Machine learning and systems engineering: [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles]
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht [u.a.]
Springer
2010
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| Schriftenreihe: | Lecture notes in electrical engineering; 68
68 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XXII, 612 S. Ill., graph. Darst. 235 mm x 155 mm |
| ISBN: | 9789048194186 |
Internformat
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| 245 | 1 | 0 | |a Machine learning and systems engineering |b [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] |c Sio-Iong Ao ; Burghard Rieger ; Mahyar A. Amouzegar [ed.] |
| 264 | 1 | |a Dordrecht [u.a.] |b Springer |c 2010 | |
| 300 | |a XXII, 612 S. |b Ill., graph. Darst. |c 235 mm x 155 mm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Lecture notes in electrical engineering; 68 | |
| 650 | 0 | 7 | |a Lernendes System |0 (DE-588)4120666-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Systemtechnik |0 (DE-588)4140901-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
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| 700 | 1 | |a Ao, Sio-Iong |4 edt | |
| 700 | 1 | |a Rieger, Burghard B. |d 1937-2021 |0 (DE-588)110958624 |4 edt | |
| 700 | 1 | |a Amouzegar, Mahyar A. |4 edt | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-020810562 | |
Datensatz im Suchindex
| _version_ | 1805095165431382016 |
|---|---|
| adam_text |
Contents
1 Multimodal Human
Spacecraft Interaction in
Remote
Environments . 1
1 Introduction
. 1
2 The MIT
SPHERES Program
. 2
2.1 General Information . 3
2.2 Human-SPHERES
Interaction
. 4
2.3
SPHERES Goggles .
5
3 Multimodal
Telepresence
. 6
3.1
Areas of Application
. 6
3.2
The Development of a Test Environment
. 6
4
Experimental Setup
. 8
4.1
Control via ARTEMIS
. 8
4.2
The Servicing Scenarios
. 9
5
Results of the Experiments
. 11
5.1
Round Trip Delays due to the Relay Satellite
. 11
5.2
Operator Force Feedback
. 12
6
Summary
. 14
References
. 14
2
A Framework for Collaborative Aspects of Intelligent
Service Robot
. 17
1
Introduction
. 17
2
Related Works
. 18
2.1
Context-Awareness Systems
. 18
2.2
Robot Grouping and Collaboration
. 19
3
Design of the System
. 20
3.1
Context-Awareness Layer
. 21
3.2
Grouping Layer
. 22
3.3
Collaboration Layer
. 24
viii Contents
4
Simulated Experimentation
. 25
4.1
Robot Grouping
. 25
4.2
Robot Collaboration
. 27
5
Conclusion
. 28
References
. 28
3
Piecewise
Bezier
Curves Path Planning with Continuous
Curvature Constraint for Autonomous Driving
. 31
1
Introduction
. 31
2 Bezier
Curve
. 32
2.1
The
de
Casteljau Algorithm
. 33
2.2
Derivatives, Continuity and Curvature
. 34
3
Problem Statement
. 34
4
Path Planning Algorithm
. 36
4.1
Path Planning Placing
Bezier
Curves within Segments (BS)
. 37
4.2
Path Planning Placing
Bezier
Curves on Corners
(ВС)
. 38
5
Simulation Results
. 43
6
Conclusions
. 45
References
. 45
4
Combined Heuristic Approach to Resource-Constrained Project
Scheduling Problem
. 47
1
Introduction
. 47
2
Basic Notions
. 48
3
Algorithm
. 49
4
Generalisation for Multiproject Schedule
. 51
5
KNapsack-Based Heuristic
. 51
6
Stochastic Heuristic Methods
. 53
7
Experimentation
. 55
8
Conclusion
. 56
References
. 56
5
A Development of Data-Logger for Indoor Environment
. 59
1
Introduction
. 59
2
Sensors Module
. 60
2.1
Temperature Sensor
. 60
2.2
Humidity Sensor
. 61
2.3
CO and CO2 Sensor
. 62
3
LCD Interface to the Microcontroller
. 62
4
Real Time Clock Interface to the Microcontroller
. 62
5
EEPROM
Interface to the Microcontroller
. 63
6
PC Interface Using RS-232 Serial Communication
. 63
7
Graphical User Interface
. 63
8
Schematic of the Data Logger
. 64
Contents ix
9 Software Design
of Data
Logger . 64
9.1 Programming Steps
for I2C
Interface . 65
9.2 Programming Steps
for LCD
Interface . 67
9.3 Programming Steps
for
Sensor Data
Collection
. 67
10
Results and Discussion
. 68
11
Conclusions
. 68
References
. 69
6
Multiobjective Evolutionary Optimization and Machine Learning:
Application to Renewable Energy Predictions
. 71
1
Introduction
. 71
2
Material and Methods
. 72
2.1
Support Vector Machines
. 72
2.2
Multiobjective Evolutionary Optimization
. 74
2.3
SVM-MOPSO Trainings
. 76
3
Application
. 78
4
Results and Discussion
. 78
5
Conclusions
. 80
References
. 81
7
Hybriding Intelligent Host-Based and Network-Based
Stepping Stone Detections
. 83
1
Introduction
. 83
2
Research Terms
. 84
3
Related Works
. 85
4
Proposed Approach: Hybrid Intelligence Stepping Stone
Detection (HI-SSD)
. 85
5
Experiment
. 86
6
Result and Analysis
. 88
6.1
Intelligence Network Stepping Stone Detection (I-NSSD)
. 88
6.2
Intelligence Host-Based Stepping Stone Detection (I-HSSD)
. 89
6.3
Hybrid Intelligence Stepping Stone Detection (HI-SSD)
. 92
7
Conclusion and Future Work
. 93
References
. 94
8
Open Source Software Use in City Government
. 97
1
Introduction
. 97
2
Related Research
. 98
3
Research Goals
. 100
4
Methodology
. 100
5
Survey Execution
. 101
6
Survey Results
. 102
7
Analysis: Interesting Findings
. 104
7.1
Few Cities Have All Characteristics
. 104
* Contents
7.2
Possible
Aversion
to OSS If Not Currently Using OSS
. 105
7.3
Current OSS Support by Leadership, Management,
and IT Staff
. 106
7.4
Discrepancy of OSS Awareness: Self, Others
. 108
8
Conclusion
. 108
References
. 108
9
Pheromone-Balance Driven Ant Colony Optimization
with Greedy Mechanism
.
Ill
1
Introduction
.
Ill
2
Preliminaries
. 113
2.1
Ant Colony Optimization
. 113
2.2
Related Studies
. 114
3
Hybrid
АСО
with Modified Pheromone Update Rules
. 115
4
Experiments and Discussion
. 117
5
Conclusion
. 119
References
. 119
10
Study of Pitchfork Bifurcation in Discrete Hopfield
Neural Network
. 121
1
Introduction
. 121
2
Determination of Fixed Points
. 123
3
Local Stability Analysis
. 124
4
Pitchfork Bifurcation Direction
. 125
5
Simulations
. 128
6
Conclusion
. 128
References
. 130
11
Grammatical Evolution and
STE
Criterion
. 131
1
Introduction
. 131
2
STE
-
Sum
Epsilon
Tube Error
. 132
3
STE
-
