This paper reviews the latest development and emerging technologies on the application of process tomography to multiphase flow measurement in industrial and biomedical fields. In the industrial applications, electrical resistance tomography and electrical capacitance tomography are popularly applied in multiphase flow measurement dependent on the electrical properties of the objects. In particular, applications to circulating fluidized bed, trickle-bed reactor and temperature measurement are discussed. In the biomedical applications, in most cases, measurement is conducted in a static condition. Two latest applications in flowing condition are reviewed, which are thrombus detection in blood flow and cell sensing with a microfluidic device. Finally, the challenges to process tomography for multiphase flow measurement on industrial and biomedical applications are addressed.

Application of Process Tomography to Multiphase Flow Measurement in Industrial and Biomedical Fields: A Review

Electrical impedance tomography (EIT) is a nondestructive imaging technique used to estimate the internal conductivity distribution of a conductive domain by taking potential measurements only at the domain boundaries. If a thin electrically conductive material that responds to pressure with local changes in conductivity is used as a conductive domain, then EIT can be used to create a large-scale pressure-sensitive artificial skin for robotics applications. This paper presents a review of EIT and its application as a robotics sensitive skin, including EIT excitation and image reconstruction techniques, materials, and skin fabrication techniques. Touch interpretation via EIT-based artificial skins is also reviewed.

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Electrical Impedance Tomography for Artificial Sensitive Robotic Skin: A Review

Image reconstruction for electrical capacitance tomography (ECT) is a nonlinear problem A generalized inverse operator is usually ill-posed (unbounded) and ill-conditioned (with a large norm) Therefore, the solutions for ECT are not unique and highly sensitive to the measurement noise To improve the image quality, a new image reconstruction algorithm for ECT based on sparse representation is proposed An unconventional basis, ie, an extended sensitivity matrix consisting of some normalized capacitance vectors corresponding to the base permittivity elements is designed as an expansion frame The permittivity distributions to be reconstructed can have a natural sparse representation based on the new basis and can be represented as a linear combination of the base elements Another sparsity regularization method-the standard Landweber iteration with a threshold is also conducted for comparison The proposed algorithm has been evaluated by both simulation (with and without noise) and experimental results for different permittivity distributions

Image Reconstruction for Electrical Capacitance Tomography Based on Sparse Representation

Instantaneous readouts of an electrical resistivity probe are taken in an upward vertical air–water mixture. The signals are further processed to render the statistical moments and the probability density functions here used as objective flow pattern indicators. A series of 73 experimental runs have its flow pattern identified by visual inspection assisted by the analyses of the void fraction’s trace and associated probability density function. The flow patterns are classified into six groups and labeled as: bubbly, spherical cap, slug, unstable slug, semi-annular and annular. This work compares and analyzes the performance of artificial neural networks, ANN, and expert systems to flow pattern identification. The employed ANNs are Multiple Layer Perceptrons, Radial Basis Functions and Probabilistic Neural Network, with single and multiple outputs. The performance is gauged by the percentage of right identifications based on experimental observation. The analysis is extended to clustering algorithms to assist the formation of knowledge base employed during the learning stages of the ANNs and expert systems. The performance of the following clustering algorithms: self organized maps, K-means and Fuzzy C-means are also tested against experimental data.

Performance comparison of artificial neural networks and expert systems applied to flow pattern identification in vertical ascendant gas-liquid flows

Bubble size distributions and shapes in annular gap bubble column

Image reconstruction is a key problem for electrical resistance tomography (ERT). Because of the soft-field nature and the ill-posed problem in solving inverse problem, traditional image reconstruction methods cannot achieve high accuracy and the process is usually time consuming. Since deep learning is good at mapping complicated nonlinear function, a deep learning method based on convolutional neural network (CNN) is proposed for image reconstruction of ERT. To establish the database, 41122 samples were generated with numerical simulations. 10-fold cross validation was used to divide all samples into training set and validation set. The network structure was based on LeNet, and refined by applying dropout layer and moving average. After 346 training epochs, the image correlation coefficient (ICC) on validation set was 0.95. When white Gaussian noise with a signal-to-noise ratio of 30, 40, and 50 were added to validation set, the ICC was 0.79, 0.89, and 0.93, respectively, which proved the anti-noise capability of the network. The reconstruction results on samples which have more inclusions, different conductivity, and other shapes explained the network has good generalization ability. Furthermore, experimental data from a 16-electrode industrial ERT system was used to compare the accuracy of the proposed model with some typical reconstruction methods. Results show that the proposed CNN method has better reconstruction results than LBP, Tikhonov, and Landweber.

