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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 傅立成(Li-Chen Fu) | |
dc.contributor.author | Zhi-Yang Chen | en |
dc.contributor.author | 陳智揚 | zh_TW |
dc.date.accessioned | 2021-06-13T04:13:15Z | - |
dc.date.available | 2006-07-31 | |
dc.date.copyright | 2006-07-31 | |
dc.date.issued | 2006 | |
dc.date.submitted | 2006-07-24 | |
dc.identifier.citation | [1] Housen. http://architecture.mit.edu/housen/.
[2] S.S. Intille and K. Larson. Designing and evaluating supportive technology for homes. Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics 2003, IEEE Press., 2003. [3] J. S. Beaudin J. Nawyn E. Munguia Tapia P. Kaushik S. S. Intille, K. Larson. A living laboratory for the design and evaluation of ubiquitous computing technologies. in Extended Abstracts of the 2005 Conference on Human Factors in Computing Systems, 2005. [4] Agent-based intelligent reactive environments http://aire.csail.mit.edu/. [5] Rodney A. Brooks. The intelligent room project. In proceeding of the Second International Cognitive Technology Conference, 1997. [6] S. Peters and H. E. Shrobe. Using semantic networks for knowledge representation in an intelligent environment. pages 323{329, 2003. [7] Mav home http://mavhome.uta.edu/index.html. [8] S. K. Das, D. J. Cook, A. Battacharya, III Heierman, E. O., and Lin Tze-Yun. The role of prediction algorithms in the mavhome smart home architecture. Wire-less Communications, IEEE [see also IEEE Personal Communications], 9(6):77{84, 2002. 1536-1284. [9] D.J Cook, M Youngblood, III Heierman, E.O., K Gopalratnam, A Rao, S.; Litvin, and F Khawaja. Mavhome: an agent-based smart home. Pervasive Computing and Communications, 521 - 524, 2003. [10] G. M. Youngblood, L. B. Holder, and D. J. Cook. Managing adaptive versatile environments. pages 351{360, 2005. [11] The adaptive house http://www.cs.colorado.edu/ mozer/house/. [12] Michael C. Mozer. The neural network house: An environment that adapts to its inhabitants. Proceedings of the American Association for Artificial Intelligence Spring Symposium on Intelligent Environments, pages 110{114, 1998. [13] Aware home http://www static.cc.gatech.edu/fce/house/house.html. [14] Irfan. A Essa. Ubiquitous sensing for smart and aware environments: technologies towards the building of an aware home. In Position Paper for the DARPA/NSF/NIST workshop on Smart Environment, 1999. [15] Cory D. Kidd, Robert Orr, Gregory D. Abowd, Christopher G. Atkeson, Irfan A. Essa, Blair MacIntyre, Elizabeth Mynatt, Thad E. Starner, and Wendy Newstetter. The aware home: A living laboratory for ubiquitous computing research. 2003. [16] T; Ueno R Hara, K; Omori. Detection of unusual human behavior in intelligent house. Neural Networks for Signal Processing,2002. Proceedings of the 2002 12th IEEE Workshop on, 4-6 Sept., pages 697{706, 2002. [17] S. Yoshihama, P. Chou, and D. Wong. Managing behavior of intelligent environments. pages 330{337, 2003. [18] Tao Gu, H. K. Pung, and D. Q. Zhang. A bayesian approach for dealing with uncertain contexts. Proceedings of the 2nd International Conference on Pervasive Computing, 2004. [19] Li-Ming Chen, hao-Lin Wu, and Li-Chen Fu. Automatic personal preference learning system in intelligent e-home. Proc. of Automation 2005, 2005. [20] David Heckerman. A tutorial on learning with bayesian networks. Technical report, 1995. [21] Stuart Russell and Peter Norvig. Artificial Intelligence, volume 14. second edition edition. [22] Sugato Basu. Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments. PhD thesis, Department of Computer Sciences, University of Texas at Austin, 2005. [23] A.K Dey and G.D Abowd. Toward a better understanding of context and context-awareness. GVU Technical Report GIT-GVU-99-22. [24] N. M. Laird Dempster, A. P. and D. B. Rubin. Maximum likelihood from incomplete data via the em algorithm. JRSS-B, 39:1{38, 1977. [25] N. Sebe, I. Cohen, T. S. Huang, and T. Gevers. Skin detection: a bayesian network approach. 2:903{906 Vol.2, 2004. [26] N. Sebe, I. Cohen, T. S. Huang, and T. Gevers. Semi-supervised face detection. 3:51{51, 2005. [27] I. Cohen, F. G. Cozman, N. Sebe, M. C. Cirelo, and T. S. Huang. Semisupervised learning of classi‾ers: theory, algorithms, and their application to human-computer interaction. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(12):1553{1566, 2004. 0162-8828. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/32672 | - |
dc.description.abstract | 隨著時代的進步,電腦開始進入人類生活的每一個部分,嵌入式裝置、資訊家電、及家庭控制網路的出現讓人類生活中存在各種不同類型的電腦提供應用,人類跟電腦之間的互動不再侷限於坐在電腦前用螢幕、鍵盤、滑鼠,而是可以擴展到更多樣化的方式,也開啟了智慧型電子家庭系統的研究及應用,不論是資訊家電、健康照顧、生活協助等等,這些都是目前智慧型電子家庭系統中熱門的研究方向。