Empirical Properties
. 134
3.1
SSE (Advantages, Disadvantages)
. 134
3.2
STE
(Advantages, Disadvantages)
. 136
4
Probabilistic Mapping of SSE to
STE
. 137
5
Goodness-of-Fit Tests of Data Sets
. 139
5.1
Uncensored Data
-ET 10x50 . 140
6
Probabilistic Relationship Between
STE
and SSE
. 141
7
Conclusion
. 141
References
. 142
12
Data Quality in ANFIS Based Soft Sensors
. 143
1
Introduction
. 143
2
ANFIS Based Inferential Model
. 144
Contents
x¡
2.1 Training
and Testing Data
. 145
3
Impact of Data Quality
. 145
3.1
Experimental Methodology
. 147
3.2
Experimental Factors
. 148
3.3
Experimental Design
. 149
3.4
Experimental Result
. 150
4
Tane
Algorithm for Noisy Data Detection
. 152
5
Results
. 152
6
Conclusion
. 154
References
. 155
13
The Meccano Method for Automatic Volume Parametrization
of Solids
. 157
1
Introduction
. 157
2
The Meccano Method
. 159
3
Application of the Meccano Method to Complex
Genus-ZeroSolids
. 160
3.1
Example
1:
Bust
. 161
3.2
Example
2:
Bunny
. 163
3.3
Example
3:
Bone
. 163
4
Conclusions and Future Research
. 165
References
.,. 166
14
A Buck Converter Model for Multi-Domain Simulations
. 169
1 Introduction
. 169
2
The Mode! for Calculating Switching Events
. 170
3
The Averaged Model
. 172
4
Consideration of Switching Losses
. 176
5
Implementation of the Simulation Models
. 177
6
Simulation and Laboratory Test Results
. 178
7
Conclusion
. 180
References
. 180
15
The Computer Simulation of Shaping in Rotating
Electrical Discharge Machining
. 183
1
Introduction
. 183
2
Mathematical Modelling of Redm Shaping by End Tool
Electrode
. 185
3
Mathematical Modelling of Redm Shaping by Lateral
Surface of Tool Electrode
. 188
4
Software for Computer Simulation
.
1
90
5
Experimental Verification
. 192
6
Conclusion
. 194
References
. 194
xii Contents
16 Parameter Identification
of a Nonlinear Two Mass System
Using Prior Knowledge
. 197
1
Introduction
. 197
2
General Dynamic Neural Network.
. 198
2.1
Administration Matrices
. 199
2.2
Implementation
. 200
3
Parameter Optimization
. 200
3.1
Levenberg-Marquardt Algorithm
. 201
3.2
Jacobian Calculations
. 202
4
Two-Mass-System
. 202
5
Structured Dynamic Neural Networks
. 203
6
Identification
. 205
6.1
Excitation Signal
. 205
6.2
Engine Parameters
. 205
6.3
TMS Parameters
. 206
7
Conclusion
. 210
References
. 210
17
Adaptive and Neural Learning for Biped Robot
Actuator Control
. 213
1
Introduction
. 213
2
Problem Description
. 214
2.1
Objective
. 214
2.2
Biped Dynamics
. 215
2.3
Uncertain Actuator Dynamics
. 215
2.4
Desired Moments IVLj
. 216
2.5
Adaptive Control Approach
. 216
3
Solution
. 216
3.1
Reference Model for Actuator
. 216
3.2
Inverse Model Reference
. 216
3.3
MRACScheme
. 217
4
MRAC for Walking Biped Actuators
. 218
4.1
Dynamics of Walking Biped
. 218
4.2
Computation of Desired Moments
. 220
4.3
Dynamics of Actuators
. 220
4.4
Configuration of MRAC Actuator
. 221
4.5
Convergence Analysis of MRAC
. 221
4.6
Neural Network Learning
. 221
5
Simulation Results
. 222
5.1
First Simulation (Without Disturbance)
