Luo Advancing Computing, Communication, Control and Management

1. Auflage 2009
ISBN: 978-3-642-05173-9
Verlag: Springer
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 290 Seiten

A large 2008 ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM 2008), was held in Guangzhou, August 2008, China. Just like the name of the Colloquium, the theme for this conference is Advancing Computing, Communication, Control, and Management Technologies. 2008 ISECS International Colloquium on Computing, Communication, Control, and Management is co-sponsored by Guangdong University of Business Studies, China, Peoples' Friendship University of Russia, Russia, Central South University, China, Southwestern University of Finance & Economics, China, and University of Amsterdam, Netherlands. It is also co-sponsored IEEE Technology Management Council, IEEE Computer Society, and Intelligent Information Technology Application Research Institute. Much work went into preparing a program of high quality. We received about 972 submissions. Every paper was reviewed by 3 program committee members, about 382 were selected as regular papers, representing a 39% acceptance rate for regular papers. The CCCM conferences serve as good platforms for the engineering community to meet with each other and to exchange ideas. The conference has also stroke a balance between theoretical and application development. The conference committees have been formed with over two hundred committee members who are mainly research center heads, faculty deans, department heads, professors, and research scientists from over 30 countries. The conferences are truly international meetings with a high level of participation from many countries. The response that we have received for the congress is excellent. This volume contains revised and extended research articles written by prominent researchers participating in the conference.

Autoren/Hrsg.

Luo, Qi

Inhaltsverzeichnis

1;Title Page;2
2;Preface;5
3;Table of Contents;6
4;Study on MRF and REF to Semi-supervised Classification;9
4.1;Introduction;9
4.2;MRF with REF;10
4.2.1;MRF for Semi-supervised Classification;10
4.2.2;REF;10
4.2.3;MRF with REF;11
4.3;Algorithm;12
4.3.1;ICM;12
4.3.2;MCMC;12
4.4;Experiments;13
4.5;Conclusion;14
4.6;References;14
5;An Extension Method of Space Syntax and Application;15
5.1;Introduction;15
5.2;Extension Method of Space Syntax;16
5.2.1;Taking Road Width into Account;16
5.2.2;Extending Integration Degree;17
5.3;Extension Method of Space Syntax;17
5.3.1;Data Selection and Processing;17
5.3.2;Data Calculation;19
5.4;Results and Discussion;19
5.4.1;Negative Correlation between Total Depth and Logarithm of Road Width;19
5.4.2;Comparison of Integration Degrees before and after Improvement;20
5.5;Conclusion;20
5.6;References;21
6;A Feasible Registration Method for Underwater SLAM;23
6.1;Introduction;23
6.2;Current Data Association Methods;24
6.3;Problem Formulation;25
6.4;Experiment Results;27
6.4.1;Straight Sailing Mode;28
6.4.2;Local Searching Mode;29
6.5;Conclusions;30
6.6;References;30
7;Promoted Global Convergence Particle Swarm Optimization Algorithm;31
7.1;Introduction;31
7.2;The Basic PSO;32
7.3;Convergence Problem of PSO;32
7.4;A Promoted Global Convergence PSO;34
7.5;Performance Test;36
7.6;Inclusion;37
7.7;References;38
8;A Web-Based Integrated System for Construction Project Cost Prediction;39
8.1;Introduction;39
8.2;Construction Project Cost Analysis Based on FCE and RS;40
8.2.1;Date Deal with FCE;40
8.2.2;Date Based on RS;41
8.3;ANN Based on PSO;43
8.4;Web-Base Conceptual Cost Pridiction;44
8.4.1;Conceptual Design;44
8.4.2;Configuration and Simulation of Neural Network;44
8.5;Conclusions;45
8.6;References;46
9;Research of Corporate Credit for Anhui Province’s Listed Companies Based on Computer Technology;47
9.1;Introduction;47
9.2;The Related Theories;48
9.2.1;Credit and Credit Risk;48
9.2.2;Research Methods;48
9.2.3;Index Selection;49
9.3;Realization of Principal Component Analysis;49
