Milestone Deliverable D3: The ATM Target Concept, 2007. ,
Next Generation Air Transportation System (NGATS) Air Traffic Management (ATM)-Airspace Project, National Aeronautics and Space Administration, Tech. Rep, 2006. ,
, Continuous climb operations (cco) manual, 2013.
Sensitivity of aircraft performance to variability of input data, EUROCONTROL Doc. CoE-TP-02005, 2003. ,
Introduction à la prévision de trajectoire, 2006. ,
Revision of atmosphere model in bada aircraft performance model, EUROCONTROL, Tech. Rep, 2010. ,
Getting to grips with the cost index, 1998. ,
Fuel and time savings by flying continuous cruise climbs: Estimating the benefit pools for maximum range operations, Transportation Research Part D: Transport and Environment, vol.35, pp.62-71, 2015. ,
Advanced aircraft performance modeling for atm: Analysis of bada model capabilities, Proceedings of the 29th IEEE/AIAA Digital Avionics Systems Conference (DASC), 2010. ,
Derated climb performance in large civil aircraft, Conference Boeing Performance and Flight Operations Engineering, 2005. ,
, Ac 25-13 -reduced and derated takeoff thrust (power) procedures, 1988.
High confidence intervals applied to aircraft altitude prediction, IEEE Transactions on Intelligent Transportation Systems, vol.17, issue.9, pp.2515-2527, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01284915
Learning probabilistic trajectory models of aircraft in terminal airspace from position data, IEEE Transactions on Intelligent Transportation Systems, 2018. ,
Performance of an Adaptive Trajectory Prediction Algorithm for Climbing Aircraft, p.2013 ,
, Aviation Technology, Integration, and Operations Conference. 08, 2013.
Comparison of Two Ground-based Mass Estimation Methods on Real Data (regular paper), International Conference on Research in Air Transportation (ICRAT), 2014. ,
Data-driven trajectory uncertainty quantification for climbing aircraft to improve ground-based trajectory prediction, Anadolu Üniversitesi Bilim Ve Teknoloji Dergisi A-Uygulamal? Bilimler ve Mühendislik, vol.18, pp.323-345, 2017. ,
Bayesian inference of aircraft initial mass, Proceedings of the 12th USA/Europe Air Traffic Management Research and Development Seminar. FAA/EUROCONTROL, 2017. ,
Aircraft initial mass estimation using bayesian inference method, Transportation Research Part C: Emerging Technologies, vol.90, pp.59-73, 2018. ,
Aircraft mass and thrust estimation using recursive bayesian method, 2018. ,
Modeling of aircraft takeoff weight using gaussian processes, Journal of Air Transportation, pp.1-10, 2018. ,
Predictive Distribution of the Mass and Speed Profile to Improve Aircraft Climb Prediction, ATM Seminar, p.13, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02138151
, , 2019.
User manual for base of aircraft data (bada) rev.3.14, EUROCONTROL, 2017. ,
Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research, Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, ser. IPSN '14, pp.83-94, 2014. ,
Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study, Transportation Research Part C: Emerging Technologies, vol.96, pp.72-95, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01878615
Machine learning applied to airspeed prediction during climb, Proceedings of the 11th ,
URL : https://hal.archives-ouvertes.fr/hal-01168664
, Europe Air Traffic Management R & D Seminar, 2015.
Bayesian learning for neural networks, vol.118, 2012. ,
Probabilistic backpropagation for scalable learning of bayesian neural networks, International Conference on Machine Learning, pp.1861-1869, 2015. ,
Practical variational inference for neural networks, Advances in neural information processing systems, pp.2348-2356, 2011. ,
Information theory and statistics. Courier Corporation, 1997. ,
Dropout as a bayesian approximation: Representing model uncertainty in deep learning, international conference on machine learning, pp.1050-1059, 2016. ,
Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014. ,
Gaussian processes in machine learning, Advanced lectures on machine learning, pp.63-71, 2004. ,
Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration, Advances in Neural Information Processing Systems, pp.7576-7586, 2018. ,
When gaussian process meets big data: A review of scalable gps, 2018. ,
Simple and scalable predictive uncertainty estimation using deep ensembles, Advances in Neural Information Processing Systems, pp.6402-6413, 2017. ,
Mixture density networks, 1994. ,
Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift, 2019. ,
Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. ,
Structured uncertainty prediction networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5477-5485, 2018. ,
Decoupled weight decay regularization, International Conference on Learning Representations, 2019. ,
Cyclical learning rates for training neural networks, Applications of Computer Vision (WACV, pp.464-472, 2017. ,
Selfnormalizing neural networks, Advances in neural information processing systems, pp.971-980, 2017. ,