成功大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	陳哲文 Chen, Che-Wen
論文名稱：	基於機器學習與深度學習之關懷科技研究與應用 Research and Application of Care Technology based on Machine Learning and Deep Learning
指導教授：	王駿發 Wang, Jhing-Fa
學位類別：	博士 Doctor
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	109
中文關鍵詞：	橘色科技、關懷科技、深度學習、機器學習、物聯網、自然語言處理
外文關鍵詞：	Orange Technology, Care Technology, Deep Learning, IOT, NLP
相關次數：	點閱：130 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

科技發展確實帶來了經濟與生活的改善，但也造成了物質化和貧富懸殊等社會問題，尤其隨著忙碌的生活步調，使得人與人之間的關係逐漸冷漠、疏遠。生活壓力的驟增，也造成了人們心靈空虛寂寞的感受。因此許多有識之士高呼必須回歸人本層面和加強人文精神，2002年諾貝爾經濟學獎得主丹尼爾卡內曼也透過他的著作"Well-Being"，闡明快樂、幸福及心理學的基礎。王駿發教於2008年提出「橘色科技」概念，本著以「人本」之精神，藉由跨領域的合作，讓科技成為打造幸福的泉源。目的在於實行人本主義科技的基本價值，加強人本科技的研究與提倡，包括健康科技、幸福科技，以及關懷科技，為人本科技的總稱。
為了提升人們的快樂及幸福感，本研究旨在藉由機器學習與深度學習與關懷科技領域的研究與應用，設計家用機器人之移動系統、門診分類對話機器人及幸福杯動作辨識應用在家庭環境、健康醫藥產業及日常生活領域。研究結果顯示，本研究提出的方法能有效地提高各系統的性能，進而推動關懷科技的發展，持續實踐人本主義科技的基本價值，加強關懷科技的研究與提倡。

The development of science and technology has improved the economy and life, but it has also caused social problems such as materialization and disparity between the rich and the poor. Especially with the busy rhythm of life, the relationship between people has gradually become cold and alienated. The sudden increase in life pressure has also caused people to feel empty and lonely. Therefore, many scholars shouted that they must return to the human level and strengthen the humanistic spirit. In 2002, the Nobel Prize winner in economics - Daniel Kaneman explained the foundation of happiness, happiness and psychology through his book "Well-Being". After that, Jhing-Fa Wang taught the concept of "Orange Technology" in 2008. In the spirit of "humanism", through cross-disciplinary cooperation, technology has become a source of happiness. The purpose is to implement the basic value of humanistic technology and strengthen the research and promotion of humanistic technology, mainly including health technology, happiness technology and care technology. In order to enhance people's happiness and well-being, this research aims to use research and application in the eld of machine learning and deep learning and care technology. We proposed mobile systems for home robots, outpatient classifi cation dialogue robots, and happiness-cup system. And we implemented them in house environment, health medicine industry and daily life.
The research results show that the method proposed in this study can e ffectively improve the performance of each system. Then promote the development of care technology, practice the basic value of humanistic technology, strengthen the research and promotion of care technology.

