Cao / Li / Zhang Fuzzy Information and Engineering Volume 2

1. Auflage 2009
ISBN: 978-3-642-03664-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 62, 1754 Seiten

Reihe: Advances in Intelligent and Soft Computing

This book is the proceedings of the Third International Conference on Fuzzy Information and Engineering (ICFIE 2009) held in the famous mountain city Chongqing in Southwestern China, from September 26-29, 2009. Only high-quality papers are included. The ICFIE 2009, built on the success of previous conferences, the ICFIE 2007 (Guangzhou, China), is a major symposium for scientists, engineers and practitioners in the world to present their updated results, ideas, developments and applications in all areas of fuzzy information and engineering. It aims to strengthen relations between industry research laboratories and universities, and to create a primary symposium for world scientists in fuzzy fields as follows: Fuzzy Information; Fuzzy Sets and Systems; Soft Computing; Fuzzy Engineering; Fuzzy Operation Research and Management; Artificial Intelligence; Fuzzy Mathematics and Systems in Applications, etc.

Autoren/Hrsg.

Cao, Bingyuan

Li, Tai-Fu

Zhang, Cheng-Yi

Inhaltsverzeichnis

1;Title Page;2
2;Preface;6
3;Organization;8
4;Contents;11
5;Fuzzy Information;27
5.1;Multi-step Offline Handwritten Chinese Characters Segmentation with GA;27
5.1.1;Introduction;27
5.1.2;Rough Segmentation Based on GA;28
5.1.2.1;HIT-MW Dataset;28
5.1.2.2;Project Profile Histogram Method;28
5.1.2.3;Coding;29
5.1.2.4;Fitness Function;29
5.1.2.5;GA Parameters Selection;30
5.1.2.6;GA Parameters Selection;30
5.1.3;Over-Segmentation Characters Mergence;30
5.1.3.1;Punctuation and Digital Number Estimation;30
5.1.3.2;Centroids Normalization;31
5.1.3.3;Merging Result;31
5.1.4;Re-segment of Insufficient Segmentation Characters;32
5.1.4.1;Viterbi Algorithm;32
5.1.4.2;Hidden Markov Model of Character Block;32
5.1.4.3;Re-segmentation by Viterbi Algorithm;34
5.1.4.4;Paths Optimization;34
5.1.4.5;Modified New Optimal Rules;35
5.1.5;Experiments and Analysis;36
5.1.6;References;36
5.2;A New Covert Image Communication Approach with HVS and GFCM;38
5.2.1;Introduction;38
5.2.2;Chaotic System and Sequences Modulation;39
5.2.3;GFCM of Several Parameters of Image Blocks;40
5.2.4;Selection of DCT Coefficients and Maximal Strength;41
5.2.5;Digital Information Embedding and Extracting;42
5.2.6;Simulation Results and Analysis;43
5.2.7;Conclusions;45
5.2.8;References;45
5.3;Sliding Model Fuzzy Control for a Bridge Crane;47
5.3.1;Introduction;47
5.3.2;Mathematical Model of Bridge Crane;48
5.3.3;Experiment Analysis;50
5.3.4;Simulation and Experiments;51
5.3.5;Conclusions;53
5.3.6;References;53
5.4;Apply Multi-class Fuzzy Support Vector Machines to Product-Form-Image Prediction;55
5.4.1;Introduction;55
5.4.2;Kansei Image Prediction Modeling;56
5.4.2.1;Kansei Information System;56
5.4.2.2;Kansei Image Prediction;56
5.4.3;Multi-Class Fuzzy SVMs;57
5.4.3.1;Support Vector Machines;57
5.4.3.2;Multi-class SVMs;59
5.4.3.3;Fuzzy SVMs;59
5.4.3.4;Kansei Image Formalization Based on MF-SVM;61
5.4.4;Case Study;62
5.4.4.1;Kansei Image Information;62
5.4.4.2;Classification by MF-SVMs;63
5.4.4.3;Training and Predict for New Products;63
5.4.5;Conclusions and Discussion;63
5.4.6;References;64
5.5;The Study on Motor Fuzzy Neural Network Controller Based on Fuzzy Soft Handoff;65
5.5.1;Introduction;65
5.5.2;Paralleled FNN Controller with Switching Control;66
5.5.2.1;Fuzzy Sub-controller;66
5.5.2.2;Neural Network PID Sub-controller;67
5.5.3;Fuzzy Soft Handoff Control Technology;68
5.5.3.1;Hard Handoff;68
5.5.3.2;Soft Handoff;68
5.5.3.3;Fuzzy Soft Handoff [7];70
5.5.4;Simulation of DC Motor System;72
5.5.5;Conclusion;73
5.5.6;References;73
5.6;Study on Armament System of System Based on Fuzzy Cognitive Map;75
5.6.1;Introduction;75
5.6.2;Characteristics of SoS;75
5.6.3;The Challenge;76
5.6.4;Fuzzy Cognitive Map;77
5.6.5;Research on Armament SoS Based on FCM;78
5.6.5.1;System Modeling;79
5.6.5.2;Simulation Analysis;81
5.6.6;Conclusion;82
5.6.7;References;82
5.7;Research on Multiple Objective Decision Model of the Security of On-Line Shopping Based on Fuzzy Information Theory;84
5.7.1;Introduction;84
5.7.2;Multilevel Multiple Objective Desision Theory;85
5.7.2.1;Build Up the Multiple Objective Decision Set;85
5.7.2.2;Build Up the Multilevel Multiple Objective Comment Set;85
5.7.2.3;Build Up the Weight Set;85
5.7.2.4;Build Up the Multiple Objective Evaluation Membership Matrix;86
5.7.2.5;Second-Level Multiple Objective Evaluation;87
5.7.2.6;Evaluation of the Security of on-line Shopping;87
5.7.3;Instance;87
5.7.3.1;Build Up Evaluation Set of the Multiple Objective Decision;87
5.7.3.2;Gather Information about the Fuzzy Multiple Objective Decision;88
5.7.3.3;Calculation of Fuzzy Index’s Weight;88
5.7.3.4;Fuzzy Multiple Objective Decision;88
5.7.4;Conclusion;90
5.7.5;References;91
5.8;Recognition of Blood Cell Images Based on Color Fuzzy Clustering;92
5.8.1;Introduction;92
5.8.2;Methods and Materials;93
5.8.2.1;Blood Cell Images;93
5.8.2.2;Fuzzy Clustering Algorithm;93
5.8.2.3;Color Clustering Analyzing of Blood Cell Image;94
5.8.2.4;Recognition by Shape;96
5.8.2.5;Implementation by Java Program;97
5.8.3;Results and Discussion;97
5.8.4;Conclusions;98
5.8.5;References;98
5.9;Study on Automatic Detection of Airplane Object in Remote Sensing Images;99
5.9.1;Introduction;99
5.9.2;The Research Actuality and Development Current of the Automatic Detection of the Airplane Goal;100
5.9.2.1;The Research Actuality of the Airplane Goal Recognition;100
5.9.2.2;The Existing Problems and Development Trends in the Automatic Recognition of the Airplane Goals;100
5.9.3;Basic Theory and Application of the Mathematical Morphology;101
5.9.3.1;Summary of Mathematical Morphology;101
5.9.3.2;Binary Morphological;102
5.9.3.3;The Basic Concepts and Operations of the Gray Mathematical Morphology;102
5.9.4;Aircraft Target Detection Based on Mathematical Morphology;103
5.9.4.1;MATLAB Summarization;103
5.9.4.2;The Realization of MATLAB Based on Mathematical Morphology— Aircraft Automatic Detection as an Example;104
5.9.5;Conclusion;105
5.9.6;References;106
5.10;Study on the Affection of Gear Fault Diagnosis Bases on HHT by Noises;108
5.10.1;Introduction;108
5.10.2;Theory and Method;109
5.10.2.1;EMD;109
5.10.2.2;Hilbert Transforms (HT);111
5.10.2.3;Hilbert Marginal Spectrum;111
5.10.2.4;EEMD;111
5.10.2.5;De-Noising Based on Wavelet Packet Decomposition Coefficient Shrinkage;112
5.10.2.6;Fault Diagnosis Based on HHT and Neural Network;113
5.10.3;Case Study;113
5.10.3.1;Fault Diagnosis Based on the Pattern Matching of HHT Marginal Spectrum;114
5.10.3.2;Fault Diagnosis Based on HHT-Neural Network;116
5.10.4;Conclusions;116
5.10.5;References;117
5.11;Job-Shop Scheduling Based on Improved Particle Swarm;118
5.11.1;Introduction;118
5.11.2;Improved Particle Swarm Optimization (IPSO) Algorithm;119
5.11.2.1;Standard PSO Algorithm;119
5.11.2.2;IPSO Algorithm;119
5.11.3;The Description of JSSP;121
5.11.4;Experimental Results and Discussion;122
5.11.5;Conclusions;126
5.11.6;References;126
5.12;Application of Fuzzy Optimization Decision Method on Robot Tactile Suit Data Processing;127
5.12.1;Introduction;127
5.12.2;Fuzzy Decision Tree Construction;129
5.12.2.1;Form of a Fuzzy Decision Tree;130
5.12.2.2;Setting Patterns for Fuzzy Decision Tree;131
5.12.2.3;Fuzzification of Compressing Training Data;132
5.12.3;Algorithm of Fuzzy Decision Tree;133
5.12.4;Experiment;135
5.12.5;Conclusions;135
5.12.6;References;136
5.13;Study of the Gear Fault Detective Method Based on CWT and ANN;137
5.13.1;Introduction;137
5.13.2;The Wavelet Transform;138
5.13.3;Gear Fault Judgment;140
5.13.4;Neural Network Architectures;142
5.13.5;Empirical and Discussion;143
5.13.6;Conclusions;143
5.13.7;References;144
5.14;Fault Diagnosis Based on Covariance Constraint for Complex Control System;145
5.14.1;Introduction;145
5.14.2;Construct of Model;146
5.14.3;Analysis of Algorithm;147
5.14.3.1;The Covariance of the Subsections Data;147
5.14.3.2;Acquire Error Control Tolerances;147
5.14.4;Implement of Algorithm;148
5.14.5;Application Examples;149
5.14.6;Conclusions;151
5.14.7;References;152
5.15;Application of HSI Based Fusion Control Strategy in Sewerage Disposing;153
5.15.1;Introduction;153
5.15.2;Cybernetics Characteristic of Sewerage Disposing Process;154
5.15.3;Control Strategy Choosing;155
5.15.4;Control Model and Algorithm;155
5.15.4.1;Control Model;155
5.15.4.2;Engineering Control Algorithm;156
5.15.5;Simulation Experiment and Its Analysis;157
5.15.6;Conclusions;159
5.15.7;References;160
5.16;Stochastic Fuzzy Controller Based on OCPFA and Applied on Two-Wheeled Self-balanced Robot;161
5.16.1;Introduction;161
5.16.2;Skinner OC and PFA;163
5.16.2.1;Skinner Operant Conditioning;163
5.16.2.2;Probabilistic Finite Automata;163
5.16.3;Design of Self-organization and Stochastic Controller;164
5.16.3.1;About Fuzzy Control;164
5.16.3.2;Stochastic Fuzzy Controller;165
5.16.4;Design of Self-organization and Stochastic Controller;168
5.16.5;Simulations and Experiments;169
5.16.6;Conclusions;170
5.16.7;References;171
5.17;Research on Image Fusion Based on Regional Feature and Fuzzy Neural Networks;172
5.17.1;Introduction;172
5.17.2;Review of Image Fusion Based on Regional Deviation;173
5.17.3;Image Fusion Approach Based on Fuzzy Neural Networks;173
5.17.3.1;Fuzzy Inference System;173
5.17.3.2;Image Fusion Model Based on Fuzzy Inference and RBFNN;175
5.17.3.3;Networks Training Based on GA;176
5.17.4;Experimental Results;177
5.17.5;Conclusion;179
5.17.6;References;180
5.18;Measuring Concept Similarity between Fuzzy Ontologies;181
5.18.1;Introduction;181
5.18.2;Related Work;182
5.18.3;Fuzzy Set Theory;183
5.18.4;Definition of Fuzzy Ontology;184
5.18.5;Measuring Fuzzy Concept Similarity;184
5.18.6;Conclusion;188
5.18.7;References;189
5.19;A Face Detection Based on Face Features;190
5.19.1;Introduction;190
5.19.2;Eyes Location Based on Hough Transform;191
5.19.3;Geometric Model Matching Algorithm;193
5.19.4;Face Rotation Verification Based on Geometric Relation;194
5.19.5;Experimental Result;196
5.19.6;Conclusion;196
5.19.7;References;197
5.20;Disposing Face Images Based on Kalman Filter;198
5.20.1;Introduction;198
5.20.2;Time Domain Recursive Low-Pass Based Kalman Filter Theory;198
