ZHANG Juan, HU Hongli , WANG Congcong ,CHEN Xia Dept. of Electrical Engineering , Xi’an Jiaotong University, Xi’an710049, China
Due to its many distinct advantages such as no invasion, high speed and low cost, Electrical Capacitance Tomography is becoming a very promising technology in two-phase flow process parameters measurement. Image reconstruction algorithm is an important part of ECT system. This paper is based on the study of 12-electrode electrical capacitance tomography system. Capacitances values are obtained by simulation using ANSYS, and they could be used for the sample data of the image reconstruction. BP neural network and Radial Basis Function (RBF) neural network are used in ECT image reconstruction. The algorithms are emulated and validated on Matlab. Simulation results showed that these algorithms are effective with both the accuracy and speed better than LBP algorithm, and the algorithm based on RBF neural network has a best effect.
Electrical Capacitance Tomography; Finite Element Model; Image reconstruction; Artificial neural network
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