The Sensitivity Optimization Guided Imaging Method for Electrical Capacitance Tomography

Qing, Zhao; Li, Jie; Shi, Liyi; Liu, Guoqiang; Liu, Jing

doi:10.1109/tim.2021.3106131

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…By analyzing the principle of PT technology, we can know that the sensitivity field of sensor has 'soft' sensitivity field and 'hard' sensitivity field. The former refers to the change in position causing a change in the sensitive field distribution in the sensor sensitivity field measurement region [27]. Taking the electrostatic field in ECT technology as an example, the potential distribution changes unevenly.…”

Section: Optimization Of Sensitive Field Based On Flow Pattern Identi...mentioning

confidence: 99%

ECT image reconstruction based on sensitive field expansion and optimization

Huang,

Li,

Wang

et al. 2024

Meas. Sci. Technol.

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Electrical capacitance tomography (ECT) is an efﬁcient method for addressing the issue of two-phase ﬂow monitoring. Most current methods result in low image reconstruction accuracy due to soft ﬁeld issues. This paper propose an ECT image reconstruction method based on sensitive ﬁeld expansion and optimization, which improve reconstruction efﬁciency and accuracy. Firstly, a sensitivity ﬁeld optimization method based on ﬂow pattern identiﬁcation was proposed. The ﬂow pattern recognition determines the ﬂow pattern to which the input signal belongs and selects the sensitive ﬁeld for the corresponding ﬂow pattern. Secondly, a sensitive ﬁeld expansion method based on feature extraction is proposed. The ECT image is modeled as a FRI signal, sampled using an exponential reproducing kernel. Feature information is extracted from the input signal for data fusion, and the optimized sensitive ﬁeld is expanded into a new sensitive ﬁeld distribution matrix by zero-padding and random reorganization. Then, based on optimized and expanded sensitive ﬁelds, a sparse image reconstruction method is proposed. Image reconstruction by constructing comprehensive observation equation to obtain the original permittivity distribution vector of the ECT system using the sparsity of the sensitivity matrix and capacitance signal. Finally, the ECT hardware system and the upper computer measurement and imaging integration software are designed. The experimental results show that the method outperforms other existing algorithms in terms of imaging indexes, has better imaging results.

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Section: Optimization Of Sensitive Field Based On Flow Pattern Identi...mentioning

confidence: 99%

ECT image reconstruction based on sensitive field expansion and optimization

Huang,

Li,

Wang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…According to the research conclusions of relevant scholars for decades, it has been a consensus in the industry that the image reconstruction model of the ECT can be ex pressed using the following mathematical formula when considering capacitive noise [12]:…”

Section: Solving For Capacitance Correlation Coefficientsmentioning

confidence: 99%

“…where D xx , D xy , D yx , and D yy are different difference operators, and λ 1 and λ 2 are regularization parameters. As a result of submitting Equations ( 11) and (12) into Expression (10), a new objective function can be obtained:…”

Section: Regularized Extreme Learning Machinementioning

confidence: 99%

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

Zhao,

Liu,

Chen

2024

Applied Sciences

Self Cite

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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.

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“…Besides, using numerical simulation data and experimental data, we discuss the capacitance matching correlation coefficients (CCs) in sections 3.2 and 4.1. The uniformity of the sensitivity matrix is defined by the ratio of the standard deviation and the mean value of the sensitivity matrix [32,33]…”

Section: Sensitivity Analysismentioning

confidence: 99%

Image reconstruction for ECT based on high-order approximate sensitivity matrix

Yang

Liu

2023

Meas. Sci. Technol.

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Electrical capacitance tomography (ECT), widely used for industrial measurements, reflects the permittivity distribution of the imaging region by measuring capacitance. The image reconstruction method is a crucial factor in determining image quality. Normally, the ECT image is reconstructed by sensitivity matrix. To alleviate the effects of the soft field property of the traditional sensitivity matrix, we propose an ECT reconstruction method based on the high-order approximate sensitivity matrix. The high-order approximate sensitivity matrices with four different nonlinearities, including a traditional sensitivity matrix, are derived through electromagnetic field analysis. Both numerical simulation and experiment are implemented for four sensitivity matrices using the Tikhonov regularisation algorithm and Landweber algorithm. Sensitivity evaluation indicators, image evaluation metricises, and capacitance matching correlation coefficients are used to compare the performance of sensitivity matrices. The results show that the high-order approximate sensitivity matrix with suitable nonlinearities improves image reconstruction quality. In different flow regimes, the sensitivity matrix with proper linearity has obvious advantages for regimes with high nonlinearity. The high-order approximate sensitivity matrix leads to shorter calculation time and can be used by various algorithms to enhance image reconstruction quality.

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The Sensitivity Optimization Guided Imaging Method for Electrical Capacitance Tomography

Cited by 8 publications

References 31 publications

ECT image reconstruction based on sensitive field expansion and optimization

ECT image reconstruction based on sensitive field expansion and optimization

A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography

Image reconstruction for ECT based on high-order approximate sensitivity matrix

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