Generative Adversarial Networks Methods for Electrical Impedance Tomography Reconstruction and Post-processing
- Abstract
- Electrical Impedance Tomography (EIT) is a versatile method applied for imaging in fields such as phase flow analysis, medical diagnostics, and sensing materials imaging. It provides real-time, non-invasive cross-sectional imaging, aiding in pipeline optimization, energy reduction, and environmental impact mitigation. For medical purposes, EIT has been integrated with conductive fabrics for artificial skin, enabling simultaneous monitoring of electrical conductivity and mechanical properties. This thesis proposes Generative Adversarial Network (GAN) models for EIT reconstructions related to the mentioned fields. These neural networks based models effectively handle complex conductivity distributions, learn intricate features directly from data, and support learning by simulation cases. Their adaptability to various scenarios, data-driven nature, and ability to incorporate regularization techniques make them promising tools for EIT applications. The results demonstrate that the designed GAN-based models successfully reconstruct target positions and a more uniform background. The performance is also contrasted to alternative neural network approaches, including neural network and deep neural network models. The evaluation metrics indicate superior performance of the GAN models in comparison.
- Author(s)
- Solano Sánchez Felipe Alberto
- Issued Date
- 2024
- Awarded Date
- 2024-02
- Type
- Dissertation
- URI
- https://dcoll.jejunu.ac.kr/common/orgView/000000011595
- Alternative Author(s)
- 솔라노 산체스 펠리
- Affiliation
- 제주대학교 대학원
- Department
- 대학원 에너지응용시스템학부
- Advisor
- Kim Kyung Youn
- Table Of Contents
- 1. Introduction 1
1.1 Scope of the Thesis 3
2. Theoretical Framework 4
2.1 Electrical Impedance Tomography 4
2.2 Modified Newton-Raphson Method 7
2.3 Generative Adversarial Networks 8
2.3.1 pix2pix GAN 12
2.3.2 Attention Mechanism 13
3. Materials and methods 15
3.1 Two-Phase Flow 15
3.2 Piezoresistive fabric 15
3.3 Evaluation Metrics 16
4. Two-phase flow study 17
4.1 Numerical Results. 20
4.2 Experimental Results 25
5. Piezoresistive fabric study 29
5.1 Numerical Results 31
5.2 Experimental Results 34
6. Future Works 36
7. Conclusions 37
8. References 38
- Degree
- Master
- Publisher
- 제주대학교 대학원
- Citation
- Solano Sánchez Felipe Alberto. (2024). Generative Adversarial Networks Methods for Electrical Impedance Tomography Reconstruction and Post-processing.
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- Faculty of Applied Energy System > Electronic Engineering
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