Leondes Intelligent Knowledge-Based Systems

Business and Technology in the New Millennium
1. Auflage 2010
ISBN: 978-1-4020-7829-3
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 1895 Seiten

This five-volume set clearly manifests the great significance of these key technologies for the new economies of the new millennium. The discussions provide a wealth of practical ideas intended to foster innovation in thought and, consequently, in the further development of technology. Together, they comprise a significant and uniquely comprehensive reference source for research workers, practitioners, computer scientists, academics, students, and others on the international scene for years to come.

Autoren/Hrsg.

Leondes, Cornelius T.

Inhaltsverzeichnis

1;Title Page
;2
2;Copyright Page
;3
3;Table of contents
;4
4;FOREWORD;6
5;PREFACE;8
6;CONTRIBUTORS;11
7;Title Page
;416
8;Copyright Page
;417
9;Table of contents
;418
10;FOREWORD;420
11;PREFACE;422
12;CONTRIBUTORS;425
13;Title Page
;804
14;Copyright Page
;805
15;Table of contents;806
16;FOREWORD;808
17;PREFACE;810
18;CONTRIBUTORS;813
19;Title Page
;1153
20;Copyright Page
;1154
21;Table of contents
;1155
22;FOREWORD;1157
23;PREFACE;1159
24;CONTRIBUTORS;1162
25;Title Page
;1624
26;Copyright Page
;1625
27;Table of contents
;1626
28;FOREWORD;1628
29;PREFACE;1630
30;CONTRIBUTORS;1633
31;VOLUME I. KNOWLEDGE-BASED SYSTEMS;31
32;PLATFORM-BASED PRODUCT DESIGN AND DEVELOPMENT: KNOWLEDGE SUPPORT STRATEGY AND IMPLEMENTATION
;32
32.1;1. INTRODUCTION;32
32.2;2. LITERATURE REVIEW;33
32.3;3. PLATFORM-BASED PRODUCT DESIGN AND DEVELOPMENT;36
32.4;4. PRODUCT PLATFORM AND PRODUCT FAMILY MODELING;38
32.4.1;4.1. Product family architecture modeling;38
32.4.2;4.2. Product family evolution representation;39
32.4.3;4.3. Product family generation;41
32.4.4;4.4. Product family evaluation for customization;42
32.5;5. MODULE-BASED PRODUCT FAMILYDESIGN PROCESS;44
32.6;6. KNOWLEDGE SUPPORT FRAMEWORK FOR MODULAR PRODUCT FAMILY DESIGN
;48
32.6.1;6.1. Knowledge support scheme, challenges and key issues;48
32.6.2;6.2. Product family design knowledge modeling and support;51
32.6.2.1;6.2.1. Product family design knowledge modeling issues
;51
32.6.2.2;6.2.2. Knowledge modeling /representation for product family design;54
32.7;7. KNOWLEDGE INTENSIVE SUPPORT SYSTEM FOR PRODUCT FAMILY DESIGN
;57
32.8;8. SUMMARY AND FUTURE WORK;61
32.9;REFERENCES;61
33;KNOWLEDGE MANAGEMENT SYSTEMS IN CONTINUOUS PRODUCT INNOVATION
;65
33.1;1. INTRODUCTION;65
33.2;2. KNOWLEDGE AND KNOWLEDGE MANAGEMENT;67
33.2.1;2.1. The concept of knowledge in management literature;67
33.2.2;2.2. Defining a knowledge management system;69
33.3;3. LITERATURE REVIEW;71
33.3.1;3.1. Main streams in literature;73
33.3.2;3.2. The literature evolutive trend: towards KM configurations;78
33.4;4. THE INVESTIGATION FRAMEWORK;80
33.5;5. THE RESEARCH METHODOLOGY;83
33.6;6. RESULTS;86
33.7;7. IMPLICATIONS FOR MANAGERIAL ACTION AND FUTURE RESEARCH;91
33.8;REFERENCES;92
34;KNOWLEDGE-BASED MEASUREMENT OF ENTERPRISE AGILITY;96
34.1;1. INTRODUCTION;96
34.2;2. MANAGING AN ADAPTIVE INFRASTRUCTURE;98
34.3;3. AGILITY MODELING AND MEASUREMENT FUNDAMENTALS;99
34.3.1;3.1. Dimensions of agility;100
34.4;4. MODELING OF AGILITY INFRASTRUCTURES;103
34.4.1;4.1. Production infrastructure;103
34.4.2;4.2. Market infrastructure;104
34.4.3;4.3. People infrastructure;104
34.4.4;4.4. Information infrastructure;105
34.4.5;4.5. Discussion;105
34.5;5. AN EXAMPLE;107
34.6;6. CONCLUDING REMARKS;110
34.7;REFERENCES;111
35;KNOWLEDGE-BASED SYSTEMS TECHNOLOGY IN THE MAKE OR BUY DECISION IN MANUFACTURING STRATEGY
;112
35.1;1. INTRODUCTION;112
35.2;THE MAKE OR BUY DECISION;113
35.2.1;(i) No Formal Methodfor Evaluating the Decision;113
35.2.2;(ii) Inaccurate Costing Systems;114
35.2.3;(iii) The Competitive Implications of the Decision;114
35.3;A DESCRIPTION OF THE MAKE OR BUY MODEL;114
35.3.1;Stage 1-Identification of Performance Categories
;116
35.3.2;Stage2-An Analysis of the Technical Capability Categories;118
35.3.3;Stage 3-Comparison of Retrieved Internal and External Technical Capability Profiles
;118
35.3.4;Stage 4-An Analysis ofthe Suppliers' Organisations;118
35.3.5;Stage 5-Total Acquisition Cost Analysis;119
35.4;THE MAKE OR BUY SYSTEM;119
35.5;KNOWLEDGE BASED SYSTMS (KBS) AND CASE-BASED REASONING (CBR);119
35.6;THE REQUIREMENTS;121
35.7;SYSTEM DEVELOPMENT;122
35.7.1;Stage 1-Peiformance Criteria Identification and Weighting
;122
35.7.1.1;1. Technical Capability Categories;122
35.7.1.2;2. Suppliers Organisation Categories
;123
35.7.2;Stage 2-Technical Capability Stage;123
35.7.3;Stage 3-Comparison of Retrieved Internal and External Technical Capability Profiles
;128
35.7.4;Stage 4-An Analysis of the Suppliers' Organisations
;130
35.8;EVALUATION;133
35.9;FURTHER ENHANCEMENTS;135
35.9.1;Dynamic performance analysis;135
35.9.2;A consultancy tool;135
35.9.3;Application of AI techniques;135
35.10;CONCLUSION;136
35.11;REFERENCES;137
36;INTELLIGENT INTERNET INFORMATION SYSTEMS IN KNOWLEDGE ACQUISITION: TECHNIQUES AND APPLICATIONS
;139
36.1;1. INTRODUCTION;139
36.2;2. RELATED WORK;140
36.2.1;2.1. The Web;140
36.2.2;2.2. Information retrieval;142
36.2.3;2.3. Hyperlink analysis;144
36.2.4;2.4. Information extraction;145
36.2.5;2.5. Data mining and machine learning;145
36.2.6;2.6. Document categorization;148
36.2.7;2.7. Web mining;149
36.2.8;2.8. Intelligent web agent;151
36.3;3. THE I3 SYSTEM;151
36.3.1;3.1. The architecture of the I3 system
;151
36.3.2;3.2. Semantic issues of the I3 system
;152
36.4;4. I3 WEB ANALYZER
;154
36.4.1;4.1. Web crawler and document parser;155
36.4.2;4.2. Linguistic detector;156
36.4.3;4.3. Structural analyzer;156
36.4.4;4.4. Content analyzer;157
36.4.5;4.5. Summary of I3WA;158
36.5;5. I3 METADATA EXTRACTOR;158
36.6;6. 13 KNOWLEDGE LEARNER;161
36.6.1;6.1. The ACIRD system;161
36.6.2;6.2. Mining term associations;161
36.7;7. I3 APPLICATIONS
;163
36.8;REFERENCES;164
37;AGGREGATOR: A KNOWLEDGE BASED COMPARISON CHART BUILDER FOR eSHOPPING
;169
37.1;1. INTRODUCTION;169
37.2;2. RELATED WORK;172
37.3;3. WRAPPERS AS CONCEPTUAL GRAPHS;174
37.3.1;3.1. Conceptual graphs background;174
37.3.2;3.2. Modeling and training wrappers with CGs;176
37.3.3;3.3. Reusing CG-wrappers;180
37.4;4. COMPARISON CHART BUILDING WITH CG-WRAPPERS;181
37.4.1;4.1. Locating product specification pages;181
37.4.2;4.2. Collecting and merging specification information;182
37.5;5. A FRAMEWORK FOR INFORMATION EXTRACTION WITH CG-WRAPPERS;185
37.5.1;5.1. System architecture;185
37.5.2;5.2. Case study;188
37.6;6. CONCLUSIONS AND FUTURE WORK;190
37.7;REFERENCES;191
38;IMPACT OF THE INTELLIGENT AGENT PARADIGM ON KNOWLEDGE MANAGEMENT
;193
38.1;1. INTRODUCTION;193
38.2;2. PARADIGM SHIFT: FROM DATA AND INFORMATION MANAGEMENT TO KNOWLEDGE MANAGEMENT
;196
38.3;3. KNOWLEDGE MANAGEMENT: DEFINITIONS AND ARCHITECTURE;198
38.4;4. KNOWLEDGE MANAGEMENT SUPPORT;202
38.5;5. INTELLIGENT AGENTS AND MULTIAGENT SYSTEMS;204
38.6;6. KNOWLEDGE TYPOLOGIES;209
38.6.1;6.1. Notion of Knowledge and Knowledge Possessors;210
38.6.2;6.2. Types of knowledge;213
38.6.3;6.3. Sources of knowledge;215
38.7;7. ORGANIZATIONS AS COMMUNITIES OF AGENTS AND PASSIVE OBJECTS;218
38.8;8. ORGANIZATIONS AS INTELLIGENT AGENTS;220
38.9;9. ORGANIZATIONS AS MULTIAGENT AND KNOWLEDGE MANAGEMENT SYSTEMS
;223
38.9.1;9.1. Intelligent agents for OKMS "Engine Room";227
38.9.2;9.2. Agents that provide communications;229
38.9.3;9.3. Personal agents;229
38.10;10. CONCLUSIONS;231
38.11;ACKNOWLEDGMENTS;232
38.12;REFERENCES;232
39;METHODS OF BUILDING KNOWLEDGE-BASED SYSTEMS APPLIED IN SOFTWARE PROJECT MANAGEMENT
;236
39.1;INTRODUCTION;236
39.2;1. PROBLEMS OF MODELLING SPM;237
39.2.1;1.1. Expert knowledge of project management;237
39.2.2;1.2. Methods of supporting management processes
;238
39.2.3;1.3. Description of project teams;241
39.2.4;1.4. Models for assessing team and project processes;243
39.3;2. NEW POSSIBILITIES FOR CREATING THE SPM MODEL;248
39.3.1;2.1. Use of modelling and simulation theories;250
39.3.2;2.2. Application of fuzzy set theory;251
39.3.3;2.3. Application of elements of fuzzy regulator theory;254
39.4;3. EXAMPLE OF BUILDING A FUZZY SPM MODEL;256
39.4.1;3.1. The concept of model construction;257
39.4.2;3.2. Construction of the model;264
39.4.2.1;3.2.1. Fuzzy Models of Knowledge- Based System for Software Project Management
;264
39.4.2.2;3.2.2. Hierarchical model;264
39.4.2.3;3.2.3. Structural model;266
39.4.2.4;3.2.4. Integrated model
;266
39.4.2.5;3.2.5. Tuning of the Fuzzy Model;269
39.4.2.6;3.2.6. Adaptation of the model to the needs of newprojects;269
39.4.2.6.1;3.2.6.1. THE SPM-RFM MODEL AS A SUPPORT FOR SOFTWARE PROJECT MANAGEMENT.;269
39.5;4. ASSESSMENT OF EXISTING SOLUTIONS;271
39.6;REFERENCES;271
40;SECURITY TECHNOLOGIES TO GUARANTEE SAFE BUSINESS PROCESSES IN SMART ORGANIZATIONS
;275
40.1;1. INTRODUCTION;275
40.2;2. SMART ORGANIZATIONS-ARE THEY THE FUTURE?;277
40.2.1;2.1. Main characteristics of Smart Organizations;277
40.2.1.1;2.1.1. Definition of Smart Organization;277
40.2.1.2;2.1.2. Life cycle of networked organizations;278
40.2.1.3;2.1.3. Human role in smart organization;280
40.2.2;2.2. Organizational form;280
40.2.2.1;2.2.1. Main characteristics of VE;280
40.2.2.2;2.2.2. Importance of safe communication in VE;282
40.2.3;2.3. Knowledge technologies and applications;283
40.2.3.1;2.3. 1. Knowledge technologies;283
40.2.3.1.1;2.3.1.1. ARTIFICIAL NEURAL NETWORKS.
