A Positive Data Extraction Method for Electrical Impedance Tomography (EIT) Based on the Novel MSA-Net

Zhang, Hanyu; Mu, J.; Yang, Yunjie; Li, Nan

doi:10.1109/jsen.2023.3278939

Cited by 6 publications

(1 citation statement)

References 29 publications

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“…Tan et al applied a CNN to ERT image reconstruction and proved the possibility of utilizing deep learning methods to accomplish the 'data to image' task in ERT [6]. Moreover, neural network approaches have proved to be effective in improving the quality of image reconstruction in tomography through data augmentation [7]. Rather than manually select prior information, neural networks extract prior information from training datasets automatically and incorporate the information into ERT; thus neural network approaches may have good performance in 'data to image' tasks.…”

Section: Introductionmentioning

confidence: 99%

Res2-UNet++: a deep learning image post-processing method for electrical resistance tomography

Huang,

Liang,

Tan

et al. 2024

Meas. Sci. Technol.

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It is challenging to monitor the multiphase flow distribution in the industrial processes in order to optimize the production. Electrical resistance tomography (ERT) can be used to visualize the inner distribution of multiphase flow. The image reconstruction plays a vital role in ERT. However, the nonlinearity and ill-posedness of inverse problem make the image reconstruction of ERT a challenge, and the development of advanced imaging algorithm has attracted much attention in the past. In this work, an improved U-shaped deep learning model is proposed, which combines the advantages of multi-scale feature extraction of UNet++ and residual feature fusion of Res2Net. The network is used to post-process the prereconstruction result of traditional ERT image reconstruction methods, where the generalization ability of the traditional methods and the flexible feature extraction advantage of deep learning methods can be combined. Simulations and experiments are designed to verify the generalization ability and the effectiveness of the proposed model. Both simulation and experimental results show that the proposed U-shaped network approach outperforms other deep learning methods, and the proposed deep learning model is fit for post-processing tasks of ERT.

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Section: Introductionmentioning

confidence: 99%

Res2-UNet++: a deep learning image post-processing method for electrical resistance tomography

Huang,

Liang,

Tan

et al. 2024

Meas. Sci. Technol.

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A permittivity imaging method in the polar coordinates: Electrical capacitance tomography based on the electromagnetic momentum

Yang,

Liu,

Liu

2024

Flow Measurement and Instrumentation

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Identification of two-phase flow patterns based on capacitance data of electrical capacitance tomography with semi-supervised generative adversarial network

Gao,

Ku,

Jian

2023

Review of Scientific Instruments

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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.

show abstract

A Positive Data Extraction Method for Electrical Impedance Tomography (EIT) Based on the Novel MSA-Net

Cited by 6 publications

References 29 publications

Res2-UNet++: a deep learning image post-processing method for electrical resistance tomography

Res2-UNet++: a deep learning image post-processing method for electrical resistance tomography

A permittivity imaging method in the polar coordinates: Electrical capacitance tomography based on the electromagnetic momentum

Identification of two-phase flow patterns based on capacitance data of electrical capacitance tomography with semi-supervised generative adversarial network

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