Baza Publikacji Pracowników Politechniki Lubelskiej

Status:
Autorzy:	Rymarczyk Tomasz, Kozłowski Edward, Kłosowski Grzegorz, Niderla Konrad
Dyscypliny:
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Rok wydania:	2019
Wersja dokumentu:	Elektroniczna
Język:	angielski
Numer czasopisma:	15
Wolumen/Tom:	19
Strony:	1 - 19
Impact Factor:	3,275
Web of Science® Times Cited:	99
Scopus® Cytowania:	145
Bazy:	Web of Science \| Scopus
Efekt badań statutowych	TAK
Materiał konferencyjny:	NIE
Publikacja OA:	TAK
Licencja:
Sposób udostępnienia:	Witryna wydawcy
Wersja tekstu:	Ostateczna wersja opublikowana
Czas opublikowania:	W momencie opublikowania
Data opublikowania w OA:	2 sierpnia 2019
Abstrakty:	angielski
	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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Logistic Regression for Machine Learning in Process Tomography

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