基于改进ResNet-18网络的电容层析成像图像重建

为求解电容层析成像(ECT)中的非线性病态反问题，提出了一种基于改进ResNet-18网络的ECT图像重建方法。该网络由多个残差块组成，每个残差块被分组，然后通过组内残差连接，以融合更多的特征尺度。通过MATLAB仿真实验平台构建了流型数据集，利用深度残差网络的非线性映射能力，完成训练集的学习与训练，并利用测试集进行训练效果评价。在此基础上进行了静态实验。仿真与静态实验结果均表明：与LBP、Landweber迭代算法及未改进ResNet-18算法相比，该方法的图像重建质量明显提高，并具有良好的泛化能力。     

基于非线性最小二乘的电容层析成像图像重建算法

针对电容层析成像技术中的"软场"效应和病态问题,提出了基于非线性最小二乘算法的新电容层析成像(ECT)算法。在分析非线性最小二乘问题残量原理的基础上,给出了目标函数中二阶信息项的割线近似的校正公式,并利用Lipschitz空间连续的性质对非线性最小二乘算法的收敛性进行了证明,在此基础上探讨了ECT应用该算法的可行性。该算法满足收敛条件且重建图像误差小。仿真和实验结果表明,与LBP、Landweber和共轭梯度算法相比,对于简单流型该算法兼备成像质量高、边界均匀稳定等优点,该算法的提出为ECT图像重建算法的研究提供了一个新的思路。     

基于梯度投影稀疏重建算法的电容层析成像图像重建

为解决两相流中存在中心物体、物体比较小或存在多个物体且相距较近时电容层析成像(ECT)重建图像精度较差的问题,基于稀疏分布的流型其介电常数分布满足稀疏性的先验条件,采用梯度投影稀疏重建(GPSR-BB)算法进行ECT图像重建。仿真及实验测试结果表明:GPSR-BB算法对于流体中小目标以及复杂流型的图像重建质量较好,重建图像的形状保真度高。     

数字化电容层析成像系统

基于电容敏感原理设计的数字化电容层析成像（ECT）系统，利用有限元法，分析了阵列电极结构对敏感场分布的影响，通过优化设计和仿真试验，改善了敏感场分布的均匀性；构建了以数字信号处理器（DSP）为核心、现场可编程门阵列（眦）协同工作的12电极的数字化ECT系统，提出了由离线预迭代和在线一步成像所构成的新的图像重建算法——预迭代法，其成像速度及精度均明显得到提高。     

电容层析成像三维图像重建研究

进行了电容层析成像(ECT)三维图像重建的仿真研究。首先利用COMSOL软件对ECT传感器进行三维建模,基于其高精度的有限元求解,计算ECT的灵敏度矩阵;其次,基于Matlab软件实现了基于线性反投影(LBP)及Landweber迭代算法的ECT图像重建,并利用图像显示程序获得了三维重建图像;最后,对球体及圆柱体模型进行了三维ECT图像重建,获得了较好的重建结果。     

基于改进LSQa算法的直接三维ECT图像重建

针对电容层析成像（ECT）技术逆问题的病态特性，本文将最小平方QR分解法（LSQR）应用到直接三维ECTIN像重建中，并采用动态滤波函数对重建图像进行改善。本文采用仿真实验的方法，从成像效果、误差评价及重建速度三个方面与Landweber算法进行了比较分析，结果表明，改进的LSQR算法能在较快的时间内达到收敛，数值比较稳定。     

基于改进白骨顶鸡优化算法的ECT图像重建北大核心CSCD

电容层析成像(ECT)技术求解图像重建问题存在严重的不适定性。为提高图像重建精度,提出了一种改进白骨顶鸡优化算法的ECT图像重建算法。针对标准白骨顶鸡优化算法(COA)中初始种群随机生成而导致的算法稳定性差和容易陷入局部最优的问题,引入了佳点集和正余弦优化方法,并融合了ART重建算法,根据ECT成像特点改进了目标函数。最后,进行仿真和静态实验,并与线性反投影(LBP)算法、Landweber算法、ART算法进行对比。结果表明,该方法可有效提高图像重建精度。     

