淡江大學機構典藏:Item 987654321/108946
English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62805/95882 (66%)
Visitors : 3945596      Online Users : 575
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/108946


    Title: A New Habit Pattern Learning Scheme in Smart Home
    Authors: Wang, Pingquan;Luo, Hong;Li, Xinming;Zhao, Zhongwen
    Keywords: Smart Home;Habit Pattern;Time Series;Activity Recognition
    Date: 2016-03
    Issue Date: 2016-12-20 09:46:51 (UTC+8)
    Publisher: 淡江大學出版中心
    Abstract: Most of the user’s activities are consistent with their habits, therefore in this paper, we propose
    a new habit pattern learning scheme in smart home to better obtain user’s behavior regulations and
    habits, which can make the home more intellectually interact with people. By recording the operations
    on each electric appliance in the form of time series, we firstly find out that the habit can be classified
    into fixed-length habit and timing habit. Then, we propose habit extraction methods based on the
    corresponding activity probability and calculation formulas of the habit strength. Since different habits
    have different variation characteristics on habit strength and time zone, we further propose the
    self-learning algorithms on time zone and habit strength threshold to obtain the suitable parameters.
    Furthermore, by defining the association among individual habits into selection, parallel, sequence,
    cross and inclusion, we can obtain habit set which is a group of habits with inner correlation. In order to
    adapt to the habit variations, we introduce a habit change factor into the habit pattern discovery
    algorithm so as to follow the habit changes. Finally, we construct the experimental environment in a
    real smart home, analyze and calculate the operation records of electric appliances in two months. The
    experiment results show that the proposed habit pattern learning scheme is effective and efficient.
    Relation: Journal of Applied Science and Engineering 19(1), pp.83-94
    DOI: 10.6180/jase.2016.19.1.10
    Appears in Collections:[Journal of Applied Science and Engineering] v.19 n.1

    Files in This Item:

    File SizeFormat
    index.html0KbHTML343View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback