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Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

cris.lastimport.scopus2024-09-18T01:30:28Z
dc.abstract.enElectrical tomography is a non-invasive method of monitoring the interior of objects, which is used in various industries. In particular, it is possible to monitor industrial processes inside reactors and tanks using tomography. Tomography enables real-time observation of crystals or gas bubbles growing in a liquid. However, obtaining high-resolution tomographic images is problematic because it involves solving the so-called ill-posed inverse problem. Noisy input data cause problems, too. Therefore, the use of appropriate hardware solutions to eliminate this phenomenon is necessary. An important cause of obtaining accurate tomographic images may also be the incorrect selection of algorithmic methods used to convert the measurements into the output images. In a dynamically changing environment of a tank reactor, selecting the optimal algorithmic method used to create a tomographic image becomes an optimization problem. This article presents the machine learning method’s original concept of intelligent selection depending on the reconstructed case. The long short-term memory network was used to classify the methods to choose one of the five homogenous methods—elastic net, linear regression with the least-squares learner, linear regression with support vector machine learner, support vector machine model, or artificial neural networks. In the presented research, tomographic images of selected measurement cases, reconstructed using five methods, were compared. Then, the selection methods’ accuracy was verified thanks to the long short-term memory network used as a classifier. The results proved that the new concept of long short-term memory classification ensures better tomographic reconstructions efficiency than imaging all measurement cases with single homogeneous methods.
dc.affiliationTransportu i Informatyki
dc.contributor.authorGrzegorz Kłosowski
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorKonrad Niderla
dc.contributor.authorMagdalena Rzemieniak
dc.contributor.authorArtur Dmowski
dc.contributor.authorMichał Maj
dc.date.accessioned2024-05-10T08:12:51Z
dc.date.available2024-05-10T08:12:51Z
dc.date.issued2021
dc.identifier.doi10.3390/en14217269
dc.identifier.issn1996-1073
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/318
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofEnergies
dc.rightsCC-BY
dc.subject.enelectrical tomography
dc.subject.enindustrial tomography
dc.subject.enmachine learning
dc.subject.enneural networks
dc.subject.enlong short-term memory (LSTM) networks
dc.titleComparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue21
oaire.citation.volume14