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Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports

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Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports

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Jiménez-Campfens, N.; Colomer, A.; Núñez, J.; Mogollón, JM.; Rodríguez, AL.; Naranjo Ornedo, V. (2020). Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports. Springer. 148-155. https://doi.org/10.1007/978-3-030-62365-4_14

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Metadatos del ítem

Título: Deep Learning in Aeronautics: Air Traffic Trajectory Classification Based on Weather Reports
Autor: Jiménez-Campfens, Néstor Colomer, Adrián Núñez, Javier Mogollón, Juan Manuel Rodríguez, Antonio L. Naranjo Ornedo, Valeriana
Entidad UPV: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Fecha difusión:
Resumen:
[EN] New paradigms in aviation, as the expected shortage of qualified pilots and the increasing number of flights worldwide, present big challenges to aeronautic enterprises and regulators. In this sense, a concept known ...[+]
Palabras clave: Air Traffic Management , Weather reports , METAR , Trajectory prediction , Deep learning
Derechos de uso: Reserva de todos los derechos
Fuente:
Intelligent Data Engineering and Automated Learning ¿ IDEAL 2020.
DOI: 10.1007/978-3-030-62365-4_14
Editorial:
Springer
Versión del editor: https://doi.org/10.1007/978-3-030-62365-4_14
Título del congreso: 21st International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2020)
Lugar del congreso: Online
Fecha congreso: Noviembre 04-06,2020
Serie: Lecture Notes in Computer Science;12490
Código del Proyecto:
info:eu-repo/grantAgreement/EC/H2020/831884/EU/HUMAN AIRCRAFT ROADMAP FOR VIRTUAL INTELLIGENT SYSTEM/
Agradecimientos:
This work has received funding from the Clean Sky 2 Joint Undertaking (JU) under grant agreement No 831884. The Titan V used for this research was donated by the NVIDIA Corporation
Tipo: Comunicación en congreso

References

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Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

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