. 222
5.2
Second Simulation (Disturbance)
. 222
5.3
Third Simulation (Neural Network Estimation)
. 223
6
Conclusions
. 224
References
. 225
Contents xiii
18
Modeling,
Simulation,
and Analysis for Battery
Electric Vehicles
. 227
1
Introduction
. 227
2
Steady State Analysis
. 228
2.1
Projected Gravity Force
. 229
2.2
Aerodynamic Drag
. 229
2.3
The Rolling Resistance
. 230
2.4
Power Required
. 230
2.5
Energy Required
. 230
2.6
Battery Specific Energy
. 231
2.7
Maximum Cruise Speed
. 233
3
Dynamic Analysis
. 235
3.1
Power Limited
. 236
3.2
Traction Limited
. 237
3.3
O-ôOmph
. 238
3.4
Maximum Gradeability
. 239
4
Conclusion
. 240
References
. 241
19
Modeling Confined Jets with Particles and Swril
. 243
1
Overview
. 243
2
Gas Phase and Turbulence Models
. 245
2.1
Standard
k
-ε
Model
. 246
2.2
Renormalization Group (RNG)
k
-ε
Model
. 247
2.3
Realizable it
-ε
Model
. 247
3
Dispersed Phase
. 248
4
Simulation Settings
. 250
5
Results
. 251
6
Conclusions
. 254
References
. 255
20
Robust Tracking and Control of
Mimo
Processes
with Input Saturation and Unknown Disturbance
. 257
1
Introduction
. 257
2
MRAGPC Design Scheme
. 258
2.1
MRAGPC Problem Formulation
. 259
2.2
Controllers Parameterization
. 260
3
Additional Design Schemes for MRAGPC
. 262
3.1
Robust Parallel Compensator (RPC) Scheme
for
MIMO
Processes
. 262
3.2
Unknown Disturbance Estimation Scheme
for
MIMO
Processes
. 264
4
Simulation Examples
. 265
4.1
Control of
MIMO
System Without Disturbance
. 265
xiv Contents
4.2
Control
of
MIMO
System with Disturbance
. 265
5
Conclusion
. 268
References
.,. 268
21
Analysis of Priority Rule-Based Scheduling in
Dual-Resource-Constrained Shop-Floor Scenarios
. 269
1
Introduction
. 269
2
Literature Review
. 270
2.1
Shop Scheduling
. 270
2.2
Priority Rules
. 270
2.3
Multi/dual-Resource Constrained Scheduling
. 271
3
Problem Description
. 271
4
Experiments with Static Instances
. 273
4.1
Experimental Design
. 274
4.2
Analyses of Static Instances
. 276
5
Long-Term
Simulation
. 277
5.1
Long-Term
Simulation
. 277
5.2
Analysis of
Long-Term
Simulations
. 277
6
Conclusion and Further Research
. 280
References
. 280
22
A Hybrid Framework for Servo-Actuated Systems
Fault Diagnosis
. 283
1
Introduction
. 283
2
System Under Consideration
. 285
3
Role of Fuzzy Logic
. 287
4
Design of Fuzzy Logic Controller
. 288
4.1
Inputs
. 289
4.2
Membership Functions
. 291
4.3
Rule-Based Inference
. 292
4.4
Denazification
. 293
4.5
Rule Viewer
. 293
5
Simulation
. 293
6
Conclusion
. 294
References
. 295
23
Multigrid Finite Volume Method for FGF-2 Transport
and Binding
. 297
1 Introduction
. 297
2
Methods
. 298
2.1
Mathematical Model
. 298
2.2
Collocated Finite Volume Discretization
. 299
2.3
Multigrid Methods
. 301
Contents xv
3
Results
. 303
4
Discussions
. 307
5
Concluding Remarks
. 309
References
. 309
24
Integrated Mining Fuzzy Association Rules For Mineral
Processing State Identification
. 311
1
Introduction
. 311
2
Grinding Process Modelling
. 313
3
The Controller Design
. 314
3.1
