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This study investigated the potential of the generative adversarial neural network (cGAN) image reconstruction in industrial electrical capacitance tomography. The image reconstruction quality was examined using image patterns typical for a two-phase flow. The training dataset was prepared by generating images of random test objects and simulating the corresponding capacitance measurements. Numerical simulations were performed using the ECTsim toolkit for MATLAB. A cylindrical sixteen-electrode ECT sensor was used in the experiments. Real measurements were obtained using the EVT4 data acquisition system. The reconstructed images were evaluated using selected image quality metrics. The results obtained using cGAN are better than those obtained using the Landweber iteration and simplified Levenberg–Marquardt algorithm. The suggested method offers a promising solution for a fast reconstruction algorithm suitable for real-time monitoring and the control of a two-phase flow using ECT. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of electrical capacitance tomography (ECT) for monitoring of industrial processes has been studied and proposed by many researchers. Examples can be found in monitoring of multiphase flows or mixing processes in reactors. Demonstrations of the functionality based on lab and test rig measurements have proven the potential of the proposed principles. This paper discusses the application of an ECT system in a heavy industries application. The harsh operating conditions in the industrial environment pose several challenges for the application of sophisticated measurement technology. This work addresses key aspects for the application of an ECT measurement system in such an environment. Specifically the electrical system design and the influence of the temperature are addressed. Relevant parameters and possible solutions are discussed. Furthermore the application of ECT as instrument for mass flow metering in pneumatic conveying processes is addressed. Supportive measurement studies from test rig experiments and comparative simulation studies are presented. Therefore, the paper provides a concise discussion on the application of ECT under harsh operating conditions, as well as the use of ECT as a measurement device for process measurement. The work concludes with a presentation of a measurement system in an industrial plant in which the proposed concepts were succesfully implemented. [ABSTRACT FROM AUTHOR]

LETS (Lung Electrical Tomography System) is an innovative medical system enabling comprehensive heart and lung function monitoring. It utilizes advanced techniques such as electrical impedance tomography (EIT), body surface potential mapping (BSPM), and electrical capacitance tomography (ECT) to provide accurate information about physiological parameters. The LETS system allows one-time examinations and continuous monitoring 24 hours a day. Remotely measuring and monitoring parameters such as the electrical activity of the heart muscle, blood flow, blood pressure, pulse, and lung aeration enables rapid identification and intervention in case of abnormalities. The system also allows for the detection of cardiac arrhythmias and other heart disorders, as well as the assessment of lung respiratory capacity and optimization of ventilation strategies. However, it's important to note that the LETS system has some limitations. For example, it may not be suitable for patients with certain pacemakers or other implanted devices. Overall, LETS represents a breakthrough in medical imaging, opening up new possibilities in diagnosing and monitoring heart and lung diseases. Its integration may contribute to improving patient care and increasing the effectiveness of medical interventions.

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The electrical capacitance tomography (ECT) is a visual nondestructive testing technology. The relative positional distribution between the electrodes and the phantom object affects the accuracy of the reconstructed image. To solve this problem, an image reconstruction method and image fusion algorithm of ECT system based on rotating electrodes are proposed. First, 4 image reconstruction algorithms are employed to reconstruct the experimental model, the Landweber iterative algorithm based on Tikhonov regularization presents the best performance. Then, by rotation the 12 electrodes 4 times, we can obtain 5 sets of capacitance data, and obtain 5 images. Finally, the fusion results can be obtained by performing the adaptive weighted fusion on these 5 images. Results show that the adaptive weighted image fusion method based on rotation electrodes improves the quality of reconstructed images and effectively reduces the artefacts. [ABSTRACT FROM AUTHOR]

This article explores the use of a post-processing technique called noise-based correction (NBC) in electrical impedance tomography (EIT) to improve image quality. The study involves simulations and experiments to test the effectiveness of NBC in reducing noise in reconstructed images. The results show that NBC significantly improves image quality in both 2D and 3D reconstructions, making it a recommended technique for EIT image reconstruction. The authors also suggest that NBC may have applications in other linearized methods in EIT and other impedance imaging techniques. [Extracted from the article]

This work presents an experimental study on oil–water flow downstream of a restriction. The flow pattern, volumetric phase distribution, and their impacts on pressure drop are discussed. We employed two techniques to visualize the oil–water flow patterns, a high-speed camera and an Electrical Capacitance Volume Tomography (ECVT) system. The ECVT system is a non-intrusive device that measures the volumetric phase distribution at the pipe cross-section with time, which plays a critical role in determining the continuous phase in the oil–water flow, and therefore the oil–water flow pattern. In this study, we delved into the oil–water flow pattern and volumetric phase distribution for different valve openings, flow rates, and water cuts, and how they impact the pressure drop. The experimental results have demonstrated a strong relationship between the oil–water flow pattern and the pressure gradient, while the oil–water flow pattern is significantly influenced by the flowing conditions and the valve openings. The impacts of water cuts on the oil–water flow pattern are more obvious for smaller valve openings. For large valve openings, the oil and water phases tend to be more separated. This results in a moderate variation in the pressure gradient as a function of water cuts. However, it becomes more complex as the valve opening decreases. The pressure gradient generally increases with decreasing valve openings until the flow pattern becomes an oil-in-water dispersed flow. The impact of the valve on the pressure gradient is more pronounced in water-dominated flow when the water cut is above the inversion point, while it seems to be most obvious for medium water cut conditions. [ABSTRACT FROM AUTHOR]