中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2.1 Objectives of Home Care Technology . . . . . . . . . . . . . . . 2
2.2 Objectives of Health Care Technology . . . . . . . . . . . . . . . 3
2.3 Objectives of Life Care Technology . . . . . . . . . . . . . . . . 3
3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4 Dissertation Organization . . . . . . . . . . . . . . . . . . . . . . . . . 6
Home Care Technology:
Obstacle Avoidance for
Mobile System of Home Robot . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1 Target Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Obstacles Avoidance . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3 Simultaneous Localization And Mapping . . . . . . . . . . . . . 11
3 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.1 Robot Mechanism Architecture . . . . . . . . . . . . . . . . . . 12
3.2 Hardware Architecture . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 Mobile platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.4 Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1 User Target Tracking . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2 Path obstacles Avoidance . . . . . . . . . . . . . . . . . . . . . 19
5 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5.1 Experiment Environment . . . . . . . . . . . . . . . . . . . . . . 25
5.2 Users Tracking Results . . . . . . . . . . . . . . . . . . . . . . . 26
5.3 Obstacle Avoidance Results . . . . . . . . . . . . . . . . . . . . 28
6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Health Care Technology:
Outpatient Text Classication for
Robot-Assisted Servicing in Hospital . . . . . . . . . . . . . . . . . . . . . . 31
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.1 Machine Learning-Based Model . . . . . . . . . . . . . . . . . . 34
2.2 Deep Learning-Based Model . . . . . . . . . . . . . . . . . . . . 35
3 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.1 Robot System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.2 Web Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 Experimental Environment . . . . . . . . . . . . . . . . . . . . . 38
3.4 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.1 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2 Attention-Based Bidirectional LSTM Model . . . . . . . . . . . 43
4.3 BERT Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.4 Fine-tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.5 Softmax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
5 Experimental Evaluation and Results . . . . . . . . . . . . . . . . . . . 49
5.1 Experimental Datasets . . . . . . . . . . . . . . . . . . . . . . . 50
5.2 Parameter Setting . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.3 Comparison with Other Methods . . . . . . . . . . . . . . . . . 52
5.4 Evaluation Settings . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 54
6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Life Care Technology:
On the Design of Happiness-cups System . . . . . . . . . . . . . . . . . . . . 59
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
2 Proposed System Architecture and Design . . . . . . . . . . . . . . . . 62
2.1 Happiness-cups . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.2 Android Application for Connection between Cup and Server . . 64
2.3 Server for Pairing Distant Cups . . . . . . . . . . . . . . . . . . 66
3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.1 Data Acquistion . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.3 Long Short-Term Memory Network . . . . . . . . . . . . . . . . 73
3.4 Softmax Classier . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.5 Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.1 Experimental Environment . . . . . . . . . . . . . . . . . . . . . 78
4.2 Dataset Collection . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.3 Parameter Setting . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.4 Comparison Methods . . . . . . . . . . . . . . . . . . . . . . . . 81
4.5 Evaluation Settings . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.6 Experimental Results for Motion Recognition Methods . . . . . 83
4.7 Matrix Confusion Report of Experimental Results . . . . . . . . 85
4.8 Power Consumption . . . . . . . . . . . . . . . . . . . . . . . . 89
4.9 Questionnaire Result . . . . . . . . . . . . . . . . . . . . . . . . 90
5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
                                    

[1] Jhing-Fa Wang. Origin and development of orange technology. Scientific Devel- opment, 466:6–9, 2011.
[2] Kuan-Yu Chen, Chien-Hung Chen, Chia-Yun Lin, Jing-Huei Tsai, and Hung- Ying Chung. Development of optimal path planning based on ant colony and wireless sensor network localization techniques for an autonomous mobile service robot. In Information Science, Electronics and Electrical Engineering (ISEEE), 2014 International Conference on, volume 2, pages 954–959. IEEE, 2014.
[3] Ippei Samejima, Yuma Nihei, Naotaka Hatao, Satoshi Kagami, Hiroshi Mi- zoguchi, Hiroshi Takemura, and Akihiro Osaki. Building environmental maps of human activity for a mobile service robot at the” miraikan” museum. In Field and Service Robotics, pages 409–422. Springer, 2015.
[4] A Pandey, S Pandey, and DR Parhi. Mobile robot navigation and obstacle avoid- ance techniques: A review. Int Rob Auto J, 2(3):00022, 2017.
[5] Yasuharu Kunii, Gabor Kovacs, and Naoaki Hoshi. Mobile robot navigation in natural environments using robust object tracking. In Industrial Electronics (ISIE), 2017 IEEE 26th International Symposium on, pages 1747–1752. IEEE, 2017.
