Lecture Notes in Electrical Engineering Volume 68 Sio-Iong Ao Burghard Rieger Mahyar A Amouzegar l l Machine Learning and Systems Engineering Editors Dr Sio-Iong Ao International Association of Engineers Hung To Road 37-39 Hong Kong Unit 1, 1/F Hong Kong SAR publication@iaeng.org Prof Dr Burghard Rieger Universität Trier FB II Linguistische Datenverarbeitung Computerlinguistik Universitätsring 15 54286 Trier Germany Prof Mahyar A Amouzegar College of Engineering California State University Long Beach CA 90840 USA ISSN 1876-1100 e-ISSN 1876-1119 ISBN 978-90-481-9418-6 e-ISBN 978-90-481-9419-3 DOI 10.1007/978-90-481-9419-3 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2010936819 # Springer Science+Business Media B.V 2010 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work Cover design: SPi Publisher Services Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20–22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009) The WCECS is organized by the International Association of Engineers (IAENG) IAENG is a non-profit international association for the engineers and the computer scientists, which was founded in 1968 and has been undergoing rapid expansions in recent years The WCECS conferences have served as excellent venues for the engineering community to meet with each other and to exchange ideas Moreover, WCECS continues to strike a balance between theoretical and application development The conference committees have been formed with over two hundred members who are mainly research center heads, deans, department heads (chairs), professors, and research scientists from over thirty countries with the full committee list available at our congress web site (http:// www.iaeng.org/WCECS2009/committee.html) The conference participants are truly international representing high level research and development from many countries The responses for the congress have been excellent In 2009, we received more than six hundred manuscripts, and after a thorough peer review process 54.69% of the papers were accepted This volume contains 46 revised and extended research articles written by prominent researchers participating in the conference Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications The book offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering Sio-Iong Ao Burghard B Rieger Mahyar A Amouzegar v Contents Multimodal Human Spacecraft Interaction in Remote Environments 1 Introduction The MIT SPHERES Program 2.1 General Information 2.2 Human-SPHERES Interaction 2.3 SPHERES Goggles Multimodal Telepresence 3.1 Areas of Application 3.2 The Development of a Test Environment Experimental Setup 4.1 Control via ARTEMIS 4.2 The Servicing Scenarios Results of the Experiments 11 5.1 Round Trip Delays due to the Relay Satellite 11 5.2 Operator Force Feedback 12 Summary 14 References 14 A Framework for Collaborative Aspects of Intelligent Service Robot Introduction Related Works 2.1 Context-Awareness Systems 2.2 Robot Grouping and Collaboration Design of the System 3.1 Context-Awareness Layer 3.2 Grouping Layer 3.3 Collaboration Layer 17 17 18 18 19 20 21 22 24 vii viii Contents Simulated Experimentation 4.1 Robot Grouping 4.2 Robot Collaboration Conclusion References 25 25 27 28 28 Piecewise Bezier Curves Path Planning with Continuous Curvature Constraint for Autonomous Driving Introduction Bezier Curve 2.1 The de Casteljau Algorithm 2.2 Derivatives, Continuity and Curvature Problem Statement Path Planning Algorithm 4.1 Path Planning Placing Bezier Curves within Segments (BS) 4.2 Path Planning Placing Bezier Curves on Corners (BC) Simulation Results Conclusions References 31 31 32 33 34 