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Resistance / Capacitance Tomography Dual-mode Technology

Posted on:2012-05-02Degree:MasterType:Thesis
Country:ChinaCandidate:J B ZhouFull Text:PDF
GTID:2208330338990993Subject:Circuits and Systems
Abstract/Summary:PDF Full Text Request
Process tomography provides an effective field parameters testing for complex, widely existent two-phase flow system. Electrical Resistance Tomography(ERT) and Eletrical Capacitance Tomography(ECT) are two mainstream methods in Electrical Tomography field. They have some characters: non-radiative, non-invasion, rapid-response, low-cost and visualization measurement etc. So there is a broad development prospects in the industrial process and other fields for ERT and ECT. Usually, they are uesd in different places. ERT is used in measure two-phas flow in which conductive medium is taken as continuous phase. And ECT is applied to measure two-phase flow in which non-conductive medium is taken as continuous phase. In order to get more widely measuring range, and provide the redundant information, this paper mainly studies the dual-mode tomography .Firstly,according to the comparability of ERT and ECT, the unified mathematical model is established, and deduced the functionals of the two mode.Secondly,the finite element mathod is used to solve the positive question of ERT and ECT. Basic that mathod, section of round containers is analyse and simulated.Thirdly,it is about reconstructing image. This paper adoptes the neural network thought. Error back propagation algorithm and spline weight function algorithm are used. Because of deficiencies, two new ideas are proposed. After that, Simulation results are given by the improvement methods. Based on the comparison of image reconstruction effect, it is proved that the Spline-weight-funnction has a lot of advantages.Lastly, the design of hardware and calculation of transform circuit are studied. It is proved that the design planst is feasibled by analyzing the results.
Keywords/Search Tags:Electrical capacitance tomography, Electrical resistance tomography, finite element, Neural network, Weight-funnction
PDF Full Text Request
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