APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN WALL MOISTURE IDENTIFICATION BY EIT METHOD
Grzegorz Kłosowski
g.klosowski@pollub.plLublin University of Technology, Faculty of Management (Poland)
https://orcid.org/0000-0001-7927-3674
Tomasz Rymarczyk
University of Economics and Innovation in Lublin, Institute of Computer Science and Innovative Technologies (Poland)
https://orcid.org/0000-0002-3524-9151
Abstract
The article presents the results of research in the area of using deep neural networks to identify moisture inside the walls of buildings using electrical impedance tomography. Two deep neural networks were used to transform the input measurements into images of damp places - convolutional neural networks (CNN) and recurrent long short-term memory networks LSTM. After training both models, a comparative assessment of the results obtained thanks to them was made. The conclusions show that both models are highly utilitarian in the analyzed problem. However, slightly better results were obtained with the LSTM method.
Keywords:
machine learning, deep learning, electrical impedance tomography, moisture detection in wallsReferences
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Authors
Grzegorz Kłosowskig.klosowski@pollub.pl
Lublin University of Technology, Faculty of Management Poland
https://orcid.org/0000-0001-7927-3674
Authors
Tomasz RymarczykUniversity of Economics and Innovation in Lublin, Institute of Computer Science and Innovative Technologies Poland
https://orcid.org/0000-0002-3524-9151
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