Wang / Li Practical Applications of Intelligent Systems

Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE 2011)
1. Auflage 2012
ISBN: 978-3-642-25658-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 124, 766 Seiten

Reihe: Advances in Intelligent and Soft Computing

Proceedings of the Sixth International Conference on Intelligent System and Knowledge Engineering presents selected papers from the conference ISKE 2011, held December 15-17 in Shanghai, China. This proceedings doesn't only examine original research and approaches in the broad areas of intelligent systems and knowledge engineering, but also present new methodologies and practices in intelligent computing paradigms. The book introduces the current scientific and technical advances in the fields of artificial intelligence, machine learning, pattern recognition, data mining, information retrieval, knowledge-based systems, knowledge representation and reasoning, multi-agent systems, natural-language processing, etc. Furthermore, new computing methodologies are presented, including cloud computing, service computing and pervasive computing with traditional intelligent methods.
The proceedings will be beneficial for both researchers and practitioners who want to utilize intelligent methods in their specific research fields.
Dr. Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dr. Tianrui Li is a professor at the School of Information Science and Technology, Southwest Jiaotong University, China.

Yinglin Wang is a professor at the Department of Computer Science and Engineering, Shanghai Jiao Tong University. He received a PhD in 1998 from Department of Computer Science and Technology, Nanjing University of Science and Technology. His major research interests lie in the areas of knowledge engineering, software engineering, intelligent information processing and service computing. He has supervised 16 government funded research projects and industrial projects in knowledge management and information systems. He has taught courses of data mining, machine learning, data structure and programming methodologies for many years. He has published over 80 research papers in journals and conference proceedings, published 1 book, and edited 2 international conference proceedings. He has chaired serveral the international conferences and workshops, such as PIC 2010, ICIS 2010, ICIS 2009, and NPC 2008 Workshop, etc.
Tianrui Li is a professor at the School of Information Science and Technology, Southwest Jiaotong University. He got his PhD in 2001 from Computer and Communication Engineering School, Southwest Jiaotong University. He worked in the Belgian Nuclear Research Centre as a Post doctorate researcher in 2005. His research fields includes intelligent information processing, data mining, mathematical modeling, could computing etc. He has taught courses of data mining, computing intelligence, discrete mathematics, and mathematical modeling for several years. He has published 90 research papers in journals and conference proceedings, edited 2 books, and 3 special issues of international journals.

Autoren/Hrsg.

Wang, Yinglin

Li, Tianrui

Inhaltsverzeichnis

1;Title
;1
2;Preface;5
3;Organizing Committee;8
4;Contents;13
5;Invited Talks;13
5.1;User-Centric Models of Temporal and Spatiotemporal
Data: A Perspective of Granular Computing;21
5.2;Intelligent Social Network Modeling;22
5.3;What Is Your Software Worth?;23
5.4;Personalized Recommender Systems for e-Government
and e-Business Intelligence;24
6;Part I:
Social Computing, Mobileand Service Computing;26
6.1;Defense and Compensation Status and Insurance
Requirement of Major Natural Disasters in Hebei Province –– Based on Investigation of 262 Urban and Rural Residents;27
6.1.1;Introduction;27
6.1.2;Data Sources and Research Methods;28
6.1.2.1;Data Sources;28
6.1.2.2;Research Methods;28
6.1.3;Major Natural Disasters in Hebei Province of Defense
and Compensation the Status Quo;28
6.1.4;Urban and Rural Residents in Hebei Province Major Natural
Disaster Insurance Requirements Analysis;29
6.1.4.1;Urban and Rural Residents in Hebei Province of Major Natural Disaster's
Insurance Needs Intend to Analysis;29
6.1.4.2;Urban and Rural Residents in Hebei Province Major Natural Disaster
Insurance Needs Analysis of Influence Factors of Will;30
6.1.5;Countermeasures;34
6.1.6;References;36
6.2;Design and Analysis of Credit Network over DHT
File-Sharing Network;37
6.2.1;Introduction;37
6.2.2;Credit and Credit Network;38
6.2.2.1;System Model;38
6.2.2.2;The Process of Join;38
6.2.2.3;Credit Update;39
6.2.2.4;Route Algorithm in CN;39
6.2.3;Incentvie Compatibility of Credit Game;40
6.2.4;Simulations;41
6.2.5;Summary;42
6.2.6;References;42
6.3;Research and Realization on the Community Health Care
System Based on Wireless Sensor Network;43
6.3.1;Introduction;43
6.3.2;Overall Design and Hardware Implementation of Wireless
Sensor Node;43
6.3.2.1;System Framework;44
6.3.2.2;Hardware Implementation of Wireless Sensor Node;44
6.3.3;Parts Design;44
6.3.3.1;Topology of the Health Care System;44
6.3.3.2;Analysis and Implementation of Routing Protocol;45
6.3.3.3;Overall Implementation of Wireless Sensor Nodes Program;45
6.3.3.4;Design of PC;46
6.3.4;Test and Analysis;46
6.3.4.1;Transmission Distance Test;46