Currently, there is an urgent need for a fast and portable intracerebral hemorrhage (ICH) detection technology for pre-hospital emergency scenarios. Owing to the disproportionately elevated permittivity of blood compared to other brain tissues, Electrical Capacitance Tomography (ECT) offers a viable modality for mapping the spatial distribution of permittivity within the brain, thus facilitating the imaging-based identification of ICH. Currently, ECT is confined to time-differential imaging due to limited sensitivity, and this methodology requires non-hemorrhagic measurements for comparison, data that are frequently inaccessible in clinical contexts. To overcome this limitation, in accordance with the natural bilateral symmetry of the cerebral hemispheres, a symmetrical cancellation scheme is introduced. In this method, electrodes are uniformly arrayed around the cranial periphery and strategically positioned in a symmetrical manner relative to the sagittal suture. Subsequently, the measured capacitances for each electrode pair are subtracted from those of their symmetrical counterparts aligned with the sagittal suture. As a result, this process isolates the capacitance attributable solely to hemorrhagic events within a given hemisphere, permitting the absolute imaging of ICH. To assess the feasibility of this method, simulation and empirical imaging were conducted respectively on a numerical hemorrhage model and three physical models (a water-wrapped hemorrhage model, an isolated porcine fat-wrapped hemorrhage model, and an isolated porcine brain tissue-wrapped hemorrhage model). Traditional absolute imaging, time-differential imaging and symmetrical cancellation imaging were performed on all models. The results substantiate that the proposed imaging modality is capable of obtaining absolute imaging of ICH. But a mirrored artifact, symmetrical to the site of the actual hemorrhage image appeared in each of the imaging results. This mirror artifact was characterized by identical dimensions and an inverted pixel-value schema, an intrinsic consequence of the symmetrical cancellation imaging algorithm. The real image of hemorrhage can be ascertained through pre-judgment with the symptoms of the patient. Additionally, the quality of this imaging is seriously dependent on the precise alignment between the electrodes and the sagittal suture of the brain; even a minor deviation in symmetry could introduce excessive noises. Thus, the complicated operational procedures remain as challenges for practical application. [ABSTRACT FROM AUTHOR]

Electrical Capacitance Tomography (ECT) is one of typical tomography technologies based on capacitance-sensitive fields, which can reconstruct the distribution images of permittivity distribution of different fluid in the measured field and is often used for realizing industrial detection in specific occasions. Landweber and homotopy perturbation regularization algorithms are used for image reconstruction in ECT system, but these algorithms are with many iteration steps, slow convergence speed, and relatively low quality of the reconstructed images. Aiming at these problems, this paper proposes an improved image reconstruction algorithm. Firstly, a regularization term is added to the target function by using the different dielectric constant distribution between the current moment and the previous moment. Then, using the homotopy perturbation to derive the second-order iterative formula to get the homotopy perturbation regularization algorithm, and finally the improved algorithm is obtained by combining the landweber algorithm based on weight factor. Furthermore, the improved homotopy perturbation regularization algorithm is applied for ECT image reconstruction. The numerical simulation experiment results show that the improved algorithm is with the highest comprehensive scores for the four flow patterns of laminar flow, annular flow, core flow and bubble flow, which is higher than the landweber algorithm and the improved homotopy perturbation regularization algorithm by 3% ~ 20%, and 18% ~ 31%, respectively. The relative error, correlation coefficient and subjective effect of the reconstructed image are significantly improved, and it has certain anti-noise performance. These reflect the improved algorithm is with practical value. [ABSTRACT FROM AUTHOR]

Gas–solid Fluidized bed technology has a pivotal role in coal separation. Bubble movement behavior is an important factor affecting the fluidization stability. Fluidized bed measurement is an essential link in the bubble behavior study. As the main evaluation parameters, the concentration distribution and density distribution can reflect the bubble movement behavior. This work uses a noninvasive method of electrical capacitance tomography (ECT) for fluidized bed measurements, combined with COMSOL simulation validation for real-time imaging of bubbles in Geldart Group B magnetite powder particles. Meanwhile, the most suitable reconstruction algorithm for gas–solid separation fluidized bed is selected from three image reconstruction algorithms. And then concentration distribution and density distribution are analyzed. The results show that under reasonable gas velocity conditions (U–Umf =2.28 and 3.17 cm/s), the central region ([0, 1/4]) concentrations of [0.43–0.45] and [0.39–0.42] and densities of [1.98–2.06 g/cm3] and [1.86–1.96 g/cm3] are obtained by ECT measurements, respectively. Finally, the bed density obtained from the ECT sensors in the experiment was validated using three different bed density models. The error can be controlled to within 20%, which indicates that the ECT measurement method has a fairly high reliability and accuracy in dry coal beneficiation field. [ABSTRACT FROM AUTHOR]

The reconstruction of the spatial complex conductivity σ + j ω ε 0 ε r from complex valued impedance measurements forms the inverse problem of complex electrical impedance tomography or complex electrical capacitance tomography. Regularized Gauß-Newton schemes have been proposed for their solution. However, the necessary computation of the Jacobian is known to be computationally expensive, as standard techniques such as adjoint field methods require additional simulations. In this work, we show a more efficient way to computationally access the Jacobian matrix. In particular, the presented techniques do not require additional simulations, making the use of the Jacobian, free of additional computational costs. [ABSTRACT FROM AUTHOR]

