Hagenberg Research (eBook)

Contents 5
Acknowledgement 8
Hagenberg Research: Introduction 9
References 12
Chapter I Algorithms in Symbolic Computation 13
1 The Renaissance of Algorithmic Mathematics 14
1.1 A Bit of History 14
2 Gröbner Bases Theory for Nonlinear Polynomial Systems 24
2.1 The Relevance of Gröbner Bases Theory 24
2.2 Gröbner Bases: Basic Notions and Results 27
3 Rational Algebraic Curves – Theory and Application 32
3.1 What is a Rational Algebraic Curve? 32
3.3 Proper Parametrizations 34
3.4 A Parametrization Algorithm 35
3.5 Applications of Curve Parametrization 38
4 Computer Generated Progress in Lattice Paths Theory 41
4.1 Paths in the Quarter Plane 41
4.2 Computer Algebra Support 43
4.3 Gessel’s Conjecture 44
4.4 Lattice Paths in 3D 46
5 Symbolic Summation in Particle Physics 48
5.1 The Underlying Summation Principles 49
5.2 Example 1: Simplification of Multi-Sums 52
5.3 Example 2: Solving Large Recurrence Relations 55
6 Nonlinear Resonance Analysis 57
6.1 What is Resonance? 57
6.2 Kinematics and Dynamics 60
6.3 Highlights of the Research on the NRA 64
References 66
Chapter II Automated Reasoning 71
1 Introduction 71
2 Theorema: Computer-Supported Mathematical Theory Exploration 73
2.1 The Theorema Language and the User Interface 75
2.2 “Lazy Thinking”: Invention by Formulae Schemes and Failing Proof Analysis 78
3 Natural Style Proving in Theorema 82
3.1 S-Decomposition and the Use of Algebraic Techniques 83
3.2 The Theorema Set Theory Prover 88
4 Unification 91
4.1 General Sequence Unification 91
4.2 Flat Matching 93
4.3 Context Sequence Matching 95
4.4 Relations between Context and Sequence Unification 95
5 Program Verification 96
5.1 Some Principles of Program Verification 97
5.2 Verification of Functional Programs 98
References 106
Chapter III Metaheuristic Optimization 110
1 Introduction 110
1.1 Motivation and Goal 110
1.2 Structure and Content 115
2 Metaheuristic Optimization Techniques 116
2.1 Simulated Annealing 117
2.2 Tabu Search 118
2.3 Iterated Local Search 120
2.4 Evolutionary Algorithms 121
2.5 Scatter Search 122
2.6 Further Metaheuristics 123
2.7 Hybrid Metaheuristics 124
3 Algorithmic Advances Based Upon Genetic Algorithms 125
3.1 The Unique Selling Points of Genetic Algorithms 125
3.2 Schema Theorem and Building Block Hypothesis 126
3.3 Stagnation and Premature Convergence 128
3.4 Offspring Selection (OS) 130
3.5 Consequences Arising out of O spring Selection 133
4 Route Planning 135
4.1 The Vehicle Routing Problem 137
4.2 Heuristic algorithms 140
4.3 Metaheuristic Approaches 141
5 Genetic Programming Based System Identification 143
5.1 Genetic Programming 143
5.2 Data Based Modeling and Structure Identification 146
5.3 Application Example: Time Series Analysis 148
5.4 Application Example: Solving Classification Problems 150
5.5 Analysis of Population Dynamics in Genetic Programming 152
5.6 Data Mining and Genetic Programming 153
6 Conclusion and Future Perspectives 155
References 157
Chapter IV Software Engineering – Processes and Tools 163
1 Introduction 163
2 Software Process Engineering 165
2.1 Concepts Related to Software Process Engineering 168
2.2 Software Process Engineering Research Challenges and Application-oriented Research at SCCH 176
3 Software Quality Engineering 190
3.1 Concepts and Perspectives in Engineering of Software Quality 191
3.2 Management and Automation of Software Testing 194
4 Software Architecture Engineering 206
4.1 General Research Areas and Challenges 207
4.2 Software Architecture Management – Languages and Tools 210
4.3 Software Architectures for Industrial Applications 216
5 Domain-Specific Languages and Modeling 220
5.1 Overview of the Field 221
5.2 Modeling and Code Generation 224
5.3 Textual Domain-Specific Languages 228
5.4 End-User Programming 229
References 232
Chapter V Data-Driven and Knowledge-Based Modeling 242
1 Introduction 242
2 Fuzzy Logics and Fuzzy Systems 243
2.1 Motivation 243
2.3 Fuzzy Systems 244
3 Data-Driven Fuzzy Systems 247
3.1 Motivation 247
3.2 Data-Driven Fuzzy Modeling Approaches 248
3.3 Regularization and Parameter Selection 252
4 Evolving Fuzzy Systems and On-line Modeling 253
4.1 Motivation and Solutions 253
4.2 The FLEXFIS Family 255
4.3 Handling Drifts and Unlearning E ect in Data Streams 259
5 Creating Comprehensible Fuzzy Regression Models 260
5.1 Motivation 260
5.2 The Underlying Language 260
5.3 Rule Induction 262
5.4 Post-Optimization of Fuzzy Rule Bases 264
6 Support Vector Machines and Kernel-Based Design 265
6.1 Kernels as Similarities: Motivation and Recent Developments 265
6.2 Support Vector Machines 267
7 Applications 269
7.1 On-Line Fault Detection at Engine Test Benches 269
7.2 On-Line Image Classification in Surface Inspection Systems 272
7.3 Application of SVMs to Texture Analysis 276
Acknowledgements 278
References 278
Chapter VI Information and Semantics in Databases and on the Web 285
1 Introduction 285
2 Ontologies 287
3 Semantic Networks 293
4 Adaptive Modeling 298
5 Web Information Extraction 304
6 Similarity Queries and Case Based Reasoning 323
7 Data Warehouses 330
References 333
Chapter VII Parallel, Distributed, and Grid Computing 336
1 Introduction 336
2 Parallel Symbolic Computation 345
3 Grid Computing 352
4 GPU Computing for Computational Intelligence 369
References 377
Chapter VIII Pervasive Computing 382
1 What is Pervasive Computing? 383
2 Ensembles of Digital Artifacts 385
3 Quantitative Space: Zones-of-Influence 393
4 Qualitative Space: Spatiotemporal Relations 397
5 Middleware for Space Awareness 405
6 Embodied Interaction 411
7 Outlook 424
References 428
Chapter IX Interactive Displays and Next-Generation Interfaces 435
1 Interactive Surfaces 437
2 Design Challenges 443
3 Design and Implementation of a Multi-Display Environment for Collaboration 455
4 Conclusions 470
References 471
Index 475