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Quality Assessment of the Neural Algorithms on the Example of EIT-UST Hybrid Tomography

cris.lastimport.scopus2024-09-19T01:31:20Z
dc.abstract.enThe paper presents the results of research on the hybrid industrial tomograph electrical impedance tomography (EIT) and ultrasonic tomography (UST) (EIT-UST), operating on the basis of electrical and ultrasonic data. The emphasis of the research was placed on the algorithmic domain. However, it should be emphasized that all hardware components of the hybrid tomograph, including electronics, sensors and transducers, have been designed and mostly made in the Netrix S.A. laboratory. The test object was a tank filled with water with several dozen percent concentration. As part of the study, the original multiple neural networks system was trained, the characteristic feature of which is the generation of each of the individual pixels of the tomographic image, using an independent artificial neural network (ANN), with the input vector for all ANNs being the same. Despite the same measurement vector, each of the ANNs generates its own independent output value for a given tomogram pixel, because, during training, the networks get their respective weights and biases. During the tests, the results of three tomographic methods were compared: EIT, UST and EIT-UST hybrid. The results confirm that the use of heterogeneous tomographic systems (hybrids) increases the reliability of reconstruction in various measuring cases, which is used to solve quality problems in managing production processes.
dc.affiliationTransportu i Informatyki
dc.contributor.authorGrzegorz Kłosowski
dc.contributor.authorTomasz Rymarczyk
dc.contributor.authorTomasz Cieplak
dc.contributor.authorKonrad Niderla
dc.contributor.authorŁukasz Skowron
dc.date.accessioned2024-05-10T07:22:52Z
dc.date.available2024-05-10T07:22:52Z
dc.date.issued2020
dc.identifier.doi10.3390/s20113324
dc.identifier.issn1424-8220
dc.identifier.urihttps://repo.akademiawsei.eu/handle/item/310
dc.languageen
dc.pbn.affiliationinformation and communication technology
dc.relation.ispartofSensors
dc.rightsCC-BY
dc.subject.enindustrial tomography
dc.subject.enmachine learning
dc.subject.enneural networks
dc.subject.encyber-physical system
dc.subject.enhybrid systems
dc.subject.enproduction process management
dc.subject.enquality assessment
dc.titleQuality Assessment of the Neural Algorithms on the Example of EIT-UST Hybrid Tomography
dc.typeReviewArticle
dspace.entity.typePublication
oaire.citation.issue11
oaire.citation.volume20