5.20.3;Modify Algorithm of Time Domain Recursive Low-Pass;199
5.20.4;Pick-Up Outline of Moving Object;200
5.20.5;Applications;201
5.20.6;References;203
5.21;Fuzzy Collaborative Filtering Approach Based on Semantic Distance;204
5.21.1;Introduction;204
5.21.2;Related Works;205
5.21.3;Fuzzy-Based Collaborative Filtering;206
5.21.3.1;Membership Function;206
5.21.3.2;User-Based Fuzzy CF;207
5.21.4;Experimental Evaluation;209
5.21.4.1;Dataset;209
5.21.4.2;Evaluation Metrics;209
5.21.4.3;Experimental Results;209
5.21.5;Conclusions;211
5.21.6;References;211
5.22;An Immune Based Optimization Tuning Method for Controller Parameter;213
5.22.1;Introduction;213
5.22.2;Puzzle in Parameter Tuning;214
5.22.3;Process Description for Parameter Tuning;214
5.22.4;Algorithm Design;215
5.22.4.1;Definition of Basic Conception;216
5.22.4.2;Clone Selection Algorithm;216
5.22.4.3;Mutation;217
5.22.5;Simulation and Discussion;218
5.22.6;Conclusions;219
5.22.7;References;220
5.23;The Study of the Fuzzy Net Present Value Based on Structured Element;221
5.23.1;Introduction;221
5.23.2;Fuzzy Structured Element and Relevant Theorem;222
5.23.3;Fuzzy NPV;223
5.23.4;Example;225
5.23.5;Conclusions;226
5.23.6;References;226
5.24;The Method of Fuzzy Network Shortest Path Based on the Structured Element Theory;228
5.24.1;Introduction;228
5.24.2;Fuzzy Structured Element and Relevant Theorem;229
5.24.3;The Natural Sequence;230
5.24.4;Equivalence Theorem of Fuzzy Shortest Path;231
5.24.5;Example;232
5.24.6;Conclusion;234
5.24.7;References;234
5.25;Automated Reading System on Thermometer by Machine Vision;235
5.25.1;Introduction;235
5.25.2;Reading Principles;236
5.25.3;Methodologies;237
5.25.3.1;Sensors and Lenses;237
5.25.3.2;Illumination;237
5.25.3.3;Algorithms;238
5.25.4;Experiment and Results;243
5.25.5;Conclusions;244
5.25.6;References;245
5.26;The Thought of Fuzzy Mathematics Modeling Is Infiltrated in MSMT;246
5.26.1;Introduction;246
5.26.2;The Case of FMM in MSMT;247
5.26.3;The Tactic of FMM in MSMT;252
5.26.3.1;Tactic 1: Pay Attention to Correlative Knowledge of FMM;252
5.26.3.2;Tactic 2: Pay Attention to the Training of InspirationalThought;253
5.26.3.3;Tactic 3: Pay Attention to Maths Language Ability Training;253
5.26.3.4;Tactic 4: Pay Attention to the Teaching of Maths Thought Way;254
5.26.3.5;Tactic 5: Pay Attention to Teach Mathematic Modeling Method;254
5.26.3.6;Tactic 6: Pay Attention to Scene Teaching;254
5.26.3.7;Tactic 7: Pay Attention to CAI;254
5.26.4;Conclusion;255
5.26.5;References;255
5.27;The Design and Implementation of GAG Move Intelligence Service System;256
5.27.1;Introduction;256
5.27.2;Knowledge Representation for System;257
5.27.3;Cipher Algorithm;258
5.27.3.1;Public Key Cryptography Thought;258
5.27.3.2;CPK;259
5.27.3.3;The Scheme of Encryption and Decryption;260
5.27.4;The Technical Architecture and Function Implemented of the System;262
5.27.4.1;The Technical Architecture of the System;262
5.27.4.2;The Construction of the System;262
5.27.5;Implementing Scheme of the Data Stream in System;262
5.27.5.1;Client Connects Server;262
5.27.5.2;Server Returns To Client;263
5.27.5.3;The Data Stream between Inquiry Client and Server;263
5.27.6;Conclusions;264
5.27.7;References;265
5.28;Fuzzy Integral on Credibility Measure;266
5.28.1;Introduction;266
5.28.2;Credibility Measure;266
5.28.3;Fuzzy Integral on Credibility Measure;268
5.28.4;References;274
5.29;An Algorithm of Iris Location Based on Gray Projection and Improved Hough Transform;275
5.29.1;Introduction;275
5.29.2;Locating the Inner Edge of Iris;276
5.29.2.1;Otsu Threshold Segmentation Method;276
5.29.2.2;Locate the Inner Edge of Iris;277
5.29.3;Locate the Outer Edge of Iris;278
5.29.3.1;Pretreating Image;278
5.29.3.2;Extracting Edge;279
5.29.3.3;Improved Hough Transform;279
5.29.4;Experiment Result and Conclusion;280
5.29.5;References;281
5.30;Semantic Information Retrieval Study Based on Knowledge Reasoning;282
5.30.1;Introduction;282
5.30.2;The Traditional Principle of Semantic Retrieval;283
5.30.3;Semantic Information Retrieval Based on Knowledge Reasoning (SIRKR);283
5.30.3.1;ALC and Fuzzy-ALC;284
5.30.3.2;User Query Intention;285
5.30.3.3;Relevance of Web Pages and User Queries;286
5.30.4;Prototype Design and Analysis;289
5.30.4.1;Prototype Design;289
5.30.4.2;Query Performance Analysis;290
5.30.5;Conclusions;291
5.30.6;References;291
5.31;Measurement Uncertainty Assessment of Vertical Metal Tank Based on Grey System Theory;292
5.31.1;Introduction;292
5.31.2;GreySystemTheory;293
5.31.3;Mathematical Model for Evaluation on Measurement Uncertainty of Vertical Metal Tank;294
5.31.4;Calculation and Measurement Examples;296
5.31.4.1;Analysis on Sources of Uncertainty in Measurement;296
5.31.4.2;Calculating;297
5.31.5;Conclusions;298
5.31.6;References;298
5.32;Adaptive Wavelet Thresholding Algorithm on Low-Contrast Image;300
5.32.1;Introduction;300
5.32.2;Principle of Multi-Resolution Analysis [6, 7];301
5.32.3;Filtering Algorithm Based on Discrete Wavelet Transforms (DWT);302
5.32.4;Test Results and Discussion;303
5.32.5;Conclusions;305
5.32.6;References;305
5.33;SVM-Based Soft-Monitoring Network System for Industrial Dust Emissions;306
5.33.1;Preface;306
5.33.2;System Structure;307
5.33.2.1;Physical Network Topology of the System;307
5.33.2.2;Logical Structure of the System;308
5.33.3;Key Technology of the System;309
5.33.3.1;Standard Data Format Defined by the System;310
5.33.3.2;System Protocol;310
5.33.4;SVM-Based Discrimination of Dust Concentration and Emissions;311
5.33.4.1;The SVM Algorithm;311
5.33.4.2;Selected Characteristics of Soot Images;312
5.33.5;Conclusions;314
5.33.6;References;314
5.34;Study on Gasoline Engine Misfiring Detection Using the CPS Signal;316
5.34.1;Introduction;316
5.34.2;System Introduction;317
5.34.3;Scheduling Design According to the 4 Cylinders Engine;317
5.34.4;Develop the Misfiring Detection Algorithm;318
5.34.5;OBD Alarm Function Design;321
5.34.6;Misfiring Detection Function Test on Vehicle;321
5.34.7;Conclusions;323
5.34.8;References;323
5.35;Image Sharpening and Denoising Method Based on Anisotropic Diffusion Model;324
5.35.1;Introduction;324
5.35.2;Anisotropic Diffusion Model;325
5.35.3;Edge-Sharpening Anisotropic Diffusion Model;326
5.35.3.1;Introduction of the Model;326
5.35.3.2;Parameters Choosing;327
5.35.4;Experimental Results;329
5.35.5;Conclusions;330
5.35.6;References;331
5.36;The Diagnosabilities of and Diagnosis Algorithms for Regular Networks under Two Three-Valued Models;332
5.36.1;Introduction;332
5.36.2;Preliminaries;333
5.36.2.1;Basic Notations and Terminologies;333
5.36.2.2;t/x Model;334
5.36.2.3;t[x] Model;335
5.36.2.4;PMC Model;335
5.36.3;Diagnosabilities of Regular Networks under Three-Valued Models;336
5.36.4;One-Step Diagnosis of Regular Networks under the Two Three-Valued Models;338
5.36.5;Conclusion;339
5.36.6;References;340
5.37;A New Blind Separation Algorithm of TT&C Signals Based on ICA Algorithm in Multipath Communication Environment;342
5.37.1;Introduction;342
5.37.2;The Basic Idea of Independent Component Analysis;343
5.37.3;Derivation of the Algorithm;344
5.37.3.1;Preprocessing;344
5.37.3.2;ICA Algorithm Based on the Maximization of Negentropy;345
5.37.3.3;The Fast-ICA Algorithm Based on the Maximization of Negentropy;346
5.37.4;Simulation Results and Analysis;348
5.37.5;Conclusion;350
5.37.6;References;351
5.38;An Image Segmentation Method Based on the BP Neural Network;352
5.38.1;Introduction;352
5.38.2;Basic Training of BP Network;354
5.38.3;Intensive BP Training;355
5.38.4;Application of BP;356
5.38.5;Conclusions;356
5.38.6;References;356
5.39;A Precise Method for Detecting Harmonics in Electric Power System Based on Wavelet Package Transform;358
5.39.1;Introduction;358
5.39.2;The Principle and Detection of Wavelet Package Transform;359
5.39.2.1;The Principle of Wavelet Package Transform;359
5.39.2.2;The Detection Process of Wavelet Package Transform;360
5.39.3;Simulation Algorithm Example;361
5.39.4;Conclusions;366
5.39.5;References;367
5.40;Approximation Error Bounds of Minimum Inference Fuzzy System as Function Approximator;368
5.40.1;Introduction;368
5.40.2;Minimum Inference Fuzzy System;369
5.40.3;Example;378
5.40.4;References;380
5.41;Research on the Method of Well Test Model Diagnosis Based on BP Neural Network;381
5.41.1;Introduction;381
5.41.2;Model Selection;381
5.41.3;Pressure Solution of Theoretical Model;382
5.41.4;Feature Extraction;386
5.41.5;The Model Diagnosis Method of BP Neural Network;388
5.41.6;Instance Analysis;390
5.41.7;Conclusions;391
5.41.8;References;391
5.42;Pressure and Temperature Coupling Predict Model of Sulphur Deposition Based on Multiple Phase Flow;392
5.42.1;The Development Tendency of Sulphur Deposition Model;392
5.42.2;Pressure Gradient Equation of Multiple Phase Flow;393
5.42.3;Multiple Phase Flow Temperature Gradient Equation;395
5.42.4;Calculate Simple Substance Sulphur Solubility in High-Sulphur Natural Gas;397
5.42.5;Pressure Temperature Coherent Amendment Deposition Model;399
5.42.6;Conclusion;399
5.42.7;References;400
5.43;Direct Torque Controlling of Permanent Magnet Synchronous Motor Based on the Adaptive Fuzzy Controller;401
5.43.1;Introduction;401
5.43.2;PMSM Mathematical Model;401
5.43.3;Adaptive Fuzzy Controller;402
5.43.3.1;Traditional Direct Torque ControlMechanism;402
5.43.3.2;Based on Fuzzy Logic Direct Torque Control Technology;403
5.43.3.3;Direct Torque Control System;404
5.43.3.4;Adaptive Institutions;405
5.43.4;System Simulation;407
5.43.5;Conclusion;408
5.43.6;References;409
5.44;Investigation of Petrophysical Property Distribution of Reservoirs at Exploration Stage with Integration of Fuzzy Methods into Geostatistics;410
5.44.1;Introduction;410
5.44.2;Theoretical Model;411
5.44.2.1;Absolute Correlation Degree;411
5.44.2.2;Rate Correlation Degree;412
5.44.3;Fuzzy Clustering Analysis of the Data from Wells and the FGn Interpolation;413
5.44.4;Applications;414
5.44.5;Conclusion;419
5.44.6;References;419
5.45;A Novel Approach to Modeling and Simulating of Underbalanced Drilling Process in Oil and Gas Wells;420
5.45.1;Introduction;420
5.45.2;Theoretical Model;421
5.45.3;How to Solve Mathematical Model;423
5.45.4;Simulation and Analysis;423
5.45.5;Conclusion;427
5.45.6;References;428
5.46;Low Power H.264/AVC Intra Prediction for Image Processing;429
5.46.1;Introduction;429
