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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許永真(Yung-Jen Hsu) | |
| dc.contributor.author | Shih-Chiang Lee | en |
| dc.contributor.author | 李世強 | zh_TW |
| dc.date.accessioned | 2021-06-15T05:48:19Z | - |
| dc.date.available | 2011-08-20 | |
| dc.date.copyright | 2010-08-20 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-18 | |
| dc.identifier.citation | [1] Activities of daily life.
[2] M. Chetty, D. Tran, and R. Grinter. Getting to green: understanding resource consumption in the home. In Proceedings of the 10th international conference on Ubiquitous computing, pages 242–251. ACM, 2008. [3] S. Darby. The effectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays, 2006. [4] Google. Google powermeter. [5] R. Harle and A. Hopper. The potential for location-aware power management. In Proceedings of the 10th international conference on Ubiquitous computing, pages 302–311. ACM, 2008. [6] C. Harris and V. Cahill. Exploiting user behaviour for context-aware power management. In Proceedings of the Interntaional Conference On Wireless and Mobile Computing, Networking, and Communications, pages 122–130, 2005. [7] C. Harris and V. Cahill. An empirical study of the potential for context-aware power management. In UbiComp 2007: Ubiquitous Computing, pages 235–252. Springer, 2007. [8] G. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870–1891, 1992. [9] Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. Viridiscope: design and implementation of a fine grained power monitoring system for homes. In Ubicomp ’09: Proceedings of the 11th international conference on Ubiquitous computing, pages 245–254, New York, NY, USA, 2009. ACM. [1] Activities of daily life. [2] M. Chetty, D. Tran, and R. Grinter. Getting to green: understanding resource consumption in the home. In Proceedings of the 10th international conference on Ubiquitous computing, pages 242–251. ACM, 2008. [3] S. Darby. The effectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays, 2006. [4] Google. Google powermeter. [5] R. Harle and A. Hopper. The potential for location-aware power management. In Proceedings of the 10th international conference on Ubiquitous computing, pages 302–311. ACM, 2008. [6] C. Harris and V. Cahill. Exploiting user behaviour for context-aware power management. In Proceedings of the Interntaional Conference On Wireless and Mobile Computing, Networking, and Communications, pages 122–130, 2005. [7] C. Harris and V. Cahill. An empirical study of the potential for context-aware power management. In UbiComp 2007: Ubiquitous Computing, pages 235–252. Springer, 2007. [8] G. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870–1891, 1992. [9] Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. Viridiscope: design and implementation of a fine grained power monitoring system for homes. In Ubicomp ’09: Proceedings of the 11th international conference on Ubiquitous computing, pages 245–254, New York, NY, USA, 2009. ACM. [10] P. Kuo. A Study on Electrical Consumption Survey and Evaluation System of Residential Buildings. PhD thesis, NCKU, 2005. [11] Y.-l. Kuo, K.-y. Chiang, C.-w. Chan, J.-C. Lee, R. Wang, E. Shen, and J. Y.-j. Hsu. Community-based game design: Experiments on social games for commonsense data collection. In KDD-09 Workshop on Human Computation (HCOMP2009), Paris, France, June 2009. [12] C. Lin. Ipars: Intelligent portable activity recognition system. Master’s thesis, National Taiwan University, 2006. [13] C. Lin and J. Hsu. Ipars: Intelligent portable activity recognition system via everyday objects, human movements, and activity duration. In 2006 AAAI Workshop on Modeling Others from Observations, Technical Report WS-06-13, pages 44–52. [14] G.-y. Lin, S.-c. Lee, J. Y.-j. Hsu, and W.-r. Jih. Applying power meters for appliance recognition on the electric panel. In Proceedings of The 5th IEEE Conference on Industrial Electronics and Applications (ICIEA2010), Taichung, Taiwan, June 2010. [EI, ISI]. [15] H. Liu and P. Singh. Conceptnet—a practical commonsense reasoning tool-kit. BT Technology Journal, 22(4):211–226, 2004. [16] Microsoft. Microsoft hohm. [17] B. of Energy. Taiwan energy statistics. [18] P. Palmes, H. K. Pung, T. Gu, W. Xue, and S. Chen. Object relevance weight pattern mining for activity recognition and segmentation. Pervasive Mob. Comput., 6(1):43–57, 2010. [19] M. Perkowitz, M. Philipose, K. Fishkin, and D. J. Patterson. Mining models of human activities from the web. In WWW ’04: Proceedings of the 13th international conference on World Wide Web, pages 573–582, New York, NY, USA, 2004. ACM. [20] D. Wyatt, M. Philipose, and T. Choudhury. Unsupervised activity recognition using automatically mined common sense. In In AAAI, pages 21–27, 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47126 | - |
| dc.description.abstract | Providing energy conservation services becomes a hot research topic because more and more people attach importance to environmental protection. This research proposes a framework that consists of two main elements, including activity-appliance model and irrelevant appliance detection. First of all, an activity-appliance model has been built to associate activities with appliances according to the data of common sense. For instance, oven is used for cook, draught fan is used for hair drying, etc. Then, using the relationships can help to recognize the resident’s current activity and detect irrelevant appliance, which are consuming electric power but not take part in the current activities. Finally, the analysis of activity-appliance models shows most appliances are strong activity-related. Such a property offers benefit to identify activity and detect irrelevant appliance. The experimental results show that the work can achieve higher than 74% accuracy for activity recognition, and receive a low false alarm for detection of irrelevant appliance in a real home environment. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T05:48:19Z (GMT). No. of bitstreams: 1 ntu-99-R97922026-1.pdf: 909759 bytes, checksum: 2fd6c49290bd380c51ba6c2393f2e8a1 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | Acknowledgments i
Abstract iii List of Figures ix List of Tables xi Chapter 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Chapter 2 Related Work 5 2.1 Energy Conservation Issues and Application . . . . . . . . . . . . . . . . 5 2.1.1 Providing Energy Information and Saving Tips . . . . . . . . . . 5 2.1.2 Context-Aware Applications . . . . . . . . . . . . . . . . . . . . 6 2.2 Activity Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Sensor-Based Approaches . . . . . . . . . . . . . . . . . . . . . 7 2.2.2 Web-Based Approaches . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Commonsense . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Chapter 3 Energy Conservation Service 11 3.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.1 Term and Notation Definition . . . . . . . . . . . . . . . . . . . 13 3.1.2 Activity-Appliance Model . . . . . . . . . . . . . . . . . . . . . 15 3.1.3 Detection of Irrelevant Appliance . . . . . . . . . . . . . . . . . 16 Chapter 4 Activity-Appliance Model 19 4.1 Activity Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Data Collecting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2.1 Data Source: Common Sense . . . . . . . . . . . . . . . . . . . 20 4.2.2 Common Sense Collected Question . . . . . . . . . . . . . . . . 22 4.2.3 Common Sense Relation . . . . . . . . . . . . . . . . . . . . . . 22 4.3 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.3.1 Voting Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.3.2 Concept Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.3.3 Rule Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.4 Weight Calculating . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Chapter 5 Irrelevant Appliance Detection System 29 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5.2 Processes of detecting irrelevant appliance system . . . . . . . . . . . . . 30 5.2.1 Selecting Target Appliances . . . . . . . . . . . . . . . . . . . . 30 5.2.2 Short Query Transforming . . . . . . . . . . . . . . . . . . . . . 32 5.2.3 Activities Similarity Computing . . . . . . . . . . . . . . . . . . 34 5.2.4 Irrelevant Appliances Detecting . . . . . . . . . . . . . . . . . . 34 Chapter 6 Experiment and Evaluation 37 6.1 Activity-Appliance Model . . . . . . . . . . . . . . . . . . . . . . . . . 37 6.1.1 Experiment Design . . . . . . . . . . . . . . . . . . . . . . . . . 37 6.1.2 Model Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 6.2 Irrelevant Appliances Detection . . . . . . . . . . . . . . . . . . . . . . 43 6.2.1 Evaluation Interface Design . . . . . . . . . . . . . . . . . . . . 44 6.2.2 Experiment Design . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.2.3 Result of Recognizing Electricity Activity . . . . . . . . . . . . . 48 6.2.4 Result of Detecting Irrelevant Appliances . . . . . . . . . . . . . 50 Chapter 7 Conclusion 53 Bibliography 55 | |
| dc.language.iso | en | |
| dc.subject | 活動非相關電器 | zh_TW |
| dc.subject | 節能服務 | zh_TW |
| dc.subject | 常識 | zh_TW |
| dc.subject | 詞頻-逆向文件頻 率 | zh_TW |
| dc.subject | 活動電器關聯模型 | zh_TW |
| dc.subject | commonsense | en |
| dc.subject | TF-IDF | en |
| dc.subject | activity-appliance models | en |
| dc.subject | irrelevant appliance | en |
| dc.subject | energy conservation service | en |
| dc.title | 使用活動電器關聯模型偵測活動非相關電器 | zh_TW |
| dc.title | Detection of Irrelevant Appliance using
Activity-Appliance Models | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳俊良(Jiann-Liang Chen),蔡宗翰(Tzong-Han Tsai),李育杰(Yuh-Jye Lee),梁敏雄(Min-Siong Liang) | |
| dc.subject.keyword | 節能服務,活動非相關電器,活動電器關聯模型,詞頻-逆向文件頻 率,常識, | zh_TW |
| dc.subject.keyword | energy conservation service,irrelevant appliance,activity-appliance models,TF-IDF,commonsense, | en |
| dc.relation.page | 56 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-19 | |
| 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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