APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN WALL MOISTURE IDENTIFICATION BY EIT METHOD

Autor:	Grzegorz Kłosowski, Tomasz Rymarczyk
Jazyk:	English<br />Polish
Rok vydání:	2022
Předmět:	machine learning deep learning electrical impedance tomography moisture detection in walls Environmental engineering TA170-171 Environmental sciences GE1-350
Zdroj:	Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, Vol 12, Iss 1 (2022)
Druh dokumentu:	article
ISSN:	2083-0157 2391-6761
DOI:	10.35784/iapgos.2883
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/705de88b0f1f416ea2be6f0127e6f6e8 Zobrazit plný text záznamu View record in DOAJ