5.46.2;H.264/AVC Intra Prediction;430
5.46.2.1;Parallel Processing;430
5.46.2.2;Mode Reduction in Intra Prediction;432
5.46.2.3;Design Flow;433
5.46.2.4;Experimental Results with Discussion;434
5.46.3;Conclusion;435
5.46.4;References;436
5.47;A Kind of Adaptive Energy Management Protocol for Wireless Sensor Network;437
5.47.1;Introduction;437
5.47.2;The Model;438
5.47.3;Adaptive Energy Management Protocol (AEMP);438
5.47.4;Simulations and Results;441
5.47.5;Conclusions;442
5.47.6;References;443
5.48;Prediction Model of Sulfur Deposition in the High Sulfur Gas Well Bore;444
5.48.1;Solubility Calculate Model;444
5.48.2;Single Grain Suspension Condition;445
5.48.2.1;Principle;446
5.48.2.2;The Separation Condition of Solid Sulfur Grain on the Wellbore Pipe’s Wall;447
5.48.3;Case Studies;448
5.48.4;Conclusion;450
5.48.5;References;451
5.49;Modeling for Athletic Sports Strategies Based on Fuzzy System;452
5.49.1;Introduction;452
5.49.2;Basic Idea of Fuzzy Control;453
5.49.3;Applying Fuzzy Control to Analyze an Athletic Sport Instance;455
5.49.4;Discussion on Fuzzy Control Model of Athlete Sports;458
5.49.5;Applying Prospect of Fuzzy Control Model in the Athlete Sport;459
5.49.6;Conclusion;460
5.49.7;References;460
5.50;An Approach to Determine the Productivity of a Well in a Fractured Porous Reservoir;461
5.50.1;Introduction;461
5.50.2;Dual-Seepage Model;462
5.50.2.1;Modeling;462
5.50.2.2;Equation Solution;464
5.50.3;Determination of Parameter Values;464
5.50.3.1;How to Determine Values of Parameters of Fracture System;465
5.50.3.2;How to Determine Values of Parameters of Matrix System;465
5.50.4;Case Study;465
5.50.5;Analysis of Influencing Factors;466
5.50.5.1;Effect of Different Flow Regions on Well Productivity;466
5.50.5.2;Effect of Permeability in Different Flow Regions on Well Productivity;467
5.50.6;Conclusions;468
5.50.7;References;468
5.51;The Reasonability Evaluation of Tuition of Regular Higher Education by Region in Chinese;470
5.51.1;Analysis of Existing Situation of Tuition in China;470
5.51.2;Factors Affecting Tuition Level;471
5.51.3;Fuzzy Comprehensive Evaluation Model;472
5.51.4;Example;474
5.51.5;References;475
6;Fuzzy Sets and Systems;476
6.1;Probabilistic Analysis for Tensile Failure of Metal Matrix Composites;476
6.1.1;Introduction;476
6.1.2;Model for Multiple Fiber Breaks with Matrix Tensile Yield and Interface Shear Yield;477
6.1.3;Failure Process Simulation;479
6.1.4;Results;480
6.1.5;Conclusions;482
6.1.6;References;482
6.2;..-Convergence Theory of Ideals in L.-Spaces;484
6.2.1;Introduction;484
6.2.2;Preliminaries;485
6.2.3;The ..-Convergence of Ideal;486
6.2.4;Some Applications of ..- Convergence Theory;489
6.2.5;Some Applications of ..- Convergence Theory;489
6.2.6;Conclusions;491
6.2.7;References;491
6.3;Research on Type-2 TSK Fuzzy Logic Systems;493
6.3.1;Introduction;493
6.3.2;First-Order Type-1 TSK FLSs;494
6.3.3;First-Order Type-2 TSK FLSs;495
6.3.3.1;First-Order Type-2 TSK FLSs;495
6.3.3.2;First-Order Interval Type-2 TSK FLSs;496
6.3.3.3;Unnormalized Type-2 TSK FLSs;498
6.3.4;Design Methods for Type-2 TSK FLSs;498
6.3.4.1;Common Design Methods;498
6.3.4.2;Hybrid Learning Methods;499
6.3.4.3;Design Methods for Stable Systems;499
6.3.5;Applications of Type-2 TSK FLSs;500
6.3.5.1;Suitable Situations for Type-2 TSK FLSs;500
6.3.5.2;Successful Cases;500
6.3.6;Conclusion;501
6.3.7;References;501
6.4;Theory Based on Interval-Valued Level Cut Sets of Zadeh Fuzzy Sets;503
6.4.1;Introduction;503
6.4.2;Interval-Valued Level Cut Sets on Zadeh Fuzzy Sets;504
6.4.3;Representation Theorems of Zadeh Fuzzy Sets Based on Interval-Valued Level Cut Sets;509
6.4.4;Conclusions;511
6.4.5;References;512
6.5;Research on the Thin Coal Mining Equipment Decision Model and Method Base on Catastrophe Evaluation Theory;513
6.5.1;Introduction;513
6.5.2;The Catastrophe Theory and Mining Equipment System Evaluation Method;514
6.5.3;Mining Equipment Decision Model;514
6.5.3.1;Decision Model;514
6.5.3.2;Application Example;515
6.5.4;Conclusion;517
6.5.5;References;517
6.6;Solutions of First Order Fuzzy Differential Equations by a Characterization Theorem;519
6.6.1;Introduction;519
6.6.2;Preliminaries;520
6.6.3;Solutions of FDEs by Using Characterization Theorems;521
6.6.4;Numerical Examples;524
6.6.5;Conclusions;524
6.6.6;References;525
6.7;ß-Compactness in L-Fuzzy Topological Spaces;526
6.7.1;Introduction;526
6.7.2;Preliminaries;526
6.7.3;ß-Compactness and Its Goodness;529
6.7.4;Other Characterizations;530
6.7.5;Some Properties;531
6.7.6;References;534
6.8;On Fuzzy a-I-Open Sets and a Decomposition of Fuzzy Continuity;536
6.8.1;Introduction;536
6.8.2;Preliminaries;537
6.8.3;Fuzzy a-I-Open Sets;538
6.8.4;Decomposition of Fuzzy Continuity;542
6.8.5;Conclusions;545
6.8.6;References;545
6.9;M-Fuzzifying P-Closure Operators;547
6.9.1;Introduction;547
6.9.2;Preliminaries;547
6.9.3;M-Fuzzifying P-Closure Operator;550
6.9.4;References;553
6.10;Deformation Monitoring and Forecasting Model Study Based on Grey System Theory;555
6.10.1;Introduction;555
6.10.2;Background of Grey System Theory;556
6.10.2.1;Grey Relational Generating;556
6.10.2.2;The GM(1, 1) Model;556
6.10.2.3;The Residual GM(1, 1) Model;557
6.10.2.4;The GM(1, N) Model;558
6.10.3;A Implementation Method for Deformation Monitoring;558
6.10.3.1;The Spatial Multi-point Model;558
6.10.3.2;The Residual Spatial Multi-point Model;560
6.10.4;Deformation Monitoring Application;560
6.10.4.1;Test Sites and Data;560
6.10.4.2;Data Applications;561
6.10.4.3;Discussions;561
6.10.5;Conclusions;563
6.10.6;References;564
6.11;L-Fuzzy Relative PS-Compactness;565
6.11.1;Introduction;565
6.11.2;Preliminaries;565
6.11.3;Relative PS-Compactness and Its Characterizations;567
6.11.4;Some Properties;570
6.11.5;References;571
6.12;Weighted Moore-Penrose Inverse of a Fuzzy Matrix;573
6.12.1;Introduction;573
6.12.2;The Main Result;575
6.12.3;Conclusion;582
6.12.4;References;582
6.13;Fuzzy-Inference Based Fault Diagnosis System for Pneumatic Press;583
6.13.1;Introduction;583
6.13.2;Design of Fault Diagnosis System;584
6.13.2.1;Fuzzy Inference;584
6.13.2.2;Working Principle and Characteristics;584
6.13.2.3;Membership Function of Symptom Signals;585
6.13.2.4;Fuzzy Decision Rule;587
6.13.3;Practical Results;588
6.13.4;Conclusion;590
6.13.5;References;590
6.14;Fuzzy Inference Based Fault State Diagnosis of Navigation Mark;591
6.14.1;Introduction;591
6.14.2;The Composition and Principle of Fault Diagnosis System;592
6.14.3;Fuzzy Fault Diagnosis Principles;592
6.14.4;Fuzzy Inference Based Fault Diagnosis State of Navigation Mark;593
6.14.4.1;Relationship between Symptom and Cause;594
6.14.4.2;Membership Function;595
6.14.4.3;Fuzzy Relationship Matrix between Symptom and Cause;596
6.14.5;Example of Fuzzy Inference Based Fault Diagnosis;596
6.14.6;Conclusions;598
6.14.7;References;598
6.15;Correlation Coefficient between Intuitionistic Fuzzy Sets;600
6.15.1;Introduction;600
6.15.2;Background;601
6.15.3;Correlation Coefficient between Intuitionistic Fuzzy Sets;602
6.15.4;Comparative Example and Application;605
6.15.5;Extension;606
6.15.6;Conclusions;608
6.15.7;References;608
6.16;On the Observability of Semilinear Fuzzy Dynamical Control Systems;610
6.16.1;Introduction;610
6.16.2;Preliminaries;610
6.16.3;Existence of Fuzzy Solutions;611
6.16.4;Observability of SFDCS;612
6.16.5;Example;614
6.16.6;Summary;617
6.16.7;References;617
6.17;An Extension of Rough Fuzzy Set on the Fuzzy Approximation Space;619
6.17.1;Introduction;619
6.17.2;Preliminaries;619
6.17.3;Definition of Rough Fuzzy Set on the Fuzzy Approximation Space;620
6.17.4;Properties of Rough Fuzzy Set on the Fuzzy Approximation Space;623
6.17.4.1;The Algorithm of the Upper Approximation;624
6.17.4.2;The Algorithm of Lower Approximation;624
6.17.5;References;625
6.18;Some Results and Example for Compatible Maps of Type(ß) on Intuitionistic Fuzzy Metric Space;626
6.18.1;Introduction;626
6.18.2;Preliminaries;627
6.18.3;Properties of Compatible Maps;628
6.18.4;Some Results and Example Using Compatible Maps of Type(ß);631
6.18.5;References;635
6.19;Continuity of Compatibility Function Based on T-Operation;636
6.19.1;Introduction;636
6.19.2;Preliminaries;636
6.19.3;t-Operation;637
6.19.4;Continuity of Compatibility Function Based on t-Operation;639
6.19.5;References;642
6.20;The Classification of Wine Based on PCA and ANN;643
6.20.1;Introduction;643
6.20.2;The Identification Method;644
6.20.2.1;PCA;644
6.20.2.2;The Identification Method of ANN;644
6.20.3;Materials and Methods;646
6.20.3.1;Experimental System [10];646
6.20.3.2;Experimental Sample;646
6.20.3.3;Principal Component Analysis;647
6.20.3.4;The Analysis of Data in ANN;647
6.20.3.5;All of the Final Classification Results Were Shown in Table 2;649
6.20.4;Conclusions;651
6.20.5;References;651
6.21;Controllability for the Semilinear Fuzzy Integrodifferential Equations in n-Dimension Fuzzy Vector Space;652
6.21.1;Introduction;652
6.21.2;Preliminaries;653
6.21.3;Existence and Uniqueness;654
6.21.4;Controllability;655
6.21.5;Example;658
6.21.6;References;660
6.22;Tuning of Fuzzy Controllers;661
6.22.1;Introduction;661
6.22.2;Tuning of Fuzzy Controllers;663
6.22.2.1;Tuning of the Value L for Inputs and Outputs of the FLC;663
6.22.2.2;Tuning of the Output Scaling Coefficients GU of the FLC;664
6.22.2.3;Tuning of the Input Scaling Coefficients GP, GI, GD of the FLC;666
6.22.3;Conclusion;668
6.22.4;References;668
6.22.5;Appendix 1;668
6.22.6;Appendix 2;669
6.23;The Connectedness Relative to a Subbase for the L-Fuzzy Topology;672
6.23.1;Introduction;672
6.23.2;Preliminaries;673
6.23.3;The Connectedness Relative to a Subbase;673
6.23.4;Conclusions;679
6.23.5;References;679
6.24;Some New Properties of Strongly Convex Fuzzy Sets;680
6.24.1;Introduction;680
6.24.2;Preliminaries;680
6.24.3;MainResults;682
6.24.4;References;686
6.25;The Continuity of Complex Fuzzy Function;687
6.25.1;Introduction;687
6.25.2;Preliminaries;687
6.25.3;Continuity of Complex Fuzzy Function;690
6.25.4;The Property of Continued Complex Fuzzy Function on M Closed Rectangle;693
6.25.5;References;696
6.26;Some Correlative Conception and Properties of Bounded Closed Fuzzy Complex Number Set;697
6.26.1;Introduction;697
6.26.2;Preliminaries;697
6.26.3;Fuzzy Complex Set and Fuzzy Complex Number;699
6.26.3.1;The Conception of Fuzzy Complex Set and Fuzzy Complex Number [4];699
