null

论文期刊

论文标题    An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT
作者    XiaoxinWang, Hongli Hu, Huiqin Jia and Kaihao Tang
发表/完成日期    2017-12-09
期刊名称    sensors
期卷    17
相关文章   
论文简介    Abstract: Electrical capacitance tomography (ECT) is a promising imaging technology of permittivity distributions in multiphase flow. To reduce the effect of charging phenomenon on ECT measurement, an improved extreme learning machine method combined with adaptive soft-thresholding (AST-ELM) is presented and studied for image reconstruction. This method can provide a nonlinear mapping model between the capacitance values and medium distributions by using machine learning but not an electromagnetic-sensitive mechanism. Both simulation and experimental tests are carried out to validate the performance of the presented method, and reconstructed images are evaluated by relative error and correlation coefficient. The results have illustrated that the image reconstruction accuracy by the proposed AST-ELM method has greatly improved than that by the conventional methods under the condition with charging object. Keywords: electrical capacitance tomography (ECT); charging phenomenon; extreme learning machine; adaptive soft-thresholding
返回