APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN WALL MOISTURE IDENTIFICATION BY EIT METHOD
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 walls
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Lublin University of Technology, Faculty of Management Poland
https://orcid.org/0000-0001-7927-3674
University 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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