Aerospace and Data Science

航空运输系统 • 低空经济 • 工业智能

论文发表与研究成果

论文发表

期刊文章
  1. LLi, J., Li, L., Dong, Y., Xie, H., & Qin, S. J. (2026). Transformer-Based Missing Data Imputation in Time Series With Application to Flight Test Data. IEEE Transactions on Industrial Electronics.
  2. Lyu, M., Li, L., & Liu, W. (2026). Multi-service network design with multi-function drones: Urban monitoring, data collection, and parcel delivery services. Transportation Research Part C: Emerging Technologies, 187, 105660.
  3. Zhu, L., Zhang, Q., Jian, X., Yang, Y., & Li, L. (2026). Spatio-temporal traffic accidents detection via graph based generative adversarial network. Engineering Applications of Artificial Intelligence, 165, 113488.
  4. Wang, Z., Wang, Y., Hansen, M., & Li, L. (2026). A Deep Clustering and Generative Approach for Large-Scale Air Traffic Trajectory Data. IEEE Transactions on Intelligent Transportation Systems.
  5. Wang, H., Guan, C., Yu, L., Li, L., Li, Y., Gong, L., & He, Y. (2025). ℓ0-RASC-NN: An Efficient Spatiotemporal Data Completion Method for Edge Devices. IEEE Transactions on Mobile Computing.
  6. Liu, L., He, X., Ye, Y., & Li, L*. (2025). An industrial process optimization framework: from data to deployment with case studies in food production processes. Journal of Intelligent Manufacturing, 1-28.
  7. Li, L.* (2025). A review of data science and artificial intelligence applications in air transportation systems. Artificial Intelligence for Transportation, 2, 100023.
  8. Ye, Y., Zhu, X., Shen, X., Chen, X., Qin, S. J., & Li, L.* (2025). Learning Satellite Pattern-of-Life Identification: A Diffusion-Based Approach. IEEE Transactions on Aerospace and Electronic Systems.
  9. He, X., Li, L.*, Mo, Y., Sun, Z., & Qin, S. J. (2025). Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search. Transportation Research Part E: Logistics and Transportation Review, 193, 103859.
  10. Xiang, C., Mo, Y., Liu, W., Wu, Z., & Li, L.* (2025). Path pool based transformer model in reinforcement framework for dynamic urban drone delivery problem. Transportation Research Part C: Emerging Technologies, 177, Article 105165.
  11. Wang, J., He, X., Jiang, S., Chan, P. W., Li, C., Ou, J., … & Li, L. (2025). Evaluation of urban wind effects on flight path planning of delivery drones using computational fluid dynamics simulations. Physics of Fluids, 37(8).
  12. Wang, J., Li, L., & Qin, S. J.* (2025). A hierarchical scheme for dynamic monitoring of multi-scale multi-mode systems. Computers & Chemical Engineering, 198, Article 109107.
  13. Tang, R., Ng, K. K. H., Li, L., & Yang, Z. (2025). A learning-based interacting multiple model filter for trajectory prediction of small multirotor drones considering differential sequences. Transportation Research Part C: Emerging Technologies, 174, Article 105115.
  14. He, Y.*, Hong, W., Li, L., Zhang, J., Qin, J., & Luo, Q. (2025). Forecasting short-term passenger flow via CBGC-SCI: an in-depth comparative study on Shenzhen Metro. Machine Learning, 114(1), Article 5.
  15. Zwetsloot, I. M., Lin, Y., Qiu, J., Li, L., Lee, W. K. F., Yeung, E. Y. S., … & Wong, C. C. L. (2025). Remaining useful life modelling with an escalator health condition analytic system. Quality and Reliability Engineering International, 41(8), 3746-3758.
  16. He, Y., Huang, P., Hong, W., Luo, Q., Li, L., & Tsui, K. L.* (2024). In-Depth Insights into the Application of Recurrent Neural Networks (RNNs) in Traffic Prediction: A Comprehensive Review. Algorithms, 17(9), 398. https://doi.org/10.3390/a17090398
  17. Jiang, S., Wang, J., Li, C., Ou, J., Duan, P.*, & Li, L. (2024). Identification of no-fly zones for delivery drone path planning in various urban wind environments. Physics of Fluids, 36(8). https://doi.org/10.1063/5.0221281
  18. Chen, C., Zhu, F., Xu, Z., Xie, Q., Lo, S. M., Tsui, K. L., & Li, L.* (2024). Knowledge-informed wheel wear prediction method for high-speed train using multisource signal data. IEEE Transactions on Instrumentation and Measurement. Advance online publication.  https://doi.org/10.1109/TIM.2024.3413151
  19. Wang, H., Li, Y.-F.*, Men, T., & Li, L. (2024). Physically interpretable wavelet-guided networks with dynamic frequency decomposition for machine intelligence fault prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems. Advance online publication. https://doi.org/10.1109/TSMC.2024.3389068