34 36 37 38 43 45 45 Combined Heuristic Approach to Resource-Constrained Project Scheduling Problem Introduction Basic Notions Algorithm Generalisation for Multiproject Schedule KNapsack-Based Heuristic Stochastic Heuristic Methods Experimentation Conclusion References 47 47 48 49 51 51 53 55 56 56 A Development of Data-Logger for Indoor Environment Introduction Sensors Module 2.1 Temperature Sensor 2.2 Humidity Sensor 2.3 CO and CO2 Sensor LCD Interface to the Microcontroller Real Time Clock Interface to the Microcontroller EEPROM Interface to the Microcontroller PC Interface Using RS-232 Serial Communication Graphical User Interface Schematic of the Data Logger 59 59 60 60 61 62 62 62 63 63 63 64 Contents ix Software Design of Data Logger 9.1 Programming Steps for I2C Interface 9.2 Programming Steps for LCD Interface 9.3 Programming Steps for Sensor Data Collection 10 Results and Discussion 11 Conclusions References 64 65 67 67 68 68 69 Multiobjective Evolutionary Optimization and Machine Learning: Application to Renewable Energy Predictions Introduction Material and Methods 2.1 Support Vector Machines 2.2 Multiobjective Evolutionary Optimization 2.3 SVM-MOPSO Trainings Application Results and Discussion Conclusions References 71 71 72 72 74 76 78 78 80 81 Hybriding Intelligent Host-Based and Network-Based Stepping Stone Detections Introduction Research Terms Related Works Proposed Approach: Hybrid Intelligence Stepping Stone Detection (HI-SSD) Experiment Result and Analysis 6.1 Intelligence Network Stepping Stone Detection (I-NSSD) 6.2 Intelligence Host-Based Stepping Stone Detection (I-HSSD) 6.3 Hybrid Intelligence Stepping Stone Detection (HI-SSD) Conclusion and Future Work References Open Source Software Use in City Government Introduction Related Research Research Goals Methodology Survey Execution Survey Results Analysis: Interesting Findings 7.1 Few Cities Have All Characteristics 83 83 84 85 85 86 88 88 89 92 93 94 97 97 98 100 100 101 102 104 104 Index Gonza´lez, E., 121 Gonza´lez, L., 397 Goose, S., 361, 365 Government, 97–108 Gragger, J.V., 169 Grammatical evolution, 131–142 Graphical user interface, 63–64, 471 Greedy mechanism, 111–119 Grid scheduling, 440, 441, 445, 447 Group delay, 353–358 Gulbinowicz, Z., 183 Gupta, D., 466 H Hammer, D., 298 Hamming weight, 399, 404–406 Haptics, 2, 6, 7, 11, 13, 14 Hasan, M., 227 Haumer, A., 169 Haupt, R., 270 Head-related impulse responses (HRIR), 364–366 Heartbeat, 548–552, 554, 555 Heger, J., 269 Herberlein, L.T., 84, 86 Heuristic method, 48, 53–55 He, X., 298 Hildebrandt, T., 269 Hjelsvold, R., 361 Holland, J.H., 586 HRIR See Head-related impulse responses (HRIR) Huang, S.S., 84 Huckaby, E.D., 243 Huffmire, T., 412 Human spacecraft interaction, 1–14 Hybrid intelligence, 85–86, 92–93 Hydraulic system, 283–288, 293 I Ibrahim, M.S., 533 Imam, I.F., 526 In-car multimedia, 496–500, 505 Incomparable binary n-tuples, 398, 407, 409 Inferential model, 143–145, 147, 154 Inoue, A., 257 Input saturation, 257–268 Intelligent service robot, 17–28 Intensification and diversification, 114, 116, 119 Interaction networks, 582, 585–586, 588, 592 Intrinsic order, 397–409 Inverse reference model, 216–217 609 IPv6, 451–463, 467, 468, 470, 472, 474, 476, 478, 479 Ivanova, N.B., 545 J Janiszewski, A., 413 Jassar, S., 143 Java, 359–370, 457, 458, 460, 462, 474 Jee, S.H., 569 Jianhua, Y., 86 Jones, C.P., 483 K Kaashoek, F., 466 Kaheil, Y., 71 Kamich, B.M., 482 Kamruzzaman, J., 483 Kandil, O.A.D., 533 Kaneda, S., 523 Katabi, D., 466 Katsuyama, T., 547 Kauffman, R., 145 Kennedy, J., 74 Kennel, R., 197 Khalil, A., 71 Kihm, J.L., 413 Kim, J., 569 Kinicki, R., 466 Knapsack