스마트홈 서비스를 위한 사용자 피드백 기반 상황인지 모델 학습
Learning Context Awareness Model based on User Feedback for Smart Home Service
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IRecently, researches on the recognition of indoor user situations through various sensors in a smart home environment are under way. In this paper, the case study was conducted to determine the operation of the robot vacuum cleaner by inferring the user 's indoor situation through the operation of home appliances, because the indoor situation greatly affects the operation of home appliances. In order to collect learning data for indoor situation awareness model learning, we received feedbacks from user when there was a mistake about the cleaning situation. In this paper, we propose a semi-supervised learning method using user feedback data. When we receive a user feedback, we search for the labels of unlabeled data that most fit the feedbacks collected through genetic algorithm, and use this data to learn the model. In order to verify the performance of the proposed algorithm, we performed a comparison experiments with other learning algorithms in the same environment and confirmed that the performance of the proposed algorithm is better than the other algorithms.
Abstract
IRecently, researches on the recognition of indoor user situations through various sensors in a smart home environment are under way. In this paper, the case study was conducted to determine the operation of the robot vacuum cleaner by inferring the user 's indoor situation through the operation of home appliances, because the indoor situation greatly affects the operation of home appliances. In order to collect learning data for indoor situation awareness model learning, we received feedbacks from user when there was a mistake about the cleaning situation. In this paper, we propose a semi-supervised learning method using user feedback data. When we receive a user feedback, we search for the labels of unlabeled data that most fit the feedbacks collected through genetic algorithm, and use this data to learn the model. In order to verify the performance of the proposed algorithm, we performed a comparison experiments with other learning algorithms in the same environment and confirmed that the performance of the proposed algorithm is better than the other algorithms.
참고문헌 (14)
- Brdiczka, Oliver, "Learning situation models in a smart home", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(1), pp.56~63 (2009)
- Jalal, Ahmad, "Robust human activity recognition from depth video using spatiotemporal multi-fused features", Pattern recognition, 61, pp.295~308 (2017)
- Wongpatikaseree, Konlakorn, "Activity recognition using context-aware infrastructure ontology in smart home domain", Knowledge, Information and Creativity Support Systems (KICSS), 2012 Seventh International Conference on. IEEE, pp.50~57 (2012)
- Guo, Lei, "Real-time human activity recognition in smart home with binary tree SVM", Control Conference (CCC), 2016 35th Chinese. IEEE, pp.6977~6981 (2016)
- Chahuara, Pedro, "Context aware decision system in a smart home: knowledge representation and decision making using uncertain contextual information", The 4th International Workshop on Acquisition, Representation and Reasoning with Contextualized Knowledge (ARCOE-12), pp.52~64 (2012)
- Ha, Eu Tteum, "Learning a Bayesian Network for Situation-Aware Smart Home Service: A Case Study with a Robot Vacuum Cleaner", International Science Index Computer and Information Engineering, 2(2), pp.877~884 (2015)
- Kwon, Seongcheol, "Interactive Learning of Bayesian Network for Situation-Aware Smart Home Service", Korean Institute of Information Scientists and Engineers, pp.486~488 (2015)
- Kwon, Seongcheol, "Performance Comparison of Situation-Aware Models for Activating Robot Vacuum Cleaner in a Smart Home", World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 10(2), pp.312~316 (2016)
- Zhu, Xiaojin, "Semi-supervised learning literature survey" (2005)
- Friedman, Nir, "Bayesian network classifiers", Machine learning, 29(2-3), pp.131~163 (1997)
- Murphy, Kevin P, "Naive bayes classifiers", pp.2006
- Umbarkar, A. J, "Crossover Operators in Genetic Algorithms: a review", ICTACT Journal on Soft Computing, 6(1), pp.1083~1092 (2015)
- Soni, Nitasha, "Study of various mutation operators in genetic algorithms", IJCSIT) International Journal of Computer Science and Information Technologies, 5(3), pp.4519~4521 (2014)
- Blickle, Tobias, "A comparison of selection schemes used in genetic algorithms" (1995)