K / Kostina / Rannacher Modeling, Simulation and Optimization of Complex Processes

Proceedings of the Third International Conference on High Performance Scientific Computing, March 6-10, 2006, Hanoi, Vietnam
1. Auflage 2008
ISBN: 978-3-540-79409-7
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 666 Seiten

This proceedings volume covers the broad interdisciplinary spectrum of scientific computing and presents recent advances in theory, development of methods, and applications in practice.

Autoren/Hrsg.

K, Ekaterina

Kostina, Ekaterina

Rannacher, Rolf

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;Development of a Fault Detection Model- Based Controller;12
3.1;1 Introduction;12
3.2;2 Fault Modeling;13
3.3;3 Fault Detection and Diagnosis (FDD);15
3.4;4 Controller Reconfiguration (CR);19
3.5;5 Conclusion;22
3.6;References;23
4;Sensitivity Generation in an Adaptive BDF- Method;26
4.1;1 Introduction;26
4.2;2 Efficient Solution of the Initial Value Problems;27
4.3;3 Sensitivity Generation;28
4.4;4 Numerical Examples;33
4.5;5 Conclusion and Outlook;34
4.6;References;35
5;The gVERSE RF Pulse: An Optimal Approach to MRI Pulse Design;36
5.1;1 Introduction to the Problem;36
5.2;2 MRI Background;37
5.3;3 The gVERSE Model;40
5.4;4 Results;46
5.5;5 Image Reconstruction;53
5.6;6 Conclusions and Future Work;56
5.7;Future Work;58
5.8;References;58
6;Modelling the Performance of the Gaussian Chemistry Code on x86 Architectures;60
6.1;1 Introduction;60
6.2;2 Background;62
6.3;3 Observed Timings and Hardware Counter Data;65
6.4;4 Previous Work;68
6.5;5 Conclusions and Future Work;68
6.6;Acknowledgments;69
6.7;References;69
7;Numerical Simulation of the December 26, 2004: Indian Ocean Tsunami;70
7.1;1 Introduction;70
7.2;2 Source and Propagation Models;71
7.3;3 Tsunami Simulations;73
7.4;4 Discussion of Results;74
7.5;5 Final Remarks;78
7.6;6 Acknowledgments;78
7.7;References;79
8;Approximate Dynamic Programming for Generation of Robustly Stable Feedback Controllers;80
8.1;1 Introduction;80
8.2;2 Robust Dynamic Programming with Constraints;82
8.3;3 Polyhedral Dynamic Programming;84
8.4;4 Approximate Robust Dynamic Programming;86
8.5;5 Stable Epigraphs and the Uroborus;89
8.6;6 Stability of a Tutorial Example;92
8.7;7 Conclusions;94
8.8;References;95
9;Integer Programming Approaches to Access and Backbone IP Network Planning.;98
9.1;1 Introduction;98
9.2;2 Problem Description;100
9.3;3 Access Network Planning;103
9.4;4 Backbone Network Planning;105
9.5;5 Computation;111
9.6;6 Results;115
9.7;Acknowledgments;119
9.8;References;119
10;An Adaptive Fictitious-Domain Method for Quantitative Studies of Particulate Flows;122
10.1;1 Introduction;122
10.2;2 Adaptive Fictitious-Domain Method;123
10.3;3 Numerical Results;127
10.4;4 Conclusion;131
10.5;References;131
11;Adaptive Sparse Grid Techniques for Data Mining;132
11.1;1 Introduction;132
11.2;2 Solving the System of Linear Equations;135
11.3;3 Adaptivity;137
11.4;4 Boundary Considerations;139
11.5;5 Summary;141
11.6;References;141
12;On the Stochastic Geometry of Birth- and- Growth Processes. Application to Material Science, Biology and Medicine;142
12.1;1 Introduction;143
12.2;2 Birth-and-Growth Processes;144
12.3;3 A Volume Growth Model;151
12.4;4 Closed Sets as Distributions – The Deterministic Case;154
12.5;5 Stochastic Geometry;154
12.6;6 The Hazard Function;156
12.7;7 Mean Densities of Stochastic Tessellations;162
12.8;8 Interaction with an Underlying Field;164
12.9;9 Numerical Simulations;167
12.10;Acknowledgments;170
12.11;References;170
