Phase boundary estimation in electrical resistance tomography with weighted multilayer neural networks

Jae Hyoung Kim; Byoung Chae Kang; Bong Yeol Choi; Min Chan Kim; Sin Kim; Kyung Youn Kim

doi:10.1109/TMAG.2006.871671

Phase boundary estimation in electrical resistance tomography with weighted multilayer neural networks

Jae Hyoung Kim, Byoung Chae Kang, Bong Yeol Choi, Min Chan Kim, Sin Kim, Kyung Youn Kim

School of Electronics Engineering

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

This work presents a boundary estimation approach in electrical resistance imaging for binary mixture fields based on weighted multilayer neural network. The interfacial boundaries are expressed with the truncated Fourier series and the unknown Fourier coefficients are estimated with the weighted multilayer neural network. In doing so, normalized boundary voltages are used for training the neural network and the results from real experiments show that the proposed approach has strong possibility for real-time monitoring of binary mixtures.

Original language	English
Pages (from-to)	1191-1194
Number of pages	4
Journal	IEEE Transactions on Magnetics
Volume	42
Issue number	4
DOIs	https://doi.org/10.1109/TMAG.2006.871671
State	Published - Apr 2006

Keywords

Binary mixtures
Boundary estimation
Electrical resistance tomography
Multilayer neural network

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10.1109/TMAG.2006.871671

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title = "Phase boundary estimation in electrical resistance tomography with weighted multilayer neural networks",

abstract = "This work presents a boundary estimation approach in electrical resistance imaging for binary mixture fields based on weighted multilayer neural network. The interfacial boundaries are expressed with the truncated Fourier series and the unknown Fourier coefficients are estimated with the weighted multilayer neural network. In doing so, normalized boundary voltages are used for training the neural network and the results from real experiments show that the proposed approach has strong possibility for real-time monitoring of binary mixtures.",

keywords = "Binary mixtures, Boundary estimation, Electrical resistance tomography, Multilayer neural network",

author = "Kim, {Jae Hyoung} and Kang, {Byoung Chae} and Choi, {Bong Yeol} and Kim, {Min Chan} and Sin Kim and Kim, {Kyung Youn}",

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AU - Kang, Byoung Chae

AU - Choi, Bong Yeol

AU - Kim, Min Chan

AU - Kim, Sin

AU - Kim, Kyung Youn

PY - 2006/4

Y1 - 2006/4

N2 - This work presents a boundary estimation approach in electrical resistance imaging for binary mixture fields based on weighted multilayer neural network. The interfacial boundaries are expressed with the truncated Fourier series and the unknown Fourier coefficients are estimated with the weighted multilayer neural network. In doing so, normalized boundary voltages are used for training the neural network and the results from real experiments show that the proposed approach has strong possibility for real-time monitoring of binary mixtures.

AB - This work presents a boundary estimation approach in electrical resistance imaging for binary mixture fields based on weighted multilayer neural network. The interfacial boundaries are expressed with the truncated Fourier series and the unknown Fourier coefficients are estimated with the weighted multilayer neural network. In doing so, normalized boundary voltages are used for training the neural network and the results from real experiments show that the proposed approach has strong possibility for real-time monitoring of binary mixtures.

KW - Binary mixtures

KW - Boundary estimation

KW - Electrical resistance tomography

KW - Multilayer neural network

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ER -

Phase boundary estimation in electrical resistance tomography with weighted multilayer neural networks

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