6.3.4.2;Wireless Multi-hop Test;46
6.3.5;Conclusion;47
6.3.6;References;47
6.4;A Novel Multi-Strategy Routing for UWB Ad Hoc
Networks;48
6.4.1;Introduction;48
6.4.2;Routing Algorithm;49
6.4.2.1;Network Position Map;49
6.4.2.2;Influence Factor;49
6.4.2.3;Routing Algorithm;50
6.4.2.4;Routing Strategies;50
6.4.3;Numerical Simulations;51
6.4.4;Conclusion;53
6.4.5;References;53
6.5;Probability Based Timed Compatibility of Web
Service Composition;54
6.5.1;Introduction;54
6.5.2;Preliminary Formalism;55
6.5.2.1;Timed Service Model Based on Normal Distribution;55
6.5.2.2;Temporal Constraints and Their Probability Based Consistency;56
6.5.3;Probability Based Timed Compatibility and Its Checking;57
6.5.3.1;Probability Based Timed Compatibility;57
6.5.3.2;Checking of Probability Based Timed Compatibility;58
6.5.4;Case Study;58
6.5.4.1;Case Specification;58
6.5.4.2;Analysis of Case;60
6.5.5;Conclusion;61
6.5.6;References;62
6.6;Designing an Adaptation Management
Framework for Mobile Payment;63
6.6.1;Introduction;63
6.6.2;Context Aware Mobile Payment;65
6.6.3;Adaptation Management Frameworks for Mobile Payment
Service;66
6.6.3.1;Fourth Layer;67
6.6.3.2;Fifth Layer;69
6.6.4;Evaluation;69
6.6.5;Conclusion;70
6.6.6;References;71
6.7;Research of Source Mobility of Source Specific Multicast;74
6.7.1;Introduction;74
6.7.2;An Overview of Tree Morphing Protocol;75
6.7.3;Improved Tree Morphing Protocol;76
6.7.4;Simulation Experiment and Result;78
6.7.5;Conclusion;79
6.7.6;References;79
6.8;A Parallel Method for Unpacking Original High
Speed Rail Data Based on MapReduce;80
6.8.1;Introduction;80
6.8.2;A Parallel Algorithm of Unpacking Original High Speed Rail Data (PAUOHSRD) Based on MapReduce;81
6.8.2.1;Analysis of the Original High Speed Rail Data Format;81
6.8.2.2;The MapReduce Model;82
6.8.2.3;Algorithms for Unpacking Original High Speed Rail Data;82
6.8.2.4;PAUOHSRD Based on MapReduce;83
6.8.3;Exprimental Analysis;86
6.8.3.1;Speedup;86
6.8.3.2;Scaleup;87
6.8.3.3;Sizeup;87
6.8.4;Conclusion;88
6.8.5;References;88
7;Part II:
Intelligent Game and HumanComputer Interaction;90
7.1;A Design for Children-Oriented Human-Computer
Interaction;91
7.1.1;Introduction;91
7.1.2;A Structure for Children-Oriented Human-Computer
Interaction: WisPad;92
7.1.2.1;Design Principles;92
7.1.2.2;WisPad System Design;92
7.1.3;Application Prototype System of WisPad: iNature;93
7.1.3.1;System Structure;93
7.1.3.2;Scene Management;95
7.1.4;System Evaluation;97
7.1.4.1;User Satisfaction Evaluation;97
7.1.4.2;Learning Time Evaluation;97
7.1.5;Conclusion;98
7.1.6;References;98
7.2;Place Concept Teaching through Sketch Map for Robot
Place Perception Based on Prototype Mechanism;99
7.2.1;Introduction;99
7.2.2;Prototype Mechanism for Place Perception;100
7.2.2.1;Prototype of Place Concept;100
7.2.2.2;Similarity Measure for Prototype;101
7.2.3;Sketch Map Understanding;103
7.2.3.1;Primitive Definition;103
7.2.3.2;Sketch Map Analysis;104
7.2.4;Simulation Experiment;106
7.2.5;Conclusion;108
7.2.6;References;108
7.3;Analysis and Application of Design Principle for Mobile
Web: Using 19k Wind Website as Example;110
7.3.1;Introduction;110
7.3.2;Related Works;111
7.3.2.1;Web-Based Cycling Community;111
7.3.2.2;Mobile Interface Design;111
7.3.2.3;Interactive Design Principle;112
7.3.3;Methodology;112
7.3.3.1;Research Process;112
7.3.4;Results;113
7.3.4.1;19K Wind Mobile Web Design;113
7.3.5;Conclusion and Discussion;114
7.3.6;References;114
7.4;Navigation and Visualisation Tools Usage in Large
Internet and Multimedia Resources;116
7.4.1;Introduction;116
7.4.2;Background;117
7.4.3;Navigational Patterns Research;117
7.4.4;Main Focus of the Paper
;118
7.4.4.1;Developing Audit Trails;118
7.4.4.2;Recognising Navigation Patterns;119
7.4.4.3;Investigating Navigation Tools;119
7.4.5;Producing New Navigation Tools;120
7.4.6;New Research Areas, Applying Navigation and Visualisation
Tools;120
7.4.7;ARCH Project Developments;121
7.4.8;Discussion;121
7.4.9;Conclusions;122
7.4.10;References;122
7.5;Establishment of Interactive Virtual Exhibition System
Based on Quest3D;124
7.5.1;Introduction;124
7.5.2;Whole Design Plan of Virtual Exhibition System;125
7.5.3;The Preparation of Three - Dimensional Model;126
7.5.4;Role Access Control Settings in Quest3D;127
7.5.5;Camera Switching Technology;129
7.5.6;Enhanced 3D Sound Control Technology of Immersed Sense;130
7.5.7;Two - Dimensional Interactive Interface Production;131
7.5.8;Conclusion;131
7.5.9;References;132
7.6;A Diving Posture Recognition Method Based
on Multiple Features Fusion;133
7.6.1;Introduction;133
7.6.2;Object Segmentation;134
7.6.2.1;Lucas_Kanade Optical Flow;134
7.6.2.2;Global Motion;136
7.6.2.3;Skin Detection;137
7.6.2.4;Projection;138
7.6.3;Features Extraction;138
7.6.3.1;Color;139
7.6.3.2;Aspect Ratio of Object Rectangle;139
7.6.3.3;Object Area Proportion;139
7.6.3.4;SIFT;140
7.6.4;Experiment;141
7.6.5;Conclusion;142
7.6.6;References;143
8;Part III:
Intelligent Engineering System;144
8.1;Fusion of Text and Image Features: A New
Approach to Image Spam Filtering;145
8.1.1;Introduction;145
8.1.2;Related Work;146
8.1.3;Hybrid Framework for Image Spam Filtering;147
8.1.3.1;Keyword Detection;148
8.1.3.2;Text-Related Features Extraction;149
8.1.3.3;Image Features Extraction;151
8.1.3.4;Bottom-Layer Classifiers;151
8.1.3.5;Classifier Fusion;153
8.1.4;Experiment;153
8.1.4.1;Experimental Setup;153
8.1.4.2;Experimental Results;154
8.1.5;Conclusion;155
8.1.6;References;155
8.2;An Auto-Tuning PI Controller for the Speed Control of a
Permanent Magnet Synchronous Motor Drive;157
8.2.1;Introduction;157
8.2.2;Controller Description;158
8.2.2.1;Design of ATPIC;158
8.2.2.2;Auto-tuning Strategy;159
8.2.2.3;Stability;159
8.2.3;Modeling of a PMSM Drive System;160
8.2.4;Simulation;160
8.2.5;Conclusion;162
8.2.6;References;162
8.3;Modified Quasilinearization Method for Optimal Launch
Mission Planning Problems;163
8.3.1;Introduction;163
8.3.2;Modified Quasilinearization Method;164
8.3.2.1;The System formation of MQM;164
8.3.2.2;The Verification of the Convergent Ability of Modified Quasilinearization
Method;165
8.3.2.3;The Algorithmic Process of Modified Quasilinearization Method;166
8.3.3;Numerical Simulation;167
8.3.3.1;The Description of the Problem;167
8.3.3.2;Simulation Result and Analysis;169
8.3.4;Conclusion;171
8.3.5;References;171
8.4;Topology Analysis and Fault Diagnosis Scheme
with OOCPN Model for Supply System in Urban Mass Transit;173
8.4.1;Introduction;173
8.4.2;Protection Layout in Mass Transit Supply;174
8.4.3;Topology Analysis Based on OOCPN Model;175
8.4.3.1;Object-Oriented Colored Petri-Net (OOCPN);175
8.4.3.2;The Application of OOCPN into Topology Analysis in Mass Transit
Supply;176
8.4.4;The Backward Reasoning with Protection Information;179
8.4.5;Conclusions;180
8.4.6;References;181
8.5;The Design of FBG Strain Sensors Based on Data
Acquisition System;182
8.5.1;Introduction;182
8.5.2;FBG Strain Sensors Demodulation Principle;182
8.5.2.1;FBG Strain Sensor Demodulation System Components;182
8.5.2.2;The Selection of Fiber F-P Filter;184
8.5.2.3;FBG Strain Sensing Properties;184
8.5.3;DSP Microcomputer Processing System;185
8.5.3.1;The Hardware Design of the Overall Structure;185
8.5.3.2;The Design of A/D Sampling Module;186
8.5.3.3;The Design of D/A Converter Module;186
8.5.4;Test Results Analysis;186
8.5.5;Summary;187
8.5.6;References;187
8.6;Negative Selection Algorithm-Based Motor
Fault Diagnosis;188
8.6.1;Introduction;188
8.6.2;Principles of Negative Selection Algorithm (NSA);189
8.6.3;Negative Selection Algorithm (NSA) in Fault Diagnosis;190
8.6.4;Simulations;193
8.6.5;Conclusions;197
8.6.6;References;197
8.7;Real-Time Steel Inspection System Based on Support
Vector Machine and Multiple Kernel Learning;199
8.7.1;Introduction;199
8.7.2;Defect Detection;200
8.7.3;Feature Extraction and Classification via MKL;201
8.7.4;Experiment Results;203
8.7.5;Conclusion;204
8.7.6;References;204
8.8;Predicting Subcellular Localizations of Membrane
Proteins in Eukaryotes with Weighted Gene Ontology Scores;205
8.8.1;Introduction;205
8.8.2;Materials and Methods;206
8.8.2.1;Datasets;206
8.8.2.2;Algorithms;206
8.8.2.3;Evaluation Protocols;207
8.8.3;Results and Discussions;208
8.8.4;Conclusions;209
8.8.5;References;209
8.9;Prediction Mining in the Market
Impact Cost of Securities Investment;210
8.9.1;Introduction;210
8.9.2;Analysis of Market Impact Cost in Stock Transactions;210
8.9.2.1;Variables Correlation Analysis;211
8.9.2.2;Impact Cost Prediction Model
;211
8.9.3;Testing and Evaluation of Cost Prediction Model;213
8.9.3.1;Model Testing;213
8.9.3.2;Application and Comparison of the Model in the Transaction Optimization;214
8.9.4;Conclusion and Future Work;214
8.9.5;References;215
8.10;Digital Watermarking Algorithm Based on Iris Features;216
8.10.1;Introduction;216
8.10.2;The Iris Texture Feature Extraction Algorithm;216
8.10.3;Iris Recognition Method of SVD and DWT Combination;218
8.10.3.1;Wavelet Transform;218
8.10.3.2;Singular Value Decomposition;218
8.10.3.3;The Algorithm Description;218
8.10.4;The Experimental Results and Performance Analysis;220
8.10.4.1;The Experimental Results
;220
8.10.4.2;The Attack Test;220
8.10.5;Conclusion;221
8.10.6;References;221
8.11;Improved Adaptive Algorithm for Ship
Trajectory Estimation;222
8.11.1;Introduction;222
8.11.2;Basic Theory of Ship Motion Model;223
8.11.2.1;Ship Dynamic Motion Model;223
8.11.2.2;Kalman Filter Model;223
8.11.3;Improved Ship Trajectory Estimation;224
8.11.3.1;Attention and Improvement;224
8.11.3.2;Improved Adaptive Filtering Algorithm;225
8.11.4;Experiments and Discussion;226
8.11.5;Conclusion;227
8.11.6;References;227
8.12;Research on the Fault Diagnosis of Wind Turbine
Gearbox Based on Bayesian Networks;229
8.12.1;Introduction;229
8.12.2;Application of Bayesian Networks in the Field of Fault Diagnosis;229
8.12.3;Structure of Bayesian Networks;230
8.12.3.1;Directed Acyclic Graph;230
8.12.3.2;Conditional Probability Table Associated With Each Node;230
8.12.4;State Identification Based on Bayesian Networks Classifier;231
8.12.4.1;Bayesian Formula;231
8.12.4.2;Decision Rule of Bayesian Minimum Error Rate;231
8.12.4.3;Bayesian Classifier Theories;232
8.12.5;Establishment of Speed-Up Gearbox Bayesian Model;232
8.12.6;A Case of Calculation and Analysis;233
8.12.7;Conclusion;234
8.12.8;References;235
8.13;Test Selection for Complex System
Based on Clonal Selection Algorithm;236
8.13.1;Introduction;236
8.13.2;Multi-signal Modeling;237
8.13.3;Test Selection Based on Clonal Selection Algorithm;237
8.13.3.1;Biological Mechanism of CSA;237
8.13.3.2;CSA-Based Method for Test Selection
;238
8.13.4;Experiments;239
8.13.5;Conclusion;240
8.13.6;References;240
8.14;A Fast and Efficient Algorithm for Intelligent Test
Paper Generating;242
8.14.1;Introduction;242
8.14.2;Applied Environmental and the Nature of the Problem of the
Algorithm;243
8.14.3;Intelligent Generating Test Paper Algorithm Based on Chapter
Ratio Score and Adaptive Releasing Strategy;244
8.14.3.1;Optimize the To-Be-Selected Question Set;244
8.14.3.2;Preprocess Before Generating Test Paper;244
8.14.3.3;Intelligent Generating Test Paper Based on Chapter Ratio Score and
Releasing Strategy;244
8.14.4;Experiment Analysis;247
8.14.5;References;247
8.15;Automated Vulnerability Assessment and Intrusion
for Server Vulnerabilities;248
8.15.1;Introduction;248
8.15.2;Overview of Vulnerabilities;248
8.15.2.1;Definition of Vulnerabilities;248
8.15.2.2;Buffer Related Vulnerabilities;249
8.15.3;Fuzzing Technique and Fuzzer;250
8.15.3.1;Introduction to Fuzzing Technique and Fuzzer;250
8.15.3.2;Design an FTP-Oriented Fuzzer;250
8.15.3.3;Automated Mining Approach Based on FTP Fuzzer;251
8.15.4;Illustrative Experiments;252
8.15.5;Conclusions;253
8.15.6;References;253
8.16;Design of New Aircraft Sensor Bus System;254
8.16.1;Introduction;254
8.16.2;Aircraft Sensor Bus Architecture Design;254
8.16.2.1;The Design of Range-Extended Motherboard;255
8.16.2.2;Buffer-Switch Design;256
8.16.2.3;Bus Optical Isolation Design;256
8.16.2.4;Bus Emergency Treatment Design;257
8.16.3;Communication Protocol Design;258
8.16.4;The Upper Computer Software Design;259
8.16.5;Tests and Result;259
8.16.6;Conclusion;261
8.16.7;References;261
8.17;A Reinforcement Learning Based Tag
Recommendation;262
8.17.1;Introduction;262
8.17.2;Proposed Algorithm;263
8.17.3;Empirical Cases;265
8.17.3.1;Data Source;265
8.17.3.2;Evaluation;265
8.17.3.3;Algorithm Comparison;266
8.17.4;Conclusions and Future Work;269
8.17.5;References;269
8.18;Online Customer Value Structure: A Network
Analysis Approach;270
8.18.1;Introduction;270
8.18.2;Models;270
8.18.3;Methods;271
8.18.4;Discussion;272
8.18.5;Conclusion;274
8.18.6;References;275
8.19;A Dynamic Fuzzy Multi-criteria Group Decision
Support System for Manager Selection;276
8.19.1;Introduction;276
8.19.2;Features Analysis of the Main Fuzzy Ranking Algorithms;277
8.19.2.1;Simple Additive Weighting (SAW) Algorithm;278
8.19.2.2;Chen and Hwang Fuzzy Ranking Algorithm for Solving MADM Problems;278
8.19.3;The Conditions for Choosing the Most Appropriate Fuzzy
Ranking Algorithm;279
8.19.4;Decision Making System Simulation;280
8.19.5;Software Development;282
8.19.6;Conclusion and Future Works;284
8.19.7;References;284
8.20;Elementary Algebra Proof Exercises Using
a Theorem Proving System;286
8.20.1;Introduction;286
8.20.2;Parsing;287
8.20.3;Proving;288
8.20.4;Related Work and Conclusion;289
8.20.5;References;290
8.21;Design and Implementation of an ETL Approach
in Business Intelligence Project;292
8.21.1;Introduction;292
8.21.2;An Overview of ETL Method;293
8.21.3;Implementation and Detail;294
8.21.3.1;Data Extraction and Data Loading;294
8.21.3.2;Data Transformation;295
8.21.3.3;Metadata Management;295
8.21.3.4;ETL Cycle and Table Partition;295
8.21.3.5;Automation;296
8.21.3.6;Log;296
8.21.3.7;Others;296
8.21.4;Conclusion;296
8.21.5;References;297
8.22;Center Conditions and Bifurcations of Limit Cycles
in a Quartic Lyapunov System;298
8.22.1;Introduction;298
8.22.2;Preliminary Knowledge;299
8.22.3;Quasi-lyapunov Constants and Center Conditions;300
8.22.4;Multiple Bifurcations of Limit Cycles;302
8.22.5;Conclusion;303
8.22.6;References;303
8.23;Extending the SCORM Standard to Support the Project
of Educational Contents for t-Learning;304
8.23.1;Introduction;304
8.23.2;Interactive Digital TV and Distance Education;305
8.23.3;Learning Objects;306
8.23.4;Extension Proposal of SCORM Standard;308
8.23.4.1;New Metadata Model;308
8.23.5;Authoring Tool T-SCORM ADAPTER;310
8.23.6;Related Work;312
8.23.7;Conclusions and Future Work;312
8.23.8;References;313
8.24;Research and Realization of Improved Layer
Management and Implementation for MapObjects;314
8.24.1;Introduction;314
8.24.2;Limitations of MapObjects;314
8.24.3;The Track of Solution;315
8.24.4;Detail of Solution;316
8.24.5;Conclusions;319
8.24.6;References;319
8.25;Automatically Extracting Chinese Aliases of Prohibited
Items Based on Web Searching;320
8.25.1;Introduction;320
8.25.2;Investigating on Some Chinese Shopping Websites;322
8.25.3;Proposed Method: ProhibitedItemFinder;323
8.25.3.1;The Expander Component;324
8.25.3.2;The Filter Component;324
8.25.3.3;The Ranker Component;325
8.25.4;Experiments and Results;326
8.25.5;Conclusion and Future Work;328
8.25.6;References;329
8.26;Technological Dynamics and National Innovation
System: A Quantitative Focus of a Neoschumpeterian Approach;330
8.26.1;Introduction;330
8.26.2;National Innovation Systems;331
8.26.2.1;The Technological Trajectories within the National Innovation System;333
8.26.3;Complexity;334
8.26.3.1;An Example of a Complex Model;334
8.26.4;Conclusions;338
8.26.5;References;339
8.27;An ETL Strategy for Real-Time Data Warehouse;340
8.27.1;Introduction;340
8.27.2;Related Works;341
8.27.2.1;Table Records;341
8.27.2.2;Copy;341
8.27.2.3;Trigger;342
8.27.2.4;Database Snapshots;342
8.27.2.5;Capturing the Changes Based on Data Log
