Electrical impedance tomography methods and algorithms processed with a GPU

Dušek, Ján; Hladky, D.; Mikulka, Jan

doi:10.1109/piers.2017.8262025

Cited by 35 publications

(16 citation statements)

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“…Electrical resistance tomography in a process tomography can be interchangeably called electrical impedance tomography (EIT). In the following part of this work, we will mainly use the name, EIT [49,50,51].…”

Section: Methodsmentioning

confidence: 99%

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Rymarczyk

Kłosowski

Kozłowski

et al. 2019

Sensors

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The main goal of this work was to compare the selected machine learning methods with the classic deterministic method in the industrial field of electrical impedance tomography. The research focused on the development and comparison of algorithms and models for the analysis and reconstruction of data using electrical tomography. The novelty was the use of original machine learning algorithms. Their characteristic feature is the use of many separately trained subsystems, each of which generates a single pixel of the output image. Artificial Neural Network (ANN), LARS and Elastic net methods were used to solve the inverse problem. These algorithms have been modified by a corresponding increase in equations (multiply) for electrical impedance tomography using the finite element method grid. The Gauss-Newton method was used as a reference to machine learning methods. The algorithms were trained using learning data obtained through computer simulation based on real models. The results of the experiments showed that in the considered cases the best quality of reconstructions was achieved by ANN. At the same time, ANN was the slowest in terms of both the training process and the speed of image generation. Other machine learning methods were comparable with the deterministic Gauss-Newton method and with each other.

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Section: Methodsmentioning

confidence: 99%

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Rymarczyk

Kłosowski

Kozłowski

et al. 2019

Sensors

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“…Electrical impedance tomography (EIT) [19,20] sometimes referred to more generally as electrical tomography (ET) [21][22][23][24][25][26] involves several techniques for image reconstruction. The literature includes the following methods: electrical capacitance tomography (ECT) [27][28][29][30], electrical resistive tomography (ERT) [31] and electrical impedance tomography [32,33]. Electrical capacitance tomography is based on the reconstruction of the electrical permittivity ε [34][35][36], while the electrical impedance tomography reconstructs the conductivity γ [37][38][39].…”

Section: Electrical Impedance Tomographymentioning

confidence: 99%

Intelligent System for Analysis and Monitoring of Flood Embankments Based on Electrical Impedance Tomography, Machine Learning and Internet of Things

Rymarczyk¹,

Kozłowski²,

Kłosowski³

2018

Preprint

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“…EIT is included in a wider group of methods referred to as electrical tomography (ET) [22,23,24]. ET involves several techniques for image reconstruction including: EIT [25], ERT which is a variation of EIT, and ECT [15,18,19,26,27]. In ECT, we determine the value of the electrical permittivity ε [28,29,30], while EIT and ERT reconstruct the conductivity σ [31].…”

Section: Introductionmentioning

confidence: 99%

Logistic Regression for Machine Learning in Process Tomography

Rymarczyk

Kozłowski

Kłosowski

et al. 2019

Sensors

120

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The main goal of the research presented in this paper was to develop a refined machine learning algorithm for industrial tomography applications. The article presents algorithms based on logistic regression in relation to image reconstruction using electrical impedance tomography (EIT) and ultrasound transmission tomography (UST). The test object was a tank filled with water in which reconstructed objects were placed. For both EIT and UST, a novel approach was used in which each pixel of the output image was reconstructed by a separately trained prediction system. Therefore, it was necessary to use many predictive systems whose number corresponds to the number of pixels of the output image. Thanks to this approach the under-completed problem was changed to an over-completed one. To reduce the number of predictors in logistic regression by removing irrelevant and mutually correlated entries, the elastic net method was used. The developed algorithm that reconstructs images pixel-by-pixel is insensitive to the shape, number and position of the reconstructed objects. In order to assess the quality of mappings obtained thanks to the new algorithm, appropriate metrics were used: compatibility ratio (CR) and relative error (RE). The obtained results enabled the assessment of the usefulness of logistic regression in the reconstruction of EIT and UST images.

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Electrical impedance tomography methods and algorithms processed with a GPU

Cited by 35 publications

References 1 publication

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

Intelligent System for Analysis and Monitoring of Flood Embankments Based on Electrical Impedance Tomography, Machine Learning and Internet of Things

Logistic Regression for Machine Learning in Process Tomography

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