Identification of moisture inside walls in buildings using machine learning and ensemble methods
Artykuł w czasopiśmie
MNiSW
70
Lista 2021
| Status: | |
| Autorzy: | Rymarczyk Tomasz, Kłosowski Grzegorz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2022 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 3 |
| Wolumen/Tom: | 69 |
| Strony: | 375 - 388 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 1 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| According to the article, locating moisture within the walls of buildings using electrical impedance tomography is discussed in detail. The algorithmic approach, whose role is to convert the input measurements into images, received excellent attention during the development process. Numerous models have been trained to generate tomographic images based on individual pixels in a given image based on machine learning methods. An array of categorisation data was then generated, which enabled the development of a classification model to solve the problem of optimal model selection for a given point on the screen. It was achieved in this manner by developing a pixel-oriented ensemble model (POE), the goal of which is to provide tomographic reconstructions of at least the same quality as homogeneous algorithmic approaches. Artificial neural networks (ANN), linear regression (LR), and the long short-term memory network (LSTM) were employed in the current research to get homogeneous machine learning results. An image reconstruction algorithm such as the ANN or the LR reconstructs the image pixel by pixel, which means that a different prediction model is trained for each image pixel. In the case of LSTM, a single network is responsible for creating the entire image. Then, using the POE algorithm, the best reconstruction method was fitted to each pixel of the output image while considering the measurement scenario provided to the program. As a result, each measurement consequences in a unique assignment of reconstructive procedures to individual pixels, which is different for each measurement. It is the capacity to maximise the selection of a prediction model while considering both a given pixel and a specific measurement vector that distinguishes the provided POE concept from other approaches. |