基于鲁棒正则化极限学习机的电容层析成像图像重建

电容层析成像图像重建是一个非线性及病态性逆问题。基于此,提出了基于迭代重加权最小二乘法的鲁棒正则化极限学习机(RELM-IRLS)算法的电容层析成像图像重建方法,以油/气两相流为研究对象,通过有限元仿真构建随机分布流型,对RELM-IRLS算法完成训练,并与Landweber迭代算法及极限学习机算法进行对比,RELM-IRLS算法的测试集平均误差相比极限学习机算法减小4.6%。仿真及静态实验结果均表明, RELM-IRLS算法所得重建图像质量得到明显提升,且算法具有良好的泛化性能。     

基于改进线性Bregman算法的ECT图像重建算法

针对电容层析成像(ECT)逆问题求解的欠定性,将分别利用奇异值分解(SVD)和二阶迭代思想改进后的线性Bregman算法应用到ECT图像重建过程中.仿真实验结果表明,它们比SVD、Landweber、CG和线性Bregman算法的成像分辨率更高,速度也更快;在2种改进算法中,基于奇异值分解改进的图像重建伪影更少,而基于二阶迭代改进的速度明显提升.     

基于改进线性Bregman算法的ECT图像重建算法

针对电容层析成像(ECT)逆问题求解的欠定性,将分别利用奇异值分解(SVD)和二阶迭代思想改进后的线性Bregman算法应用到ECT图像重建过程中.仿真实验结果表明,它们比SVD、Landweber、CG和线性Bregman算法的成像分辨率更高,速度也更快;在2种改进算法中,基于奇异值分解改进的图像重建伪影更少,而基于二阶迭代改进的速度明显提升.     

Image reconstruction algorithm based on the extended regularised total least squares method for electrical capacitance tomography

Electrical capacitance tomography (ECT) is a non-invasive imaging technology that aims at the visualisation of the cross-sectional permittivity distribution of a dielectric object based on the measured capacitance data. Successful applications of ECT depend greatly on the precision and speed of the image reconstruction algorithms. ECT image reconstruction is a typical ill-posed problem, and its solution is unstable, that is, the solution is sensitive to noises in the input data. Methods that ensure the stability of a solution while enhancing the quality of the reconstructed images should be used to obtain a meaningful reconstruction result. An image reconstruction algorithm based on the regularised total least squares (TLS) method that considers the errors in both the sensitivity field matrix and the capacitance data for ECT is presented. The regularised TLS method is extended using a combination robust estimation technique and an extended stabilising functional according to the ill-posed characteristics of ECT, which transforms the image reconstruction problem into an optimisation problem. In addition, the Newton algorithm is employed to solve the objective functional. Numerical simulations indicate that the algorithm is feasible and overcomes the numerical instability of ECT image reconstruction; for the cases of the reconstructed objects considered here, the spatial resolution of the reconstructed images obtained using the algorithm is enhanced; as a result, an efficient method for ECT image reconstruction is introduced.     

Electrical Capacitance Volume Tomography

A dynamic volume imaging based on the principle of electrical capacitance tomography (ECT), namely, electrical capacitance volume tomography (ECVT), has been developed in this study. The technique generates, from the measured capacitance, a whole volumetric image of the region enclosed by the geometrically three-dimensional capacitance sensor. This development enables a real-time, 3-D imaging of a moving object or a real-time volume imaging (4-D) to be realized. Moreover, it allows total interrogation of the whole volume within the domain (vessel or conduit) of an arbitrary shape or geometry. The development of the ECVT imaging technique primarily encloses the 3-D capacitance sensor design and the volume image reconstruction technique. The electrical field variation in three-dimensional space forms a basis for volume imaging through different shapes and configurations of ECT sensor electrodes. The image reconstruction scheme is established by implementing the neural-network multicriterion optimization image reconstruction (NN-MOIRT), developed earlier by the authors for the 2-D ECT. The image reconstruction technique is modified by introducing into the algorithm a 3-D sensitivity matrix to replace the 2-D sensitivity matrix in conventional 2-D ECT, and providing additional network constraints including 3-to-2-D image matching function. The additional constraints further enhance the accuracy of the image reconstruction algorithm. The technique has been successfully verified over actual objects in the experimental conditions     

A Multimodal Tomography System Based on ECT Sensors

A new noninvasive system for multimodal electrical tomography based on electrical capacitance tomography (ECT) sensor hardware is proposed. Quasistatic electromagnetic fields are produced by ECT sensors and used for interrogating the sensing domain. The new system is noninvasive and based on capacitance measurements for permittivity and power balance measurements for conductivity (impedance) imaging. A dual sensitivity map of perturbations in permittivity and conductivity is constructed. The measured data along with the sensitivity matrix are used for the actual image reconstruction. The new multimodal tomography system has the advantage of using already established reconstruction techniques, and the potential for combination with new reconstruction techniques by taking advantage of combined conductivity/permittivity data. Moreover, it does not require direct contact between the sensor and the region of interest. The system performance has been tested on representative data, producing good results     

基于深度信念网络的ECT图像重建算法

为提高图像重建质量,针对电容层析成像技术(ECT)中的电容数据复杂多样且与介电常数呈非线性关系的特点,提出一种基于深度信念网络(DBN)的重建算法,利用DBN的深层非线性网络结构来实现电容值与重建图像灰度值非线性关系。并对DBN进行了改进,将自适应步长(AS)引入到对比散度(CD)算法中,解决固定步长寻找全局最优困难的问题,改善图像质量;在微调阶段采用拟牛顿法加快收敛速度,减少训练时间。在COMSOL5.3软件上进行仿真试验,通过MATLAB2014a对图像进行重建。试验结果表明:DBN能够有效地重建图像,并且要优于传统算法;改进后的DBN训练时间缩短了5.51s,图像误差低至0.0094,相关系数高达0.9973,是研究ECT图像重建的新方法和手段。     

一种电容层析成像图像重建优化算法

提出一种电学层析成像(ECT)图像重建优化算法。通过将传统正则化算法转化为最小二乘问题进行求解,结合lp范数逼近正则化最小化问题,利用重新加权的方法进行迭代计算。以油-气两相流模型进行仿真及静态实验,将所提出的优化算法与常用的LBP、Landweber迭代及Tikhonov正则化算法进行对比。结果表明,与常用算法相比,采用该优化算法对管道中心物体及多物体分布流型进行图像重建,其图像相对误差均为最低,且重建图像的形状保真度明显提高。     

基于电容层析成像系统测量信号稀疏性的两相流流型辨识

提出了基于电容层析成像(ECT)测量电容信号稀疏性的两相流流型辨识算法,该算法首先使用所有流型对应的归一化测量电容值信号构建一个过完备字典,并将待辨识样本通过该过完备字典进行稀疏表示,使其具有稀疏性并满足稀疏重构的基本要求,然后以压缩感知的正交匹配追踪(OMP)算法求取各标准样本对应于完备样本集的稀疏解,最后根据待辨识样本与标准样本稀疏解之间的线性相关程度进行流型辨识。使用该方法对5种典型的两相流流型识别进行了仿真及实验研究,结果表明:该方法的流型正确识别率均高于98%。     

改进的共轭梯度算法的图像重建算法

针对电容层析成像图像重建问题的病态性,在Tikhonov正则化的基础上,以正则化解决方案的规模和给定数据的质量为理论依据,引入一个数学变换,提出了一种新的正则化方法,该方法克服了常规正则化方法扰乱原系统的缺陷。同时,将ECT物理模型进行规范化,并对共轭梯度算法进行改进。仿真实验表明,改进的共轭梯度算法的成像质量高于LBP算法、 Tikhonov正则化算法和共轭梯度算法,并利用相关系数进行了验证。