Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Issue title: Electromagnetic Fields in Mechatronics, Computer Sciences, Electrical and Electronic Engineering
Guest editors: Sławomir Wiak, Paolo Di Barba and Evelina Mognaschi
Article type: Research Article
Authors: Kłosowski, Grzegorza; | Rymarczyk, Tomaszb; c;
Affiliations: [a] Lublin University of Technology, Lublin, Poland | [b] University of Economics and Innovation in Lublin, Lublin, Poland | [c] Research & Development Centre Netrix S.A., Lublin, Poland
Correspondence: [*] Corresponding authors: Grzegorz Kłosowski, Lublin University of Technology, Lublin 20-618, Poland. E-mail: g.klosowski@pollub.pl. Tomasz Rymarczyk, University of Economics and Innovation in Lublin, Lublin, 20-209, Poland. E-mail: tomasz@rymarczyk.com
Abstract: This paper refers to a new resilient cyber-physical machine learning-based system that enables the generation of high-resolution tomographic images. The research object was a model of a tank filled with tap water. Using electrical impedance tomography (EIT) with 16 electrodes, the possibility of identifying inclusions inside the reservoir was investigated. A two-stage hybrid approach was proposed. In the first stage, three independent models were trained for the Elastic Net, Artificial Neural Networks (ANN) and Support Vector Machine (SVM) methods. In the second stage, a k-Nearest Neighbors (kNN) classification model was trained, that optimizes tomographic reconstructions by selecting the best method for each pixel, taking into account the specificity of a given measurement vector. Research has shown that applying the new concept results in a higher reconstruction quality than other methods used singly. It should be emphasized that our research is not intended to develop a new homogenous machine learning method. Instead, the goal is to invent an innovative, original, and flexible way to simultaneously use multiple machine learning methods for image optimization in industrial electrical impedance tomography.
Keywords: Machine learning, ensemble learning, electrical tomography, process tomography, hybrid tomography
DOI: 10.3233/JAE-210160
Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 69, no. 2, pp. 169-178, 2022
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl