Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
Electrical capacitance tomography holds significant potential for multiphase flow parameter measurements, but its application has been limited by the challenge of reconstructing high-quality images, especially under complex and uncertain conditions. We propose an innovative multi-objective robust op...
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MDPI AG
2025-04-01
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| author | Xuejie Yang Jing Lei Qibin Liu |
| author_facet | Xuejie Yang Jing Lei Qibin Liu |
| author_sort | Xuejie Yang |
| collection | DOAJ |
| description | Electrical capacitance tomography holds significant potential for multiphase flow parameter measurements, but its application has been limited by the challenge of reconstructing high-quality images, especially under complex and uncertain conditions. We propose an innovative multi-objective robust optimization model to alleviate this limitation. This model integrates advanced optimization methods, multimodal learning, and measurement physics, structured as a nested upper-level optimization problem and lower-level optimization problem to tackle the challenges of complex image reconstruction. By integrating supervised learning methodologies with optimization principles, our framework synchronously achieves parameter tuning and performance enhancement. Utilizing the regularization theory, the multimodal learning prior image, sparsity prior, and measurement physics are incorporated into a novel lower-level optimization problem. To enhance the inference accuracy of the prior image, a new multimodal neural network leveraging multimodal data is developed. An innovative nested algorithm that mitigates computational difficulties arising from the interactions between the upper- and lower-level optimization problems is proposed to solve the proposed multi-objective robust optimization model. Qualitative and quantitative evaluation results demonstrate that the proposed method surpasses mainstream imaging algorithms, enhancing the automation level of the reconstruction process and image quality while exhibiting exceptional robustness. This study pioneers a novel imaging framework for enhancing overall reconstruction performance. |
| format | Article |
| id | doaj-art-2d254a62ab1c4bbeb459a7c1f11d8dac |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-2d254a62ab1c4bbeb459a7c1f11d8dac2025-08-20T01:49:27ZengMDPI AGApplied Sciences2076-34172025-04-01159477810.3390/app15094778Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance TomographyXuejie Yang0Jing Lei1Qibin Liu2China Nuclear Power Engineering Co., Ltd., Haidian District, Beijing 100840, ChinaSchool of Energy, Power and Mechanical Engineering, North China Electric Power University, Changping District, Beijing 102206, ChinaInstitute of Engineering Thermophysics, Chinese Academy of Sciences, Haidian District, Beijing 100190, ChinaElectrical capacitance tomography holds significant potential for multiphase flow parameter measurements, but its application has been limited by the challenge of reconstructing high-quality images, especially under complex and uncertain conditions. We propose an innovative multi-objective robust optimization model to alleviate this limitation. This model integrates advanced optimization methods, multimodal learning, and measurement physics, structured as a nested upper-level optimization problem and lower-level optimization problem to tackle the challenges of complex image reconstruction. By integrating supervised learning methodologies with optimization principles, our framework synchronously achieves parameter tuning and performance enhancement. Utilizing the regularization theory, the multimodal learning prior image, sparsity prior, and measurement physics are incorporated into a novel lower-level optimization problem. To enhance the inference accuracy of the prior image, a new multimodal neural network leveraging multimodal data is developed. An innovative nested algorithm that mitigates computational difficulties arising from the interactions between the upper- and lower-level optimization problems is proposed to solve the proposed multi-objective robust optimization model. Qualitative and quantitative evaluation results demonstrate that the proposed method surpasses mainstream imaging algorithms, enhancing the automation level of the reconstruction process and image quality while exhibiting exceptional robustness. This study pioneers a novel imaging framework for enhancing overall reconstruction performance.https://www.mdpi.com/2076-3417/15/9/4778imaging methodmulti-objective robust optimizationmultimodal learninginformation fusionelectrical capacitance tomography |
| spellingShingle | Xuejie Yang Jing Lei Qibin Liu Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography Applied Sciences imaging method multi-objective robust optimization multimodal learning information fusion electrical capacitance tomography |
| title | Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography |
| title_full | Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography |
| title_fullStr | Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography |
| title_full_unstemmed | Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography |
| title_short | Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography |
| title_sort | multi objective robust optimization reconstruction algorithm for electrical capacitance tomography |
| topic | imaging method multi-objective robust optimization multimodal learning information fusion electrical capacitance tomography |
| url | https://www.mdpi.com/2076-3417/15/9/4778 |
| work_keys_str_mv | AT xuejieyang multiobjectiverobustoptimizationreconstructionalgorithmforelectricalcapacitancetomography AT jinglei multiobjectiverobustoptimizationreconstructionalgorithmforelectricalcapacitancetomography AT qibinliu multiobjectiverobustoptimizationreconstructionalgorithmforelectricalcapacitancetomography |