problem, 52, 53 Kodlahalli, S., 361 Kozak, J., 183 Kra´l, A., 466 Kulkarni, M., 283 Kumar, A., 59 L Labella, T.H., 19 Laplace distribution, 134–136, 139–141 Lasserre, J.B., 271 Lau, J., 415 Launder, B.E., 246 L2 cache performance prediction, 412 Lee, M., 569 Levenberg–Marquardt, 198, 201–202, 205, 207 Levinthal, C., 583 Liang, T.P., 484 Liao, Z., 143 Liquid crystal display (LCD), 60, 62, 67, 68 Loh, R.N.K., 213 Long-term variability, 71 Losses, 40, 73, 123, 170, 172–180, 237, 264, 328, 387, 389, 391–393, 395, 469, 472, 479, 483, 503, 512, 570, 572 610 Luh, P.B.c., 271 Lyapunov, 122, 214, 217, 218, 221, 224 M Macpherson, G.B., 250 Magee, J., 482, 483 Magic square based on properties, 428–434 Magnitude of error, 218 Mani, K., 423, 427, 507 Marichal, R., 121 Marzouk, O.A., 243 Mataric, M., 19 Mati, Y., 271 Matousˇek, R., 47 Matousek, R., 131 McDermott, M., 227 Memory phase classification, 412–415 Mesh untangling and smoothing, 158, 160, 161, 163 Michalski, R.S., 526 Micro EDM, 184 Micro SD-memory, 574 MILP See Mixed integer linear program (MILP) MIMO processes, 257–268 MIMO systems, 257, 258, 260, 262–268 Mingzhe, L., 466 MiniFab, 271, 272, 274 Mixed integer linear program (MILP), 269, 274 Modelica, 177, 180 Model reference adaptive control (MRAC), 214–222, 224 Mohapatra, P., 466 Molecular dynamics, 256 Montenegro, R., 157 Montero, G., 157 Montesino, F., 466 Moon, D., 71 Motor, 6, 203, 215, 220, 228, 231, 233–236, 238–241, 312–314, 316, 317, 322 Multi-domain simulation, 169–180 Multigrid, 297–309 Multimedia streaming, 388, 389, 395 Multimodal haptics, 2, 6–7 Multi-modality, 1–14, 131 Multiobjective evolutionary algorithm, 597 Multiobjective optimization, 71–81, 601, 603 Municipal, 98–102, 105 Muntean, G.-M., 385 Murugesan, G., 439 Myszka, D., 298 Index N Naumann, A., 249 Navier–Stokes, 244, 302, 303, 307–309 Nested meshes, 158 Neural networks, 53, 121–129, 143–145, 197–201, 203–204, 214, 215, 221–224, 284, 483 Nonlinear systems, 197–210, 285, 308 Non-Shannon entropy, 341–350 Nordin, N., 250 NP-hard problem, 582 O O’Donoghue, K., 545 Omar, M.N., 83 On-orbit servicing (OOS), 1, 8, 11 Osˇmera, P., 47 Open source software, 97–108 Optimization, 32, 38, 42, 48, 71–81, 111–119, 131, 133, 158, 161–166, 194, 198, 200–202, 204, 205, 207, 208, 210, 241, 271, 321–323, 411, 423, 435, 507–518, 582, 595–597, 601–603 Ostergaard, E., 19 Osunleke, A.S., 257 P Pan, J.C.-H., 274 Parker, S., 466 Passeman, F., 122 Patel, V., 271 Path planning, 2, 31–45 Pavageau, C., 271 Pechter, W., 361 Perceived quality, 387–389, 391–393 Performance analysis, 375, 495–506 Performance evaluation, 112, 416, 479, 503–505 Pescape´, A., 466 Petrucci, L.S., 361 Pham-Trong, V., 158 Pheromone update rule, 112, 114–116 Physical activity, 569–578 Pin˜eiro, J.D., 121 Pitchfork bifurcation, 121–129 Power plant, 327–339 Predictive control, 257, 258 Premature ventricular contractions (PVC), 549–551 Priority rule scheduling, 269–280 Process control, 257–268, 335 Process modeling, 322, 328, 585 Project management, 47 Index Protein folding problem, 581–592 Proxy-client-server (PCS), 388, 391 Public-key cryptosystem, 423–436, 507, 508, 514, 518 Pun, T., 361 PVC See Premature ventricular contractions (PVC) Q Qiu, H.-H., 244 Quah, T.S., 482, 483 Quality oriented adaptive scheme (QOAS), 387–389, 392, 393, 395 R Radovnikovich, M., 213 Ramani, A.K., 495 Ranking interval, 397–409 Real time clock (RTC), 60, 62–63, 68 Re-entrant processes, 272, 274 Relay satellite, 8, 10–12 Remote environment, 1–14 Resource allocation, 439–448 Resource-constrained scheduling, 47–56, 269–280 Resource management, 440–442 Review, 19, 48, 53, 98, 257, 270, 321, 476, 569–578 Robot grouping, 18–20, 25–26, 28 Robustness, 2, 338, 382 Robust tracking, 257–268 Rockefeller, B., 482, 483, 486 Rodic, D., 19 Rodrı´guez, E., 157 Rolda´n-Villasana, E.J., 327 Rols, M.