而智慧型家庭系統主要的目的就是要讓使用者能夠感到更舒適。為了達到這個目的,就是要有一個能夠瞭解使用者生活習慣的系統,系統瞭解了使用者的需求之後就能夠進一步的提供所需要的服務給使用者,讓他們在智慧型家庭中能夠感到更舒適、更能享受科技所提供的人性化服務。 因此,在此篇論文中,我們提出了一套『動態個人偏好學習系統(Dynamic Personal Preference Modeling System)』,這個系統能夠透過家庭之中所佈置的感應器與及家電狀態收集到的各種資訊,並且加上與使用者的互動來達到動態的學習使用者的生活習慣,並且能夠適應使用者生活習慣與環境的改變動態調整,『動態個人偏好學習系統』會將各種收集到的資訊,透過學習推判出使用者的偏好行為,並且利用學習後的結果建立出『個人偏好模型』。 最後,智慧型家庭系統可以透過這個所學習得到的模型去推測目前使用者所需要的服務有哪些,並且判斷此服務的正當性之後建議服務給使用者,當所提供的服務不正確時,系統便會透過與使用者互動來動態學習,以求獲得更精確的個人偏好模型,進而提供更精確的服務且讓使用者。 | zh_TW |
dc.description.abstract | In this thesis, a dynamic personal preference modeling system is proposed. The system is able to learn the user's preference model and adjust this model according to some changes(the user's behavior, the environment, etc.) in a Smart Home, and hence the system can provide adequate services to the inhabitant.
First, we need a data collection mechanism to collect all the information in a Smart Home, and there are two kinds of data which have close relationships with the personal preference model: the environment data and the personal predefined data. The First kind consists of all pure data gathered from the environment, including sensory information, electric appliance state, personal information, time information, etc. The other kind consists of personal predefined data, i.e., data predefined by the user, which can help the system to adjust the personal preference model automatically. After collecting these two kinds of data, the system uses Bayesian network with semi-supervised learning to build the personal preference model and predicts the personal preference via this model. Our system can provide service to the user and infer whether the user's behavior changes or not. Then, the system can interact with the user to obtain some useful information and adjust the preference model according to such information. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T04:13:15Z (GMT). No. of bitstreams: 1 ntu-95-R93922110-1.pdf: 1568683 bytes, checksum: f30cff286d7754ca1d081f905c87bb98 (MD5) Previous issue date: 2006 | en |
dc.description.tableofcontents | 1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Preliminaries 7 2.1 Overview of Bayesian Network . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.2 Inference in a Bayesian Network . . . . . . . . . . . . . . . . . 10 2.2 Overview of Semi-supervised Learning . . . . . . . . . . . . . . . . . 13 2.3 Overview of Context-Aware . . . . . . . . . . . . . . . . . . . . . . . 14 3 Data Definition and Preference Modeling Overview 16 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Definition of Smart Home Data . . . . . . . . . . . . . . . . . . . . . 17 3.2.1 Definition of Environment Data . . . . . . . . . . . . . . . . . 17 3.2.2 Collection of Environment Data . . . . . . . . . . . . . . . . . 20 3.2.3 Environment Data Preprocessing . . . . . . . . . . . . . . . . 21 3.2.4 Context Interpreter . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.5 Personal Predefined Data . . . . . . . . . . . . . . . . . . . . 29 3.3 Personal Preference Modeling . . . . . . . . . . . . . . . . . . . . . . 30 4 Dynamic Preference Modeling and Context-Aware Service Providing 33 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Training and Updating . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.2.1 Dynamic Personal Preference Model . . . . . . . . . . . . . . 36 4.2.2 Adjust Parameter of Preference Model . . . . . . . . . . . . . 45 4.3 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3.1 Infer What Service The User Wants . . . . . . . . . . . . . . . 47 4.3.2 Service Recommendation and Providing . . . . . . . . . . . . 50 4.3.3 Interaction with User for Labeled Data . . . . . . . . . . . . . 52 5 Experiment and System Evaluation 54 5.1 Experiment Environment . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.2 System Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 6 Conclusion and Future Work 61 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Reference 64 | |
dc.language.iso | en | |
dc.title | 智慧型家庭之動態個人偏好學習系統及環境感知服務提供 | zh_TW |
dc.title | Dynamic Personal Preference Modeling and Context-Aware Service Providing in Smart Home | en |
dc.type | Thesis | |
dc.date.schoolyear | 94-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 吳政忠,蔡超人,溫琇玲,朱浩華 | |
dc.subject.keyword | 貝式網路,半監督學習,個人偏好模型,情境感知, | zh_TW |
dc.subject.keyword | Bayesian Network,Semi-supervised Learning,Personal Preference Modeling,Context-Aware, | en |
dc.relation.page | 67 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2006-07-26 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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