Electrical capacitance tomography technique reconstructs dielectric constant distribution in an object by measuring the capacitances between the eletrode pairs which are mounted around this object. Because of the limi...Electrical capacitance tomography technique reconstructs dielectric constant distribution in an object by measuring the capacitances between the eletrode pairs which are mounted around this object. Because of the limitation of measurement condition, the measured data are imcomplet. This paper describes a multiresolution reconstructive algorithm which is based on network theory for electrical capacitance tomography technique. The dielectric constant distribution of flow of two components in a pipeline is reconstructed. The algorithm is as follows: Firstly, construct a rough, first level system model, and assume the dielectric constant distribution of the region to be reconstructed. After iteration, the dielectic constant of each unit can be reconstructed. Secondly, construct a finer, second level the system model and determine the initial dielectric constant of each unit in the region to be reconstructed according to related information between two levels. After iteration, the image of the pipeline's cross section can be reconstructed. The results of simulated experiments about different kinds of medium distributions show that this algorithm is effective and can converge.展开更多
Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power...Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.展开更多
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
In the long distance transportation of slurry filled for mining filling,there exist complex variation rules of pressure and flow velocity,pipe distribution location and other influencing factors.Electrical capacitance...In the long distance transportation of slurry filled for mining filling,there exist complex variation rules of pressure and flow velocity,pipe distribution location and other influencing factors.Electrical capacitance tomography(ECT)is a technique for visualizing two-phase flow in a pipe or closed container.In this paper,a visual detection method was proposed by image reconstruction of core,laminar,bubble and annular flow based on ECT technology,which reflects distribution of slurry in deep filling pipeline and measures the degree of blockage.There is an error between the measured and the real two-phase flow distribution due to two factors,which are immature image reconstruction algorithm of ECT and difference of flow patterns leading to degrees of error.In this paper,convolutional neural networks(CNN)is used to recognize flow patterns,and then the optimal image is calculated by the improved particle swarm optimization(PSO)algorithm with weights using simulated annealing strategy,and the fitness function is improved based on the results of the shallow neural network.Finally,the reconstructed binary image is further processed to obtain the position,size and direction of the blocked pipe.The realization of this method provides technical support for pipeline detection technology.展开更多
In this research thin film layers have been prepared at alternate layers of resistive and dielectric deposited on appropriate substrates to form four – terminal R-Y-NR network. If the gate of the MOS structures depos...In this research thin film layers have been prepared at alternate layers of resistive and dielectric deposited on appropriate substrates to form four – terminal R-Y-NR network. If the gate of the MOS structures deposited as a strip of resistor film like NiCr, the MOS structure can be analyzed as R-Y-NR network. A method of analysis has been proposed to measure the shunt capacitance and the shunt conductance of certain MOS samples. Mat lab program has been used to compute shunt capacitance and shunt conductance at different frequencies. The results computed by this method have been compared with the results obtained by LCR meter method and showed perfect coincident with each other.展开更多
为解决多接收全方向无线电能传输(Wireless Power Transfer,WPT)技术传输效率不高的问题,采用谐振网络优化设计的方法,通过增加一个串联抵消电容来减小接收线圈间互感的影响,并利用多目标优化算法中的加权法选择合适的抵消电容值,最后...为解决多接收全方向无线电能传输(Wireless Power Transfer,WPT)技术传输效率不高的问题,采用谐振网络优化设计的方法,通过增加一个串联抵消电容来减小接收线圈间互感的影响,并利用多目标优化算法中的加权法选择合适的抵消电容值,最后设计了全方向WPT试验平台,对比加入抵消电容前后系统传输效率的变化。结果表明:加入抵消电容前,系统的平均传输效率在61.9%,加入抵消电容后,系统的平均传输效率达到73.1%,系统的平均传输效率提高了11.2%;当负载由35Ω减小至25Ω时,系统输出电流基本保持不变;当传输距离在150~200 mm,接收线圈以任意角度旋转时,系统恒流输出,且传输效率保持在70%以上。研究结果为解决多接收全方向WPT系统中传输效率问题提供了改进的方法。展开更多
A cryogenic visible calibration and image evaluation facility(VCCIEF) was constructed to assess the effectiveness of electrical capacitance tomography systems in cryogenic conditions,known as Cryo-ECT.This facility wa...A cryogenic visible calibration and image evaluation facility(VCCIEF) was constructed to assess the effectiveness of electrical capacitance tomography systems in cryogenic conditions,known as Cryo-ECT.This facility was utilized to conduct dynamic,real-time imaging trials with liquid nitrogen(LN2).The actual flow patterns were captured using a camera and contrasted with the imaging outcomes.The capacitance data collected from these experiments were subsequently processed using three distinct methods:linear back projection,Landweber iteration,a fully connected deep neural network,and a convolutional neural network.This allowed for a comparative analysis of the performance of these algorithms in practical scenarios.The findings from the LN2 experiments demonstrated that the Cryo-ECT system,when integrated with the VCCIEF,was capable of successfully executing calibration,generating flow patterns,and performing imaging tasks.The system provided observable,clear,and precise phase distributions of the liquid nitrogen-vaporous nitrogenflow within the pipeline.展开更多
Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measure...Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance.To solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image enhancement.Firstly,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured.Then,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image feature-enhanced.Through multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be constructed.Finally,the image is reconstructed with adjusting wavelet coefficients.In this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm.Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].展开更多
This paper presents a new earth-fault detection algorithm for unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm is based on capacitance calculation from transient im...This paper presents a new earth-fault detection algorithm for unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm is based on capacitance calculation from transient impedance and dominant transient frequency. The Discrete Fourier Transform （DFT） method is used to determine the dominant transient frequency. The values of voltage and current earth modes are calculated in the period of the dominant transient frequency, then the transient impedance can be determined, from which we can calculate the earth capacitance. The calculated capacitance gives an indication about if the feeder is faulted or not. The algorithm is less dependent on the fault resistance and the faulted feeder parameters; it mainly depends on the background network. The network is simulated by ATP/EMTP program. Several different fault conditions are covered in the simulation process, different fault inception angles, fault locations and fault resistances.展开更多
文摘Electrical capacitance tomography technique reconstructs dielectric constant distribution in an object by measuring the capacitances between the eletrode pairs which are mounted around this object. Because of the limitation of measurement condition, the measured data are imcomplet. This paper describes a multiresolution reconstructive algorithm which is based on network theory for electrical capacitance tomography technique. The dielectric constant distribution of flow of two components in a pipeline is reconstructed. The algorithm is as follows: Firstly, construct a rough, first level system model, and assume the dielectric constant distribution of the region to be reconstructed. After iteration, the dielectic constant of each unit can be reconstructed. Secondly, construct a finer, second level the system model and determine the initial dielectric constant of each unit in the region to be reconstructed according to related information between two levels. After iteration, the image of the pipeline's cross section can be reconstructed. The results of simulated experiments about different kinds of medium distributions show that this algorithm is effective and can converge.
基金Shivaji University,Kolhapur for financial assistance through Research Strengthening Scheme。
文摘Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.
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Project(51704229)supported by the National Natural Science Foundation of ChinaProject(2018YQ2-01)supported by the Outstanding Youth Science Fund of Xi’an University of Science and Technology,China。
文摘In the long distance transportation of slurry filled for mining filling,there exist complex variation rules of pressure and flow velocity,pipe distribution location and other influencing factors.Electrical capacitance tomography(ECT)is a technique for visualizing two-phase flow in a pipe or closed container.In this paper,a visual detection method was proposed by image reconstruction of core,laminar,bubble and annular flow based on ECT technology,which reflects distribution of slurry in deep filling pipeline and measures the degree of blockage.There is an error between the measured and the real two-phase flow distribution due to two factors,which are immature image reconstruction algorithm of ECT and difference of flow patterns leading to degrees of error.In this paper,convolutional neural networks(CNN)is used to recognize flow patterns,and then the optimal image is calculated by the improved particle swarm optimization(PSO)algorithm with weights using simulated annealing strategy,and the fitness function is improved based on the results of the shallow neural network.Finally,the reconstructed binary image is further processed to obtain the position,size and direction of the blocked pipe.The realization of this method provides technical support for pipeline detection technology.