Fuzzy Logic Controller
. 317
3.2
Association Rules Miming Algorithm
. 319
4
Simulation Results
. 322
5
Conclusion
. 323
References
. 324
25
A Combined Cycle Power Plant Simulator: A Powerful,
Competitive, and Useful Tool for Operator's Training
. 327
1
Introduction
. 327
2
Antecedent
. 328
3
Architecture Configuration
. 329
3.1
Software Architecture
. 329
3.2
Software Platform
. 332
3.3
Hardware Architecture
. 333
4
Modeled Systems
. 334
4.1
Control System
. 334
4.2
DCS Model for
Real-Time
Simulation
. 335
4.3
The Graphic Visualization Tool
. 335
4.4
Processes System
. 335
5
Project Control
. 336
6
Results
. 337
7
Conclusions
. 338
8
Future Works
. 338
References
. 339
26
Texture Features Extraction in
Mammograms
Using Non-Shannon Entropies
. 341
1
Introduction
. 341
2
Gray Level Histogram Moments
. 343
3
Experimental Results
. 344
4
Conclusions and Future
. 349
References
. 350
xvi Contents
27
A Wideband
DOA
Estimation Method Based on Arbitrary
Group Delay
. 353
1
Introduction
. 353
2
Method of Digital Group Delay
. 354
3
DOA
Estimation Based on Digital Group Delay
. 356
4
Simulation
. 356
5
Conclusion
. 357
References
. 358
28
Spatial Speaker Spatial Positioning of Synthesized
Speech in Java
. 359
1
Introduction
. 359
2
RelatedWork
. 360
2.1
Our Research Contribution
. 362
3
System Design and Architecture
. 362
3.1
FreeTTS
. 363
3.2
MIT Media Lab HRTF Library
. 364
3.3
Signal Processing Module
. 365
3.4
JOALLibrary
. 365
3.5 Soundcard . 366
4
Prototype Applications and Preliminary User Studies
. 366
4.1
Spatial Audio Representation of a Text File
. 367
4.2
Spatial Story Reader
. 367
4.3
Multiple Simultaneous Files Reader
. 368
5
Conclusion and Future Work
. 370
References
. 370
29
Commercial Break Detection and Content Based
Video Retrieval
. 373
1
Introduction
. 373
2
Preprocessing and Feature Extraction
. 375
2.1
Audio Feature Extraction
. 376
2.2
Video Feature Extraction
. 377
3
Commercial Detection Scheme
. 379
3.1
Audio Feature Based Detection
. 379
3.2
Video Feature Based Detection
. 379
4
Mechanism for Automatic Annotation and Retrieval
. 379
4.1
Automatic Annotation
. 379
4.2
Content Based Video Retrieval
. 380
5
Results and Discussion
. 380
6
Conclusion and Future Work
. 382
References
. 383
Contents xvii
30 ClusterDAM:
Clustering Mechanism for Delivery of Adaptive
Multimedia Content in Two-Hop Wireless Networks
. 385
1
Introduction
. 385
2
Cluster-Dam Architecture
. 387
2.1
Cluster-based Two-Hop Design for WiMAX Networks
. 387
2.2
QOAS
-
Quality Oriented Adaptive Scheme
. 388
2.3
Other Adaptive Solutions
. 389
3
Simulation Model and Testing
. 389
3.1
Dumbbell and Double Dumbbell Topology
. 389
3.2
Simulation Setup
. 391
4
Results
. 392
5
Conclusions
. 395
References
. 395
31
Ranking Intervals in Complex Stochastic Boolean Systems
Using Intrinsic Ordering
. 397
1
Introduction
. 397
2
The Intrinsic Ordering
. 399
2.1
Intrinsic Order Relation on
10,1}" . 399
2.2
The Intrinsic Order Graph
. 401
2.3
Three Sets of Bitstrings Related to a Binary
н
-tuple .
