng, Nan IV-402 Deng, Xianhe II-396 Deng, Xiaoyun VI-343 Deng, Xubin VI-26 Deng, Yibing V-316 Deqian, Xue VI-383 Ding, Feng II-350 Dong, Hao VI-1 Dong, Liu III-292 Dong, Xu III-39, III-47 Dong, Yu VI-50 Dong-Ping, Liu II-303 Du, Jiang V-523 Du, Maobao V-365 Du, Wencai III-1 E., Shiju

VI-398

Fan, Hongda VI-1 Fan, Jihua III-515 Fan, Tongliang IV-433 Fan, Zhao IV-441 Fang, He II-172 Fang, Ligang VI-430 Fang, Qiang VI-166 Fang, Sun IV-242 Fang, Yuan II-274 Fanjie, Bu II-382 Fei, Zhou II-281 Feng, Lei V-304 Feng, Lou III-312 Feng, Lv II-101 Feng, Pengxiao IV-172 Feng, Wenlong III-1 Feng, Yuan II-194 Fengling, Wang I-262 Fengxiang, Chen V-234 Fu, Wenzhi IV-172 Fu, Xixu V-43 Fu, Yizhe VI-179 Fuhua, Xuan I-275 Furong, Wang VI-445, V-511 Gaijuan, Tan V-234 Gai-ning, Han VI-39 Gan, Jing III-427

Gang, Chen I-492 Gao, Cheng I-359 Gao, Fei III-427 Gao, Haiyan III-433 Gao, Jin IV-306 Gao, Junli VI-166 Gao, Li VI-357 Gao, Shuli V-226 Gao, Wei VI-188, VI-197 Gao, Xin II-391 Gao, Xiuju V-365 Gao, Zhijie III-17 Gong, Jun V-529 Gong, Xiaoyan II-194 Gong, Xizhang V-43 Gu, Caidong VI-424, VI-430 Guan, Xianjun I-21 Guangyu, Zhai IV-503 Guilin, Lu VI-232 Guo, Changgeng III-442 Guo, Fachang IV-382 Guo, Lejiang II-194 Guo, Lina III-494 Guo, Lu VI-452 Guo, Shuting V-288 Guo, Wei V-428, V-435 Guo, Wenping III-488 Guo, Xinbao I-403, I-409 Guo, Yanli V-226 Guo, Zhiyun III-284 Guo, Zirui I-359 Guohong V-64 Guohong, Li I-248 Guojin, Chen III-299, III-305 Guojing, Xiong II-267 Guo-song, Jiang I-320, I-328 Haicheng, Xu IV-10 Hailong, Sun I-161 Hai-qi, Feng V-374 Haitao, Hong VI-50 Haiwen, Li IV-335 Haixia, Wan I-484 Haixia, Yu VI-522 HamidehJafarian V-36 Han, Baoyuan IV-450 Han, Dong IV-464 Han, Hua I-478 Han, Xinchao III-401 Han, Xu V-100

Author Index Hang, Ling-li V-268 Hantian, Wei VI-445, V-511 Hao, Fei Lin V-304 Hao, Hong VI-551 Hao, Yitong II-143 Hau, Chuan-Shou V-239 He, Jilin V-137 He, Juan III-103 He, Li III-174 He, Lijuan III-337 He, Siqi II-420 He, Weisong VI-94, VI-100 He, Xiangguang VI-188, VI-197 He, Yang II-312 He, Yinghao IV-32 He, Yong V-304 He, Zhuzhu II-458 Heng, Chen III-89 Hengkai, Li I-213 Hong, Liang IV-181 Hong, Lu I-465 Hongbing, Zhang VI-551 Hongjun, Liu I-533 Hong-li, Zhang I-302 Hongmei, Jiang IV-159 Hongmei, Tang III-345, III-355, III-363 Hongwei, Luo III-183 Hou, Shouming III-337 Hou, Xuefeng IV-369 Hu, Caimei IV-81 Hu, Jianfeng IV-456 Hu, Jun V-409 Hu, Wenfa V-281, V-288 Hu, YongHong VI-452 Hu, Zhigang V-246 Hu, Zhiwei IV-392 Hu, Zong IV-54 Hua, Wang Guo VI-157 Huan, Wang V-518 Huang, Changqin V-258 Huang, De-Fa V-239 Huang, Haifeng I-428 Huang, Hanmin I-94 Huang, Hexiao III-508 Huang, Jun V-268 Huang, Qiong II-287 Huang, Tao III-174 Huang, Weitong V-117 Huang, Xiaodi V-468 Huang, Yu-Chun V-239