-P., 542, 543 Romero-Jime´nez, G., 327 Rotating electrode, 184, 185 Roth, P., 361 Roux, W., 271 RSA, 424, 425, 435, 436, 455, 456, 459, 460, 508, 514 S Saad, A., 85 Saenz-Otero, A., Sˇandera, C., 47 Sarker, R., 483 Scaling exponent, 548–555 Schaniel, C., 545 Schatner, J.D., 597 Schiller, L., 249 611 Schmechel, C., 466 Schoenmackers, S., 415 Scholz-Reiter, B., 269 Secure neighbor discovery protocol (SEND), 452, 454–457, 460, 462, 463 Security, 83, 85, 423–436, 452, 455, 462, 483, 507, 508 Sˇeda, M., 47 Self-organizing map (SOM), 85, 87–91, 93 Semiconductor gas sensors, 60 Settle, A., 413, 420 Shanmugam, I.B., 85 Shannon entropy and texture-featureextraction, 341–350 Sharda, R., 484 Sharma, B.I., 246 Sharma, H., 495 Sharma, K., 495 Shen, W., 297 Sherwood, T., 412 Shimoda, Y., 547 Shi, Y., 484 Shi, Y.Y., 484 Shi, Z., 484 Shi, Z.K., 484 Shop-floor control, 280 Shou-Hsuan, S.H., 86 Signal processing, 353, 362, 365 Simulation, 7, 24, 25, 32, 43–45, 112, 123, 128, 137, 169–180, 183–194, 199, 200, 202, 215, 222–224, 227–241, 244, 250–251, 253, 258, 265–270, 273, 277–280, 289, 293–294, 303, 304, 322–323, 327–331, 333–335, 355–357, 387, 389–392, 411, 413, 416, 420, 436, 502, 503, 534, 536, 541–545, 581–592 Simulator training, 327–339 Singh, A.P., 341 Singh, B., 341 Singh, I.P., 59 SIR model, 557, 559–560 Sodnik, J., 359–361 Soft sensor, 143–154 Sommerfeld, M., 244 Spatial sound, 361, 364, 366 Stachowicz, M., 283 Staniford-Chen, S., 84, 86 STE See Sum epsilon tube error (STE) Stepping stone detection (SSD), 83–94 Stepping stone intrusion, 83, 85 Stock price prediction, 482–484, 491, 492 Stoll, E., 612 Strauss, J., 466 Sud, S.K., 59 Suh, J., 17 Sukhatme, S., 19 Sukhorukov, V., 542 Sum epsilon tube error (STE), 131–142 Sum of squares, 135, 376, 508–516 Support vector machines (SVM), 72–74, 76–81 Surface and volume parametrization, 157–166 Swarm intelligence, 74 System identification, 197–210 T Taib, S.M., 481 Tanaka, K., 548 Tao, E.Y., 97 Task planning, 21, 24–25, 27, 28 Teissie, J., 542, 543 Telepresence, 2, 5–8, 12 Tetrahedral mesh generation, 159–160, 163 Thames, J.L., 85 Time series analysis, 548–550, 552, 554 Tomazˇicˇ, S., 359, 361 Torres, J., 121 Traffic generation, 468, 469, 471, 473, 479 Traore, I., 521 Turban, E., 484 Tweddle, B., Two mass system, 197–210 U Ubik, S., 466 Unknown disturbances, 257–268 V Vehicle dynamics, 31, 235, 238 Vela´squez, K., 465 Vempaty, P.K., 213 Venkataraman, H., 385 Venkatesh, N., 373 Index Ventre, G., 466 Virtual reality (VR), 5, 7, 10, 13 W Ward, D.B., 353 Ward, D.J., 97 Weber, G., 361 Wei, M., 146 Weisser, R., 47 Weitz, R., 145 Weller, H.G., 250 Wideband, 353–358 Wind energy, 72, 80, 81 Wojtusiak, J., 527 Woo, C.-W., 17 Wu, D., 466 Wu, H., 84 Wurz, W., 361 X Xie, X., 271 Y Yazawa, T., 547 Yoo, S.K., 569 Yoshikawa, M., 111 Z Zabre, E., 327 Zadeh, L.A., 285 Zhang, C., 297 Zhang, J., 297 Zhang, W., 353 Zhang, X., 353 Zhan, W., 227 Zhao, L., 143 Zimmermann, U., 542 Zoghi, B., 227 Zwick, M., 411 ... Avenue, Cambridge, MA 0 2139 -4307, USA e-mail: estoll@MIT.edu S.-I Ao et al (eds.), Machine Learning and Systems Engineering, Lecture Notes in Electrical Engineering 68, DOI 10. 1007/978-90-481-9419-3_1,... excellent reference text for researchers and graduate students, working on machine learning and systems engineering Sio-Iong Ao Burghard B Rieger Mahyar A Amouzegar v Contents Multimodal Human... References 131 131 132 134 134 136 137 139 140 141 141 142 12 Data Quality in ANFIS Based Soft Sensors