6.26.3.2;Fuzzy Complex Set and Fuzzy Complex Number Operation [4];700
6.26.4;Bounded Closed Complex Fuzzy Complex Number Set and Some Properties;700
6.26.5;Fuzzy Complex Number Value Mapping on 0F (C) and an Important Property;705
6.26.6;Conclusions;707
6.26.7;References;707
7;Soft Computing;708
7.1;Improved BP-Based Decoding Algorithms Integrated with GA for LDPC Codes;708
7.1.1;Introduction;708
7.1.2;BP Algorithm and Genetic Algorithm;709
7.1.3;Proposed GABP Algorithms;711
7.1.4;Simulation Results and Analysis;713
7.1.5;Conclusions;716
7.1.6;References;716
7.2;The Research on the Multi-project Human Resource Configuration Based on the Virus Evolutionary Genetic Algorithm;717
7.2.1;Introduction;717
7.2.2;Scheduling Question Based on the Virus Evolutionary Genetic Algorithm;719
7.2.2.1;Task Description;719
7.2.2.2;The Description of Back-Scheduling Method;720
7.2.2.3;The Design of the Virus Evolutionary Genetic Algorithm;721
7.2.3;Calculation Example;722
7.2.4;Conclusion;723
7.2.5;References;724
7.3;Recognizing the Taste Signals of Tea Using T-S Fuzzy Model;726
7.3.1;Introduction;726
7.3.2;T-SModel;727
7.3.3;Hierarchical Genetic Algorithms;727
7.3.4;The Description of the Algorithm;727
7.3.4.1;Chromosome Representation;727
7.3.4.2;Objective Functions;728
7.3.4.3;Crossover;728
7.3.4.4;Mutation;729
7.3.5;Numerical Simulations;729
7.3.6;Conclusion;730
7.3.7;References;730
7.4;Expression Recognition Based on Genetic Algorithm and SVM;732
7.4.1;Introduction;732
7.4.2;Equable Principal Component Analysis;733
7.4.3;Support Vector Machines Based on Genetic Algorithm;735
7.4.3.1;Principal;735
7.4.3.2;Multi-SVM Classifiers Model;736
7.4.3.3;Support Vector Machine Based on Genetic Algorithm;736
7.4.4;Experiment;738
7.4.4.1;Experiment Data and Pretreatment;738
7.4.4.2;Experiment Projects;738
7.4.4.3;Experiment Results and Analysis;739
7.4.5;Conclusion;739
7.4.6;References;740
7.5;A Mixed-Coding Genetic Algorithm and Its Application on Gear Reducer Optimization;741
7.5.1;Introduction;741
7.5.2;Improved Genetic Algorithm;742
7.5.3;Mathematical Models: The Objective Function and Design Variables;743
7.5.4;Mathematical Models: Constraint Conditions;744
7.5.5;Practical Applications Examples;744
7.5.6;Conclusions;746
7.5.7;References;747
7.6;Fault Tolerance Controller Is Designed for Linear Continuous Large-Scale Systems with Sensor Failures;748
7.6.1;Introduction;748
7.6.2;System Description;749
7.6.3;TheTheorem;749
7.6.4;The Simulation of Illustration;751
7.6.5;Conclusions;753
7.6.6;References;753
7.7;A Modified Particle Swarm Optimizer with Dynamical Inertia Weight;754
7.7.1;Introduction;754
7.7.2;Method;755
7.7.2.1;Standard PSO;755
7.7.2.2;Modified PSO With Dynamical Inertia Weight;755
7.7.3;Experimental Results and Discussion;757
7.7.3.1;Test Functions;757
7.7.3.2;Experimental Steps;758
7.7.3.3;Results and Performance Analysis;759
7.7.4;Conclusions;761
7.7.5;References;762
7.8;A Genetic Algorithm Based on Evolutionary Direction;764
7.8.1;Introduction;764
7.8.2;An Introduction to Genetic Algorithms;765
7.8.3;Evolutionary Direction;766
7.8.4;The Genetic Algorithm Based Evolutionary Direction;766
7.8.4.1;Evolutionary Direction Based Genetic Algorithm;767
7.8.4.2;Description of GABED;768
7.8.5;Experiments;769
7.8.5.1;Experiment1: Convergence Analysis of GABED;769
7.8.5.2;Experiment2: Comparison of Searching Ability;770
7.8.6;Conclusions;771
7.8.7;References;772
7.9;Efficient Algorithm for Attribute Reduction of Incomplete Information Systems Based on Assignment Matrix;773
7.9.1;Introduction;773
7.9.2;Preliminaries;775
7.9.3;The Analysis of Old Algorithm;776
7.9.4;New Attribute Reduction Algorithm;777
7.9.5;Example and Analysis;779
7.9.6;Conclusions;780
7.9.7;References;781
7.10;Principal Component Analysis of Triangular Fuzzy Number Data;783
7.10.1;Introduction;783
7.10.2;Basic Concepts and Notation;784
7.10.3;The Idea and Description of TFNPCA Algorithm;786
7.10.3.1;Primary Ideas;786
7.10.3.2;PCA Algorithm for Triangular Fuzzy Number Data;787
7.10.4;An Applicative Example of the TFNPCA Algorithm;789
7.10.5;Conclusions;793
7.10.6;References;793
7.11;Parameters Optimization of Cognitive Radio Based on DNA Genetic Algorithm;795
7.11.1;Introduction;795
7.11.2;GA Based on DNA Encoding;796
7.11.2.1;DNA Encoding;796
7.11.2.2;Genetic Operators;796
7.11.2.3;The Process of DNA-GA;797
7.11.3;Simulation Results;797
7.11.3.1;Test Function;797
7.11.3.2;Results and Analysis;797
7.11.4;Cognitive Radio Parameters Adjustment;799
7.11.4.1;Cognitive Radio Adjustable Parameters;799
7.11.4.2;Cognitive Radio Objective Functions;799
7.11.4.3;CR Optimal Algorithm Based on DNA-GA;800
7.11.4.4;Simulation Result and the Analysis;800
7.11.5;Conclusions;801
7.11.6;References;801
7.12;Genetic Simulated-Annealing Algorithm for Robust Job Shop Scheduling;803
7.12.1;Introduction;803
7.12.2;Problem Description;804
7.12.2.1;Job Shop Scheduling Problem with Uncertain Processing Times;804
7.12.2.2;Robust Job Shop Scheduling under Scenario Approach;805
7.12.3;Genetic Simulated-Annealing Algorithm;806
7.12.4;Computational Experiment and Result Analysis;810
7.12.5;Conclusions;812
7.12.6;References;813
7.13;Measurement and Analysis of Self-similarity for Chaotic Dynamics;814
7.13.1;Introduction;814
7.13.2;Self-similar Sets;815
7.13.3;Related Analysis and Results;817
7.13.3.1;Self-similarity of Experimental Data;817
7.13.3.2;Self-similarity of Land Clutter and Detection of Objection in Clutter;819
7.13.4;Conclusion;821
7.13.5;References;821
7.14;Multi-objective Optimal Grid Workflow Scheduling with QoS Constraints;823
7.14.1;Introduction;823
7.14.2;Grid Workflow Model Based on AGWL;823
7.14.3;Grid Workflow Scheduling Algorithm Based on AGWL Model;824
7.14.3.1;Scheduling Model and Thought;824
7.14.3.2;INSGA-.Scheduling Algorithm;825
7.14.3.3;Improved Population Initialization Algorithm;826
7.14.3.4;Improved Fast-Non-dominated-sort Algorithm;826
7.14.4;The Time Complexity of INSGA-II;827
7.14.5;Experiments and Analysis;828
7.14.5.1;Experimental Design;828
7.14.5.2;Experimental Analysis;829
7.14.6;Conclusion;830
7.14.7;References;831
7.15;Study on Identification of Oil/Gas and Water Zones in Geological Logging Base on Support-Vector Machine;832
7.15.1;Introduction;832
7.15.2;Support Vector Regression [1, 2];833
7.15.3;Case Study;836
7.15.3.1;Parameter Selection;836
7.15.3.2;Set Up the Database of Parameter;837
7.15.3.3;To Identify of Oil/Gas and Water Layer with SVM;838
7.15.4;Conclusion;839
7.15.5;References;840
7.16;A Novel RBF Neural Network and Application of Optimizing Fracture Design;841
7.16.1;Introduction;841
7.16.2;RBF-Network-Model Learning Algorithm Based on Immune Principle;843
7.16.3;Application of RBF Network Model Based on Immune Principle in Optimizing Fracture Design;845
7.16.3.1;Determination of Influential Factor;845
7.16.3.2;To Establish Decision Parameter Data;846
7.16.3.3;Decision Parameter Pretreatment;847
7.16.3.4;The Application and Analysis of Examples;847
7.16.4;Conclusion;849
7.16.5;References;850
7.17;Research on Traffic Prediction Model Based on KPCA;851
7.17.1;Introduction;851
7.17.2;Kernel Function Principal Component Analysis;852
7.17.2.1;Principal of PCA[11];852
7.17.2.2;Principle of KPCA[8, 12-14];853
7.17.3;Radial Basis Function Neural Network;854
7.17.4;Design of Traffic Prediction Model Based on KPCA;855
7.17.4.1;Traffic Data Classification;855
7.17.4.2;Design of Input/Output Vector;855
7.17.4.3;Data Preprocessing;855
7.17.4.4;PredictionModel Structure;855
7.17.5;Simulation of Prediction Model;856
7.17.6;Conclusions;859
7.17.7;References;859
7.18;A Novel Approach to Robust Optimization of Complex System without Objective Function;861
7.18.1;Introduction;861
7.18.2;Problem Definitions;862
7.18.3;A Novel Approach;863
7.18.3.1;The Performance Modeling of Complex System Based on NN;863
7.18.3.2;The Design of Fitness Function to Robust Optimization Based on Novel Approach;863
7.18.3.3;The Robust Optimization of Complex System;864
7.18.4;Experiment Study;865
7.18.4.1;The Modeling of Voltage Gain Based on NN;865
7.18.4.2;The Robust Optimization Design of Amplifier Based on Novel Approach;867
7.18.4.3;Results;867
7.18.5;Conclusion;870
7.18.6;References;870
7.19;Temperature Mode Recognition of Metallurgical Slag Based on KPCA and NN;872
7.19.1;Introduction;872
7.19.2;Method of Mode Recognition by KPCA and NN;873
7.19.2.1;Fundamental Principle of KPCA;873
7.19.2.2;The System of Temperature Mode Recognition;874
7.19.3;Modeling of Temperature Measurement of Metallurgical Slag;874
7.19.3.1;Image Data Sampling;874
7.19.3.2;Binary Image Treatment;875
7.19.3.3;Neural Network Modeling;876
7.19.4;Conclusions;879
7.19.5;References;879
7.20;Field Simulation of Liquid-Liquid Hydrocyclone Based on Large Eddy Theory;881
7.20.1;Introduction;881
7.20.2;Theory and Application of Large Eddy Simulation;881
7.20.2.1;Large Eddy Simulation Control Equation;881
7.20.2.2;Sub-Grid-Scale Model;882
7.20.3;Numerical Simulation and Result Analysis;882
7.20.3.1;Pressure Distribution;882
7.20.3.2;Tangential Velocity Distribution;883
7.20.3.3;Axial Velocity Distribution;883
7.20.3.4;Radial Velocity Distribution;884
7.20.3.5;Oil and Water Separation Efficiency;886
7.20.4;Conclusion;886
7.20.5;References;886
7.21;Numerical Simulation and Experimental Study of Cavitation Jet Flow;887
7.21.1;Introduction;887
7.21.2;Mathematic Model;887
7.21.3;Numerical Simulation;889
7.21.3.1;Physical Model;889
7.21.3.2;Boundary Condition;890
7.21.3.3;Numerical Simulation Results;890
7.21.4;Experimental Studies;892
7.21.5;Conclusion;893
7.21.6;References;893
7.22;Numerical Simulation of Hydraulic Transient for Pipeline Leakage;894
7.22.1;Introduction;894
7.22.2;Mathematical Modeling;894
7.22.3;Simulation Calculation;896
7.22.3.1;Boundary Conditions;896
7.22.3.2;Initial Conditions;897
7.22.4;Results and Analysis;897
7.22.4.1;Flow Rate Variation at Pump Station Exit with Time;897
7.22.4.2;Discharge Pressure Head of Pump Station with Time;898
7.22.4.3;Flow Rate through Leakage Hole;898
7.22.4.4;Pressure Head at Leakage Point;898
7.22.5;Conclusions;899
7.22.6;References;899
7.23;Dynamics of a Higher Order Nonlinear Difference Equation;901
7.23.1;Introduction;901
7.23.2;Preliminary Results;902
7.23.3;The Case 0q+q2r;911
7.23.6.1;Persistence;911
7.23.6.2;Global Attractivity;911
7.23.7;References;912