Image Reconstruction Based on Convolutional Neural Network for Electrical Resistance Tomography

Identification of gas/liquid two-phase flow regime through ERT-based measurement and feature extraction

Frequency-difference electrical impedance tomography (fdEIT) was originally developed to mitigate the systematic artifacts induced by modeling errors when a baseline dataset is unavailable. Instead of fine anatomical imaging, only coarse anomaly detection has been addressed in current fdEIT research mainly due to its low spatial resolution. On the other hand, there has been not enough study on fdEIT reconstruction algorithm as well. In this article, we propose an efficient and high-spatial-resolution algorithm for simultaneously reconstructing multiple fdEIT frames corresponding to inject currents with multiple frequencies. The electrical impedance tomography reconstruction problem is considered within a hierarchical Bayesian framework, where both intratask spatial clustering and intertask dependency are automatically learned and exploited in an unsupervised manner. The computation is accelerated by adopting a modified marginal likelihood maximization approach. Real-data experiments are conducted to verify the recovery performance of the proposed algorithm.

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Efficient Multitask Structure-Aware Sparse Bayesian Learning for Frequency-Difference Electrical Impedance Tomography

As a noninvasive and radiation-free imaging modality, electrical impedance tomography (EIT) has attracted much attention in the last two decades and owns many industry and biomedical applications. However, due to the nonlinearity and ill-posedness of its inverse problem, the EIT images always suffer from low spatial resolution and are sensitive to the modeling errors. To achieve high resolution and modeling error robust EIT image, a two-stage deep learning (TSDL) method is proposed. The proposed method consists of a prereconstruction block and a convolutional neural network (CNN). The prereconstruction block learns the regularization pattern from the training data set and provides a rough reconstruction of the target. The CNN postprocesses the prereconstruction result in a multilevel feature analysis strategy and eliminates the modeling errors with prior information of the observation domain shape. The prereconstruction and CNN blocks are trained together by using a minimum square approach. To evaluate the performance of the TSDL method, the lung EIT problem was studied. The training data set is calculated from more than 100 000 EIT simulation models generated from computed tomography (CT) scans across 792 patients. Lung injury, measurement noise, and model errors are randomly simulated during the model generation process. The trained TSDL model is evaluated with simulation testes, as well as the experimental tests from a laboratory setting. According to the results, the TSDL method could achieve high accuracy shape reconstructions and is robust against measurement noise and modeling errors.

A Two-Stage Deep Learning Method for Robust Shape Reconstruction With Electrical Impedance Tomography

Monitoring multiphase flow process and measuring its parameters have been a research topic that received broad attentions for the past decades. Ultrasonic process tomography (UPT) obtains the distribution of the two-phase flow based on ultrasound propagation in different fluids, making it valuable to the industrial monitoring and measurement. UPT can nonintrusively explore transient hydrodynamics of the multiphase flow, but the reconstruction quality of single modality is always dependent on phase distribution. In this paper, a dual-mode UPT that fuses the attenuation and time-of-flight of ultrasound is presented. To implement this scheme, a digital dual-mode UPT system that works under both the transmission-mode and reflection-mode is designed. A 16-channel ultrasound data acquisition system is proposed, which is flexibly programmable and reconfigurable through an field-programmable gate array (FPGA). The system includes a series of functional modules, and is based on the compact peripheral component interconnect bus for real-time data transmission to meet industrial field requirements. Static experiments show that the system can distinguish the interface of the different inclusions and preferably reconstruct the position and area of the inclusions through fusing the transmission and reflection reconstruction, the relative error and correlation coefficient of the reconstructed images are presented, respectively, which show preferable and stable distinguishability of different inclusions.

Chao Tan

Papers

Image Reconstruction Based on Convolutional Neural Network for Electrical Resistance Tomography

Identification of gas/liquid two-phase flow regime through ERT-based measurement and feature extraction

Efficient Multitask Structure-Aware Sparse Bayesian Learning for Frequency-Difference Electrical Impedance Tomography

A Two-Stage Deep Learning Method for Robust Shape Reconstruction With Electrical Impedance Tomography

An Ultrasonic Transmission/Reflection Tomography System for Industrial Multiphase Flow Imaging