9.4;Analysis of Corporate Credit Risk;50
9.5;References;54
10;Evaluation of Industrial Parks’ Industrial Transformations and Environmental Reform Actualized by AHP Based on MatLab Software;55
10.1;Introduction;55
10.2;Patterns of Industrial Transformations and Environmental Reform for Industrial Parks;56
10.2.1;Transformational Patterns for Industrial Parks;56
10.2.2;Formation of Symbiotic Effect in Industrial Parks;57
10.3;Evaluation of Industrial Transformations and Environmental Reform Actualized by AHP Based on MatLab Software;57
10.4;Conclusions;60
10.5;References;61
10.6;Appendix: MatLab Program;62
11;A Model Integrated Development of Embedded Software for Manufacturing Equipment Control;63
11.1;Introduction;63
11.2;Overview on MIC;64
11.3;System Architecture;65
11.4;An Example for System Development Using MIC;66
11.4.1;Meta-modeling;66
11.4.2;Modeling;67
11.4.3;Mapping;68
11.5;Conclusions;68
11.6;References;68
12;Two Stage Estimation Method in Data Processing and Simulation Analysis;70
12.1;Introduction and Motivation;70
12.2;The Mathematical Model;71
12.3;Estimation Method and Models Solution;72
12.4;Fitted Values and Hat Matrix;74
12.5;Bandwidth Selection;74
12.6;Simulation Experiment;76
12.7;Conclusion;77
12.8;References;78
13;Research and Implementation of a Reconfigurable Parallel Low Power E0 Algorithm;79
13.1;Introduction;79
13.2;Description of E0 Algorithm;80
13.3;Design for Low-Power Consumption;81
13.4;Reconfigurable Parallel Low Power Architecture of E0 Algorithm;82
13.4.1;The Implementation of Finite State Machine;83
13.5;Analysis and Comparison of Performance;84
13.5.1;The Realization of FPGA and ASIC;84
13.6;Conclusion;86
13.7;References;86
14;A Model of Car Rear-End Warning by Means of MAS and Behavior;87
14.1;Introduction;87
14.2;MCRWMB;88
14.2.1;Driving Behavior;88
14.2.2;MCRWMB Structure;89
14.2.3;Multi-agent Communications Based on Extended KQML;89
14.3;Bayes Decision with Driving Behavior;90
14.3.1;The Modal of Environment and Danger;90
14.3.2;Learning of Driving Behavior Based on Ensemble ANN;91
14.3.3;MCRWMB Algorithm;92
14.4;Simulation Experiment and Analysis;92
14.5;Conclusion;94
14.6;References;94
15;Novel Hue Preserving Algorithm of Color Image Enhancement;96
15.1;Introduction;96
15.2;The Algorithm Based on His Color Space;97
15.2.1;HIS Color Space;97
15.2.2;I Component Enhancement;98
15.2.3;S Component Enhancement;101
15.3;Result Analysis;102
15.4;Conclusions;103
15.5;References;103
16;Artificial Immune System Clustering Algorithm and Electricity Customer Credit Analysis;105
16.1;Introduction;105
16.2;Artificial Immune System Cluster Analysis Principles;106
16.3;Process of Artificial Immune System Cluster Analysis;107
16.4;Electricity Customer Credit Analysis Based on Artificial Immune System Cluster Algorithm;108
16.5;Conclusions;109
16.6;References;110
17;Virtual Space Sound Technique and Its Multimedia Education Application;111
17.1;Introduction;111
17.2;About Space Sound Technique;111
17.3;VRML Sound Effect Principles;113
17.3.1;Principle of AudioClip Nodes;113
17.3.2;Principle about Sound Node;115
17.4;Virtual Sound Application;117
17.5;References;118
18;Survey on Association Rules Mining Algorithms;119
18.1;Introduction;119
18.2;Basic Principles of Association Rules;119
18.3;The Research Direction of Association Rules;120
18.3.1;Improving the Algorithm to Increase Mining Efficiency;120
18.3.2;Proposing New Algorithm to Extend the Notion of Association Rules;121
18.3.3;The Integration of Association Rules and Classification;122
18.3.4;The Research on Parameter Such as Support and Confidence;123
18.4;Future Works;123
18.5;Summarize;124
18.6;References;124
19;Application of Rough Set Theory and Fuzzy LS-SVM in Building Cooling Load;127
19.1;Introduction;127
19.2;Basic Principle of RST and Fuzzy LS-SVM;128
19.2.1;Basic Concept of Rough Set Theory;128
19.2.2;Fuzzy Least Square Support Vector Machine;129
19.3;Data Preparation Based on RST and Fuzzy LS-SVM Model;132