;342
8.27.3;Design of Real-Time Strategy;343
8.27.3.1;Architecture of Capture Changed Data;343
8.27.3.2;Design of Intercept Mechanism;345
8.27.3.3;Design of Real-Time ETL;345
8.27.4;Conclusion;346
8.27.5;References;346
8.28;iDNABar: A Rapid Species Identification Toolbox
for DNA Barcoding, Collection, Preservation, Identification and Tracing;348
8.28.1;Introduction
;348
8.28.2;Data of DNA Barcoding and Its Flow Features;349
8.28.3;Species Identification;351
8.28.4;Key Elements to Management and Sharing in iDNAbar;352
8.28.4.1;User Security;352
8.28.4.2;Data Integrating and Tracing;352
8.28.4.3;Statistics;352
8.28.4.4;Service Interfaces;353
8.28.5;Discussion and Future Work;353
8.28.6;References;353
8.29;Regression Testing of Bug-Fixes with AI Techniques;355
8.29.1;Introduction;355
8.29.2;SUT Model and Test Cases;356
8.29.3;Adaptive Regression Testing;357
8.29.3.1;Longest Common Subsequences (LCS);358
8.29.3.2;Design of the Fitness Function;358
8.29.3.3;Supervised Learning of theWeighting
;359
8.29.4;Implementation with a Symbolic Simulator;360
8.29.5;Implementation and Experiment;360
8.29.6;Conclusions and Future Work;363
8.29.7;References
;363
8.30;Urgent Epidemic Control Mechanism
for Aviation Networks;365
8.30.1;Introduction;365
8.30.2;Model for Epidemic Spreading;366
8.30.2.1;Model Description;366
8.30.2.2;Examples Solved by the Model;368
8.30.3;Future Work;369
8.30.4;Conclusion;370
8.30.5;References;370
8.31;Trajectory Optimization in Reentry Phase
for Hypersonic Gliding Vehicles Using Swarm Intelligence Algorithms;371
8.31.1;Introduction;371
8.31.2;Reentry Trajectory Optimization Problem for Hypersonic
Gliding Vehicles;373
8.31.2.1;Reentry Dynamics for CAV;373
8.31.2.2;Constraints of Trajectory Optimization Problem;374
8.31.2.3;Penalty Functions;375
8.31.3;Particle Swarm Optimization and Artificial Fish-Swarm
Algorithm;376
8.31.3.1;Particle Swarm Optimization (PSO);376
8.31.3.2;Artificial Fish-Swarm Algorithm;376
8.31.4;Numerical Simulations;378
8.31.5;Conclusions
;380
8.31.6;References;380
8.32;Combining Non Revisiting Genetic Algorithm and Neural
Network to Generate Test Cases for White Box Testing;382
8.32.1;Introduction;382
8.32.2;Background Details;383
8.32.2.1;Non Revisiting Genetic Algorithm;383
8.32.2.2;Back Propagation Neural Network;384
8.32.3;Proposed Approach;384
8.32.4;Experimental Results;385
8.32.5;Conclusion;387
8.32.6;References;387
8.33;Fuzzy Information Axiom Based Decision Model
for CAD System Selection;390
8.33.1;Introduction;390
8.33.2;Literature Review;391
8.33.3;Fuzzy Information Axiom;392
8.33.4;Criteria for CAM System Selection;394
8.33.5;Application of the Proposed Decision Making Model;395
8.33.6;Conclusion;398
8.33.7;References;398
8.34;Determining the Importance of Performance
Measurement Criteria Based on Total Quality Management Using Fuzzy Analytical Network Process;400
8.34.1;Introduction;400
8.34.2;Literature Review;401
8.34.3;Methodology;402
8.34.3.1;Fuzzy Analytical Hierarchy Process;402
8.34.3.2;Steps of the ANP Methodology;403
8.34.4;Determination of the Criteria Importance;403
8.34.4.1;The Criteria for Performance Measurement;403
8.34.4.2;Calculation of the Importance;404
8.34.5;Conclusion;408
8.34.6;References;408
8.35;Research for Adaptive Intelligent Underwater Vehicle
Navigation and Positioning System;410
8.35.1;Introduction;410
8.35.2;General Filter Technology in Underwater Environment;411
8.35.3;Adaptive Filtering;412
8.35.4;System Modeling;414
8.35.4.1;State Equation;414
8.35.4.2;Measurement Equation;414
8.35.5;Simulation;414
8.35.6;Conclusion;416
8.35.7;References;416
8.36;Regression Testing Based on Neural Networks and Program Slicing Techniques;417
8.36.1;Introduction;417
8.36.1.1;Related Work;418
8.36.2;Preliminary;419
8.36.2.1;Program Slicing ;419
8.36.2.2;Machine-Learning;419
8.36.2.3;Artificial Neural Network (ANN);419
8.36.3;Framework;420
8.36.4;Implementation and Experiment;421
8.36.4.1;Trace Collection;421
8.36.4.2;Experiment Platform;421
8.36.4.3;Experiment with Grep;421
8.36.4.4;Experiment with TCAS;423
8.36.4.5;Discussion of Experiments;425
8.36.5;Conclusion and Future Work;425
8.36.6;References
;426
8.37;Mapping a Resource Description Framework OLAP Ontology to the Business Intelligence Semantic Model;427
8.37.1;Introduction;427
8.37.2;Related Work and Background;428
8.37.2.1;Related Work
;428
8.37.2.2;RDF and Ontologies;428
8.37.2.3;OLAP Model;429
8.37.3;The Target Architecture;431
8.37.3.1;The Pivot Table Level;431
8.37.3.2;The Database Level;432
8.37.4;The Implementation;432
8.37.4.1;Mapping from Our RDF Ontology to BISM;432
8.37.4.2;The Implementation of the Mapping Software;434
8.37.5;Concluding Remarks;435
8.37.6;References;435
8.38;A Fuzzy Inference System for Supply Chain Risk Management;437
8.38.1;Introduction;437
8.38.2;Supply Chain Risk Management;438
8.38.3;Methodology;439
8.38.3.1;Fuzzy Inference Systems;440
8.38.4;Application Study;442
8.38.5;Conclusion;444
8.38.6;References;444
8.39;Whole Flight Envelope Aero-engine Sensor Failure Diagnosis Based on Neutral Network;447
8.39.1;Introduction;447
8.39.2;Aero-engine Sensor Failure Diagnosis Mechanism;448
8.39.2.1;Sensor Failure Mode and Signal Preprocessing;448
8.39.2.2;Sensor Failure Diagnosis Mechanism;449
8.39.2.3;Design of Neutral Network Status Observer;449