Electrical Capacitance Tomography (ECT) is a non-invasive, non-intrusive, radiation-free, robust, and cost-efficient measurement system that generates cross-sectional images of industrial operations by measuring differences in dielectric properties within a container. Despite its advantages, the poor quality of reconstructed images limits its applicability. Researchers have explored both non-iterative and iterative methods to address this issue, yet the resulting image quality remains insufficient for high-stakes applications where accurate decision-making is critical. This study proposes a novel algorithm that integrates the ECT model with a diffusion-steered image denoising functional, enhancing the quality of ECT reconstructed images beyond existing methods. Empirical comparisons show that the proposed algorithm outperforms state-of-the-art techniques such as Linear Back Projection and Projected Landweber, with Distribution Error (DE) and Correlation Coefficient (CC) improvements of 62% and 19%, respectively. Qualitative assessments further indicate the superior performance of the proposed algorithm in reconstructing high-quality images. [ABSTRACT FROM AUTHOR]

Image reconstruction is one of the important tasks in the application of electrical capacitance tomography (ECT) systems. Though numerous algorithms have been implemented, it is often challenging to obtain satisfactory images in all imaging regions by the use of a single algorithm due to the soft-field nature of ECT systems. The preferred iterative reconstruction algorithms are highly computationally inefficient. A new iterative reconstruction algorithm is proposed which combine iterative Linear Back Projection and Logistic Regression. In this method, the solution to the ECT forward problem is implemented using logistic regression, whereas the ECT inverse problem is solved using the algebraic reconstruction technique. By doing so, it is possible to obtain high quality images at relatively efficient computational cost. The simulated experimental results shows that the proposed algorithm outperforms the Projected Landweber and Iterative Linear Back Projection in terms of spatial similarity accuracy, quality of reconstruction images, and computational efficiency. There are improvements of 29% spatial similarity accuracy and 58% computational cost relative to Iterative Linear Back Projection algorithm. This is significant improvement toward using ECT system for online industrial operations. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the two-fluid model, a three-zone drag model was developed, and the kinetic theory of granular flows and the Schneiderbauer solids wall boundary model were modified to establish a new three-dimensional (3D) unsteady mathematical model for high-pressure dense-phase pneumatic conveying in horizontal pipe. With this mathematical model, the influence of the three frictional stress models, namely Dartevelle frictional stress model, Srivastava and Sundaresan frictional stress model, and the modified Berzi frictional stress model, on the simulation result was explored. The simulation results showed that the three frictional stress models accurately predicted the pressure drop and its variations with supplementary gas in the horizontal pipe, with relative errors ranging from −4.91% to +7.60%. Moreover, the predicted solids volume fraction distribution in the cross-section of the horizontal pipe using these frictional stress models exhibited good agreement with the electrical capacitance tomography (ECT) images. Notably, the influence of the three frictional stress models on the simulation results was predominantly observed in the transition region and deposited region. In the deposited region, stronger frictional stress resulting in lower solids volume fraction and a higher pressure drop in the horizontal pipe were observed. Among the three frictional stress models, the simulation results with the modified Berzi frictional stress model aligned better with the experimental data. Therefore, the modified Berzi frictional stress model is deemed more suitable for simulating high-pressure dense-phase pneumatic conveying in horizontal pipe. [ABSTRACT FROM AUTHOR]

This article discusses the importance of multi-sensor imaging and fusion technology in various fields such as medical imaging, engineering construction, and object detection. It presents different research studies related to multi-sensor imaging and fusion, including methods for imaging detection, feature extraction, and fusion, as well as objective evaluation methods. The research results demonstrate the effectiveness and potential applications of multi-sensor imaging and fusion technology in different areas. The document also provides a summary of various research topics related to the specific applications of multi-sensor technology in engineering problems, such as metal corrosion depth estimation, tunnel management, sentiment analysis, highway vehicle speed detection, road surface detection, and brainstem hematoma puncture and drainage surgery. The authors hope that this research will inspire further advancements in multi-sensor imaging and fusion. [Extracted from the article]

This study investigated the potential of the generative adversarial neural network (cGAN) image reconstruction in industrial electrical capacitance tomography. The image reconstruction quality was examined using image patterns typical for a two-phase flow. The training dataset was prepared by generating images of random test objects and simulating the corresponding capacitance measurements. Numerical simulations were performed using the ECTsim toolkit for MATLAB. A cylindrical sixteen-electrode ECT sensor was used in the experiments. Real measurements were obtained using the EVT4 data acquisition system. The reconstructed images were evaluated using selected image quality metrics. The results obtained using cGAN are better than those obtained using the Landweber iteration and simplified Levenberg–Marquardt algorithm. The suggested method offers a promising solution for a fast reconstruction algorithm suitable for real-time monitoring and the control of a two-phase flow using ECT.

