A transformer-based model for two-phase flow imaging by electrical resistance tomography

Jingfu Yan; Longteng Bai; Wanda Xu

doi:10.1117/12.3055545

12 December 2024 A transformer-based model for two-phase flow imaging by electrical resistance tomography

Jingfu Yan, Longteng Bai, Wanda Xu

Author Affiliations +

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134390T (2024) https://doi.org/10.1117/12.3055545
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

Electrical resistance tomography (ERT) is a powerful method for two-phase flow measurement in various applications, however, the image reconstruction quality of ERT is often unsatisfactory due to the ill-posed nature of the inverse problem. In order to improve the imaging quality of ERT, a deep learning approach based on Transformer architecture is proposed. To reduce the risk of overfitting on small datasets caused by the weak inductive bias, a convolutional layer is used before the Transformer Encoder module in this paper. Trained on boundary voltages and images of the target location, the Transformer model establishes a nonlinear mapping relationship between them. The simulation results demonstrate that the proposed Transformer model achieves superior image reconstruction performance compared to the traditional image reconstruction algorithm, and have good generalization and noise resistance capabilities.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jingfu Yan, Longteng Bai, and Wanda Xu "A transformer-based model for two-phase flow imaging by electrical resistance tomography", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134390T (12 December 2024); https://doi.org/10.1117/12.3055545

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