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E-raamat: Advanced Methods and Applications in Computational Intelligence

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Advanced Methods and Applications in Computational IntelligenceSuurem pilt
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This book offers an excellent presentation of intelligent engineering and informatics foundations for researchers in this field as well as many examples with industrial application. It contains extended versions of selected papers presented at the inaugural ACASE 2012 Conference dedicated to the Applications of Systems Engineering. This conference was held from the 6th to the 8th of February 2012, at the University of Technology, Sydney, Australia, organized by the University of Technology, Sydney (Australia), Wroclaw University of Technology (Poland) and the University of Applied Sciences in Hagenberg (Austria). The book is organized into three main parts. Part I contains papers devoted to the heuristic approaches that are applicable in situations where the problem cannot be solved by exact methods, due to various characteristics or dimensionality problems. Part II covers essential issues of the network management, presents intelligent models of the next generation of networks and distributed systems as well as discusses applications of modern numerical methods in large intractable systems. Part III covers salient issues of complexity in intelligent system applications. This part also contains papers and articles which discuss concurrency issues that arise when multiple systems attempt to use the same radio space and the inter-connected system applications in the field of medical simulation and training.



This book includes updates and extended versions of select contributions to ACASE 2012, the 1st Australian Conference on the Applications of Systems Engineering, held February 2012. It presents advanced methods and applications in computational intelligence.
Part I Practical Applications of Modern Heuristic Methods
1 Data Mining Approach for Decision and Classification Systems Using Logic Synthesis Algorithms
3(22)
Grzegorz Borowik
1.1 Introduction
3(2)
1.2 Information Systems and Decision Systems
5(1)
1.3 Indiscernibility and Compatibility Relation
6(3)
1.4 Redundancy
9(5)
1.4.1 Redundancy of Information System
9(2)
1.4.2 Redundancy of Decision System
11(3)
1.5 Induction of Decision Rules
14(1)
1.6 Algorithm of Complementation
15(3)
1.7 Hierarchical Decision-Making
18(7)
References
21(4)
2 Fast Algorithm of Attribute Reduction Based on the Complementation of Boolean Function
25(18)
Grzegorz Borowik
Tadeusz Luba
2.1 Introduction
25(2)
2.2 Preliminary Notions
27(2)
2.3 Elimination of Input Variables
29(2)
2.4 Computing Minimal Sets of Attributes Using COMPLEMENT Algorithm
31(5)
2.4.1 Unate Complementation
33(3)
2.5 Experimental Results
36(2)
2.6 Conclusion
38(5)
References
39(4)
3 Multi-GPU Tabu Search Metaheuristic for the Flexible Job Shop Scheduling Problem
43(18)
Wojciech Bozejko
Mariusz Uchronski
Mieczyslaw Wodecki
3.1 Introduction
43(1)
3.2 Job Shop Problem
44(3)
3.2.1 Disjunctive Model
45(1)
3.2.2 Combinatorial Model
46(1)
3.3 Flexible Job Shop Problem
47(4)
3.3.1 Problem Formulation
47(2)
3.3.2 Graph Models
49(2)
3.4 Determination of the Cost Function
51(2)
3.5 Data Broadcasting
53(2)
3.6 Solution Method
55(3)
3.6.1 GPU Implementation Details
55(1)
3.6.2 Computational Experiments
56(2)
3.7 Conclusion