文摘In this research thin film layers have been prepared at alternate layers of resistive and dielectric deposited on appropriate substrates to form four – terminal R-Y-NR network. If the gate of the MOS structures deposited as a strip of resistor film like NiCr, the MOS structure can be analyzed as R-Y-NR network. A method of analysis has been proposed to measure the shunt capacitance and the shunt conductance of certain MOS samples. Mat lab program has been used to compute shunt capacitance and shunt conductance at different frequencies. The results computed by this method have been compared with the results obtained by LCR meter method and showed perfect coincident with each other.
文摘为解决多接收全方向无线电能传输(Wireless Power Transfer,WPT)技术传输效率不高的问题,采用谐振网络优化设计的方法,通过增加一个串联抵消电容来减小接收线圈间互感的影响,并利用多目标优化算法中的加权法选择合适的抵消电容值,最后设计了全方向WPT试验平台,对比加入抵消电容前后系统传输效率的变化。结果表明:加入抵消电容前,系统的平均传输效率在61.9%,加入抵消电容后,系统的平均传输效率达到73.1%,系统的平均传输效率提高了11.2%;当负载由35Ω减小至25Ω时,系统输出电流基本保持不变;当传输距离在150~200 mm,接收线圈以任意角度旋转时,系统恒流输出,且传输效率保持在70%以上。研究结果为解决多接收全方向WPT系统中传输效率问题提供了改进的方法。
基金supported by the National Natural Science Foundation of China(51976177)the National Key Research and Development Program of China(2022YFB4000047)。
文摘A cryogenic visible calibration and image evaluation facility(VCCIEF) was constructed to assess the effectiveness of electrical capacitance tomography systems in cryogenic conditions,known as Cryo-ECT.This facility was utilized to conduct dynamic,real-time imaging trials with liquid nitrogen(LN2).The actual flow patterns were captured using a camera and contrasted with the imaging outcomes.The capacitance data collected from these experiments were subsequently processed using three distinct methods:linear back projection,Landweber iteration,a fully connected deep neural network,and a convolutional neural network.This allowed for a comparative analysis of the performance of these algorithms in practical scenarios.The findings from the LN2 experiments demonstrated that the Cryo-ECT system,when integrated with the VCCIEF,was capable of successfully executing calibration,generating flow patterns,and performing imaging tasks.The system provided observable,clear,and precise phase distributions of the liquid nitrogen-vaporous nitrogenflow within the pipeline.
基金Supported by the National Natural Science Foundation of China (50777049,51177120)the National High Technology Research and Development Program of China (2009AA04Z130)the RCUK’s Energy Programme (EP/F061307/1)
文摘Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance.To solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image enhancement.Firstly,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured.Then,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image feature-enhanced.Through multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be constructed.Finally,the image is reconstructed with adjusting wavelet coefficients.In this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm.Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].
文摘This paper presents a new earth-fault detection algorithm for unearthed （isolated） and compensated neutral medium voltage （MV） networks. The proposed algorithm is based on capacitance calculation from transient impedance and dominant transient frequency. The Discrete Fourier Transform （DFT） method is used to determine the dominant transient frequency. The values of voltage and current earth modes are calculated in the period of the dominant transient frequency, then the transient impedance can be determined, from which we can calculate the earth capacitance. The calculated capacitance gives an indication about if the feeder is faulted or not. The algorithm is less dependent on the fault resistance and the faulted feeder parameters; it mainly depends on the background network. The network is simulated by ATP/EMTP program. Several different fault conditions are covered in the simulation process, different fault inception angles, fault locations and fault resistances.