Electrical capacitance volume tomography (ECVT) is an experimental technique capable of reconstructing 3D solid volume fraction distribution inside a sensing region. This technique has been used in fluidized beds as it allows for accessing data that are very difficult to obtain using other experimental devices. Recently, artificial neural networks have been proposed as a new type of reconstruction algorithm for ECVT devices. One of the main drawbacks of neural networks is that they need a database containing previously reconstructed images to learn from. Previous works have used databases with very simple or limited configurations that might not be well adapted to the complex dynamics of fluidized bed configurations. In this work, we study two different approaches: a supervised learning approach that uses simulated data as a training database and a reinforcement learning approach that relies only on experimental data. Our results show that both techniques can perform as well as the classical algorithms. However, once the neural networks are trained, the reconstruction process is much faster than the classical algorithms. [ABSTRACT FROM AUTHOR]

High-precision images enable electrical capacitance tomography (ECT) to obtain more reliable measurement results, meaning that the reconstruction algorithm is particularly important. Some excellent numerical algorithms have successfully solved the inverse problem for ECT imaging, but their imaging quality is relatively low. To solve this problem, this paper proposes a new reconstruction algorithm based on regularized extreme learning machines (RELMs). The implementation of the algorithm is mainly divided into two steps: (1) according to a large number of training samples, the RELM model can be obtained by the iterative split Bregman (ISB) algorithm, which can describe the mapping relationship between the capacitance correlation coefficient and the imaging target well, and (2) the capacitance correlation coefficient is calculated, which is then used as input to the RELM model to predict the final imaging. Both simulation and experimental results show that the RELM algorithm achieves greater improvement in imaging quality and robustness, and provides new development ideas for the ECT. [ABSTRACT FROM AUTHOR]

This paper presents a study on applying machine learning algorithms for the classification of a two-phase flow regime and its internal structures. This research results may be used in adjusting optimal control of air pressure and liquid flow rate to pipeline and process vessels. To achieve this goal the model of an artificial neural network was built and trained using measurement data acquired from a 3D electrical capacitance tomography (ECT) measurement system. Because the set of measurement data collected to build the AI model was insufficient, a novel approach dedicated to data augmentation had to be developed. The main goal of the research was to examine the high adaptability of the artificial neural network (ANN) model in the case of emergency state and measurement system errors. Another goal was to test if it could resist unforeseen problems and correctly predict the flow type or detect these failures. It may help to avoid any pernicious damage and finally to compare its accuracy to the fuzzy classifier based on reconstructed tomography images -- authors' previous work. [ABSTRACT FROM AUTHOR]

Introduction: Current detection of intracerebral hemorrhage (ICH), whether employing Electrical Capacitance Tomography (ECT) or other electrical imaging techniques, rely on time-difference measurements. The time-difference methods necessitate baseline measurements from the patient in a non-hemorrhagic state, which is impractical to obtain, rendering rapid detection of ICH unfeasible. Methods: This study introduces a novel approach that capitalizes on the distinct dispersion characteristics of the permittivity in brain tissue and the spectral variance of the permittivity between blood and other brain components. Specifically, the frequency-dependent variations in the permittivity are employed to achieve absolute detection of ICH, thereby eliminating the need for non-hemorrhagic baseline data. The methodology entails identification of two frequency points that the frequency-dependent variation in the permittivity at these two frequency points manifest the maximal difference between blood and other brain tissues. Subsequently, this permittivity differential at the two identified frequency points is utilized for hemorrhage detection. Experimental measurements were conducted using an impedance analyzer and a parallel plate capacitor to capture the capacitance in four single-component substances -- distilled water, sheep blood, isolated pig fat, and isolated pig brain -- as well as three mixed blood compounds -- distilled water enveloping sheep blood, pig fat encapsulating sheep blood, and pig brain surrounding sheep blood -- across a frequency range of 10 kHz to 20 MHz. Results: The results show that in different frequency bands, it is indeed possible to distinguish single-component substances from mixed substances by the frequency difference of capacitance variation. Comparative analysis reveals that the 1MHz to 5 MHz frequency range is most effective for detecting blood in distilled water. For blood detection in pig fat, a 10 kHz to 1 MHz frequency range is identified as optimal, while a 10 kHz to 0.5 MHz frequency range is advantageous for blood detection in pig brain tissue. Discussion: The findings confirm that absolute detection of ICH is achievable through frequency-dependent variations in the permittivity. However, this necessitates the identification of the frequency band manifesting the largest difference of frequency-dependent variation between single-component and mixed substances. The study acknowledges limitations primarily due to the use of anticoagulant-altered sheep blood, which exhibits permittivity divergent from those of natural blood. Additionally, the in vitro pig fat and pig brain samples, having been subjected to freeze-thaw cycles, also demonstrate permittivity unrepresentative of in vivo tissue. [ABSTRACT FROM AUTHOR]

A multiphase flowmeter (MPFM) is used in the upstream oil and gas industry for continuous, in-line, real-time, oil-gas-water flow measurement without fluid separation. An MPFM typically consists of phase-fraction and velocity measurements. It is desirable to have homogeneous flow at the measurement location so that the phase-fraction measurement is representative. A horizontal blind-tee pipe-section is often installed to homogenize flow in the downstream vertical Venturi-based flowmeters; however, little information is available on the effect of horizontal blind-tee depth (HBD) on flow homogeneity. In addition, the Venturi vertical entrance length (VEL) leading to the Venturi inlet from the horizontal blind-tee outlet is another design parameter that may potentially affect the downstream phase distribution. The phase-fraction measurement principle requires liquid properties. The local liquid richness makes the horizontal blind-tee an ideal location for measuring liquid properties; however, an excessive HBD may affect the reliability of the measurements of liquid properties, because local vortices may degrade liquid measurement representativeness if the local liquid velocity is too low. This study uses a computational fluid dynamics approach to evaluate the effect of HBD and VEL on multiphase flow measurement. The computational results are validated with experimental data collected in a multiphase flow facility. [ABSTRACT FROM AUTHOR]

