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Comparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography

cris.lastimport.scopus2024-09-19T01:31:23Z
dc.abstract.enThe 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.
dc.affiliationTransportu i Informatyki
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorGrzegorz Kłosowski
dc.contributor.authorEdward Kozłowski
dc.contributor.authorPaweł Tchórzewski
dc.date.accessioned2024-05-07T09:18:26Z
dc.date.available2024-05-07T09:18:26Z
dc.date.issued2019
dc.identifier.doi10.3390/s19071521
dc.identifier.issn1424-8220
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/279
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofSensors
dc.rightsCC-BY-NC
dc.subject.enmachine learning
dc.subject.eninverse problem
dc.subject.enelectrical impedance tomography
dc.subject.enimage reconstruction
dc.subject.enindustrial tomography
dc.titleComparison of Selected Machine Learning Algorithms for Industrial Electrical Tomography
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue7
oaire.citation.volume19