Xuebin Qin1,*, Yutong Shen1, Jiachen Hu1, Mingqiao Li1, Peijiao Yang1, Chenchen Ji1, Xinlong Zhu2
CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1699-1717, 2022, DOI:10.32604/cmes.2022.018234
- 30 December 2021
Abstract Electrical capacitance tomography (ECT) has great application potential in multiphase process monitoring, and its visualization results are of great significance for studying the changes in two-phase flow in closed environments. In this paper, compressed sensing (CS) theory based on dictionary learning is introduced to the inverse problem of ECT, and the K-SVD algorithm is used to learn the overcomplete dictionary to establish a nonlinear mapping between observed capacitance and sparse space. Because the trained overcomplete dictionary has the property to match few features of interest in the reconstructed image of ECT, it is not necessary More >
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