Calderón’s Method-Guided Deep Neural Network for Electrical Impedance Tomography

Sun, Benyuan; Zhong, Hangyu; Zhao, Yu; Ma, Long; Wang, Huaxiang

doi:10.1109/tim.2023.3322501

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Neural networks [27] and deep learning algorithms [28], with strong non-linear fitting capabilities, offer a more suitable solution. Deep learning techniques have a significant impact on enhancing the resolution of EIT reconstructed images.…”

Section: Related Workmentioning

confidence: 99%

A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

Zhao,

Xu,

Zhu

et al. 2024

Applied Sciences

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Electrical impedance tomography (EIT) is a non-invasive imaging method that allows for the acquisition of resistivity distribution information within an object without the use of radiation. EIT is widely used in various fields, such as medical imaging, industrial imaging, geological exploration, etc. Presently, most electrical impedance imaging methods are restricted to uniform domains, such as pixelated pictures. These algorithms rely on model learning-based image reconstruction techniques, which often necessitate interpolation and embedding if the fundamental imaging model is solved on a non-uniform grid. EIT technology still confronts several obstacles today, such as insufficient prior information, severe pathological conditions, numerous imaging artifacts, etc. In this paper, we propose a new electrical impedance tomography algorithm based on the graph convolutional neural network model. Our algorithm transforms the finite-element model (FEM) grid data from the ill-posed problem of EIT into a network graph within the graph convolutional neural network model. Subsequently, the parameters in the non-linear inverse problem of the EIT process are updated by using the improved Levenberg—Marquardt (ILM) method. This method generates an image that reflects the electrical impedance. The experimental results demonstrate the robust generalizability of our proposed algorithm, showcasing its effectiveness across different domain shapes, grids, and non-distributed data.

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Section: Related Workmentioning

confidence: 99%

A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

Zhao,

Xu,

Zhu

et al. 2024

Applied Sciences

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Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

Traquinho,

Vale,

Ribeiro

et al. 2023

Machines

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Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable.

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Calderón’s Method-Guided Deep Neural Network for Electrical Impedance Tomography

Cited by 2 publications

References 33 publications

A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

A Graph Neural Network Approach with Improved Levenberg–Marquardt for Electrical Impedance Tomography

Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science

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