The main problem with any tomography is the transformation of measurements into images. It is the so-called "inverse problem", which, due to its indeterminacy, can never be solved perfectly. An additional factor contributing to the deterioration of the quality of tomograms is measurement noise. This article shows how to denoise electrical capacitance tomography measurements using the LSTM autoencoder. The presented model is two-staged. First, the autoencoder is trained using very noisy measurements. Then, the decoder autoencoder generates a training set to using activations ofe the latent layer. In the second stage, the LSTM network is trained, which has encoder latent layer activations at the input and pattern images at the output. The results of the experiments show that using an autoencoder to denoise the measurements improves the reconstruction quality. [ABSTRACT FROM AUTHOR]

This article presents a mathematical model for evaluating the dielectric properties of inner layers of insulation in three-core belted cables. It proposes a modified inspection scheme that allows for the evaluation of partial capacitances and dielectric dissipation factor. The article discusses the challenges in measuring individual dielectric parameters due to parasitic capacitive coupling between adjacent conductive elements. The proposed approach utilizes aggregate measurements and the least squares method to mitigate measurement errors and improve accuracy. The article also references three other articles related to capacitive mass sensors, monitoring insulation in multicore cables, and measuring the dissipation factor in three-core belted cables. The document includes the ISSN number and publication details for the journal "PRZEGL¿D ELEKTROTECHNICZNY." [Extracted from the article]

This paper presents a study on applying machine learning algorithms for the classification of a two-phase flow regime and its internal structures. This research results may be used in adjusting optimal control of air pressure and liquid flow rate to pipeline and process vessels. To achieve this goal the model of an artificial neural network was built and trained using measurement data acquired from a 3D electrical capacitance tomography (ECT) measurement system. Because the set of measurement data collected to build the AI model was insufficient, a novel approach dedicated to data augmentation had to be developed. The main goal of the research was to examine the high adaptability of the artificial neural network (ANN) model in the case of emergency state and measurement system errors. Another goal was to test if it could resist unforeseen problems and correctly predict the flow type or detect these failures. It may help to avoid any pernicious damage and finally to compare its accuracy to the fuzzy classifier based on reconstructed tomography images – authors’ previous work.

The electric capacitance tomography (ECT) technique has been widely used in phase distribution reconstruction, while the practical application raised nonideal noise and other errors for cryogenic conditions, requiring a more accurate algorithm. This paper develops a new image reconstruction algorithm for ECT by coupling the traditional Landweber algorithm with the least square support vector regression (LSSVR) for cryogenic fluids. The performance of the algorithm is quantitatively evaluated by comparing the inversion images with the experimental results for both the room temperature working medium with the dielectric constant ratio close to cryogenic fluid and the cryogenic fluid of liquid nitrogen/nitrogen vapor (LN2-VN2). The inversion images based on the conventional LBP and Landweber algorithms are also presented for comparison. The benefits and drawbacks of the developed algorithms are revealed and discussed, according to the results. It is demonstrated that the correlated coefficients of the images based on the developed algorithm reach more than 0.88 and a maximum of 0.975. In addition, the minimum void fraction error of the algorithm is reduced to 0.534%, which indicates the significant optimization of the LSSVR coupled method over the Landweber algorithm. [ABSTRACT FROM AUTHOR]

Propellant mass gauging under micro-gravity conditions is a challenging task due to the unpredictable position and shape of the fuel body inside the tank. Micro-gravity conditions are common for orbiting satellites and rockets that operate on limited fuel supplies. Capacitance sensors have been investigated for this task in recent years; however, the effect of various positions and shapes of the fuel body is not analyzed in detail. In this paper, we investigate this with various fill types, such as annular, core-annular, and stratified fills at different positions. We compare the performance among several curve-fitting-based approaches and a machine-learning-based approach, the latter of which offers superior performance in estimating the fuel content. [ABSTRACT FROM AUTHOR]

Currently, the flow pattern identification algorithms based on ECT (electrical capacitance tomography) technology have low identification accuracy for complex flow patterns and require a large amount of label data for learning. A novel flow pattern identification method based on a semi-supervised generative adversarial network (SGAN) with capacitance data of ECT is proposed. First, the principles of the ECT technique and general GAN are briefly described, and the model parameters, loss function, and training process of the SGAN are explained in detail. Second, a capacitance data sample set of 11 400 random flow patterns is constructed by co-simulations of COMSOL and MATLAB, and then, the SGAN and BP (back propagation) and SVM (support vector machine) network models are trained and validated by the training set. Finally, static experiments are conducted on the self-developed ECT system, and the identification results of different algorithms are compared and analyzed by modifying the label sample size of the training set. The experimental results show that SGAN maintains a higher average identification accuracy under the training condition where the number of label samples of SGAN is ten times smaller than that of the other two algorithms. [ABSTRACT FROM AUTHOR]

