Generalized multi-scale dynamic inversion algorithm for electrical capacitance tomography

Owing to distinct advantages such as the easy implementation, low cost, high safety and non-intrusive sensing, the electrical capacitance tomography (ECT) is considered as a promising visualization measurement technique, in which reconstructing high quality images is highly desirable for real applications. In this paper, a multi-scale dynamic reconstruction model, which simultaneously utilizes the ECT measurement information and the dynamic evolution information of a dynamic object, is presented. The original dynamic image reconstruction problem is decomposed into a sequence of inverse problems, which are solved successively from the largest scale to the original scale. A generalized objective functional that considers the ECT measurement information, the dynamic evolution information of a dynamic object, the temporal constraint and the spatial constraint is proposed. An iterative algorithm, which integrates the beneficial advantages of the evolutionary strategy (ES) algorithm and the homotopy method, is designed for solving the proposed objective functional. Numerical simulations are implemented to evaluate the feasibility of the proposed algorithm. For the cases simulated in this paper, the quality of the images reconstructed by the proposed algorithm is improved, which indicates that the proposed algorithm is successful in solving ECT inverse problems.     

Shearlet regularization and dimensionality reduction for the temperature distribution sensing

In this paper, a new dimensionality reduction based temperature distribution sensing (TDS) method is proposed to reconstruct the temperature distribution via the limited number of the scattered temperature measurement data. The projective nonnegative matrix factorization (PNMF) method is developed to exact the basis vectors, and the augmented Lagrangian multipliers (ALM) method is proposed to solve the proposed PNMF model. A dimensionality reduction model is obtained via projecting the original temperature distribution onto the spaces spanned by a set of basis. An objective functional that considers the inaccurate properties of the reconstruction model and the measurement data, the Shearlet regularization and the total variation (TV) method is proposed to convert the TDS task into an optimization problem, where the temperature distribution is indirectly reconstructed via solving a low-dimensional vector. An iteration scheme is developed to solve the objective functional. Numerical simulation results validate the feasibility of the proposed reconstruction algorithm.     

Robust dynamic inversion algorithm for the visualization in electrical capacitance tomography

Electrical capacitance tomography (ECT) is considered as a promising visualization measurement technique, in which reconstructing high-quality images is crucial for real applications. In this paper, a robust dynamic reconstruction model, which incorporates the ECT measurement information and the dynamic evolution information of a dynamic object, is presented. Under the considerations of the low rank property of an ECT image and the inaccuracies on the sensitivity matrix, the reconstruction model and the measurement data, an objective functional that fuses the ECT measurement information, the dynamic evolution information of a dynamic object, the spatial constraint, the temporal constraint and the low rank constraint is proposed. An iteration scheme that integrates the advantages of the fast composite splitting (FCS) algorithm is developed for solving the proposed objective functional. Numerical simulations are implemented to validate the feasibility of the proposed algorithm.     

Dynamic imaging method using the low n-rank tensor for electrical capacitance tomography

Imaging objects in electrical capacitance tomography (ECT) measurement are often in a dynamic evolution process, and exploiting the spatial–temporal properties of the dynamic reconstruction objects is crucial for the improvement of the reconstruction quality. Based on the multiple measurement vectors, in this paper a robust dynamic reconstruction model that incorporates the ECT measurement information and the dynamic evolution information of a dynamic object, in which a series of dynamic images is cast as a third-order tensor that the first two dimensions are space and the third is time, is proposed. Under the considerations of the two-dimensional spatial structure property of a difference image and the spatial–temporal property of a third-order image tensor, a new objective functional that fuses the ECT measurement information, the dynamic evolution information, the temporal constraint, the spatial constraint, the low rank constraint of a difference image and the low n-rank constraint of a third-order tensor is proposed, where the images are reconstructed by a batching pattern. The split Bregman iteration (SBI) algorithm is developed for solving the proposed objective functional. Numerical simulations are implemented to demonstrate the advantages of the proposed algorithm on improving the reconstruction quality and the robustness.     

Ultrasonic tomography based temperature distribution measurement method

The temperature distribution information plays an important role in industrial applications. Owing to the advantages such as non-intrusive sensing and low cost, the ultrasonic tomography (UT) is considered to be a promising method for temperature field visualization. The ultrasonic time-of-flight (TOF) measurement and the reconstruction algorithm are crucial for practical applications of the UT measurement. In this paper, a dual-threshold measurement method is proposed to ensure a high-quality TOF measurement. In view of the inaccurate nature of the reconstruction model and TOF data, a new reconstruction method that integrates the advantages of the Tikhonov regularization method and the least squares support vector machine (LSSVM) is proposed to improve the reconstruction quality. The experimental results were compared against thermocouple measurements and the results show that the temperature distribution can be reconstructed with the error of 1.3%, which validates the feasibility and effectiveness of the proposed Tikhonov-LSSVM reconstruction algorithm.     

动态电容层析成像图像重建算法

提出了融合ECT测量信息和被测对象动态演化信息的新型图像重建模型;基于Tikhonov正则化方法,建立一个同时考虑了ECT测量信息、被测对象动态演化信息、时间与空间约束的新型图像重建目标泛涵,将图像重建问题转化为最优化问题;提出了集成分裂Bregman迭代法优势的新型算法求解该目标泛涵。数值仿真结果表明,所提出的图像重建算法其图像重建质量均优于OIOR算法、STR算法及PLI算法;同时由于所提出的图像重建算法同时考虑了测量数据和重建模型的不精确性,其抵抗测量噪声的能力得以提高。     

基于模糊的框架变形重构的应变测量修正

针对三维(three-dimensional,简称3-D)框架结构弹性变形重构中存在的应变测量误差问题,提出了一种应变测量修正的方法。通过逆有限元法(inverse finite element method,简称iFEM)推导出结构表面应变量与测量点位移量之间的应变-位移关系,实测位移量代入应变-位移的转化关系得到相应的逆解应变。由于测量应变中含有安装、测量误差等,测量应变量与逆解应变量之间非线性数学关系模型难以准确得到。基于模糊网络对任意映射关系的无限逼近特性,采用模糊网络(fuzzy network,简称FN)逼近测量应变与逆解应变的关系。在确定模糊网络后,调用网络完成测量应变的修正,利用修正后应变量求解框架的变形位移。最后在三维框架模型上进行实验验证,实验结果表明结构变形重构的误差普遍减小了60%以上,说明该测量应变修正方法能够有效提高变形重构的精度。     

用于两相流测量的ECT图像重构技术研究

电容层析成像技术(ECT)具有非侵入、响应速度快、成本低等优点,是用于两相流参数检测非常有发展潜力的技术之一。而图像重构是ECT系统研究的关键技术。该文利用有限元方法对12电极ECT系统进行建模仿真,进行正问题求解,获得了图像重构的样本数据;引入改进的径向基函数神经网络,建立了ECT图像重构算法,并在MATLAB平台上进行了仿真验证。结果表明,改进的径向基神经网络算法在图像重构准确度及速度方面有了明显提高。     

在线压缩感知方法及其在漏磁检测中的应用

以长距离油管的漏磁检测系统为研究对象,研究了漏磁检测数据的在线压缩算法。针对嵌入式在线工作环境下,传统的数据压缩方法难以应用的问题,引入压缩感知(CS)理论,提出了漏磁检测数据在线CS压缩方法。确定了小波基作为漏磁信号的最佳稀疏表示基,并推导了小波稀疏基矩阵的数学表达公式;提出Welch界和PRP共轭梯度算法的测量矩阵优化算法;提出了漏磁检测数据的重要数据段筛选方法,极大地减少了数据存储量。仿真试验证明了所提出在线压缩算法极大地减少了在线环境压缩编码的运算复杂度,具有简单迅速、压缩比高、重构精度高等优点,符合漏磁检测数据在线压缩的实际要求。     

正则化优化修正的电容层析成像图像重建算法

提出一种基于正则化优化修正的电容层析成像图像重建算法。首先利用 Tikhonov正则化法求解系统的逆问题 ,解决逆问题解的不适定性 ;然后利用优化修正法对初始图像进行修正 ,以提高重建图像的质量。实验结果表明 ,该法在重建图像质量和图像重建速率两方面均具有优势。