Machine Learning-Enhanced Radio Tomographic Device for Energy Optimization in Smart Buildings
Artykuł w czasopiśmie
MNiSW
140
Lista 2023
| Status: | |
| Autorzy: | Styła Michał, Kiczek Bartłomiej, Kłosowski Grzegorz, Rymarczyk Tomasz, Adamkiewicz Przemysław, Wójcik Dariusz, Cieplak Tomasz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2023 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 1 |
| Wolumen/Tom: | 16 |
| Numer artykułu: | 275 |
| Strony: | 1 - 20 |
| Impact Factor: | 3,0 |
| Web of Science® Times Cited: | 4 |
| Scopus® Cytowania: | 4 |
| Bazy: | Web of Science | Scopus | Ei Compendex | RePEc | Inspec | CAPlus / SciFinder | and other databases |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 27 grudnia 2022 |
| Abstrakty: | angielski |
| Smart buildings are becoming a new standard in construction, which allows for many possibilities to introduce ergonomics and energy savings. These contain simple improvements, such as controlling lights and optimizing heating or air conditioning systems in the building, but also more complex ones, such as indoor movement tracking of building users. One of the necessary components is an indoor localization system, especially without any device worn by the person being located. These types of solutions are important in locating people inside smart buildings, managing hospitals of the future and other similar institutions. The article presents a prototype of an innovative energy-efficient device for radio tomography, in which the hardware and software layers of the solution are presented. The presented example consists of 32 radio sensors based on a Bluetooth 5 protocol controlled by a central unit. The preciseness of the system was verified both visually and quantitatively by the image reconstruction as a result of solving the inverse tomographic problem using three neural networks. |