[6] Kang Li, Xiaoguang Zhao, Shiying Sun, and Min Tan. Robust target tracking and following for a mobile robot. International Journal of Robotics and Automation, 33(4), 2018.
[7] Qinghua Xiao, Fuchun Sun, Rui Ge, Kunlun Chen, and Bin Wang. Human tracking and following of mobile robot with a laser scanner. In Advanced Robotics and Mechatronics (ICARM), 2017 2nd International Conference on, pages 675– 680. IEEE, 2017.
[8] Shujing Zhang, Lei Zhang, Ri Gao, and Chenxi Liu. Mobile robot moving target detection and tracking system. In Proceedings of the 2017 The 7th International Conference on Computer Engineering and Networks. 22-23 July, 2017 Shanghai, China (CENet2017) Online at href=” https://pos. sissa. it/cgi-bin/reader/conf. cgi? confid= 299”¿ https://pos. sissa. it/cgi-bin/reader/conf. cgi? confid= 299, id. 60, 2017.
[9] Emrah Benli, Yuichi Motai, and John Rogers. Human behavior-based target tracking with an omni-directional thermal camera. IEEE Transactions on Cog- nitive and Developmental Systems, 2017.
[10] Tao Feng, Yao Yu, Lin Wu, Yanru Bai, Ziang Xiao, and Zhen Lu. A human- tracking robot using ultra wideband technology. IEEE Access, 6:42541–42550, 2018.
[11] Ivan Markovi´c, Fran¸cois Chaumette, and Ivan Petrovi´c. Moving object detec- tion, tracking and following using an omnidirectional camera on a mobile robot. In Robotics and Automation (ICRA), 2014 IEEE International Conference on, pages 5630–5635. IEEE, 2014.
[12] Ryan K Williams and Gaurav S Sukhatme. Observability in topology-constrained multi-robot target tracking. In Robotics and Automation (ICRA), 2015 IEEE International Conference on, pages 1795–1801. IEEE, 2015.
[13] Li Sun, Zhi Yan, Sergi Molina Mellado, Marc Hanheide, and Tom Duckett. 3dof pedestrian trajectory prediction learned from long-term autonomous mo- bile robot deployment data. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 1–7. IEEE, 2018.
[14] Abul Al Arabi, Pranabesh Sarkar, Faysal Ahmed, Waliur Rahman Rafie, Mahfuz Hannan, and M Ashraful Amin. 2d mapping and vertex finding method for path planning in autonomous obstacle avoidance robotic system. In Control and Robotics Engineering (ICCRE), 2017 2nd International Conference on, pages 39–42. IEEE, 2017.
[15] Patrick Kapita Mvemba, Simon Kidiamboko Guwa Gua Band, Aime Lay- Ekuakille, and Nicola Ivan Giannoccaro. Advanced acoustic sensing system on a mobile robot: design, construction and measurements. IEEE Instrumentation & Measurement Magazine, 21(2):4–9, 2018.
[16] Zahra Ziaei, Reza Oftadeh, and Jouni Mattila. Global path planning with obsta- cle avoidance for omnidirectional mobile robot using overhead camera. In Mecha- tronics and Automation (ICMA), 2014 IEEE International Conference on, pages 697–704. IEEE, 2014.
[17] Alessandro Giusti, J´erˆome Guzzi, Dan C Ciresan, Fang-Lin He, Juan P Rodr´ıguez, Flavio Fontana, Matthias Faessler, Christian Forster, Ju¨rgen Schmid- huber, Gianni Di Caro, et al. A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robotics and Automation Letters, 1(2):661– 667, 2016.
[18] Abdulla Al-Kaff, Fernando Garc´ıa, David Mart´ın, Arturo De La Escalera, and Jos´e Mar´ıa Armingol. Obstacle detection and avoidance system based on monoc- ular camera and size expansion algorithm for uavs. Sensors, 17(5):1061, 2017.
[19] Albert S Huang, Abraham Bachrach, Peter Henry, Michael Krainin, Daniel Mat- urana, Dieter Fox, and Nicholas Roy. Visual odometry and mapping for au- tonomous flight using an rgb-d camera. In Robotics Research, pages 235–252. Springer, 2017.
[20] Weina Xi, Yongsheng Ou, Jiansheng Peng, and Gang Yu. A new method for indoor low-cost mobile robot slam. In Information and Automation (ICIA), 2017 IEEE International Conference on, pages 1012–1017. IEEE, 2017.
[21] Xiaotong Xie, Yao Yu, Xiaobo Lin, and Changyin Sun. An ekf slam algorithm for mobile robot with sensor bias estimation. In Automation (YAC), 2017 32nd Youth Academic Annual Conference of Chinese Association of, pages 281–285. IEEE, 2017.
[22] Tae Hyeon Nam, Jae Hong Shim, and Young Im Cho. A 2.5 d map-based mo- bile robot localization via cooperation of aerial and ground robots. Sensors, 17(12):2730, 2017.
[23] Renaud Dub´e, Abel Gawel, Hannes Sommer, Juan Nieto, Roland Siegwart, and Cesar Cadena. An online multi-robot slam system for 3d lidars. In Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on, pages 1004–1011. IEEE, 2017.
[24] Fei ZHONG and Yu-ning ZHONG. The application research of mamdani and sugeno style fuzzy inference [j]. Journal of Hubei Polytechnic University, 2, 2005.
[25] Spyros Tzafestas. Roboethics: Fundamental concepts and future prospects. In- formation, 9(6):148, 2018.
[26] Moran Ju, Haibo Luo, Zhongbo Wang, Bin Hui, and Zheng Chang. The ap- plication of improved yolo v3 in multi-scale target detection. Applied Sciences, 9(18):3775, 2019.
[27] Haiyan Wang and Zhiyu Zhou. A heuristic elastic particle swarm optimization algorithm for robot path planning. Information, 10(3):99, 2019.
[28] Khulan Batsuren and Dongwon Yun. Soft robotic gripper with chambered fingers for performing in-hand manipulation. Applied Sciences, 9(15):2967, 2019.
[29] Min Seop Lee, Yun Kyu Lee, Dong Sung Pae, Myo Taeg Lim, Dong Won Kim, and Tae Koo Kang. Fast emotion recognition based on single pulse ppg signal with convolutional neural network. Applied Sciences, 9(16):3355, 2019.
[30] Jaco Badenhorst and Febe De Wet. The usefulness of imperfect speech data for asr development in low-resource languages. Information, 10(9):268, 2019.
[31] Stuart J Russell and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.
[32] KC Santosh. Speech processing in healthcare: Can we integrate? In Intelligent Speech Signal Processing, pages 1–4. Elsevier, 2019.
[33] Qin Li, Shaobo Li, Sen Zhang, Jie Hu, and Jianjun Hu. A review of text corpus- based tourism big data mining. Applied Sciences, 9(16):3300, 2019.
[34] Chuan-Han Cheng, Chih-Yen Chen, Jie-Jhong Liang, Ting-Nan Tsai, Chih-Yin Liu, and Tzuu-Hseng S Li. Design and implementation of prototype service robot for shopping in a supermarket. In Advanced Robotics and Intelligent Systems (ARIS), 2017 International Conference on, pages 46–51. IEEE, 2017.
[35] Alessandro Massaro, Vincenzo Maritati, Nicola Savino, Angelo Galiano, Daniele Convertini, Emanuele De Fonte, and Maurizio Di Muro. A study of a health resources management platform integrating neural networks and dss telemedicine for homecare assistance. Information, 9(7):176, 2018.
[36] Lu Fei, Lu Na, and Li Jian. A new service composition method for service robot based on data-driven mechanism. In Computer Science & Education (ICCSE), 2014 9th International Conference on, pages 1038–1043. IEEE, 2014.
[37] Elena Garcia, Maria Antonia Jimenez, Pablo Gonzalez De Santos, and Manuel Armada. The evolution of robotics research. IEEE Robotics & Automation Mag- azine, 14(1):90–103, 2007.
[38] ASUS. Zenbo: Your smart little companion. https://zenbo.asus.com/tw/. Accessed 8 Oct 2018.
[39] Sepp Hochreiter and Ju¨rgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
[40] Hanna M Wallach. Topic modeling: beyond bag-of-words. In Proceedings of the 23rd international conference on Machine learning, pages 977–984. ACM, 2006.
[41] Marc Damashek. Gauging similarity with n-grams: Language-independent cate- gorization of text. Science, 267(5199):843–848, 1995.
[42] Thorsten Joachims. A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. Technical report, Carnegie-mellon univ pittsburgh pa dept of computer science, 1996.
[43] Andrew McCallum, Kamal Nigam, et al. A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization, volume 752, pages 41–48. Citeseer, 1998.
[44] Bruno Trstenjak, Sasa Mikac, and Dzenana Donko. Knn with tf-idf based frame- work for text categorization. Procedia Engineering, 69:1356–1364, 2014.
[45] Thorsten Joachims. Text categorization with support vector machines: Learning with many relevant features. In European conference on machine learning, pages 137–142. Springer, 1998.
[46] Prem Melville, Wojciech Gryc, and Richard D Lawrence. Sentiment analysis of blogs by combining lexical knowledge with text classification. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1275–1284. ACM, 2009.
[47] Zhang Yun-tao, Gong Ling, and Wang Yong-cheng. An improved tf-idf approach for text classification. Journal of Zhejiang University-Science A, 6(1):49–55, 2005.
[48] Hanhoon Kang, Seong Joon Yoo, and Dongil Han. Senti-lexicon and improved na¨ıve bayes algorithms for sentiment analysis of restaurant reviews. Expert Sys- tems with Applications, 39(5):6000–6010, 2012.

[49] Rie Johnson and Tong Zhang. Semi-supervised convolutional neural networks for text categorization via region embedding. In Advances in neural information processing systems, pages 919–927, 2015.
[50] Rie Johnson and Tong Zhang. Supervised and semi-supervised text categorization using lstm for region embeddings. arXiv preprint arXiv:1602.02373, 2016.
[51] Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empir- ical methods in natural language processing (EMNLP), pages 1532–1543, 2014.
[52] Duyu Tang, Furu Wei, Bing Qin, Nan Yang, Ting Liu, and Ming Zhou. Sen- timent embeddings with applications to sentiment analysis. IEEE Transactions on Knowledge and Data Engineering, 28(2):496–509, 2015.
[53] Yoon Kim. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882, 2014.
[54] Chunting Zhou, Chonglin Sun, Zhiyuan Liu, and Francis Lau. A c-lstm neural network for text classification. arXiv preprint arXiv:1511.08630, 2015.
[55] Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101, 2016.
[56] Fabian Pedregosa, Ga¨el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. Journal of machine learning research, 12(Oct):2825–2830, 2011.
[57] Mart´ın Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. Tensorflow: A system for large-scale machine learning. In 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pages 265–283, 2016.
[58] Shane Ryoo, Christopher I Rodrigues, Sara S Baghsorkhi, Sam S Stone, David B Kirk, and Wen-mei W Hwu. Optimization principles and application performance evaluation of a multithreaded gpu using cuda. In Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, pages 73–82. ACM, 2008.
[59] Gerard Salton, Anita Wong, and Chung-Shu Yang. A vector space model for automatic indexing. Communications of the ACM, 18(11):613–620, 1975.
[60] J Sun. ’jieba’ chinese word segmentation tool, 2012.
[61] Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov. Unsupervised learning of video representations using lstms. In International conference on machine learning, pages 843–852, 2015.
[62] Yoshua Bengio, Patrice Simard, and Paolo Frasconi. Learning long-term depen- dencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2):157–166, 1994.