Currently, most of the electrical tomography measurement systems are developed on microcontroller units, digital signal processors, or field programmable gate arrays, whereas image reconstruction and display functions are implemented in a separate host computer. Therefore, the complete hardware usually comprises a measurement system and an image reconstruction computer. The current trend is to develop industrial standard electrical tomography systems, which can implement the image reconstruction and measurement functions into an integrated processor/chip. However, most of the above mentioned processors do not provide the necessary computational resources as required by the computationally intensive image reconstruction function. This paper describes a hardware scheme for implementing image reconstruction functions on a heterogeneous hardware platform, wherein the processor system and programmable logics (PLs) are tightly coupled and can achieve better resource utilization and overall system performance. By adopting a high-level synthesis method, the image reconstruction algorithms can be realized jointly by the PLs and processor system. In this hardware scheme, the algorithms are properly optimized to achieve better data throughput and execution efficiency, that is, by utilizing parallel computation in PLs. Details of the hardware scheme and method of accelerating the image reconstruction process are presented in this paper. Implementation results show that the proposed heterogeneous hardware scheme can achieve the image reconstruction rates of 24 and 1700 frames per second while employing the iterative and non-iterative algorithms, respectively. [ABSTRACT FROM AUTHOR]

Accurate and real-time measurement of fluid flow velocity is crucial in various industrial processes, especially when dealing with multiple phase fluids. Traditional flow measurement methods often struggle to accurately quantify the velocity of complex multiphase flows within pipes. This challenge necessitates the exploration of innovative techniques capable of providing reliable measurements. This paper proposes the utilization of Electrical Capacitance Tomography (ECT) as a promising approach for measuring the velocity of multiple phase fluids in pipes. The ECT technique involves the non-intrusive imaging of the electrical capacitance distribution within the pipe. By utilizing an array of electrodes placed around the pipe circumference, the capacitance distribution can be reconstructed, offering insight into the fluid flow patterns. By analyzing the temporal changes in the capacitance distribution, the velocity of different phases within the pipe can be estimated. To achieve accurate velocity measurements, an ECT system needs to account for the complexities introduced by multiphase flows. Various image reconstruction algorithms, such as linear back-projection and iterative algorithms like Gauss-Newton and Levenberg-Marquardt, are employed to reconstruct the capacitance distribution. Additionally, advanced signal processing techniques, such as cross-correlation analysis and time-difference methods, are used to extract velocity information from the reconstructed images. This paper presents an experimental investigation of measuring the velocity of multiple-phase fluids in pipes using the ECT technique. The study aims to address the challenges associated with different flow regimes, fluid properties, and pipe geometries by exploring advancements in electrode design, system calibration, and data processing techniques to enhance the accuracy and robustness of ECT-based velocity measurements. [ABSTRACT FROM AUTHOR]

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper's operation safety. [ABSTRACT FROM AUTHOR]

Purpose: As one of the tomographic imaging techniques, electrical capacitance tomography (ECT) is widely used in many industrial applications. While most ECT sensors have electrodes placed around a cylindrical chamber, the planar ECT sensor has been investigated for depth and defect detection. However, the planar ECT sensor has limited height and depth sensing capability due to its single-sided assessment with the use of only a single-plane design. The purpose of this paper is to investigate a dual-plane miniature planar 3D ECT sensor design using the 3 × 3 matrix electrode array. Design/methodology/approach: The sensitivity map of dual-plane miniature planar 3D ECT sensor was analysed using 3D visualisation, the singular value decomposition and the axial resolution analysis. Then, the sensor was fabricated for performance analysis based on 3D imaging experiments. Findings: The sensitivity map analysis showed that the dual-plane miniature planar 3D ECT sensor has enhanced the height sensing capability, and it is less ill-posed in 3D image reconstruction. The dual-plane miniature planar 3D ECT sensor showed a 28% improvement in reconstructed 3D image quality as compared to the single-plane sensor set-up. Originality/value: The 3 × 3 matrix electrode array has been proposed to use only the necessary electrode pair combinations for image reconstruction. Besides, the increase in number of electrodes from the dual-plane sensor setup improved the height reconstruction of the test sample. [ABSTRACT FROM AUTHOR]

Abstract The objective of this study is to develop and validate a noncontact, nondestructive inspection and imaging method for rapidly assessing the subsurface condition of carbon fiber-reinforced polymer (CFRP) composite structures. The method works on the principle of planar electrical capacitance tomography (ECT). Unlike conventional ECT systems, electrodes are arranged on a rectangular planar surface in the form of a grid. The volume on one side of the electrode plane is interrogated with an electric field, and the mutual capacitances between the electrodes are measured. The volumetric electrical permittivity distribution of the interrogated region is then reconstructed from the measured capacitance responses. In this work, an ECT image reconstruction algorithm was implemented, and a customized planar capacitive imaging system was prototyped. First, different objects were placed near the electrode array, and the corresponding volumetric change in electrical permittivity was successfully captured. Second, the planar ECT system was employed for detecting artificially introduced subsurface defects in 3D-printed objects. Third, CFRP specimens with different sizes of single-layer delamination were fabricated and subjected to ECT interrogation. The results confirmed that the planar ECT system could detect the location and size of delamination in CFRP panels. The accuracy and resolution of the planar ECT prototype were also characterized.