13;Inverse Problem of Lindenmayer Systems on Branching Structures;174
13.1;1 Introduction;174
13.2;2 Lindenmayer Systems (L-systems);175
13.3;3 The Flow Diagram of Reconstruction Process of Branching Structure;177
13.4;4 Anisotropic Difuusion Filtering;178
13.5;5 Region and Volume Growing Method;179
13.6;6 Skeletonization Process;184
13.7;7 Construction of Branching Structure;189
13.8;8 Resolution Reduction;189
13.9;9 Experiment and Results;191
13.10;10 Conclusion and Further Works;192
13.11;References;195
14;3D Cloud and Storm Reconstruction from Satellite Image;198
14.1;1 Introduction;198
14.2;2 Satellite Image and Its Properties;199
14.3;3 Cloud and Storm Segmentation;201
14.4;4 Volume Rendering by Sliced Reconstruction;206
14.5;5 Visualization and Animation;207
14.6;6 Conclusion and Further Works;215
14.7;7 Acknowledgment;216
14.8;References;216
15;Providing Query Assurance for Outsourced Tree- Indexed Data;218
15.1;1 Introduction;218
15.2;2 Related Work;220
15.3;3 A Pragmatic Approach to Managing Outsourced Tree- Indexed Data;224
15.4;4 Query Assurance for Outsourced Tree-Indexed Data;226
15.5;5 Experimental Results;230
15.6;6 Conclusion and Future Work;232
15.7;References;233
16;An Adaptive Space-Sharing Scheduling Algorithm for PC- Based Clusters;236
16.1;1 Introduction;236
16.2;2 Background Knowledge;237
16.3;3 Problem De.nition;238
16.4;4 Related Works;238
16.5;5 Partitioning-Function;239
16.6;6 Job-Selection Rules;241
16.7;7 Overall Evaluation;242
16.8;8 Conclusions and Future Work;244
16.9;References;245
17;Fitting Multidimensional Data Using Gradient Penalties and Combination Techniques;246
17.1;1 Introduction;246
17.2;2 Regularised Least Squares Regression;247
17.3;3 Combination Technique;248
17.4;4 Projections and the Combination Technique;254
17.5;5 Conclusions;257
17.6;References;259
18;Mathematical Modelling of Chemical Diffusion through Skin using Grid- based PSEs;260
18.1;1 Introduction;260
18.2;2 Chemical Diffusion Through Skin;261
18.3;3 Problem Solving Environments;263
18.4;4 Remote Grid Simulation;264
18.5;5 Visualization and Steering;265
18.6;6 Conclusions;267
18.7;Acknowledgments;268
18.8;References;268
19;Modelling Gene Regulatory Networks Using Galerkin Techniques Based on State Space Aggregation and Sparse Grids;270
19.1;1 Introduction;270
19.2;2 Approximation of Fast Processes;274
19.3;3 Aggregation Errors;277
19.4;4 Sparse Grids;281
19.5;5 Conclusion;282
19.6;6 Acknowledgments;282
19.7;References;283
20;A Numerical Study of Active-Set and Interior- Point Methods for Bound Constrained Optimization.;284
20.1;1 Introduction;284
20.2;2 Some Comparative Tests;286
20.3;3 The Projected Conjugate Gradient Method;291
20.4;4 Preconditioning the SLQP Method;293
20.5;5 Preconditioning the Interior-Point Method;294
20.6;6 Quasi-Newton Methods;299
20.7;References;302
21;Word Similarity In WordNet;304
21.1;1 Introduction;304
21.2;2 Lexical Taxonomy;305
21.3;3 Information Theoretic vs. Conceptual Distance Approach for Measuring Similarity;307
21.4;4 A Measure for Word Similarity;310
21.5;5 Experiments;311
21.6;6 Conclusion;313
21.7;References;313
22;Progress in Global Optimization and Shape Design;314
22.1;1 Introduction;314
22.2;2 Optimization Method;315
22.3;3 Application to Shape Optimization of Coastal Structures;319
22.4;4 Conclusions;323
22.5;References;323
23;EDF Scheduling Algorithm for Periodic Messages On Switched Ethernet;324
23.1;1 Introduction;324
23.2;2 Backgrounds;325
23.3;3 Scheduability Condition on Switched Ethernet;327
23.4;4 Message Scheduling Algorithm on Switched Ethernet;330
23.5;5 Conclusions and Future Work;332
23.6;Acknowledgment;333
23.7;References;333
24;Large-Scale Nonlinear Programming for Multi- scenario Optimization;334
24.1;1 Introduction;334
24.2;2 Optimization Formulations;335
24.3;3 NLP Solution Algorithm;336
24.4;4 Implementation of Internal Linear Decomposition;340
24.5;5 Source Detection Application and Results;342
24.6;6 Conclusions;345
24.7;References;346
25;On the Efficiency of Python for High- Performance Computing: A Case Study Involving Stencil Updates for Partial Differential Equations;348
25.1;1 Introduction;349
25.2;2 Python and High-Performance Serial Computing;352
25.3;3 Python and High-Performance Parallel Computing;363
25.4;4 Conclusions;366
25.5;References;368
26;Designing Learning Control that is Close to Instability for Improved Parameter Identification;370
26.1;1 Introduction;370
26.2;2 A Condition for Decay or Growth of Error with ILC Iterations;371
26.3;3 Creating a Deliberately Non Robust ILC Law;373
26.4;4 Numerical Investigation of Sensitivity to Parameter Error;377
26.5;5 Conclusions;380
26.6;References;380
27;Fast Numerical Methods for Simulation of Chemically Reacting Flows in Catalytic Monoliths;382
27.1;1 Introduction;383
27.2;2 Mathematical Model;383
27.3;3 Numerical Methods;387
27.4;4 Numerical Results;388
27.5;5 Conclusion;390
27.6;Acknowledgments;390
27.7;References;390
28;A Deterministic Optimization Approach for Generating Highly Nonlinear Balanced Boolean Functions in Cryptography;392
28.1;1 Introduction;392
28.2;2 Preliminaries;393
28.3;3 Optimization Formulation and Reformulation;394
28.4;4 Generating Highly Nonlinear Balanced Boolean Function by DCA;396
28.5;5 Preliminary Computational Results;400
28.6;References;401
29;Project-Oriented Scheduler for Cluster Systems;404
29.1;1 Introduction;404
29.2;2 Related Work;405
29.3;3 Project-Oriented Scheduler;406
29.4;4 Experimental Results;409
29.5;5 Conclusions and Further Study;410
29.6;References;412
30;Optimizing Spring-Damper Design in Human Like Walking that is Asymptotically Stable Without Feedback;414
30.1;1 Introduction;414
30.2;2 Mathematical Models of Biped Walking;416
30.3;3 Formulation and Solution of Optimal Control Problems Involving Stability;420
30.4;4 Open-Loop Stable Walking without Springs and Dampers;423
30.5;5 Can We Do Better with Springs and Dampers?;424
30.6;6 Relationship of Springs and Dampers and Feedback Control;426
30.7;7 Conclusions;428
30.8;Acknowledgments;428
30.9;References;429
31;Stability Optimization of Juggling;430
31.1;1 Introduction;430
31.2;2 Mathematical Models of Juggling and Muscle Forces;433
31.3;3 Numerical Stability Optimization of Periodic Motions;437
31.4;4 Numerical Solution: Self-Stable Juggling;439
31.5;5 Conclusions;442
31.6;Acknowledgments;442
31.7;References;442
32;Numerical Model of Far Turbulent Wake Behind Towed Body in Linearly Stratified Media;444
32.1;1 Introduction;444
32.2;2 Problem Formulation;445
32.3;3 Algorithm of Problem Solution;448
32.4;4 Computational Results;449
32.5;5 Conclusion;453
32.6;6 Acknowledgments;453
32.7;References;453
33;A New Direction to Parallelize Winograd’s Algorithm on Distributed Memory Computers;456
33.1;1 Introduction;456
33.2;2 Background;458
33.3;3 New Direction to Parallelize Winograd’s Algorithm;460
33.4;4 Conclusion;467
33.5;References;467
34;Stability Problems in ODE Estimation;470
34.1;1 Introduction;470
34.2;2 ODE Stability;472
34.3;3 The Embedding Method;476
34.4;4 The Simultaneous Method;479
34.5;5 In Conclusion;480
34.6;Acknowledgment;480
34.7;References;480
