A novel gaussian window approach for empirical mode decomposition

Shuen-De Wu*, Chiu Wen Wu, Cha Lin Liu, Yan Hao Huang, Kung Yen Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Empirical mode decomposition (EMD) is an algorithmic construction for decomposing multi-component signals into a series of intrinsic mode functions (IMFs). However, traditional EMD may encounter the difficulty of mode mixing when a signal contains intermittency. To solve the difficulty, a Gaussian window averaging method is proposed to construct the mean envelope of a given signal in each sifting process. The numerical analysis also demonstrates promising reliability with the proposed algorithm.

Original languageEnglish
Title of host publicationAdvanced Materials and Engineering Materials
Pages274-277
Number of pages4
DOIs
Publication statusPublished - 2012
Event2011 International Conference on Advanced Materials and Engineering Materials, ICAMEM2011 - Shenyang, Liaoning, China
Duration: 2011 Nov 222011 Nov 24

Publication series

NameAdvanced Materials Research
Volume457-458
ISSN (Print)1022-6680

Other

Other2011 International Conference on Advanced Materials and Engineering Materials, ICAMEM2011
Country/TerritoryChina
CityShenyang, Liaoning
Period2011/11/222011/11/24

Keywords

  • Empirical mode decomposition
  • Gaussian
  • Intrinsic mode function
  • Mode mixing

ASJC Scopus subject areas

  • Engineering(all)

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