Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Intelligent Modeling of User Behavior based on FCM Quantization for Smart home

FCM 이산화를 이용한 스마트 홈에서 행동 모델링

  • 정우용 (연세대학교 전기전자공학과) ;
  • 이제헌 (연세대학교 전기전자공학과) ;
  • 윤숙현 (연세대학교 전기전자공학과) ;
  • 조영완 (서경대학교 컴퓨터공학과) ;
  • 김은태 (연세대학교 전기전자공학과)
  • Published : 2007.06.01
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Abstract

In the vision of ubiquitous computing environment, smart objects would communicate each other and provide many kinds of information about user and their surroundings in the home. This information enables smart objects to recognize context and to provide active and convenient services to the customers. However in most cases, context-aware services are available only with expert systems. In this paper, we present generalized activity recognition application in the smart home based on a naive Bayesian network(BN) and fuzzy clustering. We quantize continuous sensor data with fuzzy c-means clustering to simplify and reduce BN's conditional probability table size. And we apply mutual information to learn the BN structure efficiently. We show that this system can recognize user activities about 80% accuracy in the web based virtual smart home.

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References

  1. R. Want, A. Hopper, V. Falcaao, and J. Gibbons, 'The active badge location system,' ACM Transactions on lnformation Systems, pp. 91-102, January 1992 https://doi.org/10.1145/128756.128759
  2. E. M. Tapia, S. S. lntille, and K. Larson, 'Activity recognition in the home setting using simple and ubiquitous sensors,' in Proceedings of PERVASIVE 2004, Vol. LNCS 3001, pp. 158-175
  3. A. Ranganathan, J. A. Muhtadi, and R. H. Campbell, 'Reasoning about uncertain contexts in pervasive computing environments,' IEEE Trans. on Pervasive Computing, vol. 3, no. 2, pp. 62-70, 2004 https://doi.org/10.1109/MPRV.2004.1316821
  4. R. E. Neapolitan, Learning Bayesian Networks, Prentice Hall, 2004
  5. F. Fleuret, 'Fast binary feature selection with conditional mutual information.' The Journal of Machine Learning Research, vol. 5, Dec 2004
  6. 정우용, 김은태, '베이자안 네트워크에 기반한 스마트홈에서의 상황인식 기법개발' 퍼지 및 지능시스템학회 논문지, 제 17 권 제 2 호,pp. 179-184, 2007 https://doi.org/10.5391/JKIIS.2007.17.2.179
  7. A. Papoulis and S. U. Pillai, Probability, Random Variables and Stochastic Processes, McGraw Hill, 2002
  8. J. Zhang and Y. Leung, 'Robust clustering by pruning outliers,' IEEE Trans. on SMC, vol. 33, no. 6, pp. 983-999, Dec 2003 https://doi.org/10.1109/TSMCB.2003.816993