Artificial neural network model based on Kolmogorov-Arnold representation theorem and retention mechanism for real-time aircraft flight phases classification
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Tomiło Paweł, Laskowski Jan, Laskowska Agnieszka |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | Pt B |
| Wolumen/Tom: | 160 |
| Numer artykułu: | 112004 |
| Strony: | 1 - 16 |
| Impact Factor: | 8,0 |
| Web of Science® Times Cited: | 3 |
| Scopus® Cytowania: | 3 |
| Bazy: | Web of Science | Scopus |
| 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: | 12 sierpnia 2025 |
| Abstrakty: | angielski |
| In modern aviation, accurate classification of flight phases is crucial for both operational safety and efficiency, as well as for reliable air traffic modeling and prediction. This paper introduces KARMA (Kolmogorov-Arnold Retention Memory Aware), a novel artificial neural network that uniquely integrates the Kolmogorov-Arnold representation theorem with a multi-scale retention mechanism for real-time flight phase classification. KARMA incorporates Kolmogorov-Arnold Network (KAN) layers for flexible function approximation, and employs convolutional feature extraction to enhance temporal data processing. The model also features a memory aggregation block that maintains context across sequential predictions. Benchmarking against state-of-the-art models—including Transformers, Retentive Networks, Extended Long-Short Term Memory, and State Space Models—demonstrates that KARMA achieves superior accuracy. The KARMA model achieved an accuracy of 0.93, a precision of 0.93 and a recall of 0.96. Additionally, the research introduces a custom, high-quality dataset collected with the developed device, enabling real-time, expert-labeled sensor data. KARMA's compact design ensures suitability for embedded avionics systems, setting a new standard for flight phase classification. |