[63] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre- training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
[64] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[65] Dell Zhang and Wee Sun Lee. Question classification using support vector ma- chines. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 26–32. ACM, 2003.
[66] Yufang Zhang, Shiming Peng, and Jia Lv. Improvement and application of tfidf method based on text classification. Jisuanji Gongcheng/ Computer Engineering, 32(19):76–78, 2006.
[67] Ed Diener. Subjective well-being. Psychological bulletin, 95(3):542, 1984.
[68] Carol D Ryff. Happiness is everything, or is it? explorations on the meaning of psychological well-being. Journal of personality and social psychology, 57(6):1069, 1989.
[69] Richard M Ryan and Edward L Deci. Self-determination theory and the facil- itation of intrinsic motivation, social development, and well-being. American psychologist, 55(1):68, 2000.
[70] H David Banta and Bryan R Luce. Health care technology and its assessment: an international perspective. 1993.
[71] Don E Detmer, Elaine B Steen, Richard S Dick, et al. The computer-based patient record: an essential technology for health care. National Academies Press, 1997.
[72] Richard J Holden and Ben-Tzion Karsh. The technology acceptance model: its past and its future in health care. Journal of biomedical informatics, 43(1):159– 172, 2010.
[73] Carol D Ryff and Corey Lee M Keyes. The structure of psychological well-being revisited. Journal of personality and social psychology, 69(4):719, 1995.
[74] Virginia SY Kwan, Michael Harris Bond, and Theodore M Singelis. Pancultural explanations for life satisfaction: Adding relationship harmony to self-esteem. Journal of personality and social psychology, 73(5):1038, 1997.
[75] Clare Madge, Julia Meek, Jane Wellens, and Tristram Hooley. Facebook, social integration and informal learning at university:”it is more for socialising and talking to friends about work than for actually doing work”. Learning, media and technology, 34(2):141–155, 2009.
[76] Robert Kraut, Michael Patterson, Vicki Lundmark, Sara Kiesler, Tridas Mukophadhyay, and William Scherlis. Internet paradox: A social technology that reduces social involvement and psychological well-being? American psy- chologist, 53(9):1017, 1998.
[77] Keith N Hampton, Lauren F Sessions, Eun Ja Her, and Lee Rainie. Social isolation and new technology. Pew Internet & American Life Project, 4, 2009.
[78] J. F. Wang, B. W. Chen, Y. Y. Chen, and Y. C. Chen. Orange computing: Challenges and opportunities for affective signal processing. In Signal Processing, Communications and Computing (ICSPCC), 2011 IEEE International Confer- ence on, pages 1–4, Sept 2011.
[79] Jhing-Fa Wang and Bo-Wei Chen. Orange computing: Challenges and opportu- nities for awareness science and technology. In Awareness Science and Technology (iCAST), 2011 3rd International Conference on, pages 533–535. IEEE, 2011.
[80] Hyemin Chung, Chia-Hsun Jackie Lee, and Ted Selker. Lover’s cups: drinking interfaces as new communication channels. In CHI’06 extended abstracts on Human factors in computing systems, pages 375–380. ACM, 2006.
[81] Paul R Hunter, Denis Zmirou-Navier, and Philippe Hartemann. Estimating the impact on health of poor reliability of drinking water interventions in developing countries. Science of the total environment, 407(8):2621–2624, 2009.
[82] Richard Feachem et al. Water, health and development. An interdisciplinary evaluation. 1978.
[83] Fereydoon Batmanghelidj. Water for Health, for Healing, for Life: You’re Not Sick, You’re Thirsty! Grand Central Publishing, 2008.
[84] Thomas Schl¨omer, Benjamin Poppinga, Niels Henze, and Susanne Boll. Ges- ture recognition with a wii controller. In Proceedings of the 2nd international conference on Tangible and embedded interaction, pages 11–14. ACM, 2008.