7.24;Improvement Study and Application Based on K-Means Clustering Algorithm;914
7.24.1;Introduction;914
7.24.2;K-Means Algorithm Analyses;914
7.24.3;The Improvement of K-Means Algorithm;915
7.24.3.1;Methods of Choosing Clustering Centers;916
7.24.3.2;Algorithm Analyses;916
7.24.3.3;New Method of Choosing Clustering Centers;917
7.24.3.4;Steps of Improved K-Means Algorithm;917
7.24.4;Achievement and Result of Algorithm;917
7.24.4.1;Processing and Standardization of Data;917
7.24.4.2;Results of Algorithms;919
7.24.5;Conclusions;920
7.24.6;References;920
7.25;Optimizing the Properties of Tyrosine and It’s Oxidation Derivatives Based on Quantum Computation;922
7.25.1;Introduction;922
7.25.2;Calculation Methods;923
7.25.3;Results and Discussion;924
7.25.3.1;Mulliken Charges Distribution;924
7.25.3.2;The Effect of Temperature on the Thermodynamic Properties;924
7.25.3.3;Frontier Orbital Energies;925
7.25.4;Conclusions;927
7.25.5;References;927
7.26;Application of Numerical Simulation Technique to the Study of Well Killing While Drilling Mud Has Completely Erupted Out from Borehole;929
7.26.1;Introduction;929
7.26.2;Theoretical Model;930
7.26.3;Mathematical Model;930
7.26.4;Initial Condition and Boundary Conditions;932
7.26.5;Solution Procedure and Algorithm;933
7.26.6;Simulation Results and Discussions;934
7.26.7;Determination of Kill Parameter Combination;938
7.26.7.1;Maximum Aiknved Surface Annular Pressure Pac;938
7.26.7.2;Kill Mud Density Pm and Injection Rate Qm;939
7.26.8;Conclusions;940
7.26.9;References;942
7.27;DC Motor Speed Control System Simulation Based on Fuzzy Self-tuning PID;943
7.27.1;Introduction;943
7.27.2;Design of the Fuzzy Controller;944
7.27.2.1;Fuzzy Control Model of a Speed Control System;944
7.27.2.2;Theory and Structure of Self-tuning Fuzzy PID Control System with Parameter;945
7.27.3;Design of Self-tuning Fuzzy PID Controller with Parameters;945
7.27.3.1;Membership Determination;946
7.27.3.2;The Principle of PID Parameter Tuning;946
7.27.3.3;Design of Fuzzy Control Rule Base;947
7.27.3.4;Defuzzification Strategy;948
7.27.4;Self-tuning Fuzzy PID Controller System with Parameter Simulation;949
7.27.5;Simulation Results;949
7.27.6;Conclusion;950
7.27.7;References;951
7.28;Modeling for Sleeping Fidget Sensor Based on Multi-source Information Fusion;952
7.28.1;Introduction;952
7.28.2;Description of Problem;953
7.28.3;Information Fusion Based on NN;954
7.28.4;Experimental System Project;955
7.28.5;NN Model of Monitoring Sleep Fidget;956
7.28.6;Simplification of Sleeping Fidget Sensor Model;958
7.28.7;Conclusion;960
7.28.8;References;960
7.29;Application of FNNS for Fracturing Candidate Optimization in Oilfield;961
7.29.1;Introduction;961
7.29.2;FNNS and Its Fuzzy Decision Method;962
7.29.2.1;Algorithm Structure of Fuzzy Neural Network;962
7.29.2.2;Weighting Vector of Fuzzy Neural Network System;963
7.29.2.3;Fuzzy Decision Method;963
7.29.3;The Method of Optimizing Layer of Candidate Well;964
7.29.3.1;Analysis of Influence Factor;964
7.29.3.2;Modeling and Simulation;965
7.29.4;Application Examples for Forecasting;966
7.29.5;Conclusion;966
7.29.6;References;967
7.30;Prediction Model for a Gas-Water Two-Phase Flow Pressure Drop Based on Pattern Classification;968
7.30.1;Introduction;968
7.30.2;Gas-Water Two-Phase Flow Experiments;969
7.30.3;Flow Pattern Identification Criteria;970
7.30.3.1;Direct Measurements to Identify Flow Patterns;970
7.30.3.2;Wavelet Singularity Analysis;973
7.30.3.3;The Judging Criteria of Flow Pattern;974
7.30.4;Based on the Two-Phase Flow Pressure Drop Model;975
7.30.4.1;Optimization of Two-Phase Flow Pressure Drop Model;975
7.30.4.2;New Combination Pressure Drop Model;976
7.30.5;Evaluation of Two-Phase Flow Pressure Drop Model;977
7.30.5.1;Evaluation of Pressure Drop Model of ShuNan Test Data;977
7.30.6;Conclusions;978
7.30.7;References;979
7.31;Power Performance Optimization of Motorcycle Based on Last Speed Ratio;980
7.31.1;Introduction;980
7.31.2;Main Methods of Improving Power Performance;980
7.31.3;Evaluation Items and Design Principles of Power Performance;981
7.31.3.1;Evaluation Items;981
7.31.3.2;Design Principles;981
7.31.4;Determination of Sliding Resistance and Air Resistance Coefficient;982
7.31.5;Simulating and Calculating Power Performance;984
7.31.5.1;Engine Performance;985
7.31.5.2;Maximum Motorcycle Speed;985
7.31.5.3;Largest Gradeability;986
7.31.5.4;Accelerating Time;986
7.31.5.5;Motorcycle Power Balance Diagram;987
7.31.6;Motorcycle Last Speed Ratio;987
7.31.7;Program Design;988
7.31.8;Conclusion;989
7.31.9;References;989
7.32;Strictly Analytic Calculation to the Renormalized Finite Quantity with Z0 Loop Propagator;990
7.32.1;Introduction;990
7.32.2;The Finite Quantity’s Separation of Z0 Loop Propagator in Momentum Regulation;991
7.32.3;Feynman Convergent Integral (1+1+4) Dimensional Calculation Method;993
7.32.4;Great Momentum Integral Limitation (1+4) Dimensional Method;994
7.32.5;Conclusions;996
7.32.6;References;996
8;Fuzzy Engineering;998
8.1;Application of Self-adjusting Quantitative Factor Fuzzy Controller in Tank System;998
8.1.1;Introduction;998
8.1.2;Design of Fuzzy Controller;998
8.1.3;Self-adjusting Quantitative Factor Fuzzy Controller;1000
8.1.3.1;The Structure of Self-adjusting Quantitative Factor Fuzzy Controller;1000
8.1.3.2;The Influence of Quantitative Factors;1001
8.1.3.3;Design of Quantitative Factor Fuzzy Controller;1001
8.1.4;Analyze of Control Effects;1004
8.1.5;Conclusion;1006
8.1.6;References;1007
8.2;Two Possibilistic Mean-Variance Models for Portfolio Selection;1008
8.2.1;Introduction;1008
8.2.2;Preliminaries;1009
8.2.3;Two Possibilistic Portfolio Selection Models;1011
8.2.4;Numerical Example;1014
8.2.5;Conclusions;1016
8.2.6;References;1016
8.3;Fault Diagnosis of Pulverizing System Based on Fuzzy Decision-Making Fusion Method;1018
8.3.1;Introduction;1018
8.3.2;Fault Condition Segmentation of Pulverizing System Based on Grey Relation Indexes;1019
8.3.3;Fault Identification of Diagnosis for Pulverizing System Using Neural Network;1023
8.3.4;Fuzzy Multi-attributes Decision-Making Model;1027
8.3.5;Conclusion;1028
8.3.6;References;1028
8.4;Fuzzy Inference Based on Similarity Measure;1030
8.4.1;Introduction;1030
8.4.2;The Mathematics Essence of Fuzzy Inference Algorithm;1031
8.4.3;The Similar Degree and Peak Drift Degree of Normal Triangle Fuzzy Sets;1033
8.4.4;Fuzzy Inference Algorithm Based on SMTT;1035
8.4.5;The Relation between SMTT Fuzzy Inference Algorithm and Interpolating Algorithm;1037
8.4.6;The Relation between SMTT Fuzzy Inference Algorithm and True Value Deferral Method-Fuzzy Inference Interpolating Algorithm;1037
8.4.7;References;1039
8.5;Fuzzy Control of Automatic Brush-Plating Process;1040
8.5.1;Introduction;1040
8.5.2;Fuzzy Control Algorithm for Brush-Plating Process;1041
8.5.2.1;Input Linguist Variables;1041
8.5.2.2;Output Linguist Variables;1042
8.5.2.3;Rule Base;1043
8.5.2.4;Fuzzifier;1045
8.5.2.5;Inference Mechanism;1046
8.5.2.6;Defuzzification;1048
8.5.2.7;The Input-Output Properties of the Fuzzy Controllers;1049
8.5.2.8;The Application Program of Fuzzy Controllers;1051
8.5.2.9;Application of the Fuzzy Controllers;1052
8.5.3;Conclusion;1053
8.5.4;References;1053
8.6;Fuzzy Control on Voltage/Reactive Power in Electric Power Substation;1055
8.6.1;Introduction;1055
8.6.2;Reactive Power and Voltage Control Methods;1056
8.6.3;Fuzzy Control Rules;1057
8.6.3.1;Setting the Domain of Input and Output Parameters and Fuzzy Sets;1057
8.6.3.2;Selecting Membership Functions;1058
8.6.3.3;Creating Control Rules;1058
8.6.4;Simulation and Discussion;1060
8.6.4.1;Designing Simulation Model;1060
8.6.4.2;Setting Simulation Parameters;1061
8.6.4.3;Simulating and Analyzing;1061
8.6.5;Conclusion;1062
8.6.6;References;1062
8.7;Fuzzy Fatigue Reliability Design Considering Model Uncertainty;1064
8.7.1;Introduction;1064
8.7.2;Model Uncertainty;1065
8.7.3;Reliability Design Based on Fuzzy Limit State;1065
8.7.4;Calculation of Fuzzy Fatigue Reliability;1069
8.7.5;Example Analysis;1069
8.7.6;Conclusion;1071
8.7.7;References;1072
8.8;An Evolvement-Based Modeling Method for Logistics Network;1073
8.8.1;Introductions;1073
8.8.2;The Evolvement Process of the Logistics Network;1074
8.8.2.1;The Growth Model with Preferential Attachment and Weighted Edge Characters;1074
8.8.2.2;The Effect of Adding Edges;1075
8.8.2.3;The Degeneration Process of the Logistics Network;1076
8.8.2.4;The Dissipation Characters and Dynamics of Logistics Network;1077
8.8.3;The Simulations and Experiment Analysis;1078
8.8.4;Conclusions;1079
8.8.5;References;1080
8.9;On-Line Inference for Fuzzy Controllers in Continuous Domains;1081
8.9.1;Introduction;1081
8.9.2;Features of the Fuzzy Simplified Reasoning Method;1083
8.9.3;Strategies for Tuning Scaling Factors On-Line;1084
8.9.4;Illustrative Example;1086
8.9.5;Conclusion;1088
8.9.6;References;1088
8.10;A Research on Fuzzy Control Method Used in STATCOM;1089
8.10.1;Introduction;1089
8.10.2;STATCOM Model;1090
8.10.3;STATCOM Multi-Objective Control;1091
8.10.4;The System Simulation of STATCOM;1095
8.10.4.1;The Establishment of Simulation Model;1095
8.10.4.2;The STATCOM Simulation of Influence on the Power System;1096
8.10.5;Conclusion;1098
8.10.6;References;1098
8.11;A Short-Term Load Forecasting Method Based on RBF Neural Network and Fuzzy Reasoning;1100
8.11.1;Introduction;1100
8.11.2;The Structure of RBF Neural Network;1101
8.11.3;The Establishment of Fuzzy Reasoning Model;1104
8.11.4;The Realization of Load Forecasting;1106
8.11.5;Conclusions;1107
8.11.6;References;1107
8.12;Application of Tread Patterns Noise-Reduction Based on Fuzzy Genetic Algorithm;1109
8.12.1;Introduction;1109
8.12.2;The Structural Parameters of Tread Patterns;1110
8.12.2.1;The Parameters of Pattern Block;1110
8.12.2.2;The Parameters of Pattern Slot;1110
8.12.2.3;The Number of Pattern Strip;1110
8.12.2.4;Pattern Pitch;1111
8.12.2.5;The Pitch Array;1111
8.12.2.6;The PatternMisplacement;1111
8.12.3;The Optimization Method of Tread Patterns Structure Parameter;1111
8.12.3.1;The Objective Function of Low-Noise Tire Curve M;1111
8.12.3.2;Determine the Fitness Function;1112
8.12.3.3;Genetic Algorithm Coding and Genetic Manipulation;1113
8.12.4;The Analysis and Comparison of Optimize Examples;1114
8.12.5;Conclusions;1115
8.12.6;References;1115
8.13;Application of Fuzzy Genetic Algorithm in Control for Heating Furnace;1117
