A novel gaussian window approach for empirical mode decomposition

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

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 Feb 21
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
CountryChina
CityShenyang, Liaoning
Period11/11/2211/11/24

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Wu, S-D., Wu, C. W., Liu, C. L., Huang, Y. H., & Lee, K. Y. (2012). A novel gaussian window approach for empirical mode decomposition. In Advanced Materials and Engineering Materials (pp. 274-277). (Advanced Materials Research; Vol. 457-458). https://doi.org/10.4028/www.scientific.net/AMR.457-458.274