Huang, Zhiqiu V-409 Huanhuai, Zhou V-334 Huijuan, Ying V-334 Hui-li, Wang VI-370 Huili, Zhang I-161 Huixia, Wang II-150, II-172 Huixin, Jin I-248 Jangamshetti, D.S. IV-351 Jen, Yen-Huai V-483 Ji, Jia VI-398 Jia, Guangshe V-125 Jia, Zhiyang VI-188, VI-197 Jian, Wang V-374 Jian, Zhou III-143 Jiang, Fuhua II-396 Jiang, Jia VI-398 Jiang, Xuping III-482 Jiang, Yuantao II-17, II-95 Jian-Hao, Xu VI-34 Jianhong, Sun IV-1, IV-10 Jian-Min, Yao III-151 Jianping, Li I-395, III-321 Jianping, Tao III-143 Jianqi, Han VI-50 Jian-tong, He VI-290 Jianwen, Cao IV-503 Jianxin, Gao V-518 Jianzheng, Yi IV-342 Jiao, Linan V-189 Jia-xin, Lin VI-224 Jie, Jin II-303 Jie, Quan I-413 Jie, Xu V-82 Jie, Yu VI-467 Jieping, Han I-132 Jin, Haiyi V-328 Jin, Min II-73 Jin, Wang V-82 Jinfa, Shi III-453, III-465 Jinfang, Zhang VI-467 Jing, Liang V-207 Jing, Tu I-184 Jing, Zhao III-292 Jing, Zhou I-66, I-71 Jing-xin, Chen I-343, I-351 Jingzhong, Liu II-318 Jin-hai, Wang VI-319 Jinhui, Lei III-207 Jinwei, Fu IV-10

529

530

Author Index

Jinwu, Yuan III-420 Jiuzhi, Mao I-313 Jou, Shyh-Jye V-10 Jun, Li VI-148 Jun, Song V-137 Jun, Wang V-82 Jun, Zhang VI-45 Jun-qi, Yang VI-297 Junsheng, Li IV-1 Jyothi, N.M. III-328 Kai, Zhang V-133 Ke, Xiaoyu II-73 Kebin, Huang II-150 Kewen, Geng VI-124 Kun, Shi VI-66 Lai, Herbert Hsuan Heng VI-500 Lan, Jingli II-518 Lee, Xuetao II-226 Lei, Xu VI-232 Lei, Yang II-109 Lei, Yu V-193 Li, Chen II-303 Li, Cungui II-499 Li, Deyang III-263 Li, Dou Hui VI-157 Li, Fengri VI-20 Li, Fengying IV-101, IV-110 Li, Guanglei III-433 Li, Guangzheng III-81 Li, Haibin I-115 Li, Haiyan IV-233 Li, Hongli VI-424 Li, Houjie I-550 Li, Hua I-380 Li, Hui III-174 Li, Jia-Hui IV-316 Li, Jianfeng IV-297 Li, Jianling IV-233 Li, Jinglin III-502 Li, Jinxiang VI-430 Li, Kuang-Yao V-239 Li, Li VI-458 Li, Liwei III-241 Li, Luyi III-192, III-394 Li, Mingzhe IV-172 Li, Na II-202 Li, Peng V-56 Li, Qi I-359