8.13.1;Introduction;1117
8.13.2;Input Membership Function Adjustment in Fuzzy Control;1118
8.13.2.1;Coding;1118
8.13.2.2;Parameter Correction in Fuzzy Control Analytic Expression;1119
8.13.3;Interval Adjustment of Output Club-Shaped;1121
8.13.3.1;Coding;1121
8.13.3.2;Genetic Operation;1121
8.13.4;Adjustment Calculation of Maximum Correction;1122
8.13.5;Analysis of Simulation Example;1123
8.13.6;Conclusions;1124
8.13.7;References;1124
8.14;Modeling of Magnetorheological Damper Using Neuro-Fuzzy System;1125
8.14.1;Introduction;1125
8.14.2;The Model of MR Damper;1126
8.14.2.1;The Direct Model of MR Damper;1126
8.14.2.2;The Mechanic Model of MR Damper;1126
8.14.3;Adaptive Neuro-Fuzzy Inference System;1127
8.14.3.1;TSK Fuzzy Model;1127
8.14.3.2;The First-Order TSK Fuzzy System and ANFIS Structure;1127
8.14.4;The Neuro-Fuzzy System of the Inverse Model of MR Damper;1128
8.14.5;Simulation Conditions;1129
8.14.6;Simulation results;1130
8.14.7;Conclusions;1131
8.14.8;References;1132
8.15;The Fuzzy Reliability Analysis for the Lining of Crack Control of the Subsea Tunnel;1133
8.15.1;Introduction;1133
8.15.2;Establishment of Fuzzy Reliability Computation Pattern;1134
8.15.3;The Calculation Example of Fuzzy Reliability Index and Sensitive Analysis;1137
8.15.4;Conclusions;1138
8.15.5;References;1139
8.16;Tracking Control of Ball and Plate System with GA-FNNC;1140
8.16.1;Introduction;1140
8.16.2;The Mathematics Model of Ball and Plate System;1141
8.16.3;Control System Structure And Design;1142
8.16.3.1;Design of FNNC;1143
8.16.3.2;The GA Optimization;1144
8.16.3.3;The BP Algorithm Online Adjustment;1145
8.16.4;Simulation Results;1146
8.16.5;Conclusions;1147
8.16.6;References;1148
8.17;The Topological and Statistical Analysis of Public Transport Network Based on Fuzzy Clustering;1149
8.17.1;Introduction;1149
8.17.2;The Definition of Space P and Fuzzy Cluster Analysis;1150
8.17.3;Weight and Strength;1152
8.17.4;Characteristic Analysis of PTN;1153
8.17.5;Conclusions;1155
8.17.6;References;1156
8.18;A Parameter Auto-Tuning Method of Fuzzy-PID Controller;1158
8.18.1;Introduction;1158
8.18.2;Parameter Tuning Principle and Its Existent Problem;1159
8.18.2.1;Tuning Principle of PID-Parameter;1159
8.18.2.2;The Existent Problem in the Process of Control Parameter Tuning;1160
8.18.3;Design of Auto-Tuning Method Based on Self-Learning System;1160
8.18.3.1;Selection for Input and Output;1161
8.18.3.2;The Design of Fuzzy Control Rule Base;1161
8.18.3.3;Fuzzy Inference and Defuzzification;1162
8.18.3.4;The Design of Fuzzy Self-Learning System;1163
8.18.4;System Simulation;1164
8.18.5;Conclusions;1165
8.18.6;References;1165
8.19;Fuzzy Optimization Design of Gas Pipeline;1166
8.19.1;Introduction;1166
8.19.2;Fuzzy Optimization Model of the Natural Gas Pipeline;1166
8.19.2.1;Objective Function;1166
8.19.2.2;Boundary Condition;1167
8.19.2.3;The Fuzzy Optimization Pattern of Pipeline;1170
8.19.3;To Solve the Fuzzy Optimization Design Mathematical Model;1170
8.19.3.1;The Inversion of Fuzzy Optimization Model;1170
8.19.3.2;Mixed-Discrete Variables Direct Search Method MDOD.;1171
8.19.4;Calculation Software;1171
8.19.5;Example;1172
8.19.6;Conclusions;1172
8.19.7;References;1173
8.20;Fuzzy Direct Torque Control of Six Phase Induction Machine Based on Torque Prediction;1174
8.20.1;Introduction;1174
8.20.2;6PIM Theoretical Analysis and Model;1175
8.20.3;Fuzzy Controller;1178
8.20.4;Experiment Results;1180
8.20.5;Conclusions;1182
8.20.6;References;1182
8.21;Study on the Optimal Charging with Neural Networks Prediction and Variable Structure Fuzzy Control;1184
8.21.1;Introduction;1184
8.21.2;Electrochemistry Theory of Lead-Acid Battery;1185
8.21.3;Charging Technique Analysis;1186
8.21.4;High Efficiency, Fast and Damage Free Charging Intelligent Control;1187
8.21.4.1;Structure of Charging Control System;1187
8.21.4.2;Design of Fuzzy Controller 2;1188
8.21.4.3;Fuzzy Neural Networks Predictor Design;1190
8.21.5;Charging Set Hardware Design;1190
8.21.6;The Experiment Result Analysis;1191
8.21.7;References;1191
8.22;Brushless DC Motor Speed Fuzzy Adaptive Control System;1193
8.22.1;Introduction;1193
8.22.2;The Structure and the Principle of Brushless DC Motor;1193
8.22.2.1;The Principle of Brushless DC Motor;1193
8.22.2.2;The Voltage and Torque Formation of Brushless DC Motor;1194
8.22.2.3;The Mathematic Model of Brushless DC Motor;1194
8.22.3;Design of Fuzzy Controller;1195
8.22.4;Design Self Adaptive Fuzzy Controller and Analogy of Steady;1196
8.22.5;Simulation;1198
8.22.6;Conclusion;1199
8.22.7;References;1200
8.23;A Kind of Nonmonotone QP-Free Method for Constrained Optimization;1201
8.23.1;Introduction;1201
8.23.2;Algorithm;1202
8.23.3;Global Convergence of Algorithm;1206
8.23.4;NumericalTests;1210
8.23.5;References;1211
8.24;Fuzzy Adaptive PID Strategy for Asynchronous Machines Direct Torque Control;1212
8.24.1;Introduction;1212
8.24.2;Asynchronous Machine DTC Control Method;1213
8.24.3;Fuzzy Adaptive PID Control;1213
8.24.4;Analysis of Simulation Result;1215
8.24.5;Conclusions;1217
8.24.6;References;1218
8.25;Electronic Throttle Control Strategy Develop;1219
8.25.1;Introduction;1219
8.25.2;System Introduction;1220
8.25.3;The Electronic Throttle Control Throttle Position Command Calculation;1221
8.25.3.1;Relationship between the Air Mass and Throttle Position;1221
8.25.3.2;Relationship between the Intake Air Mass and Manifold Absolute Pressure;1222
8.25.3.3;Build the Intake Air Mass Close Loop Control Algorithm;1223
8.25.3.4;Calculation Scheduling Develop;1224
8.25.4;Validation Test;1224
8.25.5;Conclusions;1227
8.25.6;References;1227
8.26;Study on Simulations of Hydraulic Transient as a Result of Pipeline Leakage for the Leaking Point Locating;1229
8.26.1;Introduction;1229
8.26.2;Mathematical Modeling;1229
8.26.3;Simulation Calculation;1231
8.26.3.1;Boundary Conditions;1231
8.26.3.2;Initial Conditions;1233
8.26.3.3;Calculation;1234
8.26.4;Results and Analysis;1234
8.26.4.1;Flow Rate Variation at Pump Station Exit with Time;1234
8.26.4.2;Pressure Head Variation of Pump Exit with Time;1234
8.26.4.3;Flow Rate through Leakage Hole;1235
8.26.4.4;Pressure Head at Leakage Point;1235
8.26.5;Conclusion;1235
8.26.6;References;1236
8.27;Combined Forecast Model of Gas Load Based on Grey Theory;1237
8.27.1;Introduction;1237
8.27.2;Prediction Model on the Basis of Gray Theory;1237
8.27.2.1;Gray GM (1,1) Model;1238
8.27.2.2;Residual Error Gray Prediction Model;1238
8.27.2.3;Dynamic Fill-Dimensional Gray Prediction Model;1239
8.27.2.4;Comparison of the Models;1239
8.27.3;Combined Forecasting Model;1239
8.27.4;Example Analysis;1242
8.27.5;Conclusion;1242
8.27.6;References;1243
8.28;A Fuzzy Path Selection Power-Based for MANET;1244
8.28.1;Introduction;1244
8.28.2;AODV Protocol;1245
8.28.3;Fuzzy Path Selection Power-Based AODV;1246
8.28.4;Performance Evaluation;1249
8.28.5;Conclusion;1251
8.28.6;References;1251
9;Fuzzy Operation Research and Management;1253
9.1;Dual Method to Geometric Programming with Fuzzy Variables;1253
9.1.1;Introduction;1253
9.1.2;Basic Conception;1254
9.1.3;Dual Algorithm to Geometric Programming with Fuzzy Variable;1256
9.1.4;Examples;1259
9.1.5;Conclusion;1260
9.1.6;References;1260
9.2;Average Value at Risk in Fuzzy Risk Analysis;1262
9.2.1;Introduction;1262
9.2.2;Preliminaries;1263
9.2.3;Average Value at Risk in Fuzzy Environment;1264
9.2.4;Properties of Credibilistic AVaR;1265
9.2.5;Calculating AVaR by Fuzzy Simulation;1269
9.2.6;Example;1270
9.2.7;Conclusions;1271
9.2.8;References;1271
9.3;Optimality Condition and Mixed Duality for Interval-Valued Optimization;1273
9.3.1;Introduction;1273
9.3.2;Preliminary Concepts and Results;1274
9.3.3;The KKT Optimality Sufficient Conditions;1276
9.3.4;Mixed-Type Dual Model for (IVP);1277
9.3.5;References;1280
9.4;Combined Scheduling Criteria Approach for Semiconductor Wafer Fabrication System Based on Fuzzy Cognitive Maps;1282
9.4.1;Introduction;1282
9.4.2;A Typical FCM Model;1284
9.4.3;The Single and Combined Scheduling Criteria;1285
9.4.3.1;The Scheduling Algorithm for Single Scheduling Criteria;1285
9.4.3.2;Weighted Assigning of Combined Scheduling Criteria;1286
9.4.4;Case Study;1287
9.4.5;Conclusion;1289
9.4.6;References;1290
9.5;The Method for Ranking Fuzzy Numbers Based on the Centroid Index and the Fuzziness Degree;1291
9.5.1;Introduction;1291
9.5.2;Basic Definitions;1292
9.5.2.1;Fuzzy Number;1292
9.5.2.2;Fuzziness of Fuzzy Number;1293
9.5.2.3;Geometric Dominance Degree;1293
9.5.3;Ranking Index Based on Composite Dominance;1294
9.5.4;Numerical Examples;1295
9.5.5;Conclusions;1297
9.5.6;References;1298
9.6;Fuzzy Goal Programming Model and Algorithm for Oilfield Development;1299
9.6.1;Introduction;1299
9.6.2;Preliminaries;1300
9.6.2.1;Fuzzy Set Theory;1300
9.6.2.2;Problem Description;1301
9.6.3;Fuzzy Goal Programming Model for Oilfield Development;1302
9.6.4;Hybrid Intelligent Algorithm;1304
9.6.4.1;Fuzzy Simulation;1305
9.6.4.2;Topsis;1306
9.6.4.3;Genetic Algorithm;1307
9.6.4.4;Hybrid Intelligent Algorithm;1307
9.6.5;Numerical Example;1308
9.6.6;Conclusions;1308
9.6.7;References;1309
9.7;Two Fuzzy Models for Multilayer Air Defense Disposition in Fuzzy Environment;1310
9.7.1;Introduction;1310
9.7.2;Preliminaries;1311
9.7.3;Problem Description and Two Fuzzy Models;1311
9.7.3.1;Fuzzy Chance-Constrained Programming Model;1314
9.7.3.2;Fuzzy Dependent-Chance Programming Model;1314
9.7.4;Hybrid Intelligent Algorithm;1314
9.7.4.1;Fuzzy Simulation;1315
9.7.4.2;Genetic Algorithm;1315
9.7.5;Numerical Examples;1316
9.7.6;Conclusions;1318
9.7.7;References;1318
9.8;Fuzzy Model for Portfolio Selection with Transaction Cost;1320
9.8.1;Introduction;1320
9.8.2;Fuzzy Number and Its Linear Operations and Mean;1321
9.8.3;Fuzzy Portfolio Selection Model with Transaction Cost;1322
9.8.4;Conclusion;1325
9.8.5;References;1326
9.9;A Multi-source Data Fusion Method to Establish Product Family Architecture;1328
9.9.1;Introduction;1328
9.9.2;Product Customization and Its Data;1330
9.9.3;Data Fusion Process of Establishing PFA Based on Customized Product Data;1331
9.9.4;Basal Principles and Data Fusion Algorithm;1332
9.9.4.1;Minimum Weighted Symmetric Difference between Two BOM Trees [6];1333
9.9.4.2;Data Fusion Based on DON_i and SOE_j;1333
9.9.4.3;Regrouping CK N for Product Family Architecture;1334
9.9.5;An Application Case;1335
