Retention Mechanism Based Neural Network Model for Measuring Aircraft Landing Distance
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Tomiło Paweł |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 321 - 326 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 12th International Workshop on Metrology for AeroSpace |
| Skrócona nazwa konferencji: | 12th MetroAeroSpace 2025 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 18 czerwca 2025 do 20 czerwca 2025 |
| Miasto konferencji: | Naples |
| Państwo konferencji: | WŁOCHY |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| Aircraft ground performance is one of the key parameters, the knowledge of which is extremely important in the context of certification of new aircraft types and in production testing, as well as in operational conditions that differ from typical ones. This paper presents methods for measuring the length of an aircraft's landing distance using the retention mechanism based artificial neural network model. Model is based on the involution layer and retention mechanism. The developed model, on the basis of indications (acceleration, Euler angles, geographic coordinates) of the on-board device, determines the moment of touchdown along with its geographic coordinates, which, in combination with the location where the aircraft stops, allows to determine the distance of the landing path. Four measurement series were carried out, in which the model obtained an average error of 2,18% relative to the reference method. The measurement method described in the paper using an on-board unit and an artificial neural network model makes it possible to automatically determine the landing distance of an aircraft. |
