During mine filling,the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion.Therefore,the visualization of the innermine filling of the...During mine filling,the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion.Therefore,the visualization of the innermine filling of the solid-liquid two-phase flow in the pipeline is important.This paper proposes a method based on capacitance tomography for the visualization of the solid-liquid distribution on the section of a filling pipe.A feedback network is used for electrical capacitance tomography reconstruction.This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.In the reconstruction process,the error in the linear back projection is removed;thus,the reconstruction problem becomes an accurate linear problem.The simulation results showthat the reconstruction accuracy of this algorithm is better than that of many traditional algorithms;furthermore,the reconstructed image artifacts are fewer,and the phase distribution boundary is clearer.This method can help determine the location and size of the caking and waste rock in the cross section of the pipeline more accurately and has great application prospects in the visualization of filling pipelines in mines.展开更多
Electrical impedance tomography(EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurement...Electrical impedance tomography(EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.展开更多
基金This research was supported by the National Natural Science Foundation of China(No.51704229)Outstanding Youth Science Fund of Xi’an University of Science and Technology(No.2018YQ2-01).
文摘During mine filling,the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion.Therefore,the visualization of the innermine filling of the solid-liquid two-phase flow in the pipeline is important.This paper proposes a method based on capacitance tomography for the visualization of the solid-liquid distribution on the section of a filling pipe.A feedback network is used for electrical capacitance tomography reconstruction.This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.In the reconstruction process,the error in the linear back projection is removed;thus,the reconstruction problem becomes an accurate linear problem.The simulation results showthat the reconstruction accuracy of this algorithm is better than that of many traditional algorithms;furthermore,the reconstructed image artifacts are fewer,and the phase distribution boundary is clearer.This method can help determine the location and size of the caking and waste rock in the cross section of the pipeline more accurately and has great application prospects in the visualization of filling pipelines in mines.
基金National Natural Science Foundation of China (Grant No. 60075009)
文摘Electrical impedance tomography(EIT) is a new computer tomography technology, which reconstructs an impedance (resistivity, conductivity) distribution, or change of impedance, by making voltage and current measurements on the object's periphery. Image reconstruction in EIT is an ill-posed, non-linear inverse problem. A method for finding the place of impedance change in EIT is proposed in this paper, in which a multilevel BP neural network (MBPNN) is used to express the non-linear relation between the impedance change inside the object and the voltage change measured on the surface of the object. Thus, the location of the impedance change can be decided by the measured voltage variation on the surface. The impedance change is then reconstructed using a linear approximate method. MBPNN can decide the impedance change location exactly without long training time. It alleviates some noise effects and can be expanded, ensuring high precision and space resolution of the reconstructed image that are not possible by using the back projection method.