35;A Fast, Parallel Performance of Fourth Order Iterative Algorithm on Shared Memory Multiprocessors ( SMP) Architecture;482
35.1;1 Introduction;482
35.2;2 Derivation of A Rotated Fourth Order Scheme;483
35.3;3 Implementation of A Parallel Rotated Fourth Order Algorithm;484
35.4;4 Results and Performance Evaluations;485
35.5;5 Conclusion and Future Research;489
35.6;References;489
36;Design and Implementation of a Web Services- Based Framework Using Remoting Patterns;490
36.1;1 Introduction;490
36.2;2 Related Work;491
36.3;3 Design and Implementation of the Proposed Framework;493
36.4;4 Case Studies: Building Distributed Applications Based on the Proposed Framework;497
36.5;5 Conclusion;498
36.6;References;499
37;Simulation of Tsunami and Flash Floods;500
37.1;1 Introduction;500
37.2;2 Model;501
37.3;3 Software;503
37.4;4 Validation;503
37.5;5 Case Study: Modelling Tsunamis with;505
37.6;6 Conclusions;507
37.7;7 Acknowledgments;509
37.8;References;509
38;Differentiating Fixed Point Iterations with ADOL- C: Gradient Calculation for Fluid Dynamics;510
38.1;1 Introduction;510
38.2;2 Iterative Evaluation of Derivatives of Fixed Point Iterations;512
38.3;3 Application to TAUij Code;516
38.4;4 Conclusions;518
38.5;5 Acknowledgments;518
38.6;References;518
39;Design Patterns for High-Performance Matrix Computations;520
39.1;1 Introduction;520
39.2;2 Fundamental Design Issues;521
39.3;3 The Behavior Template Pattern;524
39.4;4 Performance Comparisons;527
39.5;5 Conclusion;528
39.6;References;529
40;Smoothing and Filling Holes with Dirichlet Boundary Conditions;532
40.1;1 Introduction;532
40.2;2 Discrete Thin Plate Splines;533
40.3;3 Dirichlet Boundary Conditions;534
40.4;4 Holes in the Data Set;537
40.5;5 Example Applications;539
40.6;6 Future Research;540
40.7;References;540
41;Constraint Hierarchy and Stochastic Local Search for Solving Frequency Assignment Problem;542
41.1;1 Introduction;542
41.2;2 Local Search for FD Contraint Hierarchies;543
41.3;3 Frequency Assignment Problem;547
41.4;4 Experiments;553
41.5;5 Conclusions and Future Work;554
41.6;References;555
42;Half-Sweep Algebraic Multigrid (HSAMG) Method Applied to Diffusion Equations;558
42.1;1 Introduction;558
42.2;2 The Half-Sweep Finite Element Approximation;560
42.3;3 The Half-Sweep Algebraic Multigrid Method;562
42.4;4 Numerical Experiments;564
42.5;5 Conclusion;564
42.6;References;566
43;Solving City Bus Scheduling Problems in Bangkok by Eligen- Algorithm;568
43.1;1 Introduction;568
43.2;2 Literature Review;569
43.3;3 Mathematical Formulation;570
43.4;4 Algorithms solving MDVSP;572
43.5;5 Computational Results;574
43.6;6 Concluding Remarks;575
43.7;References;575
44;Partitioning for High Performance of Predicting Dynamical Behavior of Color Diffusion in Water using 2-D tightly Coupled Neural Cellular Network;576
44.1;1 Introduction;576
44.2;2 The 2-D Tightly Coupled Neural Cellular Network;577
44.3;3 Neural Network & Parallel Training Concept;578
44.4;4 Experimental Results;581
44.5;5 Conclusion;584
44.6;Acknowledgment;585
44.7;References;585
45;Automatic Information Extraction from the Web: An HMM- Based Approach;586
45.1;1 Introduction;586
45.2;2 HMMs for Information Extraction;588
45.3;3 Synset-Based HMMs;590
45.4;4 Experimental Results;593
45.5;5 Conclusion;595
45.6;References;595
46;Advanced Wigner Method for Fault Detection and Diagnosis System;598
46.1;1 Introduction;598
46.2;2 Background and Experimental Setup;599
46.3;3 Proposed Method;602
46.4;4 Results of Analysis;606
46.5;5 Conclusions;607
46.6;References;614
47;Appendix;616