Li, RuZhang V-18 Li, Shaokun VI-335 Li, Shenghong IV-441 Li, Shijun IV-233 Li, Wan V-416 Li, Wang V-346 Li, Wenbin IV-392 Li, WenSheng I-101 Li, Xiangdong III-401 Li, Xiumei IV-224 Li, Yang IV-42 Li, Yanlai IV-297 Li, Ying V-443, V-449, V-455 Li, Yu II-128 Li, YuJing V-18 Li, Zhenlong III-488 Lian, Jianbo I-451 Lianbo, Jiang VI-551 Liang, Wen-Qian II-450 Liang, Yuechen III-232 Liang-feng, Shen I-255 Liangtao, Sun I-492 Liao, GaoHua VI-7, V-498, V-504 Liao, Jiaping III-174 Lieya, Gu I-8 Lifen, Xie II-34 Li-jia, Chen VI-297 Lijun, Shao V-105 Li Jun, Sun II-428, II-436, II-443 Liminzhi I-513 Lin, Chien-Yu V-483 Lin, Haibo II-414 Lin, Ho-Hsiu V-483 Lin, Jing VI-179 Lina, Wang IV-59 Ling, Chen I-29, IV-59 Ling, Shen Xiao VI-511 Lingrong, Da II-373 Li-ping, Li V-221 Liping, Pang V-82 Lisheng, Wang V-234 Liu, An-Ta VI-500 Liu, Bao IV-427 Liu, Bingwu I-437 Liu, Bojia V-111 Liu, Bosong IV-32 Liu, Chunli III-255 Liu, Daohua V-491 Liu, Deli II-181 Liu, Gui-Ying II-342

Author Index Liu, Hong VI-74 Liu, Hongming III-116 Liu, Hongzhi VI-335, VI-343, VI-357 Liu, Jia V-504 Liu, Jiayi II-1 Liu, Jingwei IV-491 Liu, Jixin IV-360 Liu, June I-143 Liu, Jun-Min II-493 Liu, Li V-258 Liu, Lianchen III-158 Liu, Lianzhong II-164 Liu, LinTao V-18 Liu, Linyuan V-409 Liu, Shiwang II-181 Liu, Tao III-442 Liu, Wenbai V-316 Liu, Xiaojing V-117 Liu, Xiaojun I-59, II-164 Liu, Xin V-491 Liu, XingLi IV-181 Liu, Yang VI-110 Liu, Yanzhong II-235 Liu, Yongsheng I-471 Liu, Yongxian III-337 Liu, Yuewen II-458 Liu, Zhaotian IV-233 Liu, Zhi-qiang III-112 Liu, Zhixin I-177 Liurong, Hong V-389 Liuxiaoning V-64 Lixia, Wang VI-267 Lixing, Ding V-50 Li’yan II-22 Li-yan, Chen I-76, I-83 Liyu, Chen I-166, I-172 Liyulong I-513 Long, Chen VI-50 Long, Hai IV-25 Long, Lifang II-181 Long, Shun II-450 Long, Xingwu IV-252 Lu, Hong V-428, V-435 Lu, Hongtao IV-392, IV-402 Lu, Jing I-519 Lu, Ling V-258 Lu, Xiaocheng II-294 Lu, Y.M. V-353, V-359 Lu, Zhijian II-47 Luo, Rong I-222

Luo, Yumei II-414 Lv, Qingchu IV-93 Lv, Rongsheng II-211 Lv, Xiafu IV-280 Ma, Chunlei I-471 Ma, Jian V-164 Ma, Lixin V-328 Ma, Qing-Xun II-116, II-122 Ma, Sen IV-369 Ma, Yuan V-189 Ma, Zengjun IV-93 Ma, Zhonghua III-103 Mai, Yonghao VI-417 Mamaghani, Nasrin Dastranj III-22 Maotao, Zhu VI-210, V-211 Maoxing, Shen VI-148 Masud, Md. Anwar Hossain V-468 Meilin, Wang V-181 Meng, Hua V-69 Meng, Yi-Le V-10 Mengmeng, Gong V-82 Mi, Chao VI-492 Miao, J. V-353 Milong, Li I-457 Min, Ye Zhi VI-511 Ming qiang, Zhu I-150, I-206 Mingqiang, Zhu II-81 Mingquan, Zhou VI-528 Na, Wang I-233 Naifei, Ren V-133 Nan, Li I-533 Nan, Shizong IV-476 Nie, GuoXin V-523 Nie, Zhanglong VI-138 Ning, Ai V-334 Ning, Cai I-36 Ning, Yuan V-416 Nirmala, C.R. III-328 Niu, Huizhuo IV-369 Niu, Xiaoke IV-289 Pan, Dongming V-43 Pan, Min I-446 Pan, Rong II-1 Pan, Yingchun V-170 Pan, Zhifang IV-392 Pei, Xudong VI-105

