Image Reconstruction of Electrical Capacitance Tomography Based on Convolutional Neural Network and Finite Element Simulation

doi:10.16182/j.issn1004731x.joss.20-0849

Abstract

Abstract:

In order to resolve the nonlinear and ill-posed inverse problem of the image reconstruction of electrical capacitance tomography (ECT), an image reconstruction algorithm based on one-dimensional convolutional neural network (1D CNN) is presented. The nonlinear mapping relationship between the independent measurement value of ECT system and the gray value of reconstructed image is established by 1D CNN. Six typical flow regimes with random distribution are obtained by the finite element simulation software and a 1D CNN is successfully trained. Simulation and static experiments are carried out and the reconstructed images using linear back projection, Landweber iterative algorithm and 1D CNN are compared. Experimental results show that the 1D CNN algorithm has good generalization ability and the quality of reconstructed images by 1D CNN is obviously improved compared with the other two algorithms.

Key words: two-phase flow, electrical capacitance tomography, one-dimensional convolutional neural network, image reconstruction, finite element simulation

CLC Number:

TP391

Lifeng Zhang, Huiren Wang. Image Reconstruction of Electrical Capacitance Tomography Based on Convolutional Neural Network and Finite Element Simulation[J]. Journal of System Simulation, 2022, 34(4): 712-718.

Figures/Tables 11

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流型	指标	LBP	Landweber	1D CNN
1	RE	0.925 6	0.553 3	0.179 8
1	CC	0.587 7	0.830 0	0.983 1
2	RE	0.913 0	0.677 7	0.263 2
2	CC	0.422 0	0.753 6	0.963 4
3	RE	0.909 8	0.672 2	0.344 8
3	CC	0.429 1	0.758 1	0.930 0
4	RE	0.739 7	0.545 6	0.000 1
4	CC	0.571 1	0.771 2	0.999 9
5	RE	0.545 2	0.231 6	0.064 6
5	CC	0.859 8	0.900 3	0.992 5
6	RE	1.063 9	0.549 3	0.125 0
6	CC	0.679 4	0.717 8	0.986 8

算法	重建时间
LBP	0.005
Landweber	0.686
1D CNN	0.438

流型	指标	LBP	Landweber	1D CNN
1	RE	0.887 9	0.587 6	0.539 6
1	CC	0.517 9	0.796 0	0.840 5
2	RE	0.863 7	0.472 5	0.402 4
2	CC	0.607 4	0.868 5	0.904 0
3	RE	0.939 1	0.555 3	0.446 3
3	CC	0.433 9	0.823 6	0.877 2
4	RE	0.848 3	0.718 0	0.652 3
4	CC	0.433 4	0.655 6	0.703 6

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