Junzi Sun, Ph.D.

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mode-s.org openap.dev

Data Science & Artificial Intelligence

Data science and AI are integral to modern aviation, offering solutions for complex challenges in safety, performance, and efficiency. The research landscape includes advanced methodologies such as augmented transfer learning for environmental monitoring and statistical models for trajectory predictions. These techniques leverage open data for validation, providing a robust framework that significantly impacts the aviation research.

Highlights

Machine Learning Air Traffic Delays Prediction Models
In this open project, three Machine learning models are developed for predicting air traffic delays at different levels, which are flight-level, airport-level, and network-level. 1) Random Forest model to predict arrival delays for individual flights, 2) LSTM model to predict aggregated arrival and departure delays for a single airport, and 3) a dynamic spatial-temporal graph attention network model that predicts aggregated arrival and departure delays of all airports a network.
Contrail-Net: Neural networks models for contrail detection and segmentation
This is an open-source project implements contrail segmentation neural network models in PyTorch. The models are built using augmented transfer learning, where I applied several image augmentation on a pre-train ResUNet model. This way, the model can be quickly fine tuned with a handful of labelled satellite images. With the invention of a new loss function, SR Loss, I can further optimizes the contrail detection using contrail information in Hough space.

More publications

  1. Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space
    preprint 2023
    Sun, Junzi ; and Roosenbrand, Esther
    Copy Sun, J., & Roosenbrand, E. (2023). Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space.
    Copy 
    @preprint{sun2023flight,
  title = {Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space},
  author = {Sun, Junzi and Roosenbrand, Esther},
  year = {2023},
  eprint = {2307.12032},
  archiveprefix = {arXiv},
  primaryclass = {cs.CV},
  tag = {ai, sus}
}
  2. Designing Recurrent and Graph Neural Networks to Predict Airport and Air Traffic Network Delays
    Conference 2022
    Sun, Junzi ; Dijkstra, Tristan ; Aristodemou, Constantinos ; Buzetelu, Vlad ; Falat, Theo ; Hogenelst, Tim ; Prins, Niels ; and Slijper, Benjamin
    In Proceedings of the 10th International Conference for Research in Air Transportation
    Copy Sun, J., Dijkstra, T., Aristodemou, C., Buzetelu, V., Falat, T., Hogenelst, T., Prins, N., & Slijper, B. (2022, June). Designing Recurrent and Graph Neural Networks to Predict Airport and Air Traffic Network Delays. Proceedings of the 10th International Conference for Research in Air Transportation.
    Copy 
    @inproceedings{sun2022,
  title = {Designing Recurrent and Graph Neural Networks to Predict Airport and Air Traffic Network Delays},
  author = {Sun, Junzi and Dijkstra, Tristan and Aristodemou, Constantinos and Buzetelu, Vlad and Falat, Theo and Hogenelst, Tim and Prins, Niels and Slijper, Benjamin},
  booktitle = {Proceedings of the 10th International Conference for Research in Air Transportation},
  year = {2022},
  month = jun,
  link = {https://research.tudelft.nl/files/126274370/ICRAT2022_paper_37.pdf},
  tag = {ai},
  award = true
}
  3. Quantifying Accuracy and Uncertainty in Data-Driven Flight Trajectory Predictions with Gaussian Process Regression
    Conference 2021
    Graas, Rik ; Sun, Junzi ; and Hoekstra, Jacco
    In Proceedings of the 11th SESAR Innovation Days, Online
    Copy Graas, R., Sun, J., & Hoekstra, J. (2021, December). Quantifying Accuracy and Uncertainty in Data-Driven Flight Trajectory Predictions with Gaussian Process Regression. Proceedings of the 11th SESAR Innovation Days, Online.
    Copy 
    @inproceedings{graas2021gprtp,
  title = {Quantifying Accuracy and Uncertainty in Data-Driven Flight Trajectory Predictions with Gaussian Process Regression},
  author = {Graas, Rik and Sun, Junzi and Hoekstra, Jacco},
  booktitle = {Proceedings of the 11th SESAR Innovation Days, Online},
  year = {2021},
  month = dec,
  tag = {ai},
  link = {https://www.sesarju.eu/sites/default/files/documents/sid/2021/papers/SIDs_2021_paper_70.pdf}
}
  4. A Framework to Evaluate Aircraft Trajectory Generation Methods
    Conference 2021
    Olive, Xavier ; Sun, Junzi ; Murça, M ; and Krauth, Timothé
    In 14th USA/Europe Air Traffic Management Research and Development Seminar
    Copy Olive, X., Sun, J., Murça, M., & Krauth, T. (2021, September). A Framework to Evaluate Aircraft Trajectory Generation Methods. 14th USA/Europe Air Traffic Management Research and Development Seminar.
    Copy 
    @inproceedings{olive2021framework,
  title = {A Framework to Evaluate Aircraft Trajectory Generation Methods},
  author = {Olive, Xavier and Sun, Junzi and Mur{\c{c}}a, M and Krauth, Timoth{\'e}},
  booktitle = {14th USA/Europe Air Traffic Management Research and Development Seminar},
  organization = {FAA/EUROCONTROL},
  year = {2021},
  month = sep,
  link = {https://research.tudelft.nl/files/105023386/ATM_Seminar_2021_paper_25.pdf},
  tag = {ai}
}
  5. Modeling and detecting anomalous safety events in approach flights using ADS-B data
    Conference 2021
    Bonifazi, Alberto ; Sun, Junzi ; Hoekstra, Jacco ; and Baren, Gerben
    In 14th USA/Europe Air Traffic Management Research and Development Seminar
    Copy Bonifazi, A., Sun, J., Hoekstra, J., & van Baren, G. (2021, September). Modeling and detecting anomalous safety events in approach flights using ADS-B data. 14th USA/Europe Air Traffic Management Research and Development Seminar.
    Copy 
    @inproceedings{sun2021safety,
  title = {Modeling and detecting anomalous safety events in approach flights using ADS-B data},
  author = {Bonifazi, Alberto and Sun, Junzi and Hoekstra, Jacco and van Baren, Gerben},
  booktitle = {14th USA/Europe Air Traffic Management Research and Development Seminar},
  organization = {FAA/EUROCONTROL},
  year = {2021},
  month = sep,
  tag = {ai},
  link = {https://research.tudelft.nl/files/105023158/ATM_Seminar_2021_paper_69.pdf}
}
  6. Estimating aircraft drag polar using open flight surveillance data and a stochastic total energy model
    Journal 2020
    DOI: 10.1016/j.trc.2020.01.026
    Sun, Junzi ; Hoekstra, Jacco ; and Ellerbroek, Joost
    Transportation Research Part C: Emerging Technologies
    Copy Sun, J., Hoekstra, J., & Ellerbroek, J. (2020). Estimating aircraft drag polar using open flight surveillance data and a stochastic total energy model. Transportation Research Part C: Emerging Technologies, 114, 391–404. https://doi.org/10.1016/j.trc.2020.01.026
    Copy 
    @article{sun2020estimating,
  title = {Estimating aircraft drag polar using open flight surveillance data and a stochastic total energy model},
  author = {Sun, Junzi and Hoekstra, Jacco and Ellerbroek, Joost},
  journal = {Transportation Research Part C: Emerging Technologies},
  volume = {114},
  pages = {391--404},
  year = {2020},
  month = may,
  publisher = {Elsevier},
  link = {https://research.tudelft.nl/files/71038050/published_OpenAP_drag_polar.pdf},
  doi = {10.1016/j.trc.2020.01.026},
  tag = {ai, perf}
}
  7. Aircraft initial mass estimation using Bayesian inference method
    Journal 2018
    DOI: 10.1016/j.trc.2018.02.022
    Sun, J. ; Ellerbroek, J. ; and Hoekstra, J.M.
    Transportation Research Part C: Emerging Technologies
    Copy Sun, J., Ellerbroek, J., & Hoekstra, J. M. (2018). Aircraft initial mass estimation using Bayesian inference method. Transportation Research Part C: Emerging Technologies, 90, 59–73. https://doi.org/10.1016/j.trc.2018.02.022
    Copy 
    @article{sun2018bayes,
  title = {Aircraft initial mass estimation using Bayesian inference method},
  author = {Sun, J. and Ellerbroek, J. and Hoekstra, J.M.},
  doi = {10.1016/j.trc.2018.02.022},
  journal = {Transportation Research Part C: Emerging Technologies},
  pages = {59--73},
  publisher = {Elsevier},
  volume = {90},
  year = {2018},
  month = may,
  tag = {ai},
  link = {https://research.tudelft.nl/files/52637982/1_s2.0_S0968090X18302626_main.pdf}
}
  8. Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets
    Journal 2017
    DOI: 10.2514/1.I010520
    Sun, Junzi ; Ellerbroek, Joost ; and Hoekstra, Jacco
    Journal of Aerospace Information Systems
    Copy Sun, J., Ellerbroek, J., & Hoekstra, J. (2017). Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets. Journal of Aerospace Information Systems, 14(10), 566–571. https://doi.org/10.2514/1.I010520
    Copy 
    @article{sun2017fdp,
  title = {Flight Extraction and Phase Identification for Large Automatic Dependent Surveillance–Broadcast Datasets},
  author = {Sun, Junzi and Ellerbroek, Joost and Hoekstra, Jacco},
  doi = {10.2514/1.I010520},
  journal = {Journal of Aerospace Information Systems},
  number = {10},
  pages = {566--571},
  publisher = {American Institute of Aeronautics and Astronautics},
  volume = {14},
  year = {2017},
  month = aug,
  tag = {ai},
  link = {https://research.tudelft.nl/files/25481708/main1.pdf}
}

© Copyright 2023 Junzi Sun