402
3
Generating and Counting the Elements of C" and
C„ . 404
4
Ranking Intervals
. 406
5
Conclusions
. 409
References
. 410
32
Predicting Memory Phases
. 411
1
Introduction
. 411
2
Phase Classification Techniques
. 412
2.1
Wavelet Based Phase Classification
. 412
2.2
Activity Vectors
. 413
2.3
Stack Reuse Distances
. 413
2.4
Other Techniques
. 414
3
Setvector Based Phase Classification
. 414
4
Metrics to Compare Phase Classification Techniques
. 415
5
Results
. 416
5.1
Classification Accuracy
. 416
5.2
Computational Performance
. 420
6
Conclusion
. 420
References
. 421
xviii Contents
33 Information
Security
Enhancement
to Public-Key
Cryptosystem Through Magic Squares
. 423
1
Introduction
. 423
2
Methodology
. 424
2.1
Magic Squares and Their Construction
. 425
2.2
Construction of Doubly Even Magic Square Based
on Different Views of Fundamental Magic Square
. 427
2.3
Construction of Doubly Even Magic Square of
Order
16
Based on the Properties of
4
χ
4
Magic Square
. 428
3
Encryption/Decryption of Plain Text Using RSA Cryptosystem
with Magic Square
. 435
3.1
Wrapper Implementation-Example
. 435
4
Parallel Cryptography
. 435
5
Experimental Result
. 436
6
Conclusion
. 436
References
. 437
34
Resource Allocation for Grid Applications: An Economy Model
. 439
ł
Introduction
. 439
2
Grid Economy Model
. 440
3
Resource Management Challenges
. 441
4
Resource Allocation Model
. 442
5
Design of Economy Model
. 444
6
Experimental Results
. 445
7
RelatedWorks
. 446
8
Conclusion
. 447
References
. 448
35
A Free and Didactic Implementation of the Send Protocol
forlpv6
. 451
1
Introduction
. 451
2
Neighbor Discovery Protocol Overview
. 452
3
Vulnerabilities of the Neighbor Discovery Protocol
. 454
4
Secure Neighbor Discovery Protocol
. 454
4.1
Cryptographically Generated Address
. 456
4.2
Authorization Delegation Discovery
. 456
5
RelatedWorks
. 456
6
A Didactic Implementation of the Send Protocol
. 457
7
Conclusions and Future Work
. 462
References
. 463
36
A Survey of Network Benchmark Tools
. 465
1
Introduction
. 465
2
RelatedWorks
. 466
3
Network Benchmark Tools
. 467
Contents xix
3.1 Netperf . 467
3.2 D-itg . 468
3.3 NetStress . 470
3.4 MGEN . 471
3.5 LANforge . 473
3.6 WLAN
Traffic
Visualizer . 474
3.7 TTCP . 475
4
Comparative Analysis .
476
5
Conclusions and Future Work
. 478
References
. 480
37
Hybrid Stock Investment Strategy Decision Support System
. 481
1
Introduction
. 481
1.1
High Risk Investment
. 482
2
Finance Theories and Analysis in Stock Price Prediction
. 482
3
Data Mining (DM) and Artificial Intelligence
(AI) . 483
4
DSS Model for Stock Investment Strategy
. 484
5
Architecture of Stock Investment Strategy Decision
Support System
. 484
5.1
DM Component
. 486
5.2
TA
Component
. 487
6
Conclusion
. 492
References
. 492
38
Towards Performance Analysis of Ad hoc Multimedia Network
. 495
1
In-Vehicle Multimedia Network
. 496
1.1
System Architecture
. 498
1.2
Application Scenarios
. 500
2
Performance Modelling
. 500
2.1
NetworkModel
. 500
2.2
Packet Delay Model
. 502
2.3
Throughput Model
. 502
3
Performance Evaluation
. 503
3.1
Simulation Setup
. 503
3.2
Delay Analysis
. 504
3.3
Throughput Analysis
. 504
4
Summary
. 505
References
. 506
39
Towards the Performance Optimization of Public-key Algorithms
Using Fuzzy Modular Arithematic and Addition Chain
. 507
1
Introduction
. 507
2
Concept of Sum of Squares, Addition Chain, Elliptic Curve,
and
Fermat
Theorem
. 509
2.1
Sum of Squares
. 509
xx Contents
2.2 Addition Chain . 511
2.3
Elliptic Curve .