8.39.3;Whole Flight Envelope Fault Diagnosis;450
8.39.3.1;Partition of the Flight Envelope;450
8.39.3.2;The Two-Stage Empirical Modeling Strategy;450
8.39.4;Computer Simulation;453
8.39.5;Conclusion;454
8.39.6;References;454
8.40;Novel Design and Analysis of a Reconfigurable Parallel Manipulator Using Variable Geometry Approach;455
8.40.1;Introduction;455
8.40.2;Geometry Modeling;457
8.40.2.1;Mobility Analysis;457
8.40.2.2;Constraints Analysis;458
8.40.3;Kinematic Analysis;460
8.40.3.1;Inverse Kinematic Analysis;460
8.40.3.2;Jacobian Matrix Analysis;461
8.40.4;Dynamics Simulation;462
8.40.5;Conclusion;464
8.40.6;References;464
8.41;Formalizing Feature Selection Problem in Software Product Lines Using 0-1 Programming;466
8.41.1;Introduction;466
8.41.2;Preliminaries;467
8.41.2.1;Feature Model Background;467
8.41.2.2;0-1 Programming;468
8.41.3;Problem Formalization;469
8.41.4;Case Study;470
8.41.4.1;A Problem Example;470
8.41.4.2;A Solution Example;471
8.41.5;Related Work;471
8.41.6;Concluding Remarks;471
8.41.7;References;472
8.42;COBA: A Credible and Co-clustering Filterbot for Cold-Start Recommendations;473
8.42.1;Introduction;473
8.42.2;Related Work;474
8.42.3;COBA;475
8.42.3.1;Filtering Phase;475
8.42.3.2;Co-clustering Phase;477
8.42.3.3;Prediction Phase;477
8.42.4;Experiments;478
8.42.4.1;Dataset and Methods;478
8.42.4.2;Performance;479
8.42.5;Conclusion;481
8.42.6;References;481
8.43;Pint-Sized Airborne Fire Control System of UAV and its Key Technology;483
8.43.1;Introduction;483
8.43.2;The Necessity of Airborne Fire Control System of UAV's Development;484
8.43.3;The Elementary of Pint-Sized Airborne Fire Control System of UAV;484
8.43.3.1;The Core of Fire Control System under Autonomous Mode;484
8.43.3.2;The Key of Fire Control System under Remote Mode;485
8.43.4;The Key Technology of UAV Airborne Fire Control System;486
8.43.4.1;Information Fusion Technology;487
8.43.4.2;Knowledge Representation and Artificial Intelligence Technology;487
8.43.4.3;Data Chain Technology;488
8.43.4.4;Intelligent Sensor Technology;489
8.43.5;Conclusion;489
8.43.6;References;489
8.44;Speech Enhancement via Combination of Wiener Filter and Blind Source Separation;490
8.44.1;Introduction;490
8.44.2;Methodologies;491
8.44.2.1;Binaural Noise Reduction;492
8.44.2.2;Blind Source Separation;493
8.44.2.3;Summary of the Proposed Algorithm;496
8.44.3;Results;496
8.44.3.1;Evaluations and Results;496
8.44.4;Conclusions;498
8.44.5;References;498
8.45;Pinyin Tagging System Research and Implementation Based on Word Segmentation;500
8.45.1;Introduction;500
8.45.2;Chinese Pinyin Tagging System Model;500
8.45.3;The Local Maximum Entropy Algorithm Based on the Reverse Segmentation;501
8.45.3.1;Word Segmentation Algorithm;501
8.45.3.2;Local Maximum Entropy Algorithm Based on the Reverse Segmentation;502
8.45.4;Pinyin Dictionary Based on the Suboptimal Search Tree;502
8.45.5;The Results of Experiment;504
8.45.6;Conclusion and Prospect;504
8.45.7;References;505
8.46;Using Belief Degree-Distributed Fuzzy Cognitive Maps
for Safety Culture Assessment;506
8.46.1;Introduction;506
8.46.2;Safety Culture Assessment Database;507
8.46.3;The Hierarchical Structure of SC Indicators and Attributes;509
8.46.4;The Proposed Approach to Evaluate Safety Culture;510
8.46.5;Comparing BDD-FCMs;511
8.46.6;Application Example;511
8.46.7;Conclusions;515
8.46.8;References;515
9;Part IV:
Intelligent Control Systems;516
9.1;Design and Implementation of Low Cost Aircraft Control
Bus System upon I2C;517
9.1.1;Introduction;517
9.1.1.1;Function Introduction;517
9.1.1.2;Prototype Introduction;518
9.1.1.3;I2C Bus Time Scheduling;519
9.1.2;Hardware Design;519
9.1.2.1;The Design of Computer Board;520
9.1.2.2;The Design of AD Board;521
9.1.2.3;The Design of IO Board;522
9.1.2.4;The Design of Power Board;523
9.1.2.5;The Design of Motherboard;523
9.1.3;IP Core Design;524
9.1.4;Tests and Result;525
9.1.5;Conclusion;525
9.1.6;References;525
9.2;Messages Analysis of Siemens PPI Protocol Data Mixed
Storage Area Based on Messages Interception;526
9.2.1;Introduction;526
9.2.2;Messages Intercepted of PPI Protocol;526
9.2.3;Communication Process and Messages Basic Format of PPI Protocol;527
9.2.4;Data Mixed Storage Area Messages Analysis of I0.0, Q0.1, SMB34 and VD200;528
9.2.4.1;The Message Analysis of Read I0.0, Q0.1, SMB34 and VD20;529
9.2.4.2;The Message Analysis of Write Q0.1;531
9.2.5;Conclusion;532
9.2.6;References;532
9.3;Intelligent Control of Large Time-Delay System Based on Fuzzy Strategy;533
9.3.1;Introduction;533
9.3.2;Intelligent Controllor of Large Time-Delay System Based on Fuzzy Strategy;534
9.3.2.1;Conventional Smith Prediction Estimation Compensation Arithmetic;534
9.3.2.2;Improvment of Fuzzy Smith Predictor;535
9.3.3;Simulation Examples and Results Analysis;535
9.3.3.1;Dynamic and Static Performance Analysis;536
9.3.3.2;Robust Performance Analysis;536
9.3.3.3;Anti-interference Performance Analysis;537
9.3.4;Conclusion;538
9.3.5;References;538
9.4;A Development of Degaussing Current Controller Based on Magnetometer;539
9.4.1;Introduction;539
9.4.2;Structure and Basic Principle;540
9.4.3;Algorithm for Eliminating Interference Magnetic Field;540
9.4.3.1;Magnetic Field Detected by X-Component Sensor and Y-Component Sensor;541
9.4.3.2;Magnetic Field Detected by Z-Component Sensor;542
