Back to Search Start Over

Ensemble learning for monitoring process in electrical impedance tomography.

Authors :
Kłosowski, Grzegorz
Rymarczyk, Tomasz
Wiak, Sławomir
Di Barba, Paolo
Mognaschi, Evelina
Source :
International Journal of Applied Electromagnetics & Mechanics; 2022, Vol. 69 Issue 2, p169-178, 10p
Publication Year :
2022

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13835416
Volume :
69
Issue :
2
Database :
Complementary Index
Journal :
International Journal of Applied Electromagnetics & Mechanics
Publication Type :
Academic Journal
Accession number :
157526619
Full Text :
https://doi.org/10.3233/JAE-210160