58(3)
References
59(2)
4 Stable Scheduling with Random Processing Times
61(18)
Wojciech Bozejko
Pawel Rajba
Mieczyslaw Wodecki
4.1 Introduction
61(1)
4.2 Problem Definition and Method of Its Solution
62(1)
4.2.1 Single Machine Scheduling Problem
62(1)
4.3 Problem Description and Preliminaries
63(7)
4.3.1 The Tabu Search Method
67(1)
4.3.2 Movement and Neighborhood
68(1)
4.3.3 The Tabu Moves List
69(1)
4.4 Stochastic Processing Times
70(3)
4.4.1 Normal Distribution
71(1)
4.4.2 The Erlang's Distribution
72(1)
4.5 The Algorithms' Stability
73(1)
4.6 The Calculation Experiments
74(2)
4.7 Conclusion
76(3)
References
76(3)
5 Neural Networks Based Feature Selection in Biological Data Analysis
79(16)
Witold Jacak
Karin Proll
Stephan Winkler
5.1 Introduction
79(2)
5.2 Unsupervised Clustering and Matching Factor
81(3)
5.2.1 Matching Factor Calculation
82(1)
5.2.2 Aggregation
83(1)
5.3 Feature Selection System
84(2)
5.3.1 Cross Feature Selection Method
84(2)
5.3.2 Classifier in Feature Space
86(1)
5.4 Case Study
86(5)
5.4.1 Case Study
88(3)
5.5 Results and Conclusion
91(4)
References
93(2)
6 On the Identification of Virtual Tumor Markers and Tumor Diagnosis Predictors Using Evolutionary Algorithms
95(28)
Stephan M. Winkler
Michael Affenzeller
Gabriel K. Kronberger
Michael Kommenda
Stefan Wagner
Witold Jacak
Herbert Stekel
6.1 Introduction and Research Goals
96(2)
6.1.1 Identification of Virtual Tumor Markers
96(1)
6.1.2 Identification of. Tumor Diagnosis Estimators
97(1)
6.1.3 Organization of This
Chapter
98(1)
6.2 Data Basis
98(3)
6.3 Modeling Approaches
101(6)
6.3.1 Linear Modeling
102(1)
6.3.2 kNN Classification
102(1)
6.3.3 Artificial Neural Networks
103(1)
6.3.4 Support Vector Machines
103(1)
6.3.5 Hybrid Modeling Using Machine Learning Algorithms and Evolutionary Algorithms for Parameter Optimization and Feature Selection
103(2)
6.3.6 Genetic Programming
105(2)
6.4 Empirical Study: Identification of Models for Tumor Markers
107(6)
6.4.1 Data Preprocessing
107(1)
6.4.2 Test Series and Results
108(5)
6.5 Empirical Study: Identification of Models for Tumor Diagnoses
113(5)
6.5.1 Data Preprocessing
113(1)
6.5.2 Test Series and Results
113(5)
6.6 Conclusion
118(5)
References
119(4)
7 Affinity Based Slotting in Warehouses with Dynamic Order Patterns
123(22)
Monika Kofler
Andreas Beham
Stefan Wagner
Michael Affenzeller
7.1 Introduction
123(1)
7.2 Introduction to Slotting
124(5)
7.2.1 Random Slotting
126(1)
7.2.2 Slotting by Turnover Based Metrics
126(1)
7.2.3 Slotting by Affinity
126(1)
7.2.4 Pick Frequency / Part Affinity Score
127(2)
7.3 Multi-period Warehouse Slotting
129(3)
7.3.1 Re-warehousing
130(1)
7.3.2 Healing
131(1)
7.3.3 M-SLAP: Optimization and Evaluation
131(1)
7.4 M-SLAP Benchmark Data
132(2)
7.5 Experimental Setup and Results
134(7)
7.5.1 Algorithms
134(1)
7.5.2 Results
135(6)
7.6 Conclusion and Outlook
141(4)
References
142(3)
8 Technological Infrastructure and Business Intelligence Strategies for the EDEVITALZH eHealth Delivery System
145(20)
M.A. Perez-del-Pino
P. Garcia-Baez
J.M. Martinez-Garcia
C.P. Suarez-Araujo
8.1 Introduction
146(2)
8.2 EDEVITALZH Clinical Environment
148(13)
8.2.1 EDEVITALZH Systems Tier
149(6)
8.2.2 EDEVITALZH Databases Tier
155(1)
8.2.3 Presentation Tier: User Interfaces (PT-UI)
156(3)
8.2.4 Integration Mechanisms
159(2)
8.3 Conclusion
161(4)
References
163(2)
9 Correlation of Problem Hardness and Fitness Landscapes in the Quadratic Assignment Problem
165(32)
Erik Pitzer
Andreas Beham