531

532

Author Index

Peng, Fenglin III-482 Peng, Hao IV-335 Peng, Jianhan III-508 Peng, Jian-Liang II-136 Peng, Yan V-309 Pengcheng, Fan VI-528 Pengcheng, Zhao II-405 Piao, Linhua VI-239, VI-246, VI-253, VI-261 Ping, Li VI-273 Pinxin, Fu V-181 Qi, Lixia I-124 Qi, Zhang IV-219 Qian, Minping IV-491 Qiaolian, Cheng V-370 Qin, G.H. V-89 Qin, Zhou I-302 Qingguo, Liu III-130 Qinghai, Chen IV-335 Qingjia, Geng V-105 Qingling, Liu IV-273, V-24 Qingyun, Dai V-181 Qinhai, Ma I-238 Qiong, Long VI-467 Qiu, Biao VI-179 Qiu, YunJie IV-402 Qiuhe, Yang VI-267 Qiyi, Zhang VI-124 Qu, Baozhong III-255 Qun, Zhai III-143 Qun, Zhang III-377, III-386 Ramaswamy, V. III-328 Rao, Shuibing V-137 Ren, Chunyu VI-218 Ren, Hai Jun V-56 Ren, Honge VI-131 Ren, Jianfeng III-494 Ren, Mingming I-115 Ren, Qiang III-276 Ren, Shengbing V-246 Ren, Wei III-81 RenJie VI-376 Rijie, Cong I-132 Rubo, Zhang III-39, III-47 Rui, Chen II-303 Rui, Zhao I-248, I-313 Ruihong, Zhang II-253 Ruirui, Zhang IV-204, IV-212

Runyang, Zhong V-181 Ru’yuan, Li II-22 Saghafi, Fatemeh III-22 Samizadeh, Reza III-22 San-ping, Zhao VI-13, VI-410 Sha, Hu IV-470 Shan, Shimin IV-32 Shang, Jiaxing III-158 Shang, Yuanyuan IV-369, IV-450 Shangchun, Fan V-321 Shao, Qiang I-109 Shaojun, Qin VI-210 Shen, Ming Wei V-304 Shen, Qiqiang III-95 Shen, Yiwen II-47 Shen, Zhang VI-370 Sheng, Ye VI-232 Shi, Danda V-316 Shi, Guoliang IV-48 Shi, Li IV-289 Shi, Ming-wang V-143 Shi, Wang V-105 Shi, Yan II-414 Shi, Yi VI-131 Shidong, Li V-296 Shou-Yong, Zhang II-428, II-436, II-443 Shu, Xiaohao III-95 Shu, Xin V-164 Shuai, Wang V-221 Shuang, Pan III-30 Song, Haitao VI-166 Song, Meina III-284 Song, Yichen IV-73 Song, Yu IV-73 Sreedevi, A IV-351 Su, Donghai V-404 Sun, Qibo III-502 Sun, Zhaoyun V-189 Sun, Zhong-qiang V-100 Sunqi I-513 Sunxu I-484 Suozhu, Wang I-14 Tan, Liguo VI-110 Tang, Dejun V-328 Tang, Fang Fang V-56 Tang, Fei I-478 Tang, Hengyao II-274 Tang, Peng II-1

Author Index Tang, Xin II-17 Tang, Xinhuai V-1 Tang, Yong V-258 Tanming, Liu II-331 Tao, Li IV-204, IV-212 Tao, Zedan II-357 Tian, Fengbo IV-280 Tian, Fengqiu VI-430 Tian, Ling III-241 Tianqing, Xiao IV-10 Ting, Chen I-387 Tong, Guangji II-499, II-510, II-518 Tong, Ruo-feng IV-198 Tu, Chunxia I-46, I-59 Wan, Hong IV-289 Wan, Wei VI-452 Wan, Zhenkai III-9, IV-484 Wang, Bing II-484 Wang, Chen V-328 Wang, Chengxi I-451 Wang, Chonglu I-222 Wang, Chunhui I-500 Wang, Dan IV-433 Wang, Dongxue IV-297 Wang, Fei I-88 Wang, Feng V-201 Wang, Fengling II-128 Wang, Fumin I-198 Wang, Haiping III-224 Wang, Hongli VI-315 Wang, Huasheng II-350 Wang, Hui-Jin II-450 Wang, JiaLian V-252, V-422 Wang, Jian II-211 Wang, Jianhua IV-48 Wang, Jianqing V-111 Wang, Jie V-404 Wang, Jing V-100 Wang, Jinyu I-335 Wang, Li-Chih V-483 Wang, Lijie V-529 Wang, Linlin V-275 Wang, Luzhuang IV-93 Wang, Min VI-424 Wang, Qian III-166, III-284 Wang, Ruoyang VI-398 Wang, Ruo-Yun IV-376 Wang, Shangguang III-502

