Electrical impedance imaging of binary mixtures with boundary estimation approach based on multilayer neural network

Hae Jin Jeon, Jae Hyoung Kim, Bong Yeol Choi, Kyung Youn Kim, Min Chan Kim, Sin Kim

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This paper presents a boundary estimation approach in electrical impedance imaging for binary-mixture fields based on multilayer neural network. The interfacial boundaries are expressed with the truncated Fourier series and the unknown Fourier coefficients are estimated with the multilayer neural network. Results from numerical experiments show that the proposed approach is insensitive to the measurement noise and has a good possibility in the visualization of binary mixtures for a real time monitoring.

Original languageEnglish
Pages (from-to)313-319
Number of pages7
JournalIEEE Sensors Journal
Volume5
Issue number2
DOIs
StatePublished - Apr 2005

Keywords

  • Binary mixtures
  • Boundary estimation
  • Electrical impedance tomography
  • Multilayer neural network

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