用机器学习从电导率数据中识别材料内部异质性，仅需两次测量即可高精度判断夹杂物大小。
Electrical Impedance Tomography for Anisotropic Media: a Machine Learning Approach to Classify Inclusions
- 结合神经网络与支持向量机，从边界电测量数据推断夹杂物特征。
- 16电极设置下检测准确率高，两次测量即可实现良好尺寸预测精度。
- 适用于医学成像、无损检测等需快速识别材料异质性的场景。
本文研究二维区域Ω内导电体中一个或多个夹杂物的电导率层析成像（EIT）问题，基于边界∂Ω上的有限静电测量数据，通过狄利克雷到诺伊曼（D-N）矩阵建模。一旦确认夹杂物存在，所提出的模型结合人工神经网络（ANN）与支持向量机（SVM），可进一步判断夹杂物的尺寸、数量及内部各向异性。利用真实与模拟数据集，在16电极配置下实现了高精度夹杂物检测，结果表明仅需两次测量即可达到良好的尺寸预测准确率，验证了机器学习在传统EIT反问题分析中的巨大潜力，尤其在揭示材料各向异性方面具有重要意义。
原文摘要 · Abstract (English)
We consider the problem in Electrical Impedance Tomography (EIT) of identifying one or multiple inclusions in a background-conducting body $Ω\subset\mathbb{R}^2$, from the knowledge of a finite number of electrostatic measurements taken on its boundary $\partialΩ$ and modelled by the Dirichlet-to-Neumann (D-N) matrix. Once the presence of one inclusion in $Ω$ is established, our model, combined with the machine learning techniques of Artificial Neural Networks (ANN) and Support Vector Machines (SVM), may be used to determine the size of the inclusion, the presence of multiple inclusions, and also that of anisotropy within the inclusion(s). Utilising both real and simulated datasets within a 16-electrode setup, we achieve a high rate of inclusion detection and show that two measurements are sufficient to achieve a good level of accuracy when predicting the size of an inclusion. This underscores the substantial potential of integrating machine learning approaches with the more classical analysis of EIT and the inverse inclusion problem to extract critical insights, such as the presence of anisotropy.
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