圖模型分解運用於活動辨識與推論｜國立臺灣科技大學博碩士論文系統

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研究生：	孫郁喬 Yu-Chiao Sun
論文名稱：	圖模型分解運用於活動辨識與推論 Model Decomposition for Activity Recognition and Reasoning
指導教授：	鮑興國 Hsing-Kuo Pao
口試委員:	李育杰 Yuh-Jye Lee 項天瑞 Tien-Ruey Hsiang 孫敏德 Min-Te Sun
學位類別：	碩士 Master
系所名稱：	電資學院 - 資訊工程系 Department of Computer Science and Information Engineering
論文出版年：	2016
畢業學年度：	104
語文別：	英文
論文頁數：	55
相關次數：	點閱：442 下載：4
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Recommendation Letter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Approval Letter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Abstract in Chinese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract in English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
List of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1 Bayesian Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Quick Medical Reference . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1 Building Quick Medical Reference Model . . . . . . . . . . . . . 9
2.2 Quickscore Algorithm . . . . . . . . . . . . . . . . . . . . . . . 10
3 Unweighted Pair Group Method with Arithmetic Mean . . . . . . . . . . 13
4 Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
vi3.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.1 Sensor Deployment . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2 Activities and Location . . . . . . . . . . . . . . . . . . . . . . . 18
Experiment and result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1 Bayesian Network Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . 22
2 Experimental setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3 Data preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Experiment,Result and Analysis . . . . . . . . . . . . . . . . . . . . . . 25
4.1 Activity Recognition . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2 Hierarchical Clustering . . . . . . . . . . . . . . . . . . . . . . . 27
4.3 Power and Bandwidth Saving. . . . . . . . . . . . . . . . . . . . 31
4.4 Model Decomposition . . . . . . . . . . . . . . . . . . . . . . . 35
4.5 Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

                                

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