  20. He, X., Li, L.*, Mo, Y., Huang, J., & Qin, S. J. (2024). A distributed route network planning method with congestion pricing for drone delivery services in cities. Transportation Research Part C: Emerging Technologies, 160, Article 104536. https://doi.org/10.1016/j.trc.2024.104536
  21. Lin, Y., Guo, D., Wu, Y., Li, L., Wu, E. Q., & Ge, W.* (2024). Fuel consumption prediction for pre-departure flights using attention-based multi-modal fusion. Information Fusion, 101, Article 101983. https://doi.org/10.1016/j.inffus.2023.101983
  22. Chu, N., Ng, K. K. H., Zhu, X., Liu, Y., Li, L., & Hon, K. K. (2024). Towards dynamic flight separation in final approach: A hybrid attention-based deep learning framework for long-term spatiotemporal wake vortex prediction. Transportation Research Part C: Emerging Technologies, 169, Article 104876.
  23. Zhu, X., Hong, N., He, F., Lin, Y., Li, L.*, & Fu, X. (2023). Predicting aircraft trajectory uncertainties for terminal airspace design evaluation. Journal of Air Transport Management, 113, Article 102473. https://doi.org/10.1016/j.jairtraman.2023.102473
  24. Hao, T., Chang, H., Liang, S., Jones, P., Chan, P. W., Li, L., & Huang, J.* (2023). Heat and park attendance: Evidence from “small data” and “big data” in Hong Kong. Building and Environment, 234, Article 110123. https://doi.org/10.1016/j.buildenv.2023.110123
  25. Wang, L., Mao, J.*, Li, L., Li, X., & Tu, Y. (2023). Prediction of estimated time of arrival for multi-airport systems via “Bubble” mechanism. Transportation Research Part C: Emerging Technologies, 149, Article 104065. https://doi.org/10.1016/j.trc.2023.104065
  26. Hao, T., Huang, J.*, He, X., Li, L., & Jones, P. (2023). A machine learning-enhanced design optimizer for urban cooling. Indoor and Built Environment, 32(2), 355-374. https://doi.org/10.1177/1420326X221112857
  27. Zhu, F., Jia, X*., Li, W., Xie, M., Li, L., & Lee, J. (2023). Cross-chamber data transferability evaluation for fault detection and classification in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 36(1), 68-77. https://doi.org/10.1109/TSM.2022.3222475
  28. Huang, J.*, Cui, Y., Li, L., Guo, M., Ho, H. C., Lu, Y., & Webster, C. (2023). Re-examining Jane Jacobs’ doctrine using new urban data in Hong Kong. Environment and Planning B: Urban Analytics and City Science, 50(1), 76-93. https://doi.org/10.1177/23998083221106186
  29. Zhu, F., Feng, J.*, Xie, M., Li, L., Lei, J., & Lee, J. (2022). Profile Abstract: an optimization-based subset selection and summarization method for profile data mining. IEEE Transactions on Industrial Informatics. Advance online publication. https://doi.org/10.1109/TII.2022.3227642
  30. He, X., Jiang, C., Li, L. *, & Blom, H. (2022). A simulation study of risk-aware path planning in mitigating the third-party risk of a commercial UAS operation in an urban area. Aerospace, 9(11), Article 682. https://doi.org/10.3390/aerospace9110682
  31. Wang, Z.*, Liao, C., Hang, X., Li, L., Delahaye, D., & Hansen, M. (2022). Distribution prediction of strategic flight delays via machine learning methods. Sustainability (Switzerland), 14(22), Article 15180. https://doi.org/10.3390/su142215180
  32. Huang, J.*, Cui, Y., Chang, H., Obracht-Prondzyńska, H., Kamrowska-Zaluska, D., & Li, L. (2022). A city is not a tree: a multi-city study on street network and urban life. Landscape and Urban Planning, 226, Article 104469. https://doi.org/10.1016/j.landurbplan.2022.104469
  33. He, X., He, F., Li, L.*, Zhang, L., & Xiao, G. (2022). A route network planning method for urban air delivery. Transportation Research Part E: Logistics and Transportation Review, 166, Article 102872. https://doi.org/10.1016/j.tre.2022.102872
  34. He, Y., Li, L.*, Zhu, X., & Tsui, K. L. (2022). Multi-graph convolutional-recurrent neural network (MGC-RNN) for short-term forecasting of transit passenger flow. IEEE Transactions on Intelligent Transportation Systems, 23(10), 18155-18174. https://doi.org/10.1109/TITS.2022.3150600
  35. Fei, Z., Zhang, Z.*, Yang, F., Tsui, K.-L., & Li, L. (2022). Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features. Journal of Energy Storage, 52(B), Article 104936. https://doi.org/10.1016/j.est.2022.104936
  36. Zhao, X., Wang, Y.*, Li, L., & Delahaye, D. (2022). A queuing network model of a multi-airport system based on point-wise stationary approximation. Aerospace, 9(7), Article 390. https://doi.org/10.3390/aