Madureira / Ferreira / Vale Computational Intelligence for Engineering Systems

Emergent Applications
1. Auflage 2010
ISBN: 978-94-007-0093-2
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 46, 196 Seiten

Reihe: Intelligent Systems, Control and Automation: Science and Engineering

Computational Intelligence for Engineering Systems provides an overview and original analysis of new developments and advances in several areas of computational intelligence. Computational Intelligence have become the road-map for engineers to develop and analyze novel techniques to solve problems in basic sciences (such as physics, chemistry and biology) and engineering, environmental, life and social sciences. The contributions are written by international experts, who provide up-to-date aspects of the topics discussed and present recent, original insights into their own experience in these fields. The authors also include methods that apply to diverse fields such as manufacturing, tourism, power systems, computer science, robotics, chemistry, and biology. Topics include: Simulation and evolution of real and artificial life forms; Self-organization; Models of communication and social behaviors; Emergent collective behaviors and swarm intelligence; Adaptive, complex and biologically inspired systems; Power Systems ; Web-based Applications; Knowledge discovery; Intelligent Tutoring Systems ; Decision support Systems; Intelligent Tutoring Systems.

Autoren/Hrsg.

Madureira, Ana

Ferreira, Judite

Vale, Zita

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;Intention Recognition with Evolution Prospection and Causal Bayes Networks;10
3.1;1 Introduction;11
3.2;2 Intention Recognition;13
3.2.1;2.1 Causal Bayes Networks;13
3.2.2;2.2 Intention recognition with Causal Bayesian Networks;15
3.2.3;2.3 P-log;19
3.2.4;2.4 Recognizing Fox’s intentions - An Example;20
3.2.5;2.5 Situation-sensitive CBNs;22
3.2.6;2.6 Plan Generation;23
3.2.7;2.7 Action language Ack;24
3.2.7.1;2.7.1 Representation in the action language;24
3.3;3 Evolution Prospection;26
3.3.1;3.1 Preliminary;26
3.3.1.1;3.1.1 Language;27
3.3.1.2;3.1.2 Active Goals;27
3.3.1.3;3.1.3 Preferring abducibles;27
3.3.1.4;3.1.4 A posteriori Preferences;28
3.3.1.5;3.1.5 Evolution result a posteriori preference;28
3.4;4 Intention Recognition and Evolution Prospection for Elder Care;31
3.4.1;4.1 Elder Intention Recognition;32
3.4.2;4.2 Evolution Prospection for Providing Suggestions;36
3.5;5 Conclusions and Future Work;39
3.6;References;41
4;Scheduling a Cutting and Treatment Stainless Steel Sheet Line with Self-Management Capabilities;43
4.1;1 Introduction;43
4.2;2 Nature Inspired Optimization Techniques;44
4.3;3 Multi-Agent Systems;45
4.4;4 Autonomic Computing;46
4.5;5 AutoDynAgents System;48
4.6;6 Case Study: Cutting and Treatment Stainless Steel Sheet Line;51
4.6.1;6.1 Description of the Production Process;51
4.6.2;6.2 Scheduling Problem Description;52
4.6.3;6.3 Simulation Plans and Computational Results;53
4.7;7 Conclusions;55
4.8;References;55
5;A sensor classification strategy for robotic manipulators using multidimensional scaling technique;57
5.1;1 Introduction;57
5.2;2 Experimental platform;58
5.3;3 Main concepts;60
5.3.1;3.1 Multidimensional scaling;60
5.3.2;3.2 The Correlation coefficient;62
5.4;4 Experimental results;62
5.4.1;4.1 Analysis in the time domain;63
5.4.2;4.2 Sensor classification;65
5.5;5 Conclusion;68
5.6;References;68
6;Collective-Intelligence and Decision-Making;70
6.1;1 Introduction;70
6.2;2 Multiple simultaneous goals and uncertain causality;71
6.2.1;2.1 The preferences model and the causal effect pattern;72
6.2.2;2.2 The experimental scenario (multiple simultaneous goals);74
6.2.3;2.3 Results and prospects after this work;74
6.3;3 Decisions with collective and individual motivations;75
6.3.1;3.1 The collective ‘versus’ individual (CvI) decision model;75
6.3.1.1;3.1.1 The CvI collective and individual strata;75
6.3.1.2;3.1.2 The CvI structure and dynamics;76
6.3.2;3.2 The experimental scenario (ambulances and injured civilians);77
6.3.3;3.3 Results and prospects after this work;78
6.4;4 Decision-making for electricity markets;78
6.4.1;4.1 TEMMAS agency design;79
6.4.2;4.2 The experimental scenario (Iberian electricity market);80
6.4.3;4.3 Results and prospects after this work;81
6.5;5 Agent inferencing meets the Semantic Web;82
6.5.1;5.1 The experimental scenario (Fire-Brigade decision-making);82
6.5.2;5.2 Results and prospects after this work;83
6.6;6 Conclusions;83
6.7;References;84
7;Analysis of Crossover Operators for Cluster Geometry Optimization;86
7.1;1 Introduction;86
7.2;2 Morse Potential;87
7.2.1;2.1 Related Work;88
7.3;3 Hybrid Optimization Algorithm;89
7.3.1;3.1 Evolutionary Algorithm;89
7.3.1.1;3.1.1 Representation and Genetic Operators;90
7.4;4 Results and Discussion;92
7.5;5 Conclusions;97
7.6;References;97
8;A Support Vector Machine based Framework for Protein Membership Prediction;99
8.1;1 Introduction;100
8.2;2 SVMs with profile kernel;101
8.3;3 System architecture;103
8.3.1;3.1 The protein membership prediction algorithm;104
8.3.2;3.2 Multi-agent implementation;105
8.4;4 Experiments;108
8.4.1;4.1 Learning efficiency;108
8.4.2;4.2 Processing speed evaluation;110
8.5;5 Conclusions and future work;111
8.6;References;111
9;Modeling and Control of a Dragonfly-Like Robot;113
9.1;1 Introduction;113
9.2;2 State of the Art;114
9.3;3 The Kinematics of the Dragonfly;115
9.4;4 The Dynamics of the Dragonfly;116
9.5;5 Dynamical Analysis;117
9.6;6 Controller Performances;124
9.7;7 Conclusion;125
9.8;References;126
10;Emotion Based Control of Reasoning and Decision Making;128
10.1;1 Introduction;128
10.2;2 Modeling Artificial Emotion;129
10.2.1;2.1 The Flow Model of Emotion;130
10.3;3 Modeling Emotional Agents;132
10.3.1;3.1 Internal Representational Structures;132
10.3.2;3.2 Cognitive Space;133
10.3.3;3.3 Modeling Emotional Dynamics;134
10.4;4 Adaptive Reasoning Mechanisms;134
10.4.1;4.1 Focusing Mechanisms;135
10.4.1.1;4.1.1 Attention Focusing;136
10.4.1.2;4.1.2 Temporal Focusing;136
10.5;5 Decision-Making Based on Long-Term Adaptation;137
10.5.1;5.1 Emotional Memory;137
10.5.2;5.2 Integrating Memory and Attention Mechanisms;138
10.6;6 Discussion;139
10.7;References;140
11;A Generic Recommendation System based on Inference and Combination of OWL-DL Ontologies;143
11.1;1 Introduction;143
11.2;2 Background;144
11.2.1;2.1 Recommendation System;144
11.2.2;2.2 OWL language and reasoning;145
11.2.3;2.3 Reference architecture;146
11.3;3 Proposed Solution;147
11.3.1;3.1 Sensor-based data;147
11.3.2;3.2 Context categorization;148
11.3.3;3.3 Recommendation;150
11.3.4;3.4 Two-step generic recommendation;151
11.4;4 Conclusions & future work;153
11.5;References;154
12;GIGADESSEA – Group Idea Generation, Argumentation, and Decision Support considering Social and Emotional Aspects;156
12.1;1 Introduction;156
12.2;2 Background;157
12.2.1;2.1 Idea Generation;157
12.2.2;2.2 Argumentation;158
12.2.3;2.3 Group Decision Making;158
12.2.4;2.4 Emotion;159
12.3;3 Proposed Model;159
12.3.1;3.1 Model;160
12.3.2;3.2 Scenario;161
12.4;4 Conclusions;162
12.5;References;162
13;Electricity Markets: Transmission Prices Methods;165
13.1;1 Introduction;165
13.2;2 Methodologies for Transmission Cost Allocation;166
13.2.1;2.1 Post-Stamp Method;167
13.2.2;2.2 MW-Mile Method;168
13.2.3;2.3 Base Method;169
13.2.4;2.4 Module or Use;169
13.2.5;2.5 Zero Counterflow;170
13.2.6;2.6 Dominant Flow;170
13.2.7;2.7 Distribution Factors Methods;171
13.2.7.1;2.7.1 Generalized Generation Distribution Factors;171
13.2.7.2;2.7.2 Generalized Load Distribution Factors;172
13.2.8;2.8 Tracing Methodology and Bialek’s Tracing Method;172
13.2.8.1;2.8.1 Tracing Methodology;172
13.2.8.2;2.8.2 Bialek’s Tracing Methodology;173
13.2.9;2.9 Locational Marginal Price;174
13.2.9.1;2.9.1 Penalty Factors and Delivery Factors;175
13.3;3 Case Study;176
13.3.1;3.1 Results;178
13.3.1.1;3.1.1 Comparison of the Taxes Imputed to the Transactions;179
13.3.1.2;3.1.2 Comparison of the Taxes Imputed to the Generators;180
13.3.1.3;3.1.3 Comparison of the Taxes Imputed to the Loads;182
13.4;4 Conclusion;183
13.5;References;184
14;Computational Intelligence Applications for Future Power Systems;185
14.1;1 Power Systems – Present and Future;185
14.2;2 Computational Intelligence Methods in Power Systems;187
14.3;3 Computational Intelligence applications in Power Systems – Some Examples;190
14.3.1;3.1 Ancillary Services Dispatch using a Genetic Algorithm Approach;190
14.3.2;3.2 Reactive Power Management using a PSO Approach;193
14.3.3;3.3 Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) Scheduling using PSO;197
14.3.4;3.4 Wide Area Monitoring and Control Systems (WAMCS);198
14.4;4 Conclusions;200
14.5;References;200