;284
40.2.3.1.2;2.3.1.2. ANT ALGORITHMS AND SWARM INTELLIGENCE.;284
40.2.3.1.3;2.3.1.3. INTELLIGENT AGENTS.;285
40.2.3.1.4;2.3.1.4. KNOWLEDGE SHARING.;286
40.2.3.2;2.3.2. Knowledge management;286
40.2.4;2.4. Network technologies for smart organizations;287
40.2.4.1;2.4.1. Trends in information technology;287
40.2.4.2;2.4.2. Wired network technology;288
40.2.4.3;2.4.3. Wi-Fi (Wireless Fidelity) technology;289
40.2.4.4;2.4.4. Mobile technology;290
40.2.4.5;2.4.5. Powerline communications;292
40.2.4.6;2.4.6. The Grid computing
;293
40.3;3.
BUSINESS PROCESSES;294
40.3.1;3.1. The content of business processes;294
40.3.2;3.2. Relation between BPR & information and communication technology;295
40.4;4. SECURITY TECHNOLOGIES;296
40.4.1;4.1. Types and trends of cyber crimes;296
40.4.2;4.2. Computer system and network security;298
40.4.3;4.3. Role of trust;300
40.4.4;4.4. Security services and mechanisms;301
40.4.5;4.5. Tools, methods and techniques for security;302
40.4.5.1;4.5.1. Achieving confidentiality;302
40.4.5.2;4.5.2. Security architectures;302
40.4.5.3;4.5.3. Firewalls;302
40.4.5.4;4.5.4. Virus defense;303
40.4.5.5;4.5.5. Identification of persons
;303
40.4.5.6;4.5.6. Smart cards;304
40.4.5.7;4.5.7. Personal trusted device;304
40.4.6;4.6. Application of security technologies in networks;305
40.4.6.1;4.6.1. Wired network security;305
40.4.6.2;4.6.2. Security technoloyies for wireless communication;306
40.4.6.3;4.6.3. Mobile security;308
40.4.6.4;4.6.4. Security issues in PLC;310
40.4.6.5;4.6.5. Security in the Grid;311
40.5;5. SECURITY APPLICATIONS IN SMART ORGANIZATIONS;311
40.5.1;5.1. Security in distributed environments;311
40.5.2;5.2. Human aspects of security in smart organizations;312
40.5.3;5.3. Application of security in the life-cycle phases;313
40.6;6. CONCLUSIONS;314
40.7;REFERENCES;315
41;BUSINESS PROCESS MODELLING AND ITS APPLICATIONS IN THE BUSINESS ENVIRONMENT
;317
41.1;1. INTRODUCTION;317
41.2;2. BUSINESS PROCESS MODELLING;319
41.2.1;2.1. The model of an artificial system;319
41.2.2;2.2. Business processes and business process modelling;320
41.2.2.1;2.2.1. Business process;320
41.2.2.2;2.2.2. Business process model;320
41.2.2.2.1;2.2.2.1. WHAT IS A MODEL?;320
41.2.2.2.2;2.2.2.2. BUSINESS PROCESS MODELS AS SPECIFIC TYPES OF ENTERPRISE MODELS.;322
41.2.2.3;2.2.3. Categories of business process models and business process types;323
41.2.2.3.1;2.2.3.1. CATEGORIES OF BUSINESS PROCESS MODELS.;323
41.2.2.3.2;2.2.3.2. BUSINESS PROCESS TYPES.;324
41.2.3;2.3. Generalised enterprise reference architecture and methodology (GERAM);325
41.2.3.1;2.3.1. GERAM framework
;325
41.2.3.2;2.3.2. Generalised enterprise reference architecture (GERA);327
41.2.3.3;2.3.3. Business process modelling languages and tools;328
41.2.3.4;2.3.4. Enterprise reference models;331
41.2.4;2.4. Business process modelling principles;331
41.2.4.1;2.4.1. Process decomposition;331
41.2.4.2;2.4.2. The granularity (depth) of process models
;332
41.2.4.3;2.4.3. Modelling approach
;332
41.2.5;2.5. CIMOSA process modelling language;333
41.2.6;2.6. Workflow management;334
41.2.6.1;2.6.1. Abstraction of process management
;334
41.2.6.2;2.6.2. Architecture;335
41.2.6.3;2.6.3. Design principles and issues;336
41.2.6.4;2.6.4. Workflow from a data perspective
;336
41.3;3. WHAT ISO 9000:2000 STANDARD REQUIREMENTS MUST BUSINESS PROCESS MODELS SATISFY?
;338
41.3.1;3.1. Business process modelling related requirements of the ISO 9000:2000 standards
;339
41.3.2;3.2. Business process interactions;343
41.3.3;3.3. Product realisation and support processes;346
41.3.4;3.4. From business process modelling to enterprise modelling;348
41.3.4.1;3.4.1. Organisational view related standard requirements
;348
41.3.4.2;3.4.2. Resource view related standard requirements;350
41.3.4.3;3.4.3. Information view related standard requirements;352
41.3.5;3.5. The ISO 9000:2000 and business process reference models;354
41.4;4. BUSINESS PROCESS MODELLING IN BUSINESS PROCESS REENGINEERING;355
41.4.1;4.1. Ten-step approach to BPR;356
41.4.2;4.2. How to develop an AS-IS process model;357
41.4.3;4.3. Use documented best practice as an input to the BPR process;359
41.5;5. BUSINESS PROCESS MODELLING AND KNOWLEDGE MANAGEMENT;359
41.5.1;5.1. What is knowledge?;360
41.5.2;5.2. Need for knowledge management;361
41.5.3;5.3. The nature of knowledge and its sharing;362
41.5.4;5.4 . The knowledge process and knowledge resources;364
41.5.5;5.5. Business process modelling and knowledge management;366
41.5.6;5.6. The knowledge life-cycle model;367
41.6;6. CONCLUSION;371
41.7;REFERENCES;372
42;KNOWLEDGE BASED SYSTEMS TECHNOLOGY AND APPLICATIONS IN IMAGE RETRIEVAL
;375
42.1;1. INTRODUCTION;375
42.2;2. KNOWLEDGE REPRESENTATION AND DESCRIPTION LOGICS;376
42.3;3. RELATED WORK;378
42.3.1;3.1. Feature-based approaches;378
42.3.2;3.2. Approaches based on spatial constraints;379
42.3.3;3.3. Logic-based approaches;380
42.4;4. PROPOSED KNOWLEDGE BASED APPROACH;381
42.4.1;4.1. Syntax;381
42.4.2;4.2. Semantics;382
42.5;5. REASONING AND RETRIEVAL;390
42.5.1;5.1. Exact reasoning on images and descriptions;391
42.5.2;5.2. Approximate recognition;397
42.5.2.1;5.2.1. Spatial similarity
;399
42.5.2.2;5.2.2. Rotation similarity;401
42.5.2.3;5.2.3. Scale similarity;402
42.6;6. REPRESENTING SHAPES, OBJECTS AND IMAGES;404
42.6.1;6.1. Image features;404
42.6.2;6.2. Similarity computation;405
42.7;7. PROTOTYPE SYSTEM;407
42.7.1;7.1. Knowledge base management;408
42.8;8. DISCUSSION;411
42.9;REFERENCES;411
43;VOLUME II. INFORMATION TECHNOLOGY;445
44;TECHNIQUES IN INTEGRATED DEVELOPMENT AND IMPLEMENTATION OF ENTERPRISE INFORMATION SYSTEMS
;446
44.1;1. INTRODUCTION TO THE INTEGRATED METHODOLOGY FOR ENTERPRISE INFORMATION SYSTEMS;446
44.1.1;1.1. Development of information systems;447
44.1.2;1.2. Previous research;447
44.1.3;1.3. Overview of the integrated methodology for enterprise information systems
;448
44.2;2. TECHNIQUES OF INFORMATION STRATEGIC PLANNING;452
44.2.1;2.1. Overview;452
44.2.2;2.2. Previous researches;452
44.2.3;2.3. Information Strategic Planning Methodology (ISPM);453
44.2.4;2.4. Framework for evaluation of ISP;454
44.3;3. TECHNIQUES FOR THE EVALUATION OF INDUSTRIAL INFORMATION SYSTEMS (EIII)
;455
44.3.1;3.1. Overview;455
44.3.2;3.2. Previous researches;455
44.3.3;3.3. The improvement model of IS performance
;458
44.4;4. TECHNIQUES OF IS ECONOMIC JUSTIFICATION AND MEASUREMENT;460
44.4.1;4.1. Overview;460
44.4.2;4.2. Previous researches;461
44.4.3;4.3. Framework for economic justification and measurement system (EJMS);461
44.5;5. OTHER TECHNIQUES;464
44.5.1;5.1. Techniques of requirements analysis;464
44.5.2;5.2. UMT (Unified Modeling Tools) and repository;466
44.5.3;5.3. S3IE (Support Systems for Solution Introduction and Evaluation);468
44.6;6. FURTHER WORKS;468
44.7;REFERENCES;468
45;INFORMATION SYSTEMS FRAMEWORKS AND THEIR APPLICATIONS IN MANUFACTURING AND SUPPLY CHAIN SYSTEMS
;470
45.1;1. INTRODUCTION;470
45.2;2. INFORMATION SYSTEMS USE IN THE MANUFACTURING INDUSTRY;471
45.3;3. INFORMATION SYSTEMS EVOLUTION IN MANUFACTURING;472
45.3.1;3.1. Infrastructure as an element of information systems in manufacturing;474
45.4;4. ELECTRONIC COMMERCE AND MANUFACTURING INFORMATION SYSTEMS
;476
45.5;5. VIRTUAL ORGANISATIONS AND MANUFACTURING INFORMATION SYSTEMS
;477
45.6;6. PARADIGMS SHIFTS IN MANUFACTURING;478
45.6.1;6.1. IT and information systems for mass customisation;479
45.7;7. DEVELOPMENT OF INFORMATION SYSTEMS IN MANUFACTURING;480
45.8;8. EXAMPLES AND HIGHLIGHTS OF INFORMATION SYSTEMS DEVELOPMENTS IN MANUFACTURING
;483
45.9;9. A BROADER SCOPE OF INFORMATION SYSTEMS IN MANUFACTURING;486
45.9.1;9.1. Information systems role in improving manufacturing organisations performance
;486
45.10;10. INFORMATION SYSTEMS ENTERPRISE-WIDE SUPPORT: AN EXAMPLE;488
45.10.1;10.1. Information dependency and intensity;489
45.10.2;10.2. Information flow and operation of the supply chain;491
45.10.3;10.3. Analysis of information accuracy;496
45.11;11. ENSURING A POSITIVE CONTRIBUTION OF INFORMATION SYSTEMS TO THE ENTERPRISE
;499
45.11.1;1. Development of enhanced manufacturing operations based on a sound business strategy;500
45.11.2;2. Definition of an IT strategy to support the business strategy;500
45.11.3;3. Implement an IT strategy to lead the company once it has been possible to improve its manufacturing operations
;500
45.12;12. CONCLUSIONS;503
45.13;REFERENCES;504
46;MODELLING TECHNIQUES IN INTEGRATED OPERATIONS AND INFORMATION SYSTEMS IN MANUFACTURING SYSTEMS
;507
46.1;1. INTRODUCTION;507
46.1.1;1.1. Review of integrated modelling simulation methods or tools for manufacturing systems analysis, design and performance evaluation
;509
46.1.2;1.2. Research objectives;512
46.2;2. THE PCBA SYSTEM;513
46.3;3. SIMULATION TOOLS USED;515
46.3.1;3.1. Operational system model development based on ARENA 3.0;516
46.3.1.1;3.1.1. Operational system model development
;517
46.3.1.2;3.1.2. Experimentalframe developmentfor ARENA models;521
46.3.1.2.1;3.1.2.1. INPUT DATA ACQUISITION AND ANALYSISFOR STOCHASTIC SYSTEM MODELS.;525
46.3.1.3;3.1.3. Simulation data analysis
;527
46.3.2;3.2. Communication system model development based on COMNET III;528
46.3.2.1;3.2.1. Nctwore description and modelling constructions;528
46.3.2.1.1;3.2.1.1. MODELLING OF NETWOHK TOPOLOG IES.;530
46.3.2.1.2;3.2.1.2. NETWORK TRAFFIC AND WORKLOAD.
;533
46.3.2.2;3.2.2. Network simulation;536
46.4;4. INTEGRATED MODEL APPROACH;536
46.4.1;4.1. Establishment of an integrated model;536
46.4.1.1;4.1.1. Operational system;540
46.4.1.2;4.1.2. Information processing system
;543
46.5;5. SIMULATION RESULTS, ANALYSIS AND DISCUSSION;547
46.5.1;5.1. Operational system's aspects
;549
46.5.1.1;5.1.1. Line-balancing and collecting critical data
;550
46.5.1.2;5.1.2. Using animated simulation to investigate system performances
;551
46.5.2;5.2. Information system's aspects;552
46.5.2.1;5.2.1. Channel utilisation (%)
;554
46.5.2.2;5.2.2. Maximum message delay (ms)
;555
46.5.2.3;5.2.3. Comparative dynamic performance of LANs for the PCBA system
;556
46.5.2.3.1;5.2.3.1. CHANNEL UTILISATION (%) AND MAXIMUM MESSAGE DELAY (MS) VS TRANSMISSION RATES (MBPS) .
;557
46.5.2.3.2;5.2.3.2. CHANNEL UTILISATION (%) AND MAXIMUM MESSAGE DELAY (MS) VS MAXIMUM MESSAGE SIZES (Kb).
;558
46.6;6. DISCUSSION AND CONCLUSION;561
46.7;REFERENCES;564
47;TECHNIQUES AND ANALYSES OF SEQUENTIAL AND CONCURRENT PRODUCT DEVELOPMENT PROCESSES
;566
47.1;1. INTRODUCTION;566
47.2;2. SEQUENTIAL ENGINEERING;567
47.2.1;2.1. Sequential product development process
;567
47.2.2;2.2. Characte ristics of sequential engineering;569
47.3;3. CONCURRENT ENGINEERING;569
47.3.1;3.1. Concurrent product development process;569
47.3.1.1;3.1.1. Data transfer between activities in concurrent product development process
;570
47.3.1.2;3.1.2. Loops of concurrent product development process
;570
47.3.1.3;3.1.3. Team work;574
47.3.1.3.1;3.1.3.1. TEAM STRUCTURE IN CONCURRENT PRODUCT DEVELOPMENT PROCESS.;574
47.3.1.3.2;3.1.3.2. TEAMS IN BIG COMPANY.;576
47.3.1.3.3;3.1.3.3. TEAM STRUCTURE IN SMEs.;577
47.3.2;3.2. Organisational structures;581
47.3.2.1;3.2.1. Functional organisational structure;581
47.3.2.2;3.2.2. Project organisational structure
;583
47.3.2.3;3.2.3. Matrix organisational structure
;584
47.3.2.4;3.2.4. Organisational structure of team work in SME
;585
47.3.3;3.3. Goals and tools for support of concurrent product development process;588
47.3.3.1;a.) Considerably shorter newproduct development time;588
47.3.3.2;b.) Reduced newproduct development costs;588
47.3.3.3;c.) Better quality of newproducts regarding customer requirements;588
47.3.3.4;3.3.1. Quality Functions Deployment (QFD);589
47.3.3.4.1;3.3.1.1. HOUSE OF QUALITY STRUCTURE.;591
47.3.3.4.2;3.3.1.2. STEPS IN CONSTRUCTING THE HOUSE OF QUALITY.;592
47.3.3.4.3;3.3.1.3. EXTENDING THE HOUSE OF QUALITY.;595
47.3.3.5;3.3.2. value analysis;597
47.3.3.6;3.3.3. Failure Mode and Effects Analysis (FMEA)
;601
47.4;4. SAMPLE CASE OF INTRODUCTION OF CONCURRENT ENGINEERING IN AN SME
;606
47.4.1;4.1. Building a house of quality;607
47.4.2;4.2. Project of concurrent product development process;609
47.4.2.1;4.2.1. Goals of the project and project team
;609
47.4.2.2;4.2.4. Time and structural plan of the project
;610
47.5;5. CONCLUSION;618
47.6;REFERENCES;619
48;DESIGN AND MODELING METHODS FOR COMPONENTS MADE OF MULTI-HETEROGENEOUS MATERIALS IN HIGH-TECH APPLICATIONS
;620
48.1;1. INTRODUCTION;620
48.2;2. DESIGN METHOD FOR THE COMPONENTS MADE OF MULTI HETEROGENEOUS MATERIALS
;623
48.2.1;2.1. Design procedure;623
48.2.2;2.2. Material design;627
48.2.3;2.3. How to determine the optimal material properties needed in different regions
;630
48.2.4;2.4. How to select material constituent composition and microstructure;634
48.2.4.1;2.4.1. Select material constituent compositions from the database of material constituent composition;634
48.2.4.2;2.4.2. Select material murostructurcsirom the database of material microstructure;636
48.2.5;2.5. How to generate two material region sets;636
48.2.5.1;(1) Encode decision variables;637
48.2.5.2;(2) Determine the size of population;637
48.2.5.3;(3) Evaluation;637
48.2.5.4;(4) Selection;639
48.2.5.5;(5) Crossover operation;639
48.2.5.6;(6) Mutation;641
48.2.5.7;(7) Reproduction;642
48.2.5.8;(8) Stop criterion;642
48.2.5.9;(9) Construct the regions for different material constituent compositions and material microstructures
;642
48.2.6;2.6. An example of multi heterogeneous component design;643
48.2.6.1;(1) Requirements for material design;643
48.2.6.2;(2) Generate material regions;644
48.2.6.3;(3) Create optimization model;644
48.2.6.4;(4) Sensitivity analysis of material properties;644
48.2.6.5;(5) Search for the optimal material property vector of different regions of the flywheel
;644
48.2.6.6;(6) Select material constituent composition and microstructure for each region;645
48.3;3. CAD MODELING METHOD FOR THE COMPONENTS MADE OF MULTI HETEROGENEOUS MATERIALS
;646
48.3.1;3.1. Analyses of the requirements for representing the components made of heterogeneous materials
;646
48.3.2;3.2. Unified CAD modeling for th e compo nent made of heterogeneous materials
;648
48.3.3;3.3. Material constituent composition models
;650
48.3.4;3.4. Material microstructure models;651
48.3.4.1;3.4.1. Material microstructure modelsfor composite materials (R)
;651
48.3.4.2;3.4.2. Material microstructure models for heterogeneous materials with a periodic microstructure (P)
;652
48.3.5;3.5. Main model for integrating the two types of sub-models;656
48.3.6;3.6. An example of modeling
;657
48.4;4. FINITE ELEMENT ANALYSIS BASED ON THE MODEL;661
48.5;5. SUMMARY;663
48.6;ACKNOWLEDGEMENTS;664
48.7;REFERENCES;665
49;QUALITY AND COST OF DATA WAREHOUSE VIEWS1
;667
49.1;1. INTRODUCTION;667
49.2;2. NON-EQUIVALENT QUERY REWRITINGS;669
49.3;3. EFFICIENCY MODEL: QUALITY OF A QUERY REWRITING;670
49.3.1;3.1. Information preservation in rewritings;670
49.3.2;3.2. Information preservation on the view interface;672
49.3.2.1;3.2.1. Dispensable and replacable attributes;672
49.3.3;3.3. Information preservation on view extent;674
49.3.4;3.4. Metric of quality: Degree of Divergence (D,D)
;675
49.3.4.1;3.4.1. Degree of divergence on the query interface (DVattr(Vi))
;675
49.3.4.2;3.4.2. Degree of divergence on the query extent (DDext (Vi))
;676
49.3.4.3;3.4.3. Total degree divergence
;677
49.4;4. EFFICIENCY MODEL: VIEW MAINTENANCE COST OF A LEGAL REWRITING;677
49.4.1;4.1. View maintenance basics;677
49.4.2;4.2. Cost factor based on number of messages exchanged (CFM );678
49.4.3;4.3. Cost factor based on bytes of data transferred (CFT );678
49.4.4;4.4. Cost factor based on I/O (CFI/O)
;678
49.4.5;4.5 . Total view maintenance cost for a single data update;679
49.4.6;4.6. Overall efficiency of a legal rewriting;679
49.5;5. REVIEW OF THE EVE PROJECT;680
49.5.1;5.1. A relaxed SQL query modcl-E-SQL
;681
49.6;6. IMPLEMENTATION AND EVALUATION;683
49.6.1;6.1. Implementation of the EVE System
;683
49.6.2;6.2. Evaluation and discussion;684
49.6.2.1;6.2.1. Influence of relation distribution on view maintenance cost;685
49.6.2.2;6.2.2. Effect of relation cardinality on QC-value;686
49.6.2.3;6.2.3. Experiments on accuracy of cost model prediction;690
49.7;7. RELATED WORK;694
49.8;8. CONCLUSION;697
49.9;ACKNOWLEDGMENTS;697
49.10;REFERENCES;698
50;WEB DATA EXTRACTION TECHNIQUES AND APPLICATIONS USING THE EXTENSIBLE MARKUP LANGUAGE (XML)
;702
50.1;1. INTRODUCTION;702
50.2;2. WEB DATA EXTRACTION;703
50.2.1;2.1. Why Web data is important;703
50.2.2;2.2. Core technologies behind the World Wide Web;703
50.2.3;2.3. The challenges of web data extraction;704
50.2.4;2.4. Using XML technologies in web data extraction;706
50.3;3. FROM WEB TO SYSTEMS;706
50.3.1;3.1. Business requirements;706
50.3.2;3.2. Database-centric data extraction;707
50.3.3;3.3. Crawler-based data extraction;708
50.3.4;3.4. Challenges;712
50.3.5;3.5. Techniques for effective data extraction;716
50.4;4. OUTLINE OF A DATA EXTRACTION SYSTEM ARCHITECTURE;718
50.4.1;4.1. Data retriever;719
50.4.2;4.2 . Data extractor;719
50.4.3;4.3 . Data checker;721
50.4.4;4.4 . Data exporter;721
50.4.5;4.5. Scheduler;722
50.4.6;4.6. Administrative interface;722
50.4.7;4.7. Pattern designer
;725
50.5;5. DATA EXTRACTION PRINCIPLES;726
50.5.1;5.1. Extraction templates;726
50.5.2;5.2. Extracting XML data from HTML;728
50.5.3;5.3. Pattern creation;728
50.5.4;5.4 . Sample pattern analysis;729
50.6;6. CONCLUSION;733
50.7;BIBLIOGRAPHY;733
51;PRODUCT LIFE CYCLE MANAGEMENT IN THE DIGITAL AGE
;736
51.1;1. INTRODUCTION;736
51.2;2. THE NEW PARADIGM OF LIFE CYCLE MANAGEMENT;737
51.2.1;2.1. Partnerships for sustainabl e product life cycles;740
51.2.1.1;2.1.2. Customer's view;741
51.2.1.2;2.1.3. Life cycle objectives;741
51.2.2;2.2. Economical assessment of product life cycles;742
51.3;3. THE DIGITAL AGE-ACTIVATING HIDDEN PERFORMANCE POTENTIALS;746
51.3.1;3.1. Digital product tracing;748
51.3.2;3.2. Boosting utilization performance;748
51.3.3;3.3. Workplaces on change;750
51.3.4;3.4. Product data management for high data continuity;750
51.4;4. ALLIANCES AND LIFE-LONG NETWORKS;753
51.4.1;4.1. Product life-time value
;755
51.4.2;4.2. Selling benefit instead of usage;756
51.5;5. INDUSTRIAL PROTOTYPES OF DIGITALLY NETWORKED PRODUCT LIFE CYCLE MANAGEMENT
;758
51.5.1;5.1. Example of online process monitoring;758
51.5.2;5.2. Example of the digital factory of the future;759
51.5.2.1;5.2.1. Structure of the platform
;760
51.5.2.2;5.2.2. Data transjer;761
51.5.3;5.3. Example for the web-based control of a technical consumer product;761
51.5.3.1;5.3.1. System structure;761
51.5.3.2;5.3.2. Software-/Hardware architecture
;763
51.5.3.3;5.3.3. Extension of system bounds-afuture vision;763
51.6;6. CONCLUSION AND OUTLOOK;763
51.7;REFERENCES;765
52;PRODUCT REDESIGN AND PRICING IN RESPONSE TO COMPETITOR ENTRY: A MARKETING-PRODUCTION PERSPECTIVE
;767
52.1;1. INTRODUCTION;767
52.2;2. MODEL FORMULATION;770
52.2.1;2.1. Model notation;771
52.2.2;2.2. Attraction and market share models;771
52.2.3;2.3. Profit maximization objective;773
52.3;3. EXISTENCE OF A NASH EQUILIBRIUM;773
52.4;4. PRODUCT AND PRICE RESPONSES TO MARKET ENTRY;775
52.5;5. NUMERICAL EXAMPLE AND SENSITIVITY ANALYSIS;777
52.6;6. CONCLUSION;780
52.7;APPENDIX A;783
52.8;APPENDIX B
;786
52.9;REFERENCES;788
53;KNOWLEDGE DISCOVERY BY MEANS OF INTELLIGENT INFORMATION INFRASTRUCTURE METHODS AND THEIR APPLICATIONS
;790
53.1;INTRODUCTION;790
53.1.1;Neural Online Analytical Processing System (NOLAPS);791
53.1.2;Neural Fuzzy Model;795
53.1.3;Cross-platform intelligent information infrastructure;799
53.1.4;Conclusion;801
53.2;REFERENCES;801
54;VOLUME III. EXPERT AND AGENT SYSTEMS;833
55;TECHNIQUES IN KNOWLEDGE-BASED EXPERT SYSTEMS FOR THE DESIGN OF ENGINEERING SYSTEMS
;834
55.1;QUOTATION;834
55.2;1. INTRODUCTION;834
55.3;2. CHARACTERISTICS OF KNOWLEDGE-BASED EXPERT SYSTEMS;838
55.3.1;2.1. Domain knowledge;839
55.3.2;2.2. Inferential knowledge;841
55.4;3. KNOWLEDGE-BASED TECHNIQUES AND THEIR APPLICATION IN ENGINEERING DESIGN
;844
55.4.1;3.1. Implementation-specific knowledge-based techniques;844
55.4.1.1;3.1.1. Rule-based representation
;845
55.4.1.2;3.1.2. Semantic networks
;846
55.4.1.3;3.1.3. Frame-based representation
;846
55.4.1.4;3.1.4. Object-onented representation
;848
55.4.1.5;3.1.5. Logic-based representation
;850
55.4.1.6;3.1.6. Fuzzy logic
;852
55.4.2;3.2. Generic knowledge-based techniques
;853
55.4.2.1;3.2.1. Control strategies
;853
55.4.2.2;3.2.2 . Search strategies
;855
55.4.2.3;3.2.3. Constraint processing
;858
55.4.2.4;3.2.4. Case-based reasoning;860
55.4.2.5;3.2.5. Blackboard architecture
;861
55.5;4. KNOWLEDGE-BASED APPLICATION IN FUNCTIONAL DESIGN;862
55.5.1;4.1. B-FES functional modeling framework;863
55.5.2;4.2. Acquisition of functional design knowledge through two-level knowledge modeling
;864
55.5.3;4.3. Knowledge-based functional representation scheme;867
55.5.3.1;4.3.1. Rule-based representation in rule base
;867
55.5.3.2;4.3.2. Fuzzy logic in FMCDM model base;868
55.5.3.3;4.3.3. Knowledge-basedJunctional representation in an object-oriented behavior base;868
55.5.4;4.4. Knowledge-based functional reasoning strategy;870
55.5.5;4.5. Best-first heuristic search in functional reasoning;872
55.5.5.1;4.5.1. Weighted performallce rating aggregation of a mechanical device
;872
55.5.5.2;4.5.2. Dynamic evaluation index of a design alternative
;874
55.5.6;4.6. Case study;875
55.5.6.1;4.6.1. Problem description and user input;875
55.5.6.2;4.6.2. Automated functional design process and system output;875
55.6;5. CONCLUSION;879
55.7;REFERENCES;881
56;EXPERT SYSTEMS TECHNOLOGY IN PRODUCTION PLANNING AND SCHEDULING
;886
56.1;1. INTRODUCTION;886
56.2;2. THE EXPERT SYSTEMS TECHNOLOGY;887
56.3;3. EXPERT SYSTEMS IN PRODUCTION PLANNING & SCHEDULING;889
56.4;4. EXPERT SYSTEMS RESEARCH IN PRODUCTION PLANNING & SCHEDULING;893
56.5;5. GENESYS: A QUICK CASE STUDY
;895
56.5.1;5.1. Introduction;895
56.5.2;5.2. Problem analysis;896
56.5.3;5.3. The knowledge base;897
56.5.4;5.4. Construction & features;899
56.5.5;5.5. Performance evaluation;901
56.6;6. CONCLUSIONS/RECOMMENDATIONS;902
56.7;REFERENCES;903
57;APPLYING INTELLIGENT AGENT-BASED SUPPORT SYSTEMS IN AGILE BUSINESS PROCESSES;907
57.1;1. INTRODUCTION;907
57.2;2. INTELLIGENT AGENT FRAMEWORK;910
57.2.1;2.1. Intelligent agent system environment;911
57.2.2;2.2 . Architecture of an IA;912
57.2.3;2.3. The agent communication;916
57.2.3.1;2.3.1. The agent communication language;916
57.2.3.2;2.3.2. The content language;917
57.2.3.3;2.3.3. The agent conversation policy
;919
57.3;3. AGENT-BASED OBJECT-ORIENTED DESIGN PROCESSES;924
57.3.1;3.1. An object-oriented approach;926
57.3.1.1;3.1.1. The concept of a design object
;926
57.3.1.2;3.1.2. A design process model formalism for DwO
;927
57.3.1.3;3.1.3. Modular software components;929
57.3.1.4;3.1.4. Object-oriented approach in modular software components;931
57.3.2;3.2. Agent-based system in design process;932
57.4;4. AGENT-BASED SUPPLY CHAIN PROCESSES;936
57.4.1;4.1. Classification of intelligent supply chain agents;937
57.4.2;4.2. Architecture of agents;943
57.4.2.1;4.2.1. Message handling process;943
57.4.2.2;4.2.2. The XML-based contents of the message
;945
57.4.2.3;4.2.3. Basic alient architecture
;947
57.5;5. AGENT-BASED SYSTEMS OF KNOWLEDGE MANAGEMENT;953
57.5.1;5.1. The definition of agents;953
57.5.2;5.2. The architecture of the agent-based system;954
57.5.3;5.3. Process 4: Distribute knowledge passively;956
57.6;6. CONCLUSIONS;958
57.7;REFERENCES;959
58;THE KNOWLEDGE BASE OF A B2B eCOMMERCE MULTI-AGENT SYSTEM
;963
58.1;1. INTRODUCTION;963
58.2;2. RELATED WORK;964
58.3;3. AGENT INFERENCE MODEL (AIM);967
58.4;4. CASE STUDY SCENARIO;969
58.5;5. CREATING THE KNOWLEDGE-BASE;972
58.6;6. ARCHITECTURE;976
58.7;7. IMPLEMENTATION & RESULT;981
58.8;8. CONCLUSION;983
58.9;REFERENCES;983
59;FROM ROLES TO AGENTS: CONSIDERATIONS ON FORMAL AGENT MODELING AND IMPLEMENTATION
;985
59.1;1. INTRODUCTION;985
59.1.1;1.1. Complex systems
;985
59.1.2;1.2. Application protocols;986
59.1.3;1.3. Distributed objects;987
59.2;2. AGENT-ORIENTED PROGRAMMING;987
59.2.1;2.1. Sub-protocols;988
59.2.2;2.2. Agent-UML;988
59.2.3;2.3. From objects- to agent-oriented programming;989
59.2.4;2.4. Method and message passing unification;991
59.2.5;2.5. Internal state control of an agent;992
59.3;3. ROLES AND SCENARIOS;993
59.3.1;3.1. Definitions;993
59.3.2;3.2. Artifacts for development;995
59.3.3;3.3. Roles and scenarios as programming artifacts
;998
59.3.3.1;3.3.1. State control
;998
59.3.3.2;3.3.2. State space inheritance;999
59.3.3.3;3.3.3. Genericity and composability;1001
59.3.4;3.4. On state inheritance;1001
59.4;4. THE DHELI TOOL;1003
59.4.1;4.1. The interaction-oriented programming framework;1003
59.4.2;4.2. System runtime interfaces;1004
59.4.3;4.3. Communication interfaces;1006
59.4.4;4.4. The DHELI language;1007
59.4.4.1;4.4.1. Entities;1007
59.4.4.2;4.4.2. Communication acts;1008
59.4.4.3;4.4.3. Variables, role variables and meta-roles;1010
59.5;5. CONCLUSION AND FUTURE WORK;1011
59.6;REFERENCES;1011
60;AGENT-BASED eLEARNING SYSTEMS: A GOAL-BASED APPROACH
;1013
60.1;1. INTRODUCTION;1013
60.2;2. OVERVIEW OF THE AGENT COMPOSITE GOAL MODEL;1014
60.3;3. EXTENSION OF THE AGENT COMPOSITE GOAL MODEL;1017
60.3.1;3.1. Action selection;1017
60.3.1.1;3.1.1. Bayesian inference
;1017
60.3.1.2;3.1.2. Discussion;1018
60.3.2;3.2. Multi-agent modeling;1018
60.3.2.1;3.2.1. Agent identification;1019
60.3.2.2;3.2.2. Coordination;1019
60.3.2.3;3.2.3. Communication;1020
60.3.2.4;3.2.4. Summary;1020
60.4;4. E-LEARNING MODEL;1020
60.4.1;4.1. Goal-based modeling;1020
60.4.2;4.2. E-learning modeling;1021
60.5;5. E-LEARNING SYSTEM DEVELOPMENT;1024
60.5.1;5.1. E-learning system architecture;1024
60.5.2;5.2. E-Iearning system development;1026
60.6;6. CONCLUSION AND FUTURE WORK;1027
60.6.1;6.1. Conclusion;1027
60.6.2;6.2. Future work;1027
60.7;REFERENCES;1027
61;COMBINING TEMPORAL ABSTRACTION AND DATA MININGMETHODS IN MEDICAL DATA ANALYSIS
;1029
61.1;1. INTRODUCTION;1029
61.2;2. TEMPORAL ABSTRACTION AND DATA MINING METHODS;1031
61.2.1;2.1. Temporal abstraction methods;1031
61.2.2;2.2. Data mining methods;1032
61.3;3. THE HEPATITIS DATABASE AND A FRAMEWORK FOR COMBININGTEMPORAL ABSTRACTION WITH DATA MINING METHODS;1033
61.3.1;3.1. The hepatitis database and problems;1033
61.3.2;3.2. Preprocessing for hepatitis data;1035
61.3.2.1;3.2.1. Feature selection and data reduction
;1035
61.3.2.2;3.2.2. Extraction of data subsets;1036
61.4;4. A TEMPORAL ABSTRACTION METHOD IN THE HEPATITIS DOMAIN;1037
61.4.1;4.1. Determination of typical abstraction patterns;1037
61.4.1.1;4.1.1. The TA primitives;1037
61.4.1.2;4.1.2. Observation and determination of absttattion patterns;1038
61.4.1.3;4.1.3. Relations between TA primitives;1038
61.4.2;4.2. Temporal abstraction algorithms for extracting abstraction patterns;1040
61.4.2.1;4.2.1. Notations and parameters used in the algorithms;1041
61.4.2.2;4.2.2. Abstraction of short-term changed tests;1044
61.4.3;4.3 . Abstraction of long-term changed tests;1044
61.5;5. MINING ABSTRACTED DATA BY DATA MINING METHODS;1045
61.5.1;5.1. The statistical significance of discovered knowledge;1045
61.5.2;5.2. Mining abstracted hepatitis data with system D2MS and Clementine;1046
61.6;6. CONCLUSIONS;1052
61.7;7. ACKNOWLEDGMENTS;1052
61.8;REFERENCES;1052
62;DISTRIBUTED MONITORING: METHODS, MEANS AND TECHNOLOGIES
;1054
62.1;1. INTRODUCTION;1054