[85] Vesa-Matti M¨antyl¨a, Jani M¨antyj¨arvi, Tapio Sepp¨anen, and Esa Tuulari. Hand gesture recognition of a mobile device user. In IEEE International Conference on Multimedia and Expo (I), pages 281–284, 2000.
[86] David Sachs, Steven S Nasiri, Joseph Jiang, and Anjia Gu. Mobile devices with motion gesture recognition, October 22 2009. US Patent App. 12/252,322.
[87] Jiayang Liu, Lin Zhong, Jehan Wickramasuriya, and Venu Vasudevan. uwave: Accelerometer-based personalized gesture recognition and its applications. Per- vasive and Mobile Computing, 5(6):657–675, 2009.
[88] Patty S Freedson, Edward Melanson, and John Sirard. Calibration of the com- puter science and applications, inc. accelerometer. Medicine and science in sports and exercise, 30(5):777–781, 1998.
[89] Y. L. Hsu, C. L. Chu, Y. J. Tsai, and J. S. Wang. An inertial pen with dynamic time warping recognizer for handwriting and gesture recognition. IEEE Sensors Journal, 15(1):154–163, Jan 2015.
[90] Christoph Amma, Marcus Georgi, and Tanja Schultz. Airwriting: a wearable handwriting recognition system. Personal and Ubiquitous Computing, 18(1):191– 203, 2013.
[91] R. Xie, X. Sun, X. Xia, and J. Cao. Similarity matching-based extensible hand gesture recognition. IEEE Sensors Journal, 15(6):3475–3483, June 2015.
[92] A. Akl, C. Feng, and S. Valaee. A novel accelerometer-based gesture recognition system. IEEE Transactions on Signal Processing, 59(12):6197–6205, Dec 2011.
[93] Sian Lun Lau, Immanuel K¨onig, Klaus David, Baback Parandian, Christine Carius-Du¨ssel, and Martin Schultz. Supporting patient monitoring using ac- tivity recognition with a smartphone. In 2010 7th International Symposium on Wireless Communication Systems, pages 810–814. IEEE, 2010.
[94] Marco Cuturi, Jean-Philippe Vert, Oystein Birkenes, and Tomoko Matsui. A kernel for time series based on global alignments. In 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07, volume 2, pages II–413. IEEE, 2007.
[95] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016.
[96] Duc Ngoc Tran and Duy Dinh Phan. Human activities recognition in android smartphone using support vector machine. In 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), pages 64–68. IEEE, 2016.
[97] Sahak Kaghyan and Hakob Sarukhanyan. Activity recognition using k-nearest neighbor algorithm on smartphone with tri-axial accelerometer. International Journal of Informatics Models and Analysis (IJIMA), ITHEA International Sci- entific Society, Bulgaria, 1:146–156, 2012.
[98] Katherine Ellis, Jacqueline Kerr, Suneeta Godbole, Gert Lanckriet, David Wing, and Simon Marshall. A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiological measurement, 35(11):2191, 2014.
[99] Serge Bourobou and Younghwan Yoo. User activity recognition in smart homes using pattern clustering applied to temporal ann algorithm. Sensors, 15(5):11953–11971, 2015.
[100] Payam Refaeilzadeh, Lei Tang, and Huan Liu. Cross-validation. Encyclopedia of database systems, pages 532–538, 2009.
[101] John Cristian Borges Gamboa. Deep learning for time-series analysis. arXiv preprint arXiv:1701.01887, 2017.
[102] Felix A Gers, Douglas Eck, and Ju¨rgen Schmidhuber. Applying lstm to time series predictable through time-window approaches. In Neural Nets WIRN Vietri-01, pages 193–200. Springer, 2002.
[103] Tomoko Nemoto and David Beglar. Likert-scale questionnaires. In JALT 2013 Conference Proceedings, pages 1–8, 2014.

校內：2025-08-15公開
校外：不公開電子論文尚未授權公開，紙本請查館藏目錄

簡易檢索 / 詳目顯示

相關論文