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Logistic Regression for Machine Learning in Process Tomography

cris.lastimport.scopus2024-09-17T01:31:26Z
dc.abstract.enThe 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.
dc.affiliationTransportu i Informatyki
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorEdward Kozłowski
dc.contributor.authorGrzegorz Kłosowski
dc.contributor.authorKonrad Niderla
dc.date.accessioned2024-05-07T10:30:09Z
dc.date.available2024-05-07T10:30:09Z
dc.date.issued2019
dc.identifier.doi10.3390/s19153400
dc.identifier.issn1424-8220
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/281
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofSensors
dc.rightsCC-BY
dc.subject.enmachine learning
dc.subject.enelectrical impedance tomography
dc.subject.enultrasound tomography
dc.subject.enprocess tomography
dc.subject.enimage reconstruction
dc.titleLogistic Regression for Machine Learning in Process Tomography
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue15
oaire.citation.volume19