62.2;2. FEATURES AND FUNCTIONS OF MONITORING SYSTEMS;1055
62.3;3. MONITORING ARCHITECTURES;1056
62.3.1;3.1. Centralized monitoring;1056
62.3.2;3.2. Hierarchical monitoring;1058
62.3.3;3.3 . Distributed monitoring;1059
62.4;4. SOFTWARE PARADIGMS FOR MONITORING;1061
62.4.1;4.1. Client-Server;1061
62.4.2;4.2. Remote Evaluation (or code pushing);1062
62.4.3;4.3. Code on Demand (or code pulling);1062
62.4.4;4.4. Mobile Agents;1063
62.5;5. SOFTWARE MOBILITY FOR DISTRIBUTED MONITORING;1064
62.5.1;5.1. Location transparency and location awareness;1065
62.5.2;5.2. Mobile code systems;1065
62.5.3;5.3. Mobility mechanisms;1066
62.6;6. TECHNOLOGIES AND STANDARDS FOR MONITORING;1066
62.6.1;6.1. Internet/IETF monitoring;1067
62.6.2;6.2. OSI monitoring;1068
62.6.3;6.3. CORBA;1069
62.6.4;6.4. Java;1070
62.6.5;6.5. SOAP;1071
62.6.6;6.6. OSA, Parlay and Jain;1071
62.7;7. EMERGING APPROACHES TO DYNAMIC MONITORING;1072
62.7.1;7.1. Management by Delegation (MbD);1072
62.7.2;7.2. MbD in the context of internet management;1073
62.7.3;7.3. MbD in the context of OSI management;1074
62.7.4;7.4. Mobile agents for distributed monitoring;1075
62.7.5;7.5. Potential benefits of MA-based management;1077
62.7.6;7.6. Open issues of MA-based management
;1078
62.8;8. CONCLUSIONS;1079
62.9;ACKNOWLEDGEMENTS
;1080
62.10;REFERENCES;1080
63;FINDING PATTERNS IN IMAGE DATABASES
;1085
63.1;1. INTRODUCTION;1085
63.2;2. RELATED WORK;1086
63.2.1;2.1. Preprocessing;1086
63.2.2;2.2. Pattern discovery;1087
63.2.2.1;2.2.1. Association mining in image data
;1087
63.2.2.2;2.2.2. Clustering in image data;1088
63.2.2.3;2.2.3. Classification in image data
;1089
63.2.3;2.3. Image-specific considerations;1090
63.2.3.1;2.3.1. Semantic information;1090
63.2.3.2;2.3.2. Spatial relationship;1090
63.3;3. VIEWPOINT PATTERN DISCOVERY;1091
63.3.1;3.1. Overview;1092
63.3.2;3.2 . Algorithm ViewpointMiner;1092
63.4;4. EXPERIMENTS;1095
63.4.1;4.1. General category images;1095
63.4.2;4.2. Retinal images;1098
63.4.3;4.3. Kitchen plan images;1100
63.5;5. CONCLUSION;1101
63.6;REFERENCES;1102
64;COGNITION TECHNIQUES AND THEIR APPLICATIONS
;1104
64.1;1. EVOLUTION OF THE MODEL OF COGNITION;1104
64.1.1;1.1. The cycles of model of cognition;1105
64.2;2. SCOPE OF REALIZATION OF THE ACQUISITION CYCLE;1109
64.3;3. BUILDING PERCEPTION CYCLE;1116
64.3.1;3.1. Need for map building;1117
64.3.2;3.2. Map building by depth first search;1118
64.3.2.1;3.2.1. Algorithms for map-building by depth first search
;1120
64.3.2.2;3.2.2. A illustration of procedure traverse boundary
;1122
64.3.2.3;3.2.3. An illustration of procedure map building
;1123
64.3.2.4;3.2.4. Simulation on Superscoutt-ll Linux based graphics interface
;1123
64.3.3;3.3. Construction of 3D world map by depth first search;1124
64.4;4. LEARNING AND COORDINATION CYCLE;1129
64.4.1;4.1. Learning for obstacle avoidance;1130
64.4.1.1;4.1.1. The constraints in the navigation process;1131
64.4.1.2;4.1.2. Learning for local guidance through Neural Net
;1133
64.4.1.3;4.1.3. Building the Third Neural Net;1136
64.4.2;4.2. Planning by bi-directional associative memory;1139
64.4.2.1;4.2.1. Temporal associative memory in mobile robot navigation;1140
64.4.3;4.3. Planning using evolutionary algorithm;1144
64.4.3.1;4.3.1. Simulation for EC planning;1148
64.5;5. CONCLUSIONS;1148
64.6;REFERENCES;1150
65;VOLUME IV. INTELLIGENT SYSTEMS;1182
66;ARTIFICIAL INTELLIGENCE AND INTEGRATED INTELLIGENT SYSTEMS IN PRODUCT DESIGN AND DEVELOPMENT*
;1183
66.1;1. INTRODUCTION;1183
66.2;2. OVERVIEW OF EVOLUTION OF PRODUCT-PROCESS DESIGN METHODOLOGIES
;1184
66.2.1;2.1. Automated & integrated design
;1185
66.2.2;2.2. Concurrent engineering & concurrent design;1186
66.2.2.1;2.2.1. Concurrent engineering
;1186
66.2.2.2;2.2.2. Design for X
;1187
66.2.2.3;2.2.3. Product life cycle management
;1188
66.2.3;2.3. Intelligent computer-aided design;1188
66.2.4;2.4. Virtual prototyping;1189
66.2.5;2.5. Computer supported collaborative design
;1189
66.3;3. ARTIFICIAL INTELLIGENCE IN PRODUCT-PROCESS DESIGN;1190
66.3.1;3.1. Intelligent product design;1190
66.3.2;3.2. Intelligent process planning;1191
66.3.3;3.3. Intelligent production system layout and design;1192
66.3.4;3.4. Intelligent simulation;1193
66.4;4. INTELLIGENT SYSTEMS FOR PRODUCT-PROCESS DESIGN;1193
66.4.1;4.1. Symbolic reasoning systems
;1193
66.4.2;4.2. KBE and coupling intelligent systems;1194
66.4.3;4.3. Artificial neural network systems;1195
66.4.4;4.4. Genetic algorithms and systems;1197
66.4.5;4.5. Case-based reasoning systems;1199
66.4.5.1;4.5.1. Case-based reasoning model;1200
66.4.5.2;4.5.2. Analogical reasoning for design problem solving
;1201
66.4.5.3;4.5.3. Design prototypes and cases
;1203
66.4.6;4.6. Integrated & distributed intelligent systems;1203
66.4.7;4.7. Hybrid intelligent systems;1206
66.5;5. A GENERIC FRAMEWORK FOR INTEGRATED INTELLIGENT DESIGN;1209
66.5.1;5.1. Issues and requirements for integrated intelligent design;1210
66.5.2;5.2. Framework for integrated intelligent design;1214
66.5.2.1;5.2.1. Working environment;1214
66.5.2.2;5.2.2. AI protocol based integrated intelligent design;1214
66.5.2.3;5.2.3. Architecture of framework
;1216
66.5.2.3.1;(1)Designer Communication Layer;1216
66.5.2.3.2;(2) Core System and Control Layer;1217
66.5.2.3.3;(3) Application Layer;1218
66.6;6. IMPLEMENTATIONS OF INTEGRATED INTELLIGENT DESIGN SYSTEMS;1218
66.6.1;6.1. Integrated distributed collaborative design and assembly planning;1219
66.6.2;6.2. AI-supported internet-enabled virtual prototyping;1221
66.6.2.1;(1 ) Distributed Artificial Intelligence/Multi-Agent System;1222
66.6.2.2;(2) Feature-Based Virtual Model Representation;1223
66.6.2.3;(3)Neural Networks for Modeling Module/System's Dynamics;1223
66.6.2.4;(4) Optimization with Genetic Algorithms;1223
66.6.2.5;(5) Case-based Design;1223
66.6.3;6.3. A web-based knowledge intensive design support system;1225
66.6.3.1;6.3.1. Knowledge-based systems as knowledge servers
;1225
66.6.3.2;6.3.2 . WebDMME framework and implementation
;1226
66.7;7. CONCLUSIONS;1228
66.8;REFERENCES;1230
67;INTELLIGENT PATIENT MONITORING IN THE INTENSIVE CARE UNIT AND THE OPERATING ROOM
;1240
67.1;1. INTRODUCTION;1240
67.2;2. TEMPORAL PATTERN RECOGNITION;1245
67.2.1;2.1. Template-based methods;1246
67.2.2;2.2 . Signal processing of the ECG;1249
67.3;3. REASONING METHODS;1251
67.3.1;3.1. Fuzzy logic;1251
67.3.2;3.2 . Eviden ce-based reasoning;1254
67.3.3;3.3. Bayesian networks;1257
67.3.4;3.4 . Artificial neural networks;1259
67.4;4. INTELLIGENT INTENSIVE CARE UNIT MONITORS;1263
67.5;5. INTELLIGENT ANAESTHESIA MONITORS;1265
67.5.1;5.1. Intelligent alarms;1268
67.6;6. SMART SENSORS;1269
67.6.1;6.1. Detection of esophageal intubation;1270
67.6.2;6.2 . Depth of anaesthesia;1271
67.6.3;6.3. Cardiac output;1274
67.7;7. AUTOMATIC CONTROL IN THE ICU AND OR;1276
67.7.1;7.1. Mean arterial blood pressure;1277
67.7.2;7.2. Depth of anaesthesia;1282
67.7.3;7.3. Multiple drug infusion-cardiac output;1283
67.8;8. INTERFACE DESIGN;1285
67.8.1;8.1. Display of anaesthesia information;1287
67.8.2;8.2. Data display for intelligent monitors;1288
67.9;9. DISCUSSION;1292
67.10;ACKNOWLEDGEMENTS;1293
67.11;REFERENCES;1293
68;MISSION CRITICAL INTELLIGENT SYSTEMS
;1300
68.1;1. INTRODUCTION;1300
68.2;2. DEFINITIONS;1301
68.3;3. REAL TIME EXPERT SYSTEMS;1304
68.4;4. FAULT TOLERANCE IN INTELLIGENT SYSTEMS;1307
68.4.1;4.1. Failure detection and recovery in intelligent systems;1309
68.4.2;4.2. An architecture for a dependable intelligent system;1310
68.5;5. DISTRIBUTED MISSION CRITICAL INTELLIGENT SYSTEMS (DMCIS);1312
68.6;6. COORDINATION IN DISTRIBUTED INTELLIGENT SYSTEMS;1314
68.6.1;6.1. MINUTE: Multi issue negotiation under time constrained environments;1315
68.6.2;6.2. TRACE-Task and resource allocation in a computational economy;1317
68.6.3;6.3. MAS organization in TRACE;1318
68.6.4;6.4. Task allocation protocol;1318
68.6.5;6.5. Resource allocation protocol;1319
68.6.6;6.6. Experiments;1322
68.6.6.1;6.6.1. Reduction in decommitments
;1322
68.6.6.2;6.6.2. Fairness of resource allocation;1322
68.6.6.3;6.6.3. Adaptiveness of the TRACE MAS
;1323
68.7;7. CONCLUSIONS;1324
68.8;REFERENCES;1324
69;AN INTELLIGENT HYBRID SYSTEM FOR BUSINESS FORECASTING
;1327
69.1;1. INTRODUCTION;1327
69.2;2. PROBLEM STATEMENT;1328
69.3;3. OBJECTIVE;1329
69.4;4. NEW AREAS FOR IRS APPLICATIONS;1330
69.5;5. ARCHITECTURE OF IFS;1330
69.6;6. OPERATING PROCEDURE OF IFS;1331
69.7;7. INTELLIGENT BUSINESS FORECASTER;1332
69.7.1;7.1. Architecture of IBF
;1333
69.7.2;7.2. Operating procedure;1336
69.7.2.1;7.2.1. Self-organised learning;1336
69.7.2.2;7.2.2. Identification of Fuzzy Rules
;1337
69.7.2.3;7.2.3 . Supervised learning
;1338
69.7.2.4;7.2.4. Forecasting and retraining
;1339
69.7.3;7.3. Case studies;1340
69.7.3.1;7.3.1. IBF vs multiple regression method
;1340
69.7.3.1.1;7.3.1.1. CASE I.;1340
69.7.3.1.2;7.3.1.2. COMPARISON WITH A MULTIPLE REGRESSION MODEL.;1342
69.7.3.2;7.3.2. IBF vs conventional neural networks
;1343
69.7.3.2.1;7.3.2.1. CASE II.;1343
69.7.3.2.2;7.3.2.2. CASE III.;1348
69.7.3.2.3;7.3.2.3. COMPARISON WITH CONVENTIONAL NEURAL NETWORKS.