9.9.6;Conclusion;1336
9.9.7;References;1337
9.10;Application of Driving Fatigue Difference in Driver Selections;1338
9.10.1;Introduction;1338
9.10.2;Fuzzy Evaluation Model of Driving Fatigue;1339
9.10.3;Realization of FAHP Based on GMDM;1340
9.10.3.1;Integration of Judgment Criteria Matrix and Fuzzy Weights;1340
9.10.3.2;Sub-evaluation Integration;1341
9.10.3.3;General Evaluation Calculation;1343
9.10.4;Fuzzy Ranking Realization;1343
9.10.5;Applying Analysis;1344
9.10.6;Conclusion;1349
9.10.7;References;1349
9.11;Compatibility and Priority Method of Trapezoid Fuzzy Number Judgement Matrix;1351
9.11.1;Introduction;1351
9.11.2;Trapezoid Fuzzy Number and Judgement Matrix;1352
9.11.3;Compatibility of Trapezoid Fuzzy Number Judgement Matrix;1354
9.11.4;Priority Method of Trapezoid Fuzzy Number Judgement Matrix;1355
9.11.5;Numerical Example;1356
9.11.6;Conclusion;1357
9.11.7;References;1358
9.12;The Research of Multi-objective Asset Allocation Model with Complex Constraint Conditions in a Fuzzy Random Environment;1359
9.12.1;Introduction;1359
9.12.2;Set Up Multi-objective Programming Model with Complex Constraint Conditions;1360
9.12.2.1;The Measure of Expected Return Rate;1360
9.12.2.2;The Measure of Investment Risk;1361
9.12.2.3;The Measure of Asset Liquidity;1362
9.12.2.4;The Analysis of Complex Constraints;1363
9.12.2.5;Set Up the Model;1363
9.12.3;Design Model Algorithm;1364
9.12.3.1;Expression of the Structure;1364
9.12.3.2;Deal with the Constraints;1364
9.12.3.3;Initialization Process;1365
9.12.3.4;Evaluation Function;1365
9.12.3.5;Selection Process;1365
9.12.3.6;Crossover Operator;1366
9.12.3.7;Mutation Operation;1366
9.12.4;Empirical Analysis;1366
9.12.5;Conclusion;1368
9.12.6;References;1368
9.13;Interval-Valued Fuzzy Number and Its Expression Based on Structured Element;1370
9.13.1;Introduction;1370
9.13.2;Interval-Valued Fuzzy Set;1370
9.13.3;Interval-Valued Fuzzy Number and Its Expression of Fuzzy Structured Element;1371
9.13.4;Structured Element Representation of Mathematical Operation of Interval-Valued Fuzzy Numbers;1373
9.13.5;The Distance of Interval-Valued Fuzzy Numbers;1375
9.13.6;Conclusions;1377
9.13.7;References;1377
9.14;Lumped Parameter Analysis Method of Unsteady-State Conduction of Infinite Plate;1379
9.14.1;Introduction;1379
9.14.2;MathematicalModel;1379
9.14.3;Formula of Mean Temperature Lumped Parameter Analysis Method;1380
9.14.4;Assistant Parameter;1381
9.14.5;Dimensionless Coordinate for Mean Temperature;1383
9.14.6;Conclusions;1383
9.14.7;References;1384
9.15;R Type of Strong Connectivity in L-fuzzy Topological Spaces;1385
9.15.1;Introduction;1385
9.15.2;Preliminaries;1386
9.15.3;R Type of Strong Connectivity in L-Fuzzy Topological Spaces;1386
9.15.4;Generalization of K.Fan’s Theorem;1389
9.15.5;References;1390
9.16;Choice of Interchange Scheme Based on Grey Target Theory;1392
9.16.1;Introduction;1392
9.16.2;Grey Target Theory;1393
9.16.3;Grey Target Model;1393
9.16.3.1;Grey Mode;1393
9.16.3.2;Standard Mode;1394
9.16.3.3;Grey Target Transformation;1394
9.16.3.4;Grey Relational Discrepancy Information Spaces;1394
9.16.3.5;Comprehensive Assess Coefficient;1395
9.16.4;Project Cases;1395
9.16.5;The Lists of Grey Mode;1395
9.16.6;Project Cases;1395
9.16.6.1;The Lists of Grey Mode;1395
9.16.6.2;Construct Standard Mode;1396
9.16.6.3;Bull’s- Eye Coefficient;1396
9.16.6.4;Approaching Degree;1396
9.16.6.5;Comprehensive Assess Coefficient;1397
9.16.7;Conclusion;1398
9.16.8;References;1398
9.17;Applying FAHP to Determine the Weights of Evaluation Indices for Government Websites Satisfaction;1399
9.17.1;Introduction;1399
9.17.2;Methodology of FAHP;1399
9.17.2.1;From Crisp AHP to FAHP;1399
9.17.2.2;Establishment of TrFN;1400
9.17.2.3;The Steps of FAHP Approach;1401
9.17.3;The Application of FAHP To Government Websites Satisfaction Evaluation;1403
9.17.3.1;Hierarchy of the Satisfaction Evaluation of Government Websites;1403
9.17.3.2;Pair-Wise Comparison Matrices;1403
9.17.3.3;Defuzzification;1405
9.17.3.4;Calculation of the Eigenvalue and Eigenvector;1406
9.17.3.5;Consistency Test;1406
9.17.3.6;Overall Ranking;1406
9.17.4;Conclusions;1406
9.17.5;References;1407
10;Artificial Intelligence;1408
10.1;Interval-Valued Fuzzy Reasoning Based on Weighted Similarity Measure;1408
10.1.1;Introduction;1408
10.1.2;Weighted Similarity Measures between IvFSs;1409
10.1.3;Interval-Valued Fuzzy Approximate Reasoning Based on Weighted Similarity Measure;1411
10.1.4;Conclusion;1417
10.1.5;References;1417
10.2;A Power-Law Approach on Router-Level Internet Macroscopic Topology Modeling;1419
10.2.1;Introduction;1419
10.2.1.1;The Measured Samples from CAIDA Monitors;1420
10.2.1.2;Mathematical Description of Power-Law;1420
10.2.2;Power-Law Analysis;1421
10.2.2.1;Frequency-Degree Power-Law;1421
10.2.2.2;Degree-Rank Power-Law;1422
10.2.2.3;CCDF(d)-Degree Power-Law;1423
10.2.2.4;Power-Law Analysis Conclusions;1424
10.2.3;Internet Topology Modeling;1425
10.2.3.1;Improve BA Model by Adding A Parameter e;1425
10.2.3.2;Improve the IBA Model According to Degree-Rank Power-Law;1425
10.2.3.3;The Algorithm;1426
10.2.4;Conclusion;1426
10.2.5;References;1427
10.3;Research on the Voltage Space Vector Selection of Direct Torque Control;1428
10.3.1;Introduction;1428
10.3.2;The Relationship of Stator Flux Linkage Space Vector and Voltage Space Vector;1429
10.3.3;The Constitute of Direct Torque Control System;1430
10.3.4;The Establishment of Flux Linkage Torque Observer Model and Sector Judgment Simulation Model;1431
10.3.4.1;Flux Linkage Torque Observer Simulation Model;1431
10.3.4.2;Sector Judgment Simulation Model;1431
10.3.5;Voltage Space Vector Selection and the Establishment of Model;1432
10.3.5.1;The Selection of Switch State ID;1432
10.3.5.2;Switch State ID Changed into the Corresponding Inverter Switch State Signal with Space Voltage Vector;1433
10.3.6;Voltage Space Vector Selection and the Establishment of Model;1434
10.3.7;Conclusions;1435
10.3.8;References;1435
10.4;The Correct Expressions of Reverse Triple I Methods for Fuzzy Reasoning;1436
10.4.1;Introduction;1436
10.4.2;Reverse Triple I Methods for FMP and FMT Based on IL;1437
10.4.2.1;Reverse Triple I Method for FMP Based on IL;1438
10.4.2.2;Reverse Triple I Methods for FMT Based on IL;1440
10.4.3;a-Reverse Triple I Methods for FMP and FMT Based on IL;1441
10.4.3.1;a-Reverse Triple I Method for FMP Based on IL;1441
10.4.3.2;a-Reverse Triple I Method for FMT Based on IL;1442
10.4.4;a(u, v)-Reverse Triple I Methods for FMP and FMT Based on IL;1443
10.4.5;Conclusion;1445
10.4.6;References;1445
10.5;A BP Neural Network Model Based on Concept Lattice;1447
10.5.1;Introduction;1447
10.5.2;Prepare knowledge;1448
10.5.2.1;Concept Lattice;1448
10.5.2.2;Attribute Reduction of Concept Lattice;1450
10.5.3;BP Neural Network;1451
10.5.3.1;Network Set Up;1451
10.5.3.2;Initialization;1451
10.5.3.3;Network Training;1451
10.5.4;A BP Neural Network Model Based on Concept Lattice;1451
10.5.4.1;Concept Lattice-Based BP Neural Network Model;1452
10.5.4.2;BP Neural Network Algorithm of matlab;1452
10.5.5;The Experimental Results and Analysis;1453
10.5.5.1;Train BP Neural Network;1453
10.5.5.2;Sample Test;1454
10.5.6;Conclusion;1455
10.5.7;References;1456
10.6;A New Modeling Algorithm for Router-Level Topology;1457
10.6.1;Introduction;1457
10.6.2;Background;1458
10.6.3;Modeling Algorithm;1459
10.6.4;Simulations and Discussion;1461
10.6.5;Conclusions;1463
10.6.6;References;1463
10.7;Prediction of Heat Value of Chongqing Municipal Solid Waste Using Artificial Neural Networks;1465
10.7.1;Introduction;1465
10.7.2;A BP Neural Network Model Based on Physical Compositions of MSW for Predicting Heat Value;1466
10.7.3;A BP Neural Network Model Based on Elemental Compositions of MSW for Predicting Heat Value;1471
10.7.4;Conclusion;1473
10.7.5;References;1474
10.8;A Novel Hash Function Based on Hyperchaotic Lorenz System;1475
10.8.1;Introduction;1475
10.8.2;Hyperchaotic Lorenz System;1476
10.8.3;Hash Function Structure;1478
10.8.4;Compression Function Design;1478
10.8.5;Performance Analysis;1480
10.8.5.1;One-Way Property Analysis;1480
10.8.5.2;Birthday Attack Analysis;1481
10.8.5.3;Meet-in-the-Middle Attack Analysis;1481
10.8.5.4;Preimage Attack Analysis;1481
10.8.6;Conclusions;1482
10.8.7;References;1482
10.9;An Improved Ant Colony Algorithm for Order Picking Optimization Problem in Automated Warehouse;1483
10.9.1;Introduction;1483
10.9.2;System Description;1484
10.9.2.1;Problem Description;1484
10.9.2.2;The Basic Assumptions and Constraints;1485
10.9.2.3;Mathematical Model;1486
10.9.3;Improved Ant Colony Algorithm;1488
10.9.3.1;Ant Colony Algorithm;1488
10.9.3.2;The Improved Ant Colony Algorithm;1489
10.9.4;Analysis of Experimental Results;1490
10.9.5;Conclusions;1492
10.9.6;References;1492
10.10;An Improved Genetic Algorithm for Locations Allocation Optimization Problem of Automated Warehouse;1494
10.10.1;Introduction;1494
10.10.2;Problem Formulation;1495
10.10.3;Algorithm;1498
10.10.3.1;Coding;1498
10.10.3.2;Selection Operator;1499
10.10.3.3;Niche Technique;1499
10.10.3.4;Pareto-Optimal Solution Sets Filter;1500
10.10.3.5;Crossover Operator;1500
10.10.3.6;Mutation Operator;1501
10.10.4;TestAnalysis;1502
10.10.5;Conclusions;1504
10.10.6;References;1504
11;Fuzzy Mathematics and Systems in Applications;1506
11.1;Application of Grey System Model in Stock Prediction;1506
11.1.1;Introduction;1506
11.1.2;Brief Introduction of Grey System Thoery;1507
11.1.3;Build Up the GM(1, 1) Model Prediction of the Stock Price;1508
11.1.3.1;Build the GM(1, 1) Model;1509
11.1.3.2;Precise Inspection of Model;1509
11.1.3.3;Prediction by GM(1, 1) Model;1511
11.1.4;Conclusions;1511
11.1.5;References;1511
11.2;Dynamic Evaluation of University Science Research Capability Based on Fuzzy Theory;1513
11.2.1;Introduction;1513
11.2.2;Analysis of Effect Elements and Evaluation Index System of USRC;1513
11.2.2.1;Analysis of USRC Elements;1514
11.2.2.2;Establishment of Evaluation Index System;1514
11.2.3;Establishment of Evaluation Model of USRC;1515
11.2.3.1;The Selection of Evaluation Methods;1515
11.2.3.2;Establishment of Evaluation Model of USRC;1515
11.2.3.3;Make Sure Evaluation Rule;1516
11.2.4;Case Study;1517
11.2.5;Conclusion and Expectation;1520
11.2.6;References;1521
11.3;Wall Following of Mobile Robot Based on Fuzzy Genetic Algorithm of Linear Interpolating;1522
