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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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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Peng, Wen-ChihSubject: search.filters.subject.smart home

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Now showing 1 - 4 of 4
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    A Novel System for Extracting Useful Correlation in Smart Home Environment
    (2013)
    Chen, Yi-Cheng
    ;
    Peng, Wen-Chih
    ;
    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.
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    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.
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    An Intelligent System for Mining Usage Patterns from Appliance Data in Smart Home Environment
    (2012)
    Chen, Yi-Cheng
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    Ko, Yu-Lun
    ;
    Peng, Wen-Chih
    ;
    資訊工程學系
    ;
    Department of Computer Science
    In the last decade, considerable concern has arisen over the electricity saving due to the issue of reducing greenhouse gases. Previous studies on usage pattern utilization mainly are focused on power disaggregation and appliance recognition. Little attention has been paid to utilizing pattern mining for the target of energy saving. In this paper, we develop an intelligent system which analyzes appliance usage to extract users' behavior patterns in a smart home environment. With the proposed system, users can acquire the electricity consumption of each appliance for energy saving easily. In advance, if the electricity cost is high, users can observe the abnormal usage of appliances from the proposed system. Furthermore, we also apply our system on real-world dataset to show the practicability of mining usage pattern in smart home environment.
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    TagFree: Identifying users without tags in smart home environments
    (2007)
    Peng, Wen-Chih
    ;
    Huang, WangTing
    ;
    Chen, YiLing
    ;
    Liao, PeiChing
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    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.