19.3.1;Fuzzy LS-SVM Predictor Design;132
19.3.2;Experiment Result;133
19.4;Conclusion;134
19.5;References;134
20;An Efficient Feature Selection Algorithm Based on Hybrid Clonal Selection Genetic Strategy for Text Categorization;135
20.1;Introduction;135
20.2;Related Works;136
20.3;HCSGA Algorithm;137
20.4;Experiments;139
20.5;Conclusions;141
20.6;References;141
21;Power Demand Forecasting Based on BP Neural Network Optimized by Clone Selection Particle Swarm;143
21.1;Introduction;143
21.2;Power Demand Modeling;144
21.2.1;Variable Data Selection and Pretreatment;144
21.2.2;Determine BP Neural Network Structure;144
21.3;Learning Process of Neural Network Model Based on Clone Selection Particle Swarm Algorithm (CSPSO-BP);145
21.4;Power Demand Forecast;147
21.5;Conclusions;148
21.6;References;148
22;Research on Simplifying the Motion Equations and Coefficients Identification for Submarine Training Simulator Based on Sensitivity Index;150
22.1;Introduction;150
22.2;Submarine Space Motion Equation;151
22.3;Typical Steering Test;152
22.4;The Sensitivity Index;152
22.4.1;The Sensitivity Calculation of Hydrodynamic Coefficients;153
22.4.2;Parts of Hydrodynamic Coefficients Results;153
22.4.3;Analyses of Results;154
22.5;Particle Swarm Optimization (pso);154
22.6;Conclusion;156
22.7;References;156
23;Evaluating Quality of Networked Education via Learning Action Analysis;158
23.1;Introduction;158
23.2;Quality Evaluation Criteria;159
23.2.1;Relevance;159
23.2.2;Interpretation;160
23.3;Quality Evaluation with Learning Action Analysis;161
23.3.1;Reflection Evaluation;162
23.3.2;Tutor Support Evaluation;162
23.4;Case Study and Discussion;163
23.5;Conclusion and Future Work;164
23.6;References;164
24;Research on Face Recognition Technology Based on Average Gray Scale;166
24.1;Introduction;166
24.2;Image Pre-processing;167
24.3;Feature Vector;167
24.3.1;Face Feature Vector;167
24.3.2;Feature Vector;168
24.4;Feature Extraction Algorithm Analysis;171
24.4.1;The Feature Extraction Algorithm;171
24.4.2;Complexity Analysis;171
24.5;Experiment and Results;171
24.6;Conclusion;172
24.7;References;173
25;The Active Leveled Interest Management in Distributed Virtual Environment;174
25.1;Introduction;174
25.2;AIMNET;174
25.3;Leveled Interest Management;176
25.4;Active Leveled Interest Management;177
25.5;Conclusion;181
25.6;References;181
26;The Research of the Intelligent Fault Diagnosis Optimized by ACA for Marine Diesel Engine;182
26.1;Introduction;182
26.2;The Structure of Fuzzy Neural Network;183
26.3;The Optimization and Study Algorithm of FNN Parameters;185
26.4;The Simulation Results of Intelligent Fault Diagnosis System;186
26.5;Conclusions;188
26.6;References;189
27;Extraction and Parameterization of Eye Contour from Monkey Face in Monocular Image;190
27.1;Introduction;190
27.2;Face Segmentation;191
27.3;Face Normalization;193
27.4;Eye Contour Extraction;194
27.5;Eye Contour Parameterization;194
27.6;Conclusion;196
27.7;References;197
28;KPCA and LS-SVM Prediction Model for Hydrogen Gas Concentration;198
28.1;Introduction;198
28.2;Kernel Principal Component Analysis (KPCA);199
28.3;LS-SVM Forecasting Model;201
28.4;KCPA-LSSVM Based Hydrogen Gas Concentration Forecasting;203
28.5;Conclusions;204
28.6;References;205
29;Random Number Generator Based on Hopfield Neural Network and SHA-2 (512);206
29.1;Introduction;206
29.2;Architecture of Random Number Generator Based on Hopfield Neural Network;207
29.3;Hopfield Neural Network;208
29.4;SHA-2 Hash Function;210
29.5;Experimental Results and Conclusion;211
29.6;References;212
30;A Hybrid Inspection Method for Surface Defect Classification;214
30.1;Introduction;214
30.2;Rough Set for Feature Selection of Wood Veneer Defect;215
30.2.1;Basic Concepts;215
30.2.2;A Rough Sets Feature Selection Method;216
30.3;A Neural Network with Fuzzy Input for Inspection;217
30.3.1;Fuzzifier;217
30.3.2;A Neural Network Algorithm with Fuzzy Input;218
30.4;A Classifier Using Rough Sets Based Neural Network with Fuzzy Input;218