11.3.1;Introduction;1522
11.3.2;The Fuzzy Control System Design of TIT-.Intelligent Mobile Robot;1523
11.3.3;Linear Interpolating Based Fuzzy Control Algorithm;1525
11.3.3.1;Fuzzy Inference Mechanism;1525
11.3.3.2;Defuzzification;1526
11.3.3.3;Linear Interpolating;1526
11.3.3.4;Obtaining the Actual Output of the Fuzzy Controller;1527
11.3.4;The Optimizing Fuzzy Controller Based on Genetic Algorithm;1527
11.3.4.1;Coding Strategy and Community Initialization;1527
11.3.4.2;Determination of the Fitness Function;1528
11.3.4.3;The Searching Process of GA;1528
11.3.5;Simulation and Analysis;1529
11.3.5.1;The Simulation of Linear Interpolating Based Fuzzy Control;1529
11.3.5.2;The Simulation of Fuzzy Genetic Algorithm;1529
11.3.6;Conclusion;1531
11.3.7;References;1532
11.4;An Enhanced AODV Route Protocol Applying in the Wireless Sensor Networks;1533
11.4.1;Introduction;1533
11.4.2;The EAODV Scheme;1534
11.4.2.1;The Synopsis of the AODV Routing Protocol;1534
11.4.2.2;The Routing Protocol of the EAODV;1534
11.4.3;Simulation;1537
11.4.3.1;The Simulation Model;1537
11.4.3.2;The Effect of Node Scale for the Network Life;1537
11.4.3.3;The Moving Speed of Node Scale for the Network Life;1538
11.4.3.4;Time Delay, Throughput, Drop Ration and Delay Jitter;1539
11.4.4;Related Works;1540
11.4.5;Conclusions;1541
11.4.6;References;1541
11.5;Solution to A-C-F-L-P Based on Fuzzy Structured Element;1543
11.5.1;Introduction;1543
11.5.2;Fuzzy Structured Element and Fuzzy Number;1545
11.5.3;The Structured Element Methods of Fuzzy Number Ranking;1546
11.5.4;Solution to All-Coefficient-Fuzzy Linear Programming Base on the Structured Element Weighted Ranking;1547
11.5.5;Example;1549
11.5.5.1;Example;1549
11.5.5.2;Example;1550
11.5.6;Conclusions;1550
11.5.7;References;1551
11.6;Model on Dynamic Control of Project Costs Based on GM(1,1)for Construction Enterprises;1552
11.6.1;Introduction;1552
11.6.2;The Fuzzy Forecasting Model of the Unfinished Project Costs Based on GM(1,1);1553
11.6.2.1;Model on Gray Fuzzy Forecasting Based on GM (1,1);1553
11.6.2.2;Accuracy Testing of the Gray Fuzzy Predictive Model Based on GM (1,1);1555
11.6.2.3;The Gray Predictive Model Based on GM (1,1) Used to Predict Project Costs;1555
11.6.3;The Early Warning Control Model Based on Earned Value Theory;1555
11.6.3.1;The Control Model of Unfinished Project Costs;1556
11.6.3.2;The Early Warning Mechanism of Buffer Management of Costs Control;1556
11.6.4;Case Study;1557
11.6.4.1;Project Costs Overview;1557
11.6.4.2;The Gray Fuzzy Predictive Model Based on GM (1,1) Used to Predict the Unfinished Project Costs;1558
11.6.5;Conclusion;1560
11.6.6;References;1560
11.7;Traffic Circle Design Based Fuzzy Control Method;1562
11.7.1;Introduction;1562
11.7.2;Main Results;1563
11.7.2.1;Fuzzy Control Method;1563
11.7.2.2;Procedures of Traffic Circle Design Model Based Fuzzy Control Method;1565
11.7.3;Application Examples;1567
11.7.4;Conclusion;1567
11.7.5;References;1568
11.8;The Life Insurance Model under Fuzzy Rates of Interest;1569
11.8.1;Introduction;1569
11.8.2;The Basic Concepts of the Credibility Theory;1570
11.8.3;Full Discrete Life Insurance Model under Fuzzy Rates of Interest;1572
11.8.3.1;Formulas of Level Net Premiums under Fuzzy Rates of Interest;1573
11.8.3.2;Calculation Formulas;1575
11.8.4;Examples;1576
11.8.5;Conclusions;1576
11.8.6;References;1577
11.9;Study on POPs Emissions Prediction Based on GM(1,1) Model;1578
11.9.1;Introduction;1578
11.9.2;Gray Forecasting Model;1578
11.9.2.1;GM (1,1) Model;1579
11.9.2.2;Accuracy Test;1580
11.9.3;Application of GM (1,1) Model in Prediction of POPs Emissions;1581
11.9.4;Conclusions;1583
11.9.5;References;1583
11.10;Wellbore Trajectory Simulation Based on Fuzzy Control;1584
11.10.1;Introduction;1584
11.10.2;Basic Structure of Fuzzy Controller (FC);1585
11.10.3;Mathematical Model for Fuzzy Control System;1585
11.10.4;Design Process for Wellbore Trajectory Fuzzy Controller;1587
11.10.5;Fuzzy Control Simulation of Rate of Deviation Change;1588
11.10.6;Conclusions;1591
11.10.7;References;1591
11.11;Research of Fuzzy Comprehensive Evaluation of Intuitionistic Preference Relations;1593
11.11.1;Introduction;1593
11.11.2;Establishing of Evaluating Index System of Competitive Capability of Industrial Estate;1594
11.11.3;Establishing and Analyzing of Fuzziness Comprehensive Evaluating System of Competitive Capability of Industrial Estate;1594
11.11.3.1;The Characters of the Fuzziness Comprehensive Evaluating Method;1594
11.11.3.2;The Main Steps of the Fuzziness Comprehensive Evaluating Method;1595
11.11.4;Example Analysis of the Fuzziness Comprehensive Evaluating of Competitive Capability of Industrial Estate;1597
11.11.4.1;Establishment of the Weight Group of Evaluating Index;1597
11.11.4.2;Establishment of the Membership Grade Function of Evaluating Indexes;1597
11.11.4.3;Establishment of the Fuzziness Evaluating Matrix of Competitive Capability of Industrial Estate;1598
11.11.4.4;Calculating of the Comprehensive Score of Competitive Capability of Industrial Estate;1598
11.11.4.5;Comprehensive Evaluating and Analysis of Competitive Capability of Industrial Estate;1599
11.11.5;Conclusions;1600
11.11.6;References;1601
11.12;Study on the Fuzzy Comprehensive Classification of Occupational Injury Risk in Road Transport Enterprises;1602
11.12.1;Introduction;1602
11.12.2;Factors of Road Transport Enterprises Occupational Injury Risk;1603
11.12.2.1;Analysis on Possibility Factors;1603
11.12.2.2;Analysis on Severity Influencing Factors;1604
11.12.3;The Evaluation Index System of Road Transportation Enterprise Occupational Injury Risk;1604
11.12.4;The Fuzzy Comprehensive Evaluation of Road Transportation Enterprise Occupational Injury Risk;1605
11.12.4.1;The Fuzzy Comprehensive Evaluation of Road Transportation Enterprise Occupational Injury Risk;1606
11.12.4.2;Establishing Evaluation Set;1606
11.12.4.3;Index Quantification and Values;1606
11.12.4.4;Determination of Membership and Weight;1606
11.12.4.5;Calculation of Fuzzy Comprehensive Evaluation;1607
11.12.5;Evaluation Example;1607
11.12.5.1;Primary Fuzzy Comprehensive Evaluation;1607
11.12.5.2;Advanced Comprehensive Evaluation;1608
11.12.6;Conclusions;1608
11.12.7;References;1608
11.13;The Electric Heating Furnace Temperature Control Based on SPID;1610
11.13.1;Introduction;1610
11.13.2;Composition and Principle;1611
11.13.3;Control Methods;1611
11.13.3.1;Smith Predictive Control;1611
11.13.3.2;Single Parameter PID Control;1612
11.13.4;Conclusions;1615
11.13.5;References;1615
11.14;Research on Numerical Simulation for Ternary Complicated Uncertain Number;1617
11.14.1;Introduction;1617
11.14.2;Several Basic Definitions;1617
11.14.2.1;Definition of the Ternary Complicated Uncertain Number;1617
11.14.2.2;Definition of the Matter-Element;1618
11.14.2.3;The Ternary Uncertain Matter-Element;1618
11.14.3;Algorithm of the Ternary Complicated Uncertain Number;1618
11.14.3.1;Computing Model of the Ternary Uncertain Number;1618
11.14.3.2;Algorithm of the Ternary Uncertain Number;1619
11.14.4;Application of the Ternary Complicated Uncertain Number;1619
11.14.4.1;To Establish the Supply Ability Index;1619
11.14.4.2;To Calculate the Material Supply Ability Index;1619
11.14.5;Conclusion;1620
11.14.6;References;1620
11.15;Power System Voltage Stability Analysis Based on Data Mining;1621
11.15.1;Introduction;1621
11.15.2;The Analysis Model of Voltage Stability;1622
11.15.2.1;Data Preprocessing;1623
11.15.2.2;Data Warehouse;1623
11.15.2.3;Data Compression;1624
11.15.2.4;Support Vector Machine;1624
11.15.3;The Simulation and Conclusion;1625
11.15.4;References;1627
11.16;On the Exponential Stability of Stochastic Differential Equations;1628
11.16.1;Introduction;1628
11.16.2;Stochastic Exponent Stability;1630
11.16.3;Exponential P-Stability;1631
11.16.4;Almost Surely Exponential Stability;1633
11.16.5;Conclusion;1634
11.16.6;References;1634
11.17;The Almost .-Compactness in L.-Spaces;1636
11.17.1;Introduction;1636
11.17.2;Preliminaries;1636
11.17.3;Almost.-Compact Set and Its Characteristics;1638
11.17.4;Some Important Properties of Almost .-Compactness;1641
11.17.5;References;1644
11.18;Development of Microsturcture Based on Single-Wall Carbon Nanotube;1646
11.18.1;Introduction;1646
11.18.2;Fabrication Methods;1647
11.18.3;Results and Discussions;1649
11.18.4;Conclusions;1650
11.18.5;References;1651
11.19;Design and Application of Test Circuit of Coils’Same or Different Name Ends;1652
11.19.1;Introduction;1652
11.19.2;Concept Introduction;1652
11.19.3;Function Description;1654
11.19.4;Design Ideas and Principle;1654
11.19.5;Debugging and Application in Site;1656
11.19.6;Conclusion;1657
11.19.7;References;1657
11.20;Reduced Minimal Numerical Range of the Aluthge Transform;1658
11.20.1;Introduction;1658
11.20.2;Preliminaries and Main Results;1659
11.20.3;References;1663
11.21;The Evolution Model and Controlling Factors Analysis of the Pore Space of Oolitic Shoal Reservoir;1665
11.21.1;Pore Space Types of Reservoir and Its Characteristics;1665
11.21.1.1;Residual Intergranular Pore;1665
11.21.1.2;Residual Intergranular Pore;1665
11.21.1.3;Dissolved Pores in Grains;1666
11.21.1.4;Intracrystalline Pore and Intercrystalline Solution Pores;1667
11.21.1.5;Fracture;1667
11.21.2;Reservoir Space Evolution Model;1667
11.21.2.1;Reservoir Space Evolution Model in Beach-Facies Oolitic Limestone;1667
11.21.2.2;Reservoir Space Evolution Model in Shoal Oolitic Dolomite;1669
11.21.2.3;Reservoir Space;1669
11.21.3;The Controlling Factors of the Formation in Reservoir Space;1670
11.21.3.1;Favorable Facies Control the Distribution of Oolitic Reservoir;1670
11.21.3.2;Favorable Facies Control the Distribution of Oolitic Reservoir;1670
11.21.3.3;The Tectoclase Improve Reservoir Property in the Reservoir;1671
11.21.4;Conclusion;1671
11.21.5;References;1672
11.22;Analysis and Optimization of Contact Strength of Screw Thread;1673
11.22.1;Introduction;1673
11.22.2;The Governing Equation of Gap Element Analysis;1674
11.22.2.1;Basic Assumptions;1674
11.22.2.2;The Equation of Contact Algorithm with Gap Element;1674
11.22.3;Contact Stress Analysis of Screw Thread;1676
11.22.3.1;Structure and Finite Gap Element Model;1676
11.22.3.2;Analysis Results;1677
11.22.4;Optimization of Contact Strength;1678
11.22.5;Conclusion;1680
11.22.6;References;1680
12;Author Index;1682