( 您好!臺灣時間:2023/06/02 16:35
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::


研究生(外文):Yu-sheng Hung
論文名稱(外文):Modeling and simulation of wireless sensor and RFID-based smart house
指導教授(外文):Kung-Jeng Wang
外文關鍵詞:Smart housesimulationwireless sensorRFID
  • 被引用被引用:3
  • 點閱點閱:278
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
The purpose of this research is to develop a simulation framework that enables us to simulate and evaluate the performances of a smart house. We define three performance indexes, thermal comfort, context awareness and security, and examine the overall happiness of the humans residing in the smart house. In terms of modeling, we have completed the four building blocks for constructing a basic simulated smart house, that is, a virtual human (its physical movement and physiological status), the smart house environment, sensors and their fuzzy-based characteristics, and an optimization-based intelligence to control room temperature in the smart house. In terms of simulation experiments, this study has examined the impacts of initial room temperatures, human population, decision interval of controlling room temperature, and sensor range. This study has contributed to the development of a smart space for precisely modeling its human behaviors, and the corresponding temperature control rules. We have presented a set of systematic experiments to demonstrate the feasibility and efficiency of the proposed simulation model as evaluating a smart house.
中文摘要 I
Abstract II
Acknowledgements III
Content IV
List of Figures VI
List of Tables VIII
Chapter 1 Introduction 1
1.1 Research background, motivation, and objective 1
1.2 organization of Thesis 2
Chapter 2 Literature Review 3
2.1 Smart house – an Introduction 3
2.2 Virtual human 4
2.2.1 Physiological status of a virtual human 5
2.2.2The Brownian movement 6
2.2.3Fuzzy theory 6
2.3 Sensors 7
2.4 Decision rule 7
2.4.1 Optimization of the nonlinear model 8
2.5 Simulation approach 8
2.6 Summary 9
Chapter 3 Simulation modeling of a wireless sensor and RFID-based smart house 10
3.1 Structure of the overall simulation system for smart house 10
3.1.1 Modeling of a virtual human 14
3.1.2 Modeling of physiological status of a virtual human 21
3.1.3 Modeling of space 29
3.2 Sensors in smart house and its intelligence 31
3.2.1 Sensors 31
3.2.2 Temperature control rule and the system goal 35
3.3 Demonstration of simulation runs 42
3.3.1 Variables setting of virtual human and smart house 43
3.3.2 Experiment results 48
3.4 Summary 52
Chapter 4 Simulation experiments and discussion 53
4.1 Variables setting of virtual human and smart house 53
4.2 Validation of the simulation model 56
4.3 The effect of population of human load 64
4.4 The effect of sensor response time 69
4.5 Design of experiments on major factors 75
4.5.1 Factors settings 77
4.5.2 Experiment results 77
4.6 Summary 82
Chapter 5 Conclusion and Future Research 85
5.1 Conclusion 85
5.2 Future research 86
Reference 88
Anderson, F. C., Arnold, A. S., Pandy, M. G., Goldberg, S. R., and Delp, S. L. (2006) “Simulation of walking”, 3rd Edition, In Human Walking, Williams and Wilkins.
Aumann, C. A. (2007) “A methodology for developing simulation models of complex systems”, Ecological Modelling, 202, 385-396.
Bhattacharya, S. S. (2002) “Intelligent monitoring systems: smart room for patient's suffering from somnambulism”, Microtechnologies in Medicine and Biology 2nd Annual International IEEE-EMB Special Topic Conference,326-331.
Bobick, A. F. and Pinhanez, C. S.(1997) “Controlling view-based algorithms using approximate world models and action information”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 955-961.
Brandes, M. and Rosenbaum, D. (2004) “Correlations between the step activity monitor and the DynaPort ADL-monitor”, Clinical Biomechanics, 19(1), 91-94.
Cheek, P. (2005) “Aging well with smart technology”, Nursing Administration Quarterly, 29, 329-338.
Chou, C.-H., Hu, B. L. and Yu, T. (2008) “Quantum Brownian motion of a macroscopic object in a general environment”, Statistical Mechanics and its Applications, 387 (2-3), 432-444.
De Sevin, E., Kallmann, M. and Thalmann, D. (2001) “Towards real time virtual human life simulations”, Proceedings of Computer Graphics International Conference, 31-37.
Doukas, H., Nychtis, C. and Psarras, J. (2008) “Assessing energy-saving measures in buildings through an intelligent decision support model”, Building and Environment, Article in Press.
Evans, G. (1991) “Solving home automation problems using artificial intelligence techniques”, IEEE Transactions on Consumer Electronics, 37 (3), 395-400.
Fallman, D., Lindbergh, K., Fjellstrom, O., Johansson, L., Nilbrink, F. and Bogren, L. (2007) “An RFID-based point-and-listen interface providing library access for the visually impaired ”, Lecture Notes in Computer Science, 4556 (3), 269-278.
Fanger, P. O. (1970)“Thermal comfort. Danish technical press”,-.
Feng, J. (2008) “An intelligent decision support system based on machine learning and dynamic track of psychological evaluation criterion”, Studies in Computational Intelligence, 117, 141-157.
Ghiabaklou, Z. (2003) “Thermal comfort prediction for a new passive cooling system”, Building and Environment, 38 (7), 883-891.
Gonzàlez-Alonso, J., Teller, C., Andersen, S. L., Jensen, F. B., Hyldig, T., and Nielsen, B. (1999) “Influence of body temperature on the development of fatigue during prolonged exercise in the heat”, Journal of Applied Physiology, 86 (3), 1032-1039.
Han, J., Zhang, G., Zhang, Q., Zhang, J., Liu, J., Tian, L., Zheng, C., Haob, J.,Linb, J., Liub, Y. and Moschandreas, D. J. (2007) “Field study on occupants’ thermal comfort and residential thermal environment in a hot-humid climate of China”, Building and Environment, 42 (12), 4043-4050.
Hase, K. and Yokoi, T. (2002) “Computer simulation study of human locomotion with a three dimensional entire-body neuro-musculo-skeletal model”, JSME International Journal, 45 (4), 1065-1072.
Helal, S. (2005) “Programming pervasive spaces”, IEEE Pervasive Computing , 4 (1), 84-87.
Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y. and Jansen, E. (2005) “The Gator tech smart house: A programmable pervasive space ”, Computer , 38 (3), 50-60.
Hsu, H.-H., Cheng, Z., Huang, T. and Han, Q. (2006) “Behavior analysis with combined RFID and video information ”, Lecture Notes in Computer Science, 4159, 176-181
Jin, T.-S., Lee, J.-M. and Hashimoto, H. (2006) “Position estimation of a mobile robot using images of a moving target in intelligent space with distributed sensors”, Advanced Robotics, 20 (6), 737-762.
Jin, T.-S., Morioka, K. and Hashimoto, H. (2006) “Distributed sensor network for multi-agent motion tracking in intelligent space”, Sice-icase International Joint Conference, 4108410, 3716-3721.
Jing, X.-J. (2005) “Behavior dynamics based motion planning of mobile robots in uncertain dynamic environments”, Robotics and Autonomous Systems, 53 (2), 99-123.
Johnson, J. L., Davenport, R. and Mann, W. C. (2007) “Consumer feedback on smart home applications”, Topics in Geriatric Rehabilitation , 23 (1), 60-72.
Lee, C. C. (1990) ”Fuzzy logic in control systems: Fuzzy logic controller--part I”, IEEE Transactions on Systems, 20 (2), 404-418.
Li, H.-N. and Zhao, D.-H. (2006) “Control of structure with semi-active friction damper by intelligent algorithm”, IEEE International Conference on Fuzzy Systems, 1681919, 1584-1590.
Mateou, N. H. and Andreou, A. S. (2008) “A framework for developing intelligent decision support systems using evolutionary fuzzy cognitive maps”, Journal of Intelligent and Fuzzy Systems, 19 (2), 151-170.
Mathias, C. G. T., Wilson, D. M. and Maibacj, H. I. (1981) “Transepidermal water loss as a function of skin surface temperature”, Journal of Investigative Dermatology, 77 (2), 219-220.
Mody, N. A. and King, M. R. (2007) “Influence of Brownian motion on blood platelet flow behavior and adhesive dynamics near a planar wall ”, Langmuir, 23 (11), 6321-6328.
Nadimi, E. S., Søgaard, H. T., Bak, T. and Oudshoorn, F. W. (2008) “ZigBee-based wireless sensor networks for monitoring animal presence and pasture time in a strip of new grass”, Computers and Electronics in Agriculture, 61 (2), 79-87.
Nelson, N. (2001) “Dynamical theories of Brownian motion”, second edition, Princeton University Press,-.
Norton, J. P. (2008) “Algebraic sensitivity analysis of environmental models”, Environmental Modelling and Software, 23 (8), 963-972.
Ross, M. S. (2003) “Introduction to probability models”, Academic Press, New York.
Rougier, P. (1999) “Influence of visual feedback on successive control mechanisms in upright quiet stance in humans assessed by fractional Brownian motion modelling”, Neuroscience Letters, 266 (3), 157-160.
Roy, T. K. and Maiti, M. (1998)“Multi-objective inventory models of deteriorating items with some constraints in a fuzzy environment”, Computers and Operations Research , 25 (12), 1085-1095.
Serway, A. R. and Jewett, W. T., (2004) “Physics for scientists and engineers with modern physics”, 6th Edition, Belmont, California.
Taga, G., (2000) “Nonlinear dynamics of the human motor control - real-time and anticipatory adaptation of locomotion and development of movements”, In Proceedings of the International Symposium on Adaptive Motion of Animals and Machines, -.
Tuffnelll, C. (1992) “The influence of body temperature on a model of human respiratory system”, IEEE Proceedings of the Annual International Conference, 14, 1-8.
Roy, U. and Bharadwaj, B. (2002) “Design with part behaviors: Behavior model, representation and applications”, CAD Computer Aided Design, 34 (9), 613-636.
Verdú, F. and Villacampa, Y. (2002 ) “A computer program for a Monte Carlo analysis of sensitivity in equations of environmental modelling obtained from experimental data”, Advances in Engineering Software, 33 (6), 351-359.
Verdú, F. and Villacampa, Y. (2008) “A computational algorithm for the multiple generation of nonlinear mathematical models and stability study”, Advances in Engineering Software, 39 (5), 430-437.
Vijayan, T. and Kumaran, M. (2008) “Inventory models with a mixture of backorders and lost sales under fuzzy cost”, European Journal of Operational Research , 189 (1), 105-119.
Wang, X. L. (2005) “A course in fuzzy systems and control”, Person Education Taiwan Ltd, Taiwan.
Zaïdi, H., Taïar, R., Fohanno, S., and Polidori, G. (2007) “The influence of swimming type on the skin-temperature maps of a competitive swimmer from infrared thermography”, Acta of Bioengineering and Biomechanics, 9 (1), 47-51.
Zio, E., Baraldi, P. and Popescu, I. C. (2008) “Fuzzy decision trees as intelligent decision support systems for fault diagnosis”, Studies in Computational Intelligence, 117, 187-210.
第一頁 上一頁 下一頁 最後一頁 top