English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 62796/95837 (66%)
造访人次 : 3641384      在线人数 : 354
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻


    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/118912


    题名: Retrospective analysis for phase I statistical process control and process capability study using revised sample entropy
    作者: Chang, Shing I.;Zhang, Zheng;Koppel, Siim;Malmir, Behnam;Kong, Xianguang;Tsai, Tzong-Ru;Wang, Donghai
    关键词: Sample entropy;Change points;Process capability analysis;Statistical process control
    日期: 2018-06-13
    上传时间: 2020-07-14 12:10:38 (UTC+8)
    出版者: Springer U K
    摘要: This study explored a new nonparametric analytical method for identifying heterogeneous segments in time-series data for data-abundant processes. A sample entropy (SampEn) algorithm often used in signal processing and information theory can also be used in a time series or a signal stream, but the original SampEn is only capable of quantifying process variation changes. The proposed algorithm, the adjusted sample entropy (AdSEn), is capable of identifying process mean shifts, variance changes, or mixture of both. A simulation study showed that the proposed method is capable of identifying heterogeneous segments in a time series. Once segments of change points are identified, any existing change-point algorithms can be used to precisely identify exact locations of potential change points. The proposed method is especially applicable for long time series with many change points. Properties of the proposed AdSEn are provided to demonstrate the algorithm’s multi-scale capability. A table of critical values is also provided to help users accurately interpret entropy results.
    關聯: Neural Computing and Applications 31, p.7415-7428
    DOI: 10.1007/s00521-018-3556-4
    显示于类别:[統計學系暨研究所] 期刊論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML58检视/开启

    在機構典藏中所有的数据项都受到原著作权保护.

    TAIR相关文章

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