Issue |
MATEC Web Conf.
Volume 176, 2018
2018 6th International Forum on Industrial Design (IFID 2018)
|
|
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Article Number | 01032 | |
Number of page(s) | 5 | |
Section | Intelligent Design and Computer Technology | |
DOI | https://doi.org/10.1051/matecconf/201817601032 | |
Published online | 02 July 2018 |
An Improved Normalized Model of Electrical Capacitance Tomography
1
College of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China;
2
Yunnan Electric Power Research Institute, Kunming, 650217, China
3
Department of Automation, North China Electric Power University, 071003, China
*
Corresponding author : Yan Jiangbao:120958764@qq.com
Electrical capacitance tomography (ECT) technology is a process tomography, which is based on medical CT technology and develops together with computer and sensor technology. The measured capacitances by ECT system are usually normalized at the high and low permittivity for image. Parallel normalization model is commonly used during normalization process, which assumes the distribution of materials in parallel. And thus, the normalized capacitance is a linear function of measured capacitance. The usage of series normalization model results in the normalized capacitance as a nonlinear function of measured capacitance. The research on parallel, series normalization model and hybrid normalization model. In this paper, we propose a method based on the center electric field line to determine the model weighting factor for the hybrid normalization model. The simulation and static test results show that the reconstructed image quality is improved obviously by the hybrid normalization model based on the center electric field line.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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