9.4.4;Test of the Instrument Performance;543
9.4.5;Conclusion;544
9.4.6;References;544
9.5;Urban Traffic Control and Monitoring – An Approach for the Brazilian Intelligent Cities Project;545
9.5.1;Introduction;545
9.5.2;Related Work;546
9.5.3;Traffic Control Strategy;546
9.5.3.1;Controller Agents;546
9.5.3.2;Supervisor Agent;548
9.5.4;System Integration;549
9.5.5;Results;550
9.5.6;Current Work;552
9.5.7;Final Remarks;553
9.5.8;References;553
9.6;Guaranteed Cost Control of Polynomial Nonlinear
Uncertain Systems with Time-Delay;554
9.6.1;Introduction;554
9.6.2;Preliminaries and Problem Statement;555
9.6.3;Main Results;557
9.6.4;Illustrative Example;561
9.6.5;Conclusions;562
9.6.6;References;562
9.7;On PSO Based Fuzzy Neural Network Sliding Mode Control for Overhead Crane;564
9.7.1;Introduction;564
9.7.2;Overhead Crane Model;565
9.7.3;Sliding Mode Controller Design;566
9.7.4;The Unknown Estimation Based on FNNs;567
9.7.4.1;FNN Structure;567
9.7.4.2;Weight Adjustment;569
9.7.5;Particle Swarm Optimization (PSO);570
9.7.5.1;PSO Algorithm;570
9.7.5.2;Design Steps;570
9.7.6;Simulation Research;571
9.7.7;Conclusions;572
9.7.8;References
;572
9.8;Adaptive False Alarm Filter Using Machine Learning
in Intrusion Detection;574
9.8.1;Introduction;574
9.8.2;Related Work;576
9.8.3;The Comparisons among Machine Leaning Schemes;576
9.8.3.1;Feature Selection of Snort Alarms;577
9.8.3.2;Datasets Construction and Experimental Methodology;578
9.8.3.3;Results of Comparisons;579
9.8.4;Our Proposal;581
9.8.4.1;Architecture of Adaptive False Alarm Filter;582
9.8.4.2;Evaluation Results;582
9.8.5;Conclusion and Future Work;584
9.8.6;References;584
9.9;The Design and Implementation of Thematic Maps
Automatic Production System for Remote Sensing Image;586
9.9.1;Introduction;586
9.9.2;The Overall Design of System;587
9.9.2.1;Data Layer;587
9.9.2.2;Middleware Layer;587
9.9.2.3;Work Layer;588
9.9.2.4;User layer;588
9.9.3;The Workflow of System;588
9.9.3.1;Map Rendering;588
9.9.4;Experimental Verification;590
9.9.5;Conclusion;590
9.9.6;References;591
10;Part V: Intelligent GIS, Networks
or the Internet of Things;592
10.1;Semantic Web Enabled Intelligent Geospatial
Web Services;593
10.1.1;Introduction;593
10.1.2;Service-Oriented Geo-ontology Model;593
10.1.3;Four Constraints Matching Algorithm Considering Syntax and Semantics;594
10.1.4;Heuristic Composing Algorithm Basing on Weighted Graph
Plan;595
10.1.5;Experiment;596
10.1.5.1;Study Area and Data Preparation;596
10.1.5.2;Principle of the Intelligent Composition;597
10.1.5.3;Experimental Result;597
10.1.6;Conclusions;598
10.1.7;References;598
10.2;A Research of Approximate Entropy’s Clustering
Analysis in the Detection of Abnormal Flow;599
10.2.1;Introduction;599
10.2.2;ApEn Method and Its Significance;600
10.2.3;DARPA Data;601
10.2.4;ApEn Parameters of Test;602
10.2.5;Conclusion and Discussion;605
10.2.6;References;605
10.3;A Remote On-Line Diagnostic System for Vehicles
by Integrating OBD, GPS and 3G Techniques;606
10.3.1;Introduction;606
10.3.2;System Design;607
10.3.2.1;Vehicle Terminal;607
10.3.2.2;Monitoring Center
;610
10.3.3;Experimental Result;611
10.3.4;Conclusion;612
10.3.5;References;613
10.4;Modeling Wireless Sensor Network with Spatial Constrained Affinity
Propagation;614
10.4.1;Introduction;614
10.4.2;Spatial Constrained AÆnity Propagation for Exemplar Sensor
Selection;616
10.4.3;Experiments and Results;618
10.4.4;Conclusions;619
10.4.5;References;619
10.5;Multi-agent Communication Model and Service
Manipulation in Network Service Management;620
10.5.1;Introduction;620
10.5.2;Agent Communication Model and Service Processing;622
10.5.2.1;“Shaikh-Guide” Based Agent Communication Model;622
10.5.2.2;Service Manipulation Process;623
10.5.2.3;Service Decomposition Algorithm;624
10.5.2.4;Task Processing and Executing;625
10.5.3;Performance Analysis and Evaluation;626
10.5.4;Application to Network Service Management;627
10.5.5;Conclusions and Outlook;628
10.5.6;References;628
11;Part VI: Social Issues of Knowledge
Engineering;630
11.1;Diagnosing and Remedying Knowledge Gap
between Enterprises;631
11.1.1;Introduction;631
11.1.2;The Theoretical Interpretation on Diagnosing Knowledge Gap;631
11.1.2.1;Trading Model of Knowledge;631
11.1.2.2;Four-Dimension and Eight-Grade Knowledge Distance Model;632
11.1.3;The Basic Path Model;633
11.1.3.1;Endogenous and Exogenous Path of Knowledge;633
11.1.3.2;Knowledge Transferring Process;633
11.1.4;The Safeguard Mechanisms of Making Up for Knowledge Gap;633
11.1.4.1;Establishing the Mechanism of Knowledge Studying;634
11.1.4.2;Establishing Knowledge Innovation Mechanism;634
11.1.4.3;Establishing Knowledge Sharing Mechanism;634
11.1.5;References;635
11.2;Route Analysis of Satellite Constellation Based on
Directional Crosslink with Narrow-Beam Antenna;636
11.2.1;Introduction;636
11.2.2;Topology Features of Walker Constellation;637
11.2.3;Routing Analysis;639
11.2.3.1;Scheduling Overview;639
11.2.3.2;Routing Analysis;640
11.2.3.3;Route of Minimum Number of Hops of the Least Delay;640
11.2.3.4;Route of Least Delay of the Minimum Number of Hops;642
11.2.4;Simulation;644
11.2.5;Conclusion and Outlook;646
11.2.6;References;647
11.3;Modeling of a MIT for the Application of a Frequency
Inverter of the Electric Vehicle;648
11.3.1;Introduction;648
11.3.2;Linear Model for MIT’S Torque;649
11.3.3;Verification and Validation;653
11.3.4;Conclusions;655
11.3.5;References;656
11.4;Bridge Structural Health Evaluation Based
on Multi-level Fuzzy Comprehensive Evaluation;657
11.4.1;Introduction;657
11.4.2;Evaluation Theory;658
11.4.3;Evaluation Model;659
11.4.3.1;Establishment of the Evaluation Model;659
11.4.3.2;Model Implementation;660
11.4.4;Case Study;661
11.4.4.1;Evaluation Object;661
11.4.4.2;Results Analysis;662
11.4.5;Summary and Prospects;663
11.4.6;References;664
11.5;An Improved Active Queue Management Algorithm
Based on Queue Length and Traffic Rate Factor;665
11.5.1;Introduction;665
11.5.2;Research on RED Algorithm;665
11.5.3;Prediction of Congestion Status;666
11.5.3.1;Prediction Based on Average Queue Length;667
11.5.3.2;Prediction Based on Tracffic Rate;667
11.5.4;QTRC Algorithm;668
11.5.4.1;Influential Factors for Algorithm Design;668
11.5.4.2;Improved Algorithm QTRC;669
11.5.4.3;Simulation and Results;671
11.5.5;Conclusion and Future Work;674
11.5.6;References;674
11.6;HLM in the Study of Humanistic Quality Education;676
11.6.1;Introduction;676
11.6.2;Establishment of the Model;677
11.6.3;Model Analysis;678
11.6.3.1;Zero Model and Random Coefficients Regression Model;678
11.6.3.2;Exploratory Analysis of Latent Variables in Level-2;679
11.6.3.3;Complete Model;680
11.6.4;Conclusion;681
11.6.5;References;682
11.7;Modeling and Application of Urban Rail Transit Network for Path Finding Problem;683
11.7.1;Introduction;683
11.7.2;Network Properties Analysis;684
11.7.3;Modeling;685
11.7.3.1;Overview;685
11.7.3.2;Network Abstract;685
11.7.3.3;The Network Model;686
11.7.3.4;KSPF Algorithm;686
11.7.4;Case Study;687
11.7.4.1;Case Introduction;687
11.7.4.2;Result;687
11.7.5;Conclusion;688
11.7.6;References;688
11.8;A Novel Technique for Predicting Ship Grounding Based on Fuzzy Theory;690
11.8.1;Introduction;690
11.8.2;Factors Affecting Grounding;691
11.8.3;Basic Theory
;691
11.8.3.1;ECDIS;691
11.8.3.2;Fuzzy Theory;691
11.8.4;Computing Model;692
11.8.4.1;The First Stage Model;692
11.8.4.2;The Second Stage Model;692
11.8.5;Simulation Results;694
11.8.6;Conclusion;695
11.8.7;References;695
11.9;Prefetching Strategy for Address Translation in IA-32 Emulation;696
11.9.1;Introduction;696
11.9.2;Address Translation in IA-32 Emulation;697
11.9.3;Prefetching Strategy of Address Translation;697
11.9.3.1;Strategy Description;697
11.9.3.2;Prefetching Window;698
11.9.3.3;Pre_TLB Management;699
11.9.4;Performance Evaluation;700
11.9.5;Conclusions;701
11.9.6;References;701
11.10;Log Domain Speckle Noise Reduction in Ultrasonographic Animal Images;702
11.10.1;Introduction;702
11.10.2;Literature Review;703
11.10.3;Proposed Methodology;704
11.10.4;Results;705
11.10.5;Discussion and Conclusions;707
11.10.6;References;708
11.11;Research on High Performance Services for Future Ubiquitous Wireless Networks;709
11.11.1;Introduction;709
11.11.2;End-to-End Framework and Strategies;710
11.11.2.1;Framework Architecture;710
11.11.2.2;Bandwidth Allocation According to Real-Time Traffic Pattern;710
11.11.3;Conclusion;712
11.11.4;References;712
11.12;Science and Technology Project Post Evaluation Index
Research in Energy and Chemical Enterprises;714
11.12.1;Introduction;714
11.12.2;Preliminary Selection of the Evaluation Indexes and Design
of the Questionnaires;715
11.12.3;Screening of the Evaluation Indexes;716
11.12.3.1;Analysis on Exploratory Factors;716
11.12.3.2;Constitution of Post Evaluation Measurement Index System;718
11.12.4;Conclusion;718
11.12.5;References;719
11.13;3-D Numerical Modeling of Diffusion of Nuclide in Porous Media;720
11.13.1;Introduction;720
11.13.2;Numeric Modeling;720
11.13.2.1;Percolation Mechanism in Porous Media (Darcy Percolation);721
11.13.2.2;Governing Equation for the Nuclide’s Diffusion in Porous Media;722
11.13.2.3;Derivation of the Finite Element Discrete Format;722
11.13.3;Mesh, Results and Discussion;723
11.13.4;Summaries;725
11.13.5;References;726
11.14;Research on H.264 Dynamic Redundant Encoding
Algorithm Based on the Channel State and Video Correlation;727
11.14.1;Introduction;727
11.14.2;Redundancy Algorithm Selections;729
11.14.3;The Dynamic Redundancy Algorithm;730
11.14.3.1;Correlation Analysis of H.264;730
11.14.3.2;Algorithm Description;731
11.14.4;Dynamic Redundancy Algorithm Based on Channel Feedback;733
11.14.5;Experiments;734
11.14.5.1;Channel Feedback Redundant Encoding Experiment;735
11.14.5.2;Experiment on Dynamic Redundant Encoding Algorithm Based on Video Correlation;735
11.14.5.3;The Comprehensive Experiment and Results Analysis;736
11.14.6;Conclusion;737
11.14.7;References;737
11.15;Optimal Trajectory and Solution of the Inverse
Kinematics of a Robotic Manipulator by Genetic Algorithms;739
11.15.1;Introduction;739
11.15.2;Robotic Manipulator Description;740
11.15.3;The Genetic Algorithm;741
11.15.3.1;Individual Representation;741
11.15.3.2;Initialization;742
11.15.3.3;Evaluation;742
11.15.3.4;Selection;743
11.15.3.5;Crossing and Mutation;743
11.15.4;Results;743
11.15.4.1;Case 1: Optimal Configuration Using GA and Cubic Trajectory Selection;743
11.15.4.2;Case 2: Optimal Linear Trajectory Using GA;745
11.15.4.3;Ponderation Factors;745
11.15.5;Conclusion;748
11.15.6;References;749
12;Author Index;750