533

Wang, Shanshan II-458 Wang, Shijun IV-464 Wang, Shi-Lin IV-376, IV-441 Wang, Shimei I-428 Wang, Shuyan II-10 Wang, Tiankuo II-510 Wang, Ting V-449, V-455 Wang, Weiliang VI-544 Wang, Xia I-21 Wang, Xiaohong III-241 Wang, Xiaohui I-269 Wang, Xiaoya II-420 Wang, Xiaoying V-117 Wang, Xing VI-239, VI-246, VI-253, VI-261 Wang, Yan IV-508 Wang, Y.C. V-359 Wang, Yiran III-247 Wang, Yongping III-276 Wang, YouHua V-18 Wang, Yu IV-252 Wang, Yude VI-480 Wang, Yuqiang V-170 Wang, Zhenxing III-166 Wang, Zhizhong IV-289 Wei, Cai II-150, II-172 Wei, Cheng-Wen V-10 Wei, Fengjuan II-235 Wei, Guo IV-252 Wei, Li III-30 Wei, Lin VI-79 Wei, Ling-ling III-212, III-218 Wei, Liu I-351 Wei, Ou V-409 Wei, Xianmin IV-418, IV-422 Wei, Yang II-253 Wei, Yu-Ting III-407 Wei, Zhou VI-305 Weihong, Chen III-89 Weihua, Liu III-183, III-369 Weihua, Xie V-30 Weimin, Wu IV-242 Weiqiong, He IV-219 Weiwei, Fang III-321 Weixi, Han I-465 Wen, Chengyu I-177 Wen, Jun Hao V-56 Wendi, Ma II-364 Wenping, Zhang I-184 Wu, Bin V-246

534

Author Index

Wu, Caiyan VI-424 Wu, Di II-143 WU, Guoshi III-247 Wu, Hao I-380 Wu, Kaijun V-43 Wu, Peng VI-452 Wu, Xiaofang IV-32 Wu, Xiwei II-357 Wu, Xue-li V-69 Wu, Yanqiang IV-470 Wu, Yong II-493 Wu, Zhongbing I-109 Xi, Ba IV-325 Xi, JunMei VI-7 -xia, Gao VI-297 Xia, Li IV-273, V-24 Xiang, Hongmei VI-94, VI-100 Xiang, Jun II-181 Xiang, Qian V-215 Xiang, Song VI-305 Xiang Li, Wang II-428 Xianzhang, Feng III-453, III-465 Xiao, Weng III-130 Xiao-hong, Zhang IV-411 Xiaolin, Chen II-281 Xiao-ling, He I-320, I-328 Xiaona, Zhou I-313 XiaoPing, Hu VI-350 Xiaosai, Li V-340 Xiaosheng, Liu I-213 Xiaowei, Wei VI-391 Xiaoxia, Zhao III-207 Xiaoya, He II-259 Xiaoyan, Xu VI-528 Xiao-ying, Wang I-343 Xiaoyong, Li II-364, II-382 Xie, Dong IV-25 Xie, Hualong III-337 Xie, Li V-1 Xie, Lihui II-218 Xie, Luning III-122 Xie, Qiang-lai III-212, III-218 Xie, Xiaona IV-167 Xie, Xing-Zhe IV-316 Xie, Zhengxiang IV-280 Xie, Zhimin I-21 Xifeng, Xue VI-148 Xijun, Liu V-476