In this paper, a new approach for capacitance measurement for electrical capacitance tomography (ECT) sensors is proposed. The method is based on the digital processing of the time-domain voltage measurements at the sensor electrodes. Furthermore, a robust capacitance estimation algorithm is developed to convert the measured voltage time-series to inter-electrode capacitances. The proposed measurement technique simplifies the electronic design of the ECT sensor and is suitable for use in applications requiring a compact device with a fast scan time. The accuracy and sensitivity of the method are investigated numerically and experimentally using a prototype sensor. In particular, the sensitivity of the estimated capacitance to measurement noise levels is analyzed in detail. Additionally, an analysis of the parameters that affect the accuracy of estimated capacitances is carried out from which we are able to demonstrate that the method is immune to effects such as stray capacitances between the electrodes and the ground. A prototype sensor with an open curved geometry on a millimeter scale is used to test the method empirically. Experimental results obtained for measurements with mineral oil and water are shown and compared against capacitances obtained using a physics-based forward model of the sensor. The inter-electrode capacitances in the range of tens of femtofarads to a few picofarads are estimated and a close match with the forward model results is obtained. [ABSTRACT FROM AUTHOR]

Unstable combustion phenomena such as flame flashback, flame liftoff, extinction and blowout frequently take place during the operation of the bluff-body stabilized burner. Therefore, flame state monitoring is necessary for the safe operation of the bluff-body stabilized burner. In the present study, an electrical capacitance tomography (ECT) system was deployed to detect the permittivity distribution in the premixing channel and further characterize the flame states of stabilization, flashback, liftoff, extinction and blowout. A calderon-based reconstruction method was modified to reconstruct the permittivity distribution in the annular premixing channel. The detection results indicate that the permittivity in the premixing channel increases steeply when the flame flashback takes place and decreases obviously when the flame lifts off from the combustor rim. Based on the varied permittivity distribution at different flame states, a flame state index was proposed to characterize the flame state in quantification. The flame state index is 0, positive, in the range of −0.64–0, and lower than −0.64 when the flame is at the state of stable, flashback, liftoff and blowout, respectively. The flame state index at the flame state of extinction is the same as that at the flame state of liftoff. The extinction state and the blowout state can be distinguished by judging whether the flame flashback takes place before the flame is extinguished. These results reveal that the ECT system is capable of monitoring the flame state, and that the proposed flame state index can be used to characterize the flame state. [ABSTRACT FROM AUTHOR]

In this paper, the use of radial screens in an Electrical Capacitance Tomography (ECT) sensor is investigated with the aim of resulting in higher-resolution images. The effect of the screens on the quality of the reconstructed images was simulated. 12-electrode ECT sensors with and without screens were designed and tested. The capacitance between different electrode pairs was measured for two permittivity distributions. The obtained capacitance data were used to reconstruct images using the Projected Landweber Iteration algorithm based on linear and semi-linear ECT models. The sensitivity of the measurements and the accuracy of the obtained images for the ECT sensors with and without screens were analyzed. The main conclusion is that a little improvement in the quality of images can be achieved with the use of 12-Electrode ECT sensors with radial screen. [ABSTRACT FROM AUTHOR]

Electrical tomography techniques have been developed to monitor the internal behavior of industrial processes. Electrical capacitance gives the best benefits over other tomography modalities, as it has no radiation, is non-intrusive, and has a low cost. This study investigated the diagnosis of two-phase oil/gas flow in a closed pipe, using an image data capture system for an 8 external electrode Electrical Capacitance Tomography (ECT) sensor. The system had a high-resolution ratio, a small measurement error, and was able to remove the effects of stray capacitance. Experimental measurements were carried out on two different materials that filled the space inside the pipe in different proportions to determine the sensitivity and accuracy of the measurement. The results showed that the system had fast image data capture time, high accuracy, a very small resolution ratio, and a good signal-to-noise ratio and quality factor. [ABSTRACT FROM AUTHOR]

For the problem that noise has a great impact on the measurement data during the electrical capacitance tomography data acquisition process, a denoising method based on the truncated singular value decomposition combined with the total least squares model is proposed. Soft threshold is performed on the effective value of the truncated singular value decomposition to remove the influence of external noise interference in the measurement data, and as far as possible to retain the original characteristics of the data. For the problem of different errors in the measurement data and the coefficient matrix, a mathematical model is introduced based on total least squares. It is used to improve the total least squares iterative method and reduce both the measurement error and the coefficient matrix error. To solve the problems of slow convergence speed and low efficiency caused by the ill-posedness of the equation during the iteration, adaptive correction parameters are introduced, which effectively avoids the occurrence of local convergence and improves the speed of convergence and imaging accuracy. To solve the ill-condition of the total least squares model, a regularization matrix is added so that the imaging results can achieve the goal of overall optimization. For 12-electrode electrical capacitance tomography system, the simulation experiments are carried out based on four typical flow patterns. The results show that the algorithm effectively increases the robustness of reconstruction and improves the accuracy of the reconstructed images. [ABSTRACT FROM AUTHOR]