512
3
Fuzzy Modular Arithmetic
. 513
4
Applications of Sum of Squares, and Addition Chain in Reducing
the Number of Multiplication in Modular Exponentiation
. 514
4.1
Pseudocode
. 514
4.2
Example
. 515
5
Implementation of ECC Using Fuzzy Modular Arithmetic
. 516
6
Conclusion
. 518
References
. 518
40
RBDT-1 Method: Combining Rules and Decision Tree
Capabilities
. 521
1
Introduction
. 521
2
RelatedWork
. 523
3
Rule Generation and Notations
. 524
3.1
Notations
. 524
3.2
Rule Generation Method
. 524
4
RBDT-1 Method
. 525
4.1
Attribute Selection Criteria
. 525
4.2
Building the Decision Tree
. 528
5
Illustration of the RBDT-1 Method
. 528
5.1
Illustration
. 528
6
Experiments
. 529
7
Conclusions
. 531
References
. 531
41
Computational and Theoretical Concepts for Regulating
Stem Cells Using Viral and Physical Methods
. 533
1
Introduction
. 533
2
Methods Used in Gene Therapy
. 534
3
Proposed Model
. 536
4
Simulation Results
. 541
References
. 545
42
DF
A, a Biomedical
Checking Tool for the Heart Control System
. 547
1
Introduction
. 547
2
Methods
. 548
2.1
Finger Blood-Pressure Pulse
. 548
2.2
DFAMethods
. 548
2.3
Volunteers and Ethics
. 549
3
Results
. 549
3.1
Extra-Systole
. 549
3.2
Alternans with Low Exponent
. 552
Contents xxi
3.3
Extraordinary High Exponent
. 552
3.4
Normal Exponent
. 553
3.5
DFA Is Beneficial
. 554
4
Discussion
. 554
References
. 556
43
Generalizations in Mathematical Epidemiology
. 557
1
Introduction
. 557
2
CA And MR Applied to the SNIR Epidemic Model
. 558
2.1
The Standard SIR Model
. 559
2.2
TheS2IRModel
. 560
2.3
The S3IR, The S4IR and S5IR Models
. 561
2.4
TheS^RModel
. 562
3
CA and MR Applied to the SNIMR Epidemic Model
. 563
3.1
The SI2R Model
. 563
3.2
The S2I2R Model
. 564
3.3
The SnImR Model
. 565
4
CA and MR Applied to the Staged Progressive SIMR
Epidemic Model
. 565
4.1
The Staged Progressive SI2R Model
. 566
4.2
The Staged Progressive SI3R Model
. 567
4.3
Staged Progressive SImR Model
. 567
5
Conclusions
. 568
References
. 568
44
Review of Daily Physical Activity Monitoring System Based
on Single
Triaxial Accelerometer
and Portable Data
Measurement Unit
. 569
1
Introduction
. 569
1.1
Measurement of Physical Activity
. 570
1.2
Behavioral Observation
. 571
1.3
Pedometers
. 571
1.4
Accelerometers
. 571
2
Material and Method
. 574
2.1
Portable Data Measurement Unit
. 574
2.2
Physical Activity Data Collection
. 575
2.3
Feature Extraction
. 576
3
Results
. 576
4
Discussion and Conclusion
. 577
References
. 578
45
A Study of the Protein Folding Problem by a Simulation Model
. 581
1
Introduction
. 581
2
The Protein Folding Problem
. 582
xxii Contents
2.1 The
Levi
nthal
Paradox
. 582
2.2
Motivations
. 583
3
Approaches to Study the Protein Folding Problem
. 583
3.1
Latest Approach
. 585
3.2
The
Amino
Acid Interaction Network
. 585
4
Folding a Protein in a Topological Space by
Bio-Inspired Methods
. 586
4.1
Genetic Algorithms
. 586
4.2
Motif Prediction
. 588
4.3
Dataset
. 589
4.4
Overall Description
. 589
4.5
Genetic Operators
. 589
4.6
Algorithm
. 590
5
Conclusions
. 592
References
. 592
46
Analysing Multiobjective Fitness Function with Finite
State Automata
. 595
1
Introduction
. 595
2
Evolutionary Algorithm
. 598
2.1
Input-Output Specification
(IOS)
. 598
2.2
Syntax Term
(S)
. 599
2.3
Primitive Function
(F)
. 599
2.4
Learning Parameter (oti)
. 599
2.5
Complexity Parameters (Tmax,
β)
. 599
2.6
System Proof Plan
(t;)
. 599
3
Evolutionary Process
. 600
3.1
Single Objective Evolutionary Process
. 600
3.2
Multi
Objective Evolutionary Process
. 601
4
Result and Discussion
. 603
4.1
Input-Output Specification
. 603
4.2
Performance
. 604
5
Conclusion
. 605
References
. 605
Index
. 607 |
| any_adam_object | 1 |
| author2 | Ao, Sio-Iong Rieger, Burghard B. 1937-2021 Amouzegar, Mahyar A. |
| author2_role | edt edt edt |
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| discipline | Maschinenbau / Maschinenwesen Informatik |
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| genre | (DE-588)1071861417 Konferenzschrift 2009 Berkeley Calif. gnd-content |
| genre_facet | Konferenzschrift 2009 Berkeley Calif. |
| id | DE-604.BV036895448 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T10:54:56Z |
| institution | BVB |
| isbn | 9789048194186 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020810562 |
| oclc_num | 706858330 |
| open_access_boolean | |
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| physical | XXII, 612 S. Ill., graph. Darst. 235 mm x 155 mm |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Springer |
| record_format | marc |
| series | Lecture notes in electrical engineering; 68 |
| series2 | Lecture notes in electrical engineering; 68 |
| spelling | Machine learning and systems engineering [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] Sio-Iong Ao ; Burghard Rieger ; Mahyar A. Amouzegar [ed.] Dordrecht [u.a.] Springer 2010 XXII, 612 S. Ill., graph. Darst. 235 mm x 155 mm txt rdacontent n rdamedia nc rdacarrier Lecture notes in electrical engineering; 68 Lernendes System (DE-588)4120666-6 gnd rswk-swf Systemtechnik (DE-588)4140901-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)1071861417 Konferenzschrift 2009 Berkeley Calif. gnd-content Lernendes System (DE-588)4120666-6 s Systemtechnik (DE-588)4140901-2 s DE-604 Maschinelles Lernen (DE-588)4193754-5 s Ao, Sio-Iong edt Rieger, Burghard B. 1937-2021 (DE-588)110958624 edt Amouzegar, Mahyar A. edt Erscheint auch als Online-Ausgabe 978-90-481-9419-3 Lecture notes in electrical engineering; 68 68 (DE-604)BV022422356 68 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3458477&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020810562&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Machine learning and systems engineering [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] Lecture notes in electrical engineering; 68 Lernendes System (DE-588)4120666-6 gnd Systemtechnik (DE-588)4140901-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4120666-6 (DE-588)4140901-2 (DE-588)4193754-5 (DE-588)1071861417 |
| title | Machine learning and systems engineering [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] |
| title_auth | Machine learning and systems engineering [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] |
| title_exact_search | Machine learning and systems engineering [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] |
| title_full | Machine learning and systems engineering [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] Sio-Iong Ao ; Burghard Rieger ; Mahyar A. Amouzegar [ed.] |
| title_fullStr | Machine learning and systems engineering [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] Sio-Iong Ao ; Burghard Rieger ; Mahyar A. Amouzegar [ed.] |
| title_full_unstemmed | Machine learning and systems engineering [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] Sio-Iong Ao ; Burghard Rieger ; Mahyar A. Amouzegar [ed.] |
| title_short | Machine learning and systems engineering |
| title_sort | machine learning and systems engineering international conference on advances in machine learning and systems engineering berkeley california usa october 20 22 2009 under the auspices of the world congress on engineering and computer science wcecs 2009 revised and extended research articles |
| title_sub | [International Conference on Advances in Machine Learning and Systems Engineering ; Berkeley, California, USA, October 20 - 22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) ; revised and extended research articles] |
| topic | Lernendes System (DE-588)4120666-6 gnd Systemtechnik (DE-588)4140901-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Lernendes System Systemtechnik Maschinelles Lernen Konferenzschrift 2009 Berkeley Calif. |
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| volume_link | (DE-604)BV022422356 |
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