;1351
69.8;8. INTELLIGENT SCENARIO GENERATOR;1355
69.8.1;8.1. Truth valued flow inference
;1359
69.8.2;8.2. Architecture;1360
69.8.3;8.3. Learning algorithm;1363
69.8.4;8.4. An illustrative example;1365
69.9;9. JUDGMENTAL ADJUSTMENT OF OBJECTIVE FORECASTS;1370
69.9.1;9.1. The FSD method;1372
69.9.1.1;9.1.1. The FSD model
;1372
69.9.1.2;9.1.2. Formatting the problem
;1375
69.9.1.3;9.1.3. Combining Experts' Adjustments
;1376
69.9.1.3.1;a) Construction of the "a" Vector;1377
69.9.1.3.2;b) Construction of "R" Matrix;1378
69.9.1.3.3;c) Tra nsformat ion;1378
69.9.2;9.2. The fuzzy adjuster;1379
69.9.3;9.3. Validation;1380
69.9.4;9.3.1. Experimentation
;1381
69.9.5;9.3.2. Experimental results;1381
69.10;10. THE IFS SOFTWARE;1385
69.10.1;10.1. System manager;1385
69.10.2;10.2. Intelligent business forecaster;1385
69.10.2.1;10.2.1. IBF Setting up procedure
;1386
69.10.2.2;10.2.2. IBF Operating procedure;1386
69.10.3;10.3. Intelligent scenario generator;1386
69.10.3.1;10.3.1. ISG Setting up procedure
;1386
69.10.3.2;10.3.2. ISG Operating procedure
;1386
69.10.4;10.4. Fuzzy adjuster;1387
69.10.5;10.5. Database;1387
69.10.6;10.6. Knowledge base;1387
69.10.7;10.7 . User interface;1387
69.11;11. CONCLUSIONS;1387
69.12;REFERENCES;1388
70;INTELLIGENT SYSTEMS TECHNOLOGY IN THE FAULT DIAGNOSIS OF ELECTRONIC SYSTEMS
;1392
70.1;1. INTRODUCTION;1392
70.2;2. THE DIAGNOSTIC PROCESS;1393
70.3;3. A SIMPLIFIED MODEL OF MACHINE INTELLIGENCE;1393
70.4;4. TRADITIONAL APPROACHES;1394
70.4.1;4.1. Rule-based systems;1394
70.4.2;4.2. Fault (decision) trees;1395
70.5;5. MODEL-BASED APPROACHES;1396
70.5.1;5.1. Fault models (or fault dictionaries);1396
70.5.2;5.2. Causal models;1398
70.5.3;5.3. Models based on structure and behaviour;1398
70.5.4;5.4 . Diagnostic inference model;1401
70.6;6. APPROACHES BASED ON LEARNING;1403
70.6.1;6.1. Case-based reasoning;1403
70.6.2;6.2. Explanation-based learning;1404
70.6.3;6.3. Learning knowledge from data;1405
70.7;7. FUZZY LOGIC APPROACHES;1405
70.8;8. NEURAL NETWORK APPROACHES;1407
70.9;9. HYBRID APPROACHES;1411
70.10;10. DIAGNOSTIC STANDARDS;1413
70.11;11. COMMENTARY;1413
70.11.1;11.1. Rule-based approaches;1414
70.11.2;11.2. Model-based approaches;1414
70.11.3;11.3. Case-based approaches
;1415
70.11.4;11.4. Fuzzy logic and neural networks;1415
70.11.5;11.5. Hybrid approaches;1416
70.12;12. FUTURE RESEARCH DIRECTIONS;1416
70.13;13. TOOLS FOR THE RAPID DEPLOYMENT OF AI-BASED DIAGNOSTIC SOLUTIONS
;1418
70.13.1;13.1. Knowledge representation;1418
70.13.2;13.2. Diagnostic inference;1421
70.13.3;13.3. Summary;1425
70.14;14. CONCLUSIONS;1425
70.15;REFERENCES;1426
71;TECHNIQUES IN THE UTILIZATION OF THE INTERNET AND INTRANETS IN FACILITATING THE DEVELOPMENT OF CLINICAL DECISION SUPPORT SYSTEMS IN THE PROCESS OF PATIENT CARE
;1430
71.1;WHY DO WE NEED IT TO COORDINATE CARE?;1430
71.1.1;Prevalence of chronic disease;1430
71.1.2;Poor quality of care and escalating health care costs;1431
71.1.3;Communication and coordination issues;1431
71.1.4;Promoting support for chronic disease management;1433
71.1.5;Chapter outline;1433
71.2;DEFINING KEY CONCEPTS;1434
71.2.1;Evidence-based medicine;1434
71.2.2;Guideline;1434
71.2.3;Protocol;1438
71.2.4;Care plan;1440
71.2.5;Pathway;1441
71.2.6;Workflow;1444
71.2.7;Relationship between the concepts;1447
71.3;INTERNET/INTRANET-ENABLED HEALTH INFORMATION NETWORKS;1447
71.4;CLINICAL GUIDELINES AND DECISION SUPPORT;1451
71.5;INTEGRATING GUIDELINES AND WORKFLOW INTO EHR DESIGN;1456
71.5.1;Instruction;1459
71.5.2;ERR system architecture for CIGs and workflows;1462
71.5.3;Case study 1-hypertension in diabetes
;1465
71.5.4;Case study 2-early supported discharge;1467
71.6;CONCLUSION;1467
71.7;ACKNOWLEDGEMENTS;1472
72;RISK ANALYSIS AND THE DECISION-MAKING PROCESS IN ENGINEERING
;1477
72.1;1. INTRODUCTION;1477
72.2;2. THE NEED FO R RISK MANAGEMENT;1477
72.3;3. RISK;1478
72.4;4. DECISION-MAKING PROCESS;1480
72.4.1;4.1. Basic concepts;1480
72.4.2;4.2. Decision trees;1481
72.4.3;4.3. Defining utility criteria;1482
72.5;5. RISK-ANALYSIS BASED DECISION PROCESS;1483
72.5.1;5.1. General framework for integrating risk to the decision making process;1484
72.5.2;5.2. Final remarks;1487
72.6;6. ACCEPTABILITY OF RISK;1488
72.7;7. OPTIMIZATION;1491
72.7.1;7.1. Basic optimization concepts;1491
72.7.2;7.2. Cost of saving human lives;1493
72.7.3;7.3. Life cycle costing;1495
72.7.3.1;7.3. 1. General aspects;1496
72.7.3.2;7.3.2. Basics oflife cycle costing;1497
72.8;8. EXAMPLES;1498
72.8.1;8.1. Allocation of resources to transport networks;1498
72.8.1.1;8.1.1. Basic considerations;1498
72.8.1.2;8.1.2. Decision criteria;1498
72.8.1.3;8.1.3. Accessibility
;1499
72.8.1.4;8.1.4. Optimization of resource allocation;1500
72.8.1.5;8.1.5. Case study
;1501
72.8.1.6;8.1.6. Summary and final remarks
;1503
72.8.2;8.2. Design of structural systems;1503
72.8.2.1;8.2.1. Decision criteria;1503
72.8.2.2;8.2.2. Probabilistic model of theground motion
;1503
72.8.2.3;8.2.3. Model of the probability of failure of the structural system
;1505
72.8.2.4;8.2.4. Estimation of cost;1505
72.8.2.5;8.2.5. Optimization;1506
72.8.2.6;8.2.6. Summary and final remarks
;1507
72.9;9. CONCLUSIONS;1508
72.10;REFERENCES;1508
73;MECHATRONICS AND SMART STRUCTURES DESIGN TECHNIQUES FOR INTELLIGENT PRODUCTS, PROCESSES, AND SYSTEMS
;1510
73.1;1. INTRODUCTION;1510
73.2;2. ANALYSIS OF INDUCED-STRAIN ACTUATION;1512
73.2.1;2.1. Actuator-structure interaction;1512
73.2.1.1;2.1.1. Displacement analysis
;1513
73.2.1.2;2.1.2. Output energy analysis
;1516
73.2.2;2.2. Induced-strain actuators with compliant support;1517
73.2.2.1;2.2.1. Displacement analysis;1517
73.2.2.2;2.2.2. Output energy analysis;1518
73.2.3;2.3 . Displacement-amplified induced-strain actuators;1519
73.2.3.1;2.3.1. Displacement analysis;1519
73.2.3.2;2.3.2. Output energy analysis
;1521
73.2.3.3;2.3.3. Optimal kinematic gain, G, for a given value of .
