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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Appliance Control by TDoA-based Localization and Gesture Recognition
(2018-01-01)
Chiang, Ting-Hui
;
Chen, Yan-Ann
;
Chang, Chun-Ting
;
Chen, Ling-Jyh
;
Tseng, Yu-Chee
Smart home is one of the major Internet of Things (IoT) applications. When realizing a smart home, designing an intuitive interaction for appliance control has become a crucial issue. A control intention to an appliance comprises two main meanings, identification and control command. But, previous works seldom deliver these two meanings at the same time. To fully convey the control intention, this paper proposes a scheme of intuitive appliance control that tracks and recognizes hand moving trajectory and hand gestures respectively by exploiting acoustic information. We utilize a 3D positioning method based on time difference of arrival (TDoA) of acoustic signals to detect the trajectory of a hand movement for appliance identification and design a gesture recognition method by Doppler effect for control command. We validate the effectiveness of the proposed scheme via real-life experiments. The results show that average locating errors to the targets are 5.24 degrees and 10.35 degrees in the horizontal and vertical planes respectively and the accuracy of gesture recognition is 96.66%. Since human's eyesight is horizontal to the ground, locating an object in the vertical view is error-prone. We also provide a scroll gesture to assist the appliance selection.
Gesture-based Control in a Smart Home Environment
(2017-01-01)
Alemuda, Fariz
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Lin, Fuchun Joseph
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資訊工程學系
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電機資訊國際碩士學位學程
;
Department of Computer Science
Gesture is a convenient and natural way to control a smart home. The wearable device provides an excellent vehicle for getting a user's hand gesture. Recognition models for gestures can be divided into two types: user dependent and user independent. In this research, we propose a hybrid model that combines both user dependent and user independent models to distinguish a user's hand gestures. Our research investigates which model among three is the best approach for recognizing hand gestures. We employ ten hand gestures as the test cases for comparison. First, from a 6-axis wearable device we extract features based on the collected raw data of hand gestures. Then these extracted features are analyzed by a oneM2M-compliant platform to detect gestures based on Decision Tree and Logistic Regression algorithms. With a data set of over 7 users and 20 repetitions of tests for each user, we tested the effectiveness of recognition models and gesture detection algorithms. The results show that our proposed hybrid model could achieve the best accuracy with either of two detection algorithms.
IoTtalk-RC: Sensors As Universal Remote Control for Aftermarket Home Appliances
(2017-08-01)
Lin, Yun-Wei
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Lin, Yi-Bing
;
Hsiao, Chung-Yun
;
Wang, Yun-Yen
;
資訊工程學系
Almost half of all Americans either own smart home technology or plan to invest in it. Therefore, many commercial smart IoT solutions aim for home automation, and try to live up with high expectation for smart home. A major problem of these smart home solutions is that they cannot flexibly accommodate the existing remotely controlled aftermarket appliances. This paper proposes IoTtalk remote control (IoTtalk-RC), a mechanism that utilizes sensors as universal software-defined remote control for aftermarket home appliances. Our solution replaces the infrared remote controller of cheap aftermarket home appliances and provides extra intelligence of sensors that can be manipulated by the homeowners. We conduct measurements, analytic analysis and simulation experiments to investigate the time complexity of IoTtalk-RC. This paper indicates that the extra transmission delays of remote control incurred by IoTtalk-RC are small even if the IoTtalk server is remotely located at a virtual machine of a cloud. In other words, IoTtalk-RC is feasible and can be effectively operated with the IoT technology.
A Novel System for Extracting Useful Correlation in Smart Home Environment
(2013)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Lee, Wang-Chien
;
資訊工程學系
;
Department of Computer Science
Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this paper, a novel system, namely, Correlation Pattern Mining System (CPMS), is developed to capture the usage patterns and correlations among appliances. With several new optimization techniques, CPMS can reduce the search space effectively and efficiently. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.
Significant Correlation Pattern Mining in Smart Homes
(2015-05-01)
Chen, Yi-Cheng
;
Peng, Wen-Chih
;
Huang, Jiun-Long
;
Lee, Wang-Chien
;
資訊工程學系
Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.
A Novel Search Scheme for Nonintrusive Load Monitoring Systems
(2012)
Lai, Yi-Sheng
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Chen, Yung-Chi
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Tsao, Shiao-Li
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Tsai, Tzung-Cheng
;
資訊工程學系
A nonintrusive load monitoring (NILM) system analyzes the changes of voltage and current on an electrical circuit and can disaggregate the usage and power consumption of appliances on the same circuit loop according to different power consumption signatures of the appliances. An NILM system is regarded as one of the most important features for energy monitoring and management in a smart home and building. Advances in computing, communication and storage technologies further make an NILM system possible to accommodate a huge amount of power consumption signatures of appliances, and to detect appliances and/or appliance states through database search techniques. In this study, we propose a low-cost NILM system for a smart home and building to facilitate end users contributing the power consumption signatures of their appliances to a datacenter through confirmations or feedbacks of the NILM search. Based on those user contributed datasets, a new NILM search algorithm is then presented in this paper. To evaluate and compare the performance and accuracy of NILM search results by employing the proposed and existing approaches, an evaluation test-bed is established. The experimental results demonstrate that the proposed scheme outperforms the existing algorithms and can achieve 85% to 96% accuracy of appliance state detections.
TagFree: Identifying users without tags in smart home environments
(2007)
Peng, Wen-Chih
;
Huang, WangTing
;
Chen, YiLing
;
Liao, PeiChing
;
資訊工程學系
Since family members have their unique features when living in a smart home environment, user identifications are able to achieve without any tags. In this paper, we propose TagFree system in which users freely move in a smart home environment and TagFree system is able to intelligently identify family member according to sensed data. Specifically, TagFree system consists of two phases: the training phase and the prediction phase. In the training phase, sensed data are collected and then, given a huge amount of sensed data, the profile of users, including the most common sensed data (i.e., tones, weights and location), are discovered. Once the profile of users is built up, in the prediction phase, we propose two scoring algorithms to generate likelihood scores according to the sensed data given. A simulation is implemented to verify the correctness of our proposed system and extensive experiments are conducted. Experimental results show that our proposed TagFree is able to achieve high accuracy of identifying family member without any tags. Furthermore, from experimental results, we also provided some guidelines to set some important parameters for TagFree.

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