30.4.1;Classifier Optimization;220
30.4.2;Experiments;220
30.5;Conclusions;221
30.6;References;221
31;Study on Row Scan Line Based Edge Tracing Technology for Vehicle Recognition System;222
31.1;Introduction;222
31.2;Pre-processing for Recognition;223
31.2.1;Binarization Processing;223
31.2.2;Edge-Based Segmentation;223
31.2.3;Edge Detection and Thinning;224
31.3;Edge Tracing Algorithm;224
31.3.1;Edge Tracing;224
31.3.2;Principle of Neighbor of Tracing Algorithm;225
31.3.3;Implement of the Algorithm;225
31.3.4;Performance Analysis;226
31.4;Computer Simulation and Results;227
31.4.1;Image Data;227
31.4.2;Experiments;228
31.4.3;Results;228
31.5;Conclusion and Future Work;228
31.6;References;229
32;Study on Modeling and Simulation of Container Terminal Logistics System;230
32.1;Introduction;230
32.2;Logistics System Modeling Technology;231
32.2.1;Oversea Review;231
32.2.2;Review in China;232
32.2.3;Models;232
32.2.4;Primary Coverage and Key Problem;233
32.3;Verification Validation and Accreditation;234
32.3.1;Review;234
32.3.2;Primary Coverage and Key Problem;234
32.4;Screening Test and Robsut Design;235
32.4.1;Screening Review;235
32.4.2;Robust Review;235
32.4.3;Primary Coverage and Key Problem about Screen;236
32.4.4;Primary Coverage and Key Problem about Robust;236
32.5;Conclusion;237
32.6;References;237
33;Digitalized Contour Line Scanning for Laser Rapid Prototyping;239
33.1;Introduction;239
33.2;Principle Comparing of SLS, SL and LOM;240
33.3;Digitalized Contour Line Scanning;243
33.4;Case Study;244
33.5;Conclusion;245
33.6;References;246
34;3D Surface Texture Synthesis Using Wavelet Coefficient Fitting;247
34.1;Introduction;247
34.2;Novel Texture Synthesis Using Wavelet Coefficient Fitting;249
34.2.1;Wavelet Transform;249
34.2.2;Novel Texture Synthesis Using Wavelet Coefficient Fitting;249
34.3;Rendering and Relighting the 3D Synthesis Surface Texture;251
34.3.1;Mathematical Framework of the Gradient Method;251
34.3.2;Linear Combination of Three Synthesized Images from “a”, “b” and “c” Image;251
34.4;Experimental Results;251
34.5;Conclusion;253
34.6;References;253
35;Building Service Oriented Sharing Platform for Emergency Management – An Earthquake Damage Assessment Example;255
35.1;Introduction;255
35.2;Emergency Management;256
35.3;Architecture of Prototype;256
35.3.1;Basic Components;257
35.3.2;Indicator Management;258
35.3.3;Navigation into IQ Data;258
35.4;Component Design;260
35.4.1;Quality Service;260
35.4.2;Statistics Service;260
35.4.3;Catalog Service;261
35.4.4;Earthquake Client Application;262
35.5;Conclusion;262
35.6;References;262
36;An Interactive Intelligent Analysis System in Criminal Investigation;264
36.1;Introduction;264
36.2;Related Works;265
36.2.1;Knowledge Mapping Inversion Principle;265
36.2.2;Intuition Learning System;266
36.3;Interactive Intelligent Analysis Systems;267
36.3.1;Establishment of Cooperative Relational Database;268
36.3.2;Establishment of Intuitionistic Fuzzy Learning System;268
36.4;An Examples;269
36.5;Conclusion and Future Work;270
36.6;References;271
37;Research on Functional Modules of Gene Regulatory Network;272
37.1;Introduction;272
37.2;Discussion on Functional Modules of Gene Regulatory Network;273
37.3;Probability Boolean Network;274
37.4;Functional Modules of PBN;276
37.5;Example;277
37.6;Conclusion;278
37.7;References;278
38;Block-Based Normalized-Cut Algorithm for Image Segmentation;280
38.1;Introduction;280
38.2;Block-Based Normalized Cut;281
38.3;Experimental Results;283
38.3.1;Experimental Design;283
38.3.2;Parameter Selection;284
38.4;Conclusions and Future Work;285
38.5;References;286
39;Semantics Web Service Characteristic Composition Approach Based on Particle Swarm Optimization;287
39.1;Introduction;287
39.2;Semantics Web Service Characteristic Composition Approach;288
39.3;Semantics Web Service Characteristic Composition Optimization;291
39.4;Experiment Result;293
39.5;Conclusion;294
39.6;References;294
40;Author Index;296