Use of the Two-Stage Neural System in Electrical Impedance Tomography for Imaging Moisture inside Walls
Brak miniatury
Data
2022
Inny tytuł
Typ
Artykuł recenzyjny
Redaktor
dc.contributor.advisor
Dyscyplina PBN
Informatyka techniczna i telekomunikacja
Czasopismo lub seria
Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
ISSN
ISBN
DOI
10.1145/3560905.3568059
Strona internetowa
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Wydawca
Wydanie
Numer
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Tytuł monografii
item.page.defence
Tytuł tomu
Opis
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Abstrakt (en)
Damp walls of buildings are a serious problem due to the social and economic consequences. Moisture causes accelerated wear of facades, paint coatings, weakening of the wall structure, and high maintenance and renovation costs. The growth of fungi and bacteria worsens the indoor microclimate [1]. Effective identification of moisture inside the walls enables effective preventive actions. The paper presents an algorithmic concept that increases the quality of tomographic images showing the distribution of moisture inside the walls. The method solves the problem of monitoring the dampness of historical buildings and walls susceptible to moisture. The research focuses on solving the inverse problem of converting electrical measurements into spatial images. The study used a proprietary electrical impedance tomography system with specially designed electrodes. The measurement vector is converted to images in two stages. In the first stage, the Long Short-Term Memory (LSTM) neural network was used, which generates raw reconstructions. The task of the second LSTM network is to convert the raw images obtained in the first stage into enhanced images. The application of the presented method is not limited to one type of narrow-sphere tomography. The two-stage approach can be easily adapted to, e.g., medical and industrial or process tomography. Therefore, it is a generic, universal method with great implementation potential, which is its great advantage.