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Article

Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography

1
Engineering Tomography Laboratory (ETL), Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, UK
2
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
These two authors contribute equally for this paper.
Academic Editors: Theodore Kotsilieris, Ioannis E. Livieris and Ioannis Anagnostopoulos
Electronics 2021, 10(9), 1058; https://doi.org/10.3390/electronics10091058
Received: 9 March 2021 / Revised: 25 April 2021 / Accepted: 26 April 2021 / Published: 29 April 2021
(This article belongs to the Special Issue Regularization Techniques for Machine Learning and Their Applications)
A convolutional neural network (CNN)-based image reconstruction algorithm for two-phase material imaging is presented and verified with experimental data from a capacitively coupled electrical resistance tomography (CCERT) sensor. As a contactless version of electrical resistance tomography (ERT), CCERT has advantages such as no invasion, low cost, no radiation, and rapid response for two-phase material imaging. Besides that, CCERT avoids contact error of ERT by imaging from outside of the pipe. Forward modeling was implemented based on the practical circular array sensor, and the inverse image reconstruction was realized by a CNN-based supervised learning algorithm, as well as the well-known total variation (TV) regularization algorithm for comparison. The 2D, monochrome, 2500-pixel image was divided into 625 clusters, and each cluster was used individually to train its own CNN to solve the 16 classes classification problem. Inherent regularization for the assumption of binary materials enabled us to use a classification algorithm with CNN. The iterative TV regularization algorithm achieved a close state of the two-phase material reconstruction by its sparsity-based assumption. The supervised learning algorithm established the mathematical model that mapped the simulated resistance measurement to the pixel patterns of the clusters. The training process was carried out only using simulated measurement data, but simulated and experimental tests were both conducted to investigate the feasibility of applying a multi-layer CNN for CCERT imaging. The performance of the CNN algorithm on the simulated data is demonstrated, and the comparison between the results created by the TV-based algorithm and the proposed CNN algorithm with the real-world data is also provided. View Full-Text
Keywords: convolutional neural network (CNN); supervised deep learning; capacitively coupled electrical resistance tomography (CCERT); image reconstruction convolutional neural network (CNN); supervised deep learning; capacitively coupled electrical resistance tomography (CCERT); image reconstruction
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MDPI and ACS Style

Chen, Z.; Ma, G.; Jiang, Y.; Wang, B.; Soleimani, M. Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography. Electronics 2021, 10, 1058. https://doi.org/10.3390/electronics10091058

AMA Style

Chen Z, Ma G, Jiang Y, Wang B, Soleimani M. Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography. Electronics. 2021; 10(9):1058. https://doi.org/10.3390/electronics10091058

Chicago/Turabian Style

Chen, Zhuoran, Gege Ma, Yandan Jiang, Baoliang Wang, and Manuchehr Soleimani. 2021. "Application of Deep Neural Network to the Reconstruction of Two-Phase Material Imaging by Capacitively Coupled Electrical Resistance Tomography" Electronics 10, no. 9: 1058. https://doi.org/10.3390/electronics10091058

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