;1522
73.2.4;2.4. Electric response;1525
73.3;3. ANALYSIS OF INDUCED-STRAIN ACTUATION FOR DYNAMIC APPLICATION;1528
73.3.1;3.1. Mechanical response;1531
73.3.2;3.2. Electric response;1533
73.4;4. DESIGN OF SMART STRUCTURES WITH INDUCED-STRAIN ACTUATORS;1535
73.4.1;4.1. Efficient static design;1537
73.4.2;4.2. Efficient dynamic design;1539
73.4.3;4.3. Quasi-static dynamic operation;1541
73.4.4;4.4. Undamped dynamic operation;1542
73.4.5;4.5. The damped dynamic system;1543
73.4.6;4.6. Design example of induced-strain actuation application;1545
73.5;5. DESIGN OF EMBEDDED ULTRASONICS SMART STRUCTURES FOR STRUCTURAL HEALTH MONITORING
;1550
73.5.1;5.1. PWAS Ultrasonic transducers;1550
73.5.2;5.2. Shear-layer coupling between PWAS and structure;1554
73.5.2.1;5.2.1. Symmetric case;1556
73.5.2.2;5.2.2. Antisymmetric case;1557
73.5.2.3;5.2.3. Shear lag solution;1558
73.5.2.4;5.2.4. Pin-force model;1563
73.5.2.5;5.2.5 . Energy transier between the PWAS and the structure
;1565
73.5.2.6;5.2.6 . Conditions for optimum energy transfer
;1567
73.5.3;5.3. Lamb waves excited by PWAS;1568
73.5.3.1;5.3.1. Lamb wave solution under nonuniform shear-stress boundary excitation
;1569
73.5.3.2;5.3.2. Ideal-bonding solution
;1575
73.5.4;5.4. Pitch-catch PWAS experiments
;1578
73.5.4.1;5.4.1. Experimental setup;1578
73.5.4.2;5.4.2. Excitation signal;1580
73.5.4.3;5.4.3. Lamb mode tuning;1581
73.5.4.4;5.4.4. Pitch-catch results
;1583
73.6;6. SUMMARY AND CONCLUSIONS;1583
73.7;BIBLIOGRAPHY;1587
74;ENGINEERING INTERACTION PROTOCOLS FOR MULTIAGENT SYSTEMS
;1589
74.1;1. INTRODUCTION;1589
74.1.1;1.1. Interaction protocols in multiagent systems
;1589
74.1.2;1.2. Communication protocols;1590
74.1.2.1;1.2.1. Communication protocols in distributed systems
;1590
74.1.2.2;1.2.2. Communication protocol engineering;1590
74.1.3;1.3. Engineering interaction protocols;1591
74.1.3.1;1.3.1. One approach
;1591
74.1.3.2;1.3.2. Some tools.;1593
74.1.4;1.4. Overview of the beghera application;1594
74.2;2. THE ANALYSIS STAGE;1596
74.3;3. THE FORMAL DESCRIPTION STAGE;1598
74.3.1;3.1. Towards a new interaction modeling language;1599
74.3.2;3.2. A component-based approach;1600
74.3.3;3.3. Definition of micro-protocols;1601
74.3.4;3.4. The CPDL language;1604
74.3.5;3.5 . Graphical modeling languages for protocol's representation;1605
74.3.6;3.6. UAML and UAMLe languages;1608
74.3.7;3.7. A tool for supporting agent interaction protocol design;1608
74.4;4. THE VALIDATION STEP;1610
74.4.1;4.1. The reachability analysis;1611
74.4.2;4.2. One example;1611
74.4.3;4.3. The model-checking approach;1613
74.5;5. THE PROTOCOL SYNTHESIS STAGE;1613
74.5.1;5.1. Phase role;1613
74.5.2;5.2. Two methods for the protocol synthesis;1614
74.5.2.1;5.2.1. Protocol synthesis approach;1614
74.5.2.2;5.2.2. Direct execution of protocols in CPDL language
;1615
74.5.2.3;5.2.3. Comparison of these two approaches;1616
74.6;6. THE CONFORMANCE TESTING STAGE;1617
74.7;7. CONCLUSION AND PERSPECTIVES;1618
74.8;REFERENCES;1620
75;VOLUME V. NEURAL NETWORKS, FUZZY THEORY AND GENETIC ALGORITHMS
;1653
76;NEURAL NETWORK SYSTEMS TECHNOLOGY AND APPLICATIONS IN CAD/CAM INTEGRATION
;1654
76.1;1. INTRODUCTION;1654
76.2;2. ARTIFICIAL NEURAL NETWORKS;1655
76.3;3. ANN TECHNIQUES FOR FEATURE RECOGNITION;1655
76.3.1;3.1. The topology;1655
76.3.1.1;3.1.1. Feedforward networks
;1655
76.3.1.2;3.1.2. Competitive networks;1656
76.3.1.3;3.1.3. Recurrent networks;1657
76.3.1.4;3.1.4. The three-Iayer feed-forward neural network
;1658
76.3.1.5;3.1.5. The four-layer feed-forward neural network
;1659
76.3.1.6;3.1.6. The five-layer, perceptrons quasi-neural network
;1659
76.3.2;3.2. Input representation;1659
76.3.2.1;3.2.1. 2D feature representation
;1660
76.3.2.2;3.2.2. Face adjacency matrix code;1660
76.3.2.3;3.2.3. Face score vector;1660
76.3.2.4;3.2.5. F-adjaccncy matrix and V-adjacency matrix
;1661
76.3.2.5;3.2.6. 2D input patterns of 3D feature volume;1665
76.3.2.6;3.2.7. A vector based on the partitioned view-contours of a given object
;1665
76.3.2.7;3.2.8. Simplified sheleton
;1666
76.3.3;3.3. The output format
;1666
76.3.3.1;3.3.1. Each neuron corresponding to a feature class
;1666
76.3.3.2;3.3.2. Neurons representing the information of the recognised feature;1667
76.3.3.3;3.3.3. A matrix file containing the code for each recognised feature and its machining directions
;1667
76.3.4;3.4. The training method;1667
76.3.4.1;3.4.1. Back propagation algorithms
;1667
76.3.4.2;3.4.2. Conjugate gradient algorithm by the authors
;1668
76.3.4.3;3.4.3. Training method by Prabhakar and Henderson
;1669
76.3.5;3.5. Summary of ANN-based feature recognition
;1669
76.4;4. ANN TECHNIQUES FOR CAPP;1669
76.4.1;4.1. The topology;1670
76.4.1.1;4.1.1. Feedforward network
;1670
76.4.1.2;4.1.2. Hopfield network
;1670
76.4.1.3;4.1.3. Brain-State-in-a-Box (BSB);1672
76.4.1.4;4.1.4. MAXNET ;1672
76.4.2;4.2. Input representation;1673
76.4.2.1;4.2.1. Standardised image data
;1673
76.4.2.2;4.2.2. Input vector with value ranging from 0 to 1
;1674
76.4.2.3;4.2.3. Input vector with integer value;1674
76.4.2.4;4.2.4. Input vector in binary form
;1674
76.4.2.5;4.2.5. Input vector in mixed form
;1674
76.4.3;4.3. Output format;1675
76.4.3.1;4.3.1. Output vector in ordered binary form
;1675
76.4.3.2;4.3.2. Output vector with special values;1675
76.4.3.3;4.3.3. One-unit output in binary form
;1675
76.4.4;4.4. Training method;1676
76.4.4.1;4.4. 1. Unsupervised learning algorithm
;1676
76.4.4.2;4.4.2. Back-propagation
;1676
76.4.5;4.5. Summary of ANN-based CAPP
;1678
76.5;5. ANN-BASED HYBRID APPROACHES TO CAPP;1678
76.5.1;5.1. CAPP using expert system and ANN techniques
;1678
76.5.1.1;5.1.1. Expert system control module
;1678
76.5.1.2;5.1.2. Neural network control module
;1679
76.5.2;5.2. CAPP using ANN, fuzzy logic and expert system techniques
;1680
76.5.3;5.3. CAPP using GA, ANN and Fuzzy logic techniques
;1682
76.5.3.1;5.3.1. Input representation
;1682
76.5.3.2;5.3.2. Output format
;1685
76.5.3.3;5.3.3. Topology and the training method of the proposed neural network
;1685
76.5.4;5.4. Summary of ANN-based hybrid methods;1685
76.6;6. CONCLUSIONS;1685
76.7;7. REFERENCES;1686
77;NEURAL NETWORK SYSTEMS TECHNOLOGY AND APPLICATIONS IN PRODUCT LIFE-CYCLE COST ESTIMATES
;1689
77.1;1. INTRODUCTION;1689
77.2;2. BACKGROUND;1690
77.2.1;2.1. General product development;1690
77.3;3. AN APPROXIMATE ESTIMATION METHOD FOR THE PRODUCT LIFE CYCLE COST USING ANNS
;1691
77.3.1;3.1. The concepts;1691
77.3.2;3.2. Development of the life cycle cost factors
;1693
77.3.3;3.3. Development of product attributes
;1694
77.3.3.1;3.3.1. General product attributes
;1695
77.3.3.2;3.3.2. Maintainability attributes
;1695
77.3.3.3;3.3.3. Determining the final product attributes using statistical analysis
;1696
77.4;4. A CASE STUDY
;1699
77.4.1;4.1. Data collection
;1700
77.4.2;4.2. Development of training algorithms
;1701
77.4.2.1;4.2.1. Backpropagation algorithm
;1701
77.4.2.2;4.2.2. Development of training algorithm with backpropagation
;1702
77.4.3;4.3. Testing and the results;1703
77.4.4;4.4. Discussion;1705
77.5;5. CONCLUSIONS AND FUTURE WORKS;1707
77.6;REFERENCES;1709
78;NEURAL NETWORK SYSTEMS TECHNOLOGY IN THE ANALYSIS OF FINANCIAL TIME SERIES
;1710
78.1;INTRODUCTION AND CONTEXT;1711
78.2;TIME SERIES AND THEIR TECHNIQUES;1714
78.2.1;Financial time series;1715
78.2.2;Components of financial time series:
;1715
78.2.3;Switching time series;1726
78.3;NEURAL NETWORKS FUNDAMENTALS;1727
78.3.1;Artificial neural networks;1728
78.3.2;The single neuron element;1729
78.3.3;Training recurrent networks;1742
78.3.4;Validation;1744
78.4;DATA PREPARATION FOR NEURAL NETWORKS;1745
78.4.1;Detrending;1746
78.4.2;Smoothing;1746
78.4.3;Normalizing and scaling the data;1747
78.4.4;Structuring the data;1747
78.4.5;Time series and neural networks applications;1749
78.5;APPENDIX;1759
78.6;BIBLIOGRAPHY;1760
79;FUZZY RULE EXTRACTION USING RADIAL BASIS FUNCTION NEURAL NETWORKS IN HIGH-DIMENSIONAL DATA
;1762
79.1;INTRODUCTION;1762
79.2;1. FUZZY SET THEORY: BASIC DEFINITIONS AND TERMINOLOGY;1765
79.2.1;1.1. Fuzzy sets;1765
79.2.2;1.2. Linguistic variables and linguistic values;1766
79.2.3;1.3. Membership function formulation and parametrization;1766
79.2.4;1.4. Fuzzy set operations;1769
79.3;2. FUZZY REASONING AND FUZZY INFERENCE SYSTEMS;1770
79.3.1;2.1. Fuzzy if-then rules;1771
79.3.2;2.2. Approximate reasoning;1771
79.3.3;2.3. Fuzzy inference systems;1772
79.4;3. RADIAL BASIS FUNCTION NEURAL NETWORKS;1775
79.4.1;3.1. Definition;1776
79.4.2;3.2. Training;1778
79.4.2.1;3.2.1. Hidden layer definition :;1778
79.4.2.2;3.2.2 . Output layer definition;1778
79.4.2.3;3.2.3. Functional Equivalence to FIS;1779
79.5;4. ANFIS ARCHITECTURE;1780
79.6;5. OPTIMAL DESIGN OF RBFN BASED ON FUZZY CLUSTERING;1781
79.6.1;5.1. Fuzzy Clustering and similarity measures;1781
79.6.2;5.2. Toeplitz covariance matrix estimator;1784
79.6.3;5.3. Optimal number of clusters;1786
79.6.4;5.4. Output layer supervised training and rule extraction;1787
79.6.5;5.5. Algorithm: elliptical radial basis function network design;1789
79.7;6. EXPERIMENTS;1789
79.7.1;6.1. Synthetic data experiments;1790
79.7.1.1;6. 1. 1. Experiment 1;1790
79.7.1.2;6.1.2. Experiment 2;1792
79.7.2;6.2. Benchmark data experiments;1796
79.8;7. SUMMARY;1798
79.9;REFERENCES;1799
80;FUZZY DECISION MODELING OF PRODUCT DEVELOPMENT PROCESSES
;1802
80.1;1. INTRODUCTION;1802
80.2;2. MODELLING PRODUCT DEVELOPMENT INFORMATION WITH FUZZY SETS;1804
80.2.1;2.1. Introduction to fuzzy set theory;1804
80.2.2;2.2. Representing imprecision and preference information with fuzzy sets;1805
80.2.3;2.3 . Measures of possibility and necessity
;1806
80.3;3. A FUZZY SET APPROACH FOR PRIORITIZATION OF DESIGN REQUIREMENTS
;1807
80.3.1;3.1. Problem formulation;1807
80.3.2;3.2. A fuzzy outranking preference model to prioritize design requirements
;1809
80.3.2.1;3.2.1. Representing imprecise information in QFD;1809
80.3.2.2;3.2.2. A fuzzy outranking preference model for prioritizing design requirements;1810
80.3.3;3.3. Illustrative example;1813
80.4;4. A FUZZY SET APPROACH FOR SELECTION OF DESIGN CONCEPTS;1815
80.4.1;4.1. Problem formulation;1815
80.4.2;4.2. Fuzzy outranking preference model for concept selection;1817
80.4.2.1;4.2.1. Construction offuzzy outranking relations;1817
80.4.2.2;4.2.2. Determination ofnon-dominated design concepts;1818
80.4.3;4.3. Illustrated example;1819
80.5;5. A FUZZY SET APPROACH FOR SCHEDULING OF PRODUCT DEVELOPMENT PROJECTS
;1823
80.5.1;5.1. Problem formulation;1823
80.5.2;5.2. A fuzzy scheduling model to minimize schedule risk;1824
80.5.2.1;5.2.1. Comparison of two fuzzy temporal parameters
;1824
80.5.2.2;5.2.2. Performance measure of fuzzy project scheduling
;1825
80.5.2.3;5.2.3. Fuzzy scheduling with a genetic algorithm
;1825
80.5.3;5.3. lllustrative example;1829
80.6;6. CONCLUSION;1831
80.7;REFERENCES;1831
81;EVALUATION AND SELECTION IN PRODUCT DESIGN FOR MASS CUSTOMIZATION
;1834
81.1;1. INTRODUCTION;1834
81.2;2. CURRENT STATUS OF RESEARCH;1835
81.2.1;2.1. Design alternatives evaluation and selection;1836
81.2.2;2.2. Product family design evaluation and selection;1837
81.3;3. CUSTOMER-DRIVEN PRODUCT FAMILY DESIGN FOR MASS CUSTOMIZATION
;1838
81.3.1;3.1. Strategies and technical challenges for mass customization;1838
81.3.2;3.2. Customer-driven design for mass customization;1839
81.3.3;3.3. Module-based product family design;1841
81.3.4;3.4. Knowledge support framework for CDFMC;1843
81.4;4. PRODUCT FAMILY DESIGN EVALUATION AND SELECTION
;1844
81.4.1;4.1. Knowledge decision support scheme;1845
81.4.2;4.2. Customization/evaluation metrics;1846
81.4.3;4.3. Fuzzy clustering and design ranking methodology;1847
81.4.3.1;4.3.1. Fuzzy clustering analysis for design
;1847
81.4.3.2;4.3.2. Fuzzy ranking for design
;1850
81.4.3.3;4.3.3. Simplified fuzzy ranking for design
;1851
81.4.4;4.4. Evaluation of product family design alternatives;1852
81.4.4.1;4.4.1. Heuristic evaluation function
;1852
81.4.4.2;4.4.2. Evaluation index
;1852
81.4.5;4.5. Neural network adjustment for membership functions;1853
81.5;5. CASE STUDY AND SYSTEM PROTOTYPE;1855
81.5.1;5.1. Case study;1855
81.5.2;5.2. System prototype;1855
81.6;6. DISCUSSION;1858
81.7;7. SUMMARY AND CONCLUSIONS;1859
81.8;REFERENCES;1860
82;GENETIC ALGORITHM TECHNIQUES AND APPLICATIONS IN MANAGEMENT SYSTEMS
;1864
82.1;1. INTRODUCTION;1864
82.1.1;1.1. Resource-constrained scheduling problem
;1864
82.1.2;1.2. Classes of the generalized problem for resource-constrained scheduling;1865
82.1.3;1.3. Structure of this chapter;1867
82.2;2. RELATED WORK ON SCHEDULING;1867
82.2.1;2.1. Exact solution methods;1867
82.2.2;2.2. Heuristic solution methods;1868
82.3;3. INTRODUCTION TO GENETIC ALGORITHM;1868
82.3.1;3.1. The concept of genetic algorithms;1868
82.3.2;3.2. A simple example of genetic algorithms;1869
82.3.3;3.3. Packages of genetic algorithm components;1869
82.3.4;3.4. Applications of GA and when not to use;1871
82.4;4. SURVEY OF GA TECHNIQUES ON SCHEDULING;1871
82.4.1;4.1. Representation issues;1872
82.4.1.1;4.1.1. Indirect representation;1872
82.4.1.2;4. 1.2. Direct representation;1874
82.4.2;4.2 . Operators;1874
82.4.3;4.3. Comparison between different approaches;1875
82.4.4;4.4 . Other methods and issues
;1876
82.5;5. APPLY GENERIC ALGORITHMS TO SOFTWARE ENGINEERING;1877
82.5.1;5.1. Software project management;1877
82.5.2;5.2. Introduction to SPMNET;1878
82.5.3;5.3. GA for software project management;1878
82.5.3.1;5.3.1. Task-basedmodel;1878
82.5.3.1.1;1. Model;1879
82.5.3.1.2;2. A Test Problem and its Results;1880
82.5.3.2;5.3.2. Timeline-based model;1881
82.5.3.2.1;1. Model;1881
82.5.3.2.2;2. Numerical Experiments;1881
82.6;6. CONCLUSION AND FUTURE WORK;1881
82.7;REFERENCES;1882
83;ASSEMBLY SEQUENCE OPTIMIZATION USING GENETIC ALGORITHMS
;1885
83.1;1. INTRODUCTION;1885
83.2;2. BACKGROUND TO ASSEMBLY PLANNING AND OPTIMISATION;1886
83.3;3. THE ASSEMBLY SEQUENCE PLANNING PROBLEM;1888
83.4;4. LITERATURE REVIEW ON ASP;1889
83.5;5. THE APPROACH USED TO S/O THE ASP PROBLEM;1890
83.6;6. A MODEL OF THE ASSEMBLY PROCESS;1891
83.6.1;6.1. The graph of liaisons and the table ofliaisons;1892
83.6.2;6.2. The wave model of the assembly process;1894
83.7;7. A MODEL FOR THE ASSEMBLY SEQUENCES;1895
83.7.1;7.1. Representation of SLMC assembly sequences;1895
83.7.2;7.2. Generalisation of the representation for non-SLMC assembly plans;1896
83.8;8. A MODEL OF THE PRODUCT FOR ASP AND THE AUTOMATIC GENERATION OF FEASIBLE ASSEMBLY SEQUENCES
;1898
83.8.1;8.1. Background;1898
83.8.2;8.2. Intrinsic precedence relations;1901
83.8.3;8.3. Guided search algorithm for generation of assembly sequences considering only IPR
;1902
83.8.4;8.4. Extrinsic precedence relations;1903
83.8.5;8.5. Implementation of EPR
;1904
83.8.5.1;8.5.1. EPRfor individual liaisons;1905
83.8.5.2;8.5.2. Boolean relations;1905
83.8.5.3;8.5.3. EPR for groups of liaisons
;1906
83.8.5.4;8.5.4. Definition of the assembly table;1908
83.9;9. QUALITY MEASURES FOR ASP AND THE FITNESS FUNCTION;1909
83.9.1;9.1. The fitness function for a single optimisation criterion;1911
83.9.2;9.2. The fitness function for multi-criteria optimisation;1912
83.10;10. THE GENETIC ALGORITHM FOR THE OPTIMISATION OF ASSEMBLY SEQUENCES
;1914
83.10.1;10.1. Automatic generation of assembly sequences by guided search;1914
83.10.2;10.2. The crossover operator;1915
83.10.3;10.3. The fitness function;1916
83.10.4;10.4. The selection process;1916
83.11;11. A CASE STUDY;1916
83.12;12. CONCLUSIONS;1919
83.13;13. REFERENCES;1921
84;KERNEL-BASED SELF-ORGANIZED MAPS TRAINED WITH SUPERVISED BIAS FOR GENE EXPRESSION DATA MINING
;1923
84.1;1. INTRODUCTION;1923
84.2;2. KERNEL-BASED SELF-ORGANIZED MAP ADAPTATION;1924
84.3;3. THE KSDG-SOM ALGORITHM;1925
84.3.1;3.1. Initialization phase;1927
84.3.2;3.2. Training run adaptation phase;1927
84.3.2.1;3.2.1. Map adaptation rules;1928
84.3.2.2;3.2.2. Evaluation of the map training run convergence condition;1929
84.3.3;3.3. Expansion phase;1929
84.3.4;3.4. Fine tuning adaptation phase;1929
84.3.5;3.5. Evaluation of classification performances;1930
84.3.6;3.6. Model selection step;1930
84.3.7;3.7. Node deletion;1931
84.4;4. THE EXPANSION PROCESS;1931
84.5;5. CRITERIA FOR CONTROLLING THE KSDG-SOM DYNAMIC GROWING;1934
84.6;6. APPLICATION;1936
84.7;7. CONCLUSIONS;1938
84.8;8. ACKNOWLEDGMENT;1938
84.9;REFERENCES;1939
85;COMPUTATIONAL INTELLIGENCE FOR FACILITY LOCATION ALLOCATION PROBLEMS
;1940
85.1;1. INTRODUCTION;1940
85.1.1;1.1. Facility location problem;1940
85.1.2;1.2. Location-allocation problem;1943
85.1.3;1.3. Mathematical programming;1944
85.1.4;1.4. Organization;1945
85.2;2. BACKGROUND;1945
85.2.1;2.1. Branch-and-bound;1946
85.2.2;2.2. Lagrange relaxation and sub-gradient method;1949
85.2.3;2.3. Local search;1953
85.2.4;2.4 . Genetic algorithm;1954
85.2.5;2.5. Simulated annealing;1955
85.3;3. HYBRID METHOD FOR LOCATION-ALLOCATION PROBLEM;1957
85.3.1;3.1. Nested GA (GA + GA);1958
85.3.2;3.2. GA + Branch and Bound;1958
85.3.3;3.3. GA + Lagrange;1959
85.4;4. MIXED TYPE CHROMOSOME AND ALTERNATE LOCATION ALLOCATION;1961
85.5;5. CURRENT OPTIMIZATION SOFTWARE;1962
85.6;6. EXPERIMENTS;1964
85.7;7. CONCLUSIONS;1969
85.8;REFERENCES;1970
86;INDEX;1972