Michael Affenzeller
9.1 Introduction
165(1)
9.2 Previous Approaches
166(1)
9.3 Fitness Landscape Analysis
167(4)
9.3.1 Trajectories
167(2)
9.3.2 Measures
169(2)
9.3.3 Landscape Variants
171(1)
9.4 Quadratic Assignment Problem
171(4)
9.4.1 QAPLIB
172(1)
9.4.2 Problem Specific Measures
172(3)
9.5 Algorithms
175(1)
9.5.1 Robust Taboo Search
175(1)
9.5.2 Simulated Annealing
176(1)
9.5.3 Genetic Algorithms
176(1)
9.6 Experiments
176(5)
9.6.1 Hardness Measurement
180(1)
9.7 Results
181(10)
9.7.1 Simple Correlations
182(4)
9.7.2 Regression
186(5)
9.8 Conclusion
191(6)
References
192(5)
10 Architecture and Design of the Heuristic Lab Optimization Environment
197(68)
S. Wagner
G. Kronberger
A. Beham
M. Kommenda
A. Scheibenpflug
E. Pitzer
S. Vonolfen
M. Kolfer
S. Winkler
V. Dorfer
M. Affenzeller
10.1 Introduction
198(4)
10.1.1 Related Work
199(1)
10.1.2 Feature Overview
200(1)
10.1.3 Structure and Content
201(1)
10.2 User Groups and Requirements
202(6)
10.2.1 User Groups
202(3)
10.2.2 Requirements
205(3)
10.3 Architecture and Design
208(20)
10.3.1 HeuristicLab 1.x
208(2)
10.3.2 HeuristicLab 2.x
210(4)
10.3.3 HeuristicLab 3.x
214(12)
10.3.4 Analysis and Comparison
226(2)
10.4 Algorithm Modeling
228(12)
10.4.1 Operators
228(5)
10.4.2 Modeling Genetic Algorithms
233(4)
10.4.3 Modeling Simulated Annealing
237(3)
10.5 Problem Modeling
240(17)
10.5.1 Quadratic Assignment Problem
241(2)
10.5.2 Simulation-Based Optimization
243(3)
10.5.3 Genetic Programming
246(11)
10.6 Conclusion
257(8)
References
259(6)
Part II Network Management Essential Problems
11 A Biomimetic SANET Middleware Infrastructure for Guiding and Maneuvering Autonomous Land-Yacht Vessels
265(20)
Christopher Chiu
Zenon Chaczko
11.1 Introduction
265(1)
11.2 A Sailing Vessel as a Actor-Based Process
266(1)
11.3 Heuristic Analysis for Autonomous Sailing Craft
267(8)
11.3.1 Application of Tensor Analysis for Trajectory Mapping
268(2)
11.3.2 Developmental Approach and Methodology
270(5)
11.4 Evaluation of the Tensor Analysis Framework
275(7)
11.4.1 Experiment of Heuristics
275(1)
11.4.2 Analysis and Further Work
276(5)
11.4.3 Providing Representation and Context to Land-Yacht Systems
281(1)
11.5 Conclusion
282(3)
References
282(3)
12 Improvement of Spatial Routing in WSN Based on LQI or RSSI Indicator
285(14)
Jan Nikodem
Ryszard Klempous
Maciej Nikodem
Zenon Chaczko
12.1 Introduction
285(1)
12.2 Relation Based Spatial Routing
286(3)
12.3 Constructing Neighborhoods in WSN
289(4)
12.4 Ordering the Neighborhood Using LQI or RSSI Indicator
293(2)
12.5 Conclusion
295(4)
References
296(3)
13 Centralized and Distributed CRRM in Heterogeneous Wireless Networks
299(16)
Abdallah A.L. Sabbagh
Robin Braun
Mehran Abolhasan
13.1 Introduction
299(2)
13.2 Need for CRRM
301(2)
13.2.1 Efficient Utilization of Radio Resources
301(1)
13.2.2 Reduce Blocking and Dropping Probability
302(1)
13.2.3 Improve Network Reliability and Stability
302(1)
13.2.4 Allow Network Operators' to Gain Maximum Revenue
302(1)
13.2.5 Guarantee Required QoS across Different RATS
302(1)
13.2.6 Consider Users' Preferences and Increase Their Satisfactions
303(1)
13.3 Heterogeneous Wireless Networks with and without CRRM Algorithm
303(2)
13.4 RRM and CRRM Interactions
305(3)
13.4.1 Loose Coupling
306(1)
13.4.2 Tight Coupling
306(1)
13.4.3 Very Tight Coupling
307(1)
13.4.4 Discussion
308(1)
13.5 Distributing RRM and CRRM Entities among CN, RAT and UTs
308(2)
13.5.1 Centralized CRRM
308(1)
13.5.2 Integrated CRRM
308(2)
13.5.3 Distributed CRRM
310(1)
13.5.4 Discussion
310(1)
13.6 Distributed vs. Centralized CRRM Algorithms
311(1)
13.7 Conclusion and Future Works
312(3)
References
312(3)
14 An Intelligent Model for Distributed Systems in Next Generation Networks
315(20)
Pakawat Pupatwibul
Ameen Banjar
Abdallah A.L. Sabbagh
Robin Braun
14.1 Introduction
315(3)
14.2 The Needs of Distributed Systems
318(2)
14.2.1 Change of Traffic Patterns
319(1)
14.2.2 The Consumerization of IT
319(1)
14.2.3 The Rise of Cloud Services
320(1)
14.2.4 Huge Data Demand More Bandwidth
320(1)
14.3 Network Structure Paradigms
320(5)
14.3.1 Centralized Network Structure
320(2)
14.3.2 Hybrid Network Structure
322(1)
14.3.3 Distributed Structure Network
323(2)
14.4 Software-Defined Networking (SDN)
325(2)
14.5 Distributed Active Information Model (DAIM)
327(3)
14.6 Implementing DAIM Model in OpenFlow: A Case Study
330(2)
14.7 Conclusion and Future Works
332(3)
References
332(3)
15 The Study of the OFDM and MIMO-OFDM Networks Compatibility --- Measurements and Simulations
335(16)
Michal Kowal
Ryszard J. Zielinski
Zenon Chaczko
15.1 Introduction
335(1)
15.2 The MIMO Technology
336(1)
15.3 The Measurement Procedure
337(3)
15.4 Simulations
340(2)
15.5 The Results of the Simulations vs. Measurements
342(2)
15.6 The Measure of Convergence between the Simulation and the Measurement Results
344(2)
15.7 Conclusion
346(5)
References
346(5)
Part III Intelligent System Applications
16 EMC between WIMAX 1.5GHz and WLAN 2.4GHz Systems Operating in the Same Area
351(16)
Ryszard J. Zielinski
Michal Kowal
Slawomir Kubal
Piotr Piotrowski
16.1 Introduction
351(1)
16.2 Wireless Communications in Mine Excavation
352(2)
16.3 Reverberation Chamber
354(3)
16.4 Systems under Tests
357(1)
16.5 Testbed
358(1)
16.6 Measurement Results
359(4)
16.6.1 Reference Measurements
360(1)
16.6.2 Measurement Results for WiMAX System
361(1)
16.6.3 Measurement Results for WLAN System
361(2)
16.7 Summary
363(4)
References
363(4)
17 An Anticipatory SANET Environment for Training and Simulation of Laparoscopic Surgical Procedures
367(20)
Christopher Chiu
Zenon Chaczko
17.1 Introduction
367(1)
17.2 Modeling of Laparoscopic Surgery Using an Agent-Based Process
368(3)
17.2.1 Applying BDI Principles a Knowledge-Based System
370(1)
17.3 Application of Heuristics in a Laparoscopic Surgical Domain
371(7)
17.3.1 Extended Kohonen Map (EKM) Techniques
372(1)
17.3.2 BDI Agent Integration Process
373(3)
17.3.3 Distributed Processing by Integrating JADEX with EKM Heuristics
376(2)
17.4 Evaluation of the SANET Middleware Environment
378(6)
17.4.1 Heuristic Experiment
378(3)
17.4.2 Analysis and Further Work
381(2)
17.4.3 Enabling Multi-dimensional Heuristic Contexts for Laparoscopic Surgical Simulations
383(1)
17.5 Conclusion
384(3)
References
384(3)
18 Towards Ubiquitous and Pervasive Healthcare
387(18)
Jan Szymanski
Zenon Chaczko
Ben Rodanski
18.1 Introduction
388(6)
18.1.1 Definitions of Terms
388(6)
18.2 Background Context
394(6)
18.2.1 Body Sensor Networks as Special WSNs
394(1)
18.2.2 A Brief History of Body Sensor Networks
395(1)
18.2.3 BSN Integration into Connected Healthcare System
396(1)
18.2.4 Sensors and Actuators for BSNs
396(1)
18.2.5 Wireless Technologies for BSNs
397(2)
18.2.6 Connectivity Models for BSNs
399(1)
18.3 Challenges for Ubiquitous and Pervasive Healthcare
400(2)
18.4 Conclusion and Future Work
402(3)
References
403(2)
Index 405
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