Xilan, Feng III-453, III-465 Xiliang, Dai VI-124 Xilong, Jiang IV-219 Xin, Xiao IV-204, IV-212 Xin, Zhanhong I-222 Xing, GuiLin VI-357 Xing, Wang V-461 Xing, Xu VI-210 Xinhua, An II-40 Xinling, Wen VI-66, VI-474 Xinzhong, Xiong II-52 Xinzhong, Zhang I-373 Xi ping, Zhang I-150, I-206 Xiucheng, Dong V-340 Xu, Dawei IV-450 Xu, Jing III-166 Xu, Kaiquan II-458 Xu, Li VI-305 Xu, Ming III-95 Xu, Shuang I-550 Xu, Wanlu VI-398 xu, Wenke VI-20 Xu, Zhifeng II-17 Xu, Zhou III-64 Xuan, Dong VI-305 Xue, Qingshui IV-101, IV-110 Xuemei, Hou V-193 Xuemei, Li V-50 Xuemei, Tang V-158 Xuhua, Chen IV-342 Xuhua, Shi V-148, V-153 Xuhui, Wang II-22 Xun, Jin IV-252 Xuxiu IV-521 Xu-yang, Liu V-207 YaChao, Huang V-30 Yachun, Dai V-133 Yamin, Qin I-373 Yan, Fu I-14 Yan, Jun I-115 Yan, Peng V-74 Yan, Qingyou II-420 Yan, Shou III-158 Yan, Yunyang IV-120 Yan, Zhang IV-325 Yanbin, Shi VI-517, VI-522 Yang, Chen VI-210 Yang, Fangchun III-502

Author Index Yang, Fei IV-88 Yang, Hao III-122 Yang, Lianhe IV-476 Yang, Li Chen III-292 Yang, Liming V-365 Yang, Qian II-164 Yang, Qin VI-458 Yang, Renfa II-287 Yang, Song I-238, I-395 Yang, Wei III-112 Yang, Xinhua IV-450 Yang, Xue I-101, I-124 Yang, You-dong V-164 Yang, Yue II-87 Yanli, Shi VI-517, VI-522 Yanli, Xu IV-136, IV-144, IV-151, IV-159 Yanping, Liu III-369 Yan-yuan, Zhang VI-79 Yanzhen, Guo I-248 Yao, Lei-Yue III-218 Yaqiong, Wei I-132 Yazhou, Chen V-211 Ye, H.C. V-89 Yi, Cui V-82 Yi, Jing-bing III-54 Yi, Ru VI-474 Yifan, Shen III-312 Yihui, Wu IV-335 Yin, Jinghai IV-456 Yin, Qiuju IV-66 Yin, Zhang I-313 Yinfang, Jiang V-133 Ying, Mai I-280 Yingfang, Li IV-1 Yingjun, Feng IV-144, IV-151 Yingying, Ding III-89 Ying-ying, Zhang IV-42 Yixin, Guo VI-282 Yong, Wu I-156 Yong-feng, Li VI-39 Yongqiang, He III-413, III-420 Yongsheng, Huang V-518 Yong-tao, Zhao VI-224 Yongzheng, Kang I-161 You, Mingqing III-200 Youmei, Wang IV-18 Youqu, Lin II-326 Yu, Chen VI-66, VI-474 Yu, Cheng III-377, III-386

535

Yu, Chuanchun IV-120 Yu, Deng I-8 Yu, Jie VI-398 Yu, Liangguo VI-488 Yu, Quangang VI-239, VI-246, VI-253, VI-261 Yu, Shuxiu II-226 Yu, Siqin II-95 Yu, Tao I-244 Yu, Tingting V-281 Yu, Yan IV-48 Yu, Yao II-143 Yu, Zhichao I-52 Yuan, Fang I-76, I-83 Yuan, Feng III-473 Yuan, Qi III-312 Yuan, Xin-wei III-54 Yuanqing, Wang II-530 Yuanquan, Shi IV-204, IV-212 Yuanyuan, Zhang V-374 Yue, Wang VI-328 Yue, Xi VI-204 Yu-han, Zhang VI-319 Yuhong, Li I-465 Yuhua, He I-1 Yujuan, Liang VI-173 Yun, Wang V-133 Yun-an, Hu VI-224 Yunfang, Chen IV-242 YunFeng, Lin VI-350 Yunna, Wu IV-325 Yuxia, Hu VI-364 Yuxiang, Li V-105 Yuxiang, Yang VI-267 zelong, Xu VI-551 Zeng, Jie III-433 Zeng, Zhiyuan VI-152 Zhai, Ju-huai V-143 Zhan, Yulong II-143 Zhan, Yunjun I-421 Zhang, Bo I-437 Zhang, David IV-297 Zhang, Fan IV-32 Zhang, Fulin III-200 Zhang, Haijun I-437 Zhang, Haohan IV-172 Zhang, Hongjing II-10 Zhang, Hongzhi IV-297

536

Author Index

Zhang, Huaping VI-1 Zhang, Huiying V-416 Zhang, Jian VI-131 Zhang, Jianhua V-69 Zhang, Jie IV-93 Zhang, Kai V-404 Zhang, Kuai-Juan II-136 Zhang, Laishun IV-464 Zhang, Liancheng III-166 Zhang, Liang IV-484 Zhang, Liguo IV-508 Zhang, Ling II-466 Zhang, Minghong III-135 Zhang, Minghua I-451 Zhang, Qimin VI-376 Zhang, RuiTao V-18 Zhang, Sen V-529 Zhang, Shu V-43 Zhang, Shuang-Cai II-342 Zhang, Sixiang IV-427 Zhang, Tao VI-179 Zhang, Tingxian IV-360 Zhang, Wei II-350 Zhang, Wuyi III-64, III-74 Zhang, Xianzhi II-194 Zhang, Xiaolin I-500 Zhang, Xiuhong V-404 Zhang, Yi III-268 Zhang, Yingqian II-414 Zhang, Yong I-269 Zhang, Yu IV-189 Zhang, Yuanliang VI-117 Zhang, Yujin IV-441 Zhang, Yun II-66 Zhang, Zaichen IV-382 Zhang, Zhiyuan III-135 Zhangyanpeng I-513 Zhanqing, Ma V-399 Zhao, Hong V-275 Zhao, Huifeng II-244 Zhao, Jiyin I-550 Zhao, Liu V-321 Zhao, Min IV-306 Zhao, Xiaoming III-488 Zhao, Xiuhong III-433 Zhao, Ying III-276 Zhaochun, Wu II-26 Zhaoyang, Zhang II-530 Zhe, Yan VI-273 Zhen, Lu II-187

Zhen, Ran V-69 Zhen, Zhang VI-232 Zhen-fu, Li VI-290 Zheng, Chuiyong II-202 Zheng, Fanglin III-394 Zheng, Jun V-409 Zheng, Lin-tao IV-198 Zheng, Qian V-321 Zheng, Qiusheng III-401 Zheng, Xianyong I-94 Zheng, Yanlin III-192, III-394 Zheng, Yongliang II-181 Zhenying, Xu V-133 Zhi, Kun IV-66 Zhi’an, Wang II-22 Zhiben, Jie II-259 Zhibing, Liu II-172 Zhibo, Li V-193 Zhi-gang, Gan I-295 Zhi-guang, Zhang IV-411 Zhijun, Zhang II-109 Zhiqiang, Duan IV-342 Zhiqiang, Jiang III-453, III-465 Zhiqiang, Wang I-262 Zhisuo, Xu V-346 Zhiwen, Zhang II-101 Zhixiang, Tian II-373 Zhiyuan, Kang I-543 Zhong, Luo III-442 Zhong, Shaochun III-192 Zhong, Yuling II-181 Zhongji, Tan VI-517 Zhongjing, Liu VI-370 Zhonglin, He I-1, I-166 Zhongyan, Wang III-130 Zhou, Defu VI-430 Zhou, De-Qun II-466 Zhou, Fang II-493 Zhou, Fanzhao I-507 Zhou, Feng I-109 Zhou, Gang VI-417 Zhou, Hong IV-120 Zhou, Jing-Jing VI-58 Zhou, Lijuan V-309 Zhou, Wei IV-427 Zhou, Yonghua VI-492 Zhou, Zheng I-507 Zhu, JieBin V-498 Zhu, Jingwei III-508 Zhu, Libin I-335

Author Index Zhu, Lili II-420 Zhu, Linlin III-135 Zhu, Quanyin IV-120, IV-189 Zhu, Xi VI-152 Zhuanghua, Lu V-389

Zhuping, Du V-193 Zou, Qiong IV-129 Zunfeng, Liu V-381 ZuoMing IV-514 Zuxu, Zou II-81

537