To ensure the desired mechanical properties of adhesively bonded components, maintaining the prescribed mixing quality, i.e. mixing ratio and evenness, of two-component (2K) adhesives is essential. Although different studies proved that mixing errors may exert a negative impact on the bonding result, there are currently no methods available that would allow for continuous monitoring thereof during inline dosing processes. A way out of associated issues is offered by the Electrical Capacitance Tomography (ECT), which enables practitioners to map spatial material distributions and mixing ratios by measuring the capacitances of different media, including adhesives. Therefore, the aim of the study was to investigate the ECT's potential to be used for 2K mixture quality control. Due to the complexity of the matter, the presentation is split into two papers. In this 1st part, all preliminary experimental and analytical work aiming to determine polymer characteristics, above all permittivity, ε, needed to evaluate whether a respective 2K adhesive may be ECT-monitored is presented. For that, a permittivity measurement device (PMD) was developed, which allows for a fast and efficient determination of adhesive permittivities. Subsequently, the PMD was validated with reference dielectrics and then used to determine the permittivities of the components of 16 commercially available 2K adhesives (epoxies and polyurethanes). Finally, the relationship between permittivity and mixing ratio was investigated for a sub-set of four representative ECT-suitable adhesives. It was found that a wide variety of 2K polymers may principally be controlled by ECT and that the relationship between mixing ratio and mixture permittivity can be described by general analytical theories from the literature. As a result, preliminary experimental effort aiming to assess the adhesive suitability for inline ECT-monitoring could be significantly reduced. The latter experiments will be the topic of the 2nd part of the study. [ABSTRACT FROM AUTHOR]

Owing to the ill-conditioned nature of electrical resistivity tomography and the measurement error of the hardware equipment, the reconstructed resistivity distribution image often contains artifacts of varying degrees. Other soft-field imaging technologies, such as electrical impedance tomography and electrical capacitance tomography, also encounter artifacts. Artifacts interfere with the assessment of damaged areas. To eliminate the influence of artifacts on the reconstructed image, a novel artifact elimination algorithm called the fast artifact filtering (FAF) algorithm is proposed. Based on the calculation results of existing algorithms, such as the Newton's one-step error reconstructor (NOSER) algorithm, the FAF algorithm can remove the damaged areas with low confidence from the potentially damaged areas and only retain the damaged areas with high confidence for final imaging. Several simulation models were used to test the effectiveness of the artifact elimination algorithm proposed in this study. The test results show that the number of artifacts in the final reconstructed image is significantly reduced after the NOSER algorithm is combined with the FAF algorithm. In addition, when the number of finite element model division elements was 4802, the refresh time of a single image increased by approximately 1 ms. A structural health monitoring test for hollow structure is provided. The results show that the FAF also performs well on the measured voltage data. [ABSTRACT FROM AUTHOR]

Electrical capacitance tomography (ECT) is a noninvasive advanced measurement technique with the potential to be applied to cryogenic propellant monitoring in microgravity environments. This paper briefly introduces common propellant measurement techniques and reviews the development of capacitive methods for cryogenic fluid measurements. At present, ECT at cryogenic environment has only successfully reconstructed phase distribution images in liquid nitrogen–vapor nitrogen static stratified flow experiments. Sensor structure, micro-capacitance measurement technology, multi-flow pattern test, and dynamic experiment still need to be further studied. [ABSTRACT FROM AUTHOR]

The gas and solids in a circulating fluidized bed (CFB) are heterogeneously dispersed and a multiscale flow regime may form both in time and space. Accurate measurement of the fluidizing process is significant for investigating the multiscale gas–solid flow characteristics and the design, optimization, and control of CFBs in various applications. This article develops a deep learning‐based tomographic imaging of electrical capacitance tomography (ECT) to characterize the particle concentration distribution in a CFB. The deep tomographic imaging approach is realized through training a well‐designed convolutional neural network (CNN) with the numerically built dataset. Simulation results demonstrate that the average values of the relative image errors reconstructed by CNN in the test set are 0.1110 and 0.1114 for the 60 and 100 mm pipes, respectively, which are better than the average values of 0.1819 and 0.2519 by the Landweber algorithm. With the verification of the trained model based on the prepared data can image the unseen typical flow patterns better than Landweber, it is further used to investigate the particle flow characteristics of a lab‐scale CFB. Experimental results reveal that the developed deep tomographic imaging of ECT can successfully measure the fluidized particle distribution in both the 60 and 100 mm pipes, showing good prediction and generality of the designed CNN model. A flow regime transformation from "annular" flow to "core‐annular" flow and pneumatic conveying is observed under the tested conditions. Besides, the flow regime would be highly affected by the fluidized gas flow rate and the initial bed height. [ABSTRACT FROM AUTHOR]

High-precision images enable electrical capacitance tomography (ECT) to obtain more reliable measurement results, meaning that the reconstruction algorithm is particularly important. Some excellent numerical algorithms have successfully solved the inverse problem for ECT imaging, but their imaging quality is relatively low. To solve this problem, this paper proposes a new reconstruction algorithm based on regularized extreme learning machines (RELMs). The implementation of the algorithm is mainly divided into two steps: (1) according to a large number of training samples, the RELM model can be obtained by the iterative split Bregman (ISB) algorithm, which can describe the mapping relationship between the capacitance correlation coefficient and the imaging target well, and (2) the capacitance correlation coefficient is calculated, which is then used as input to the RELM model to predict the final imaging. Both simulation and experimental results show that the RELM algorithm achieves greater improvement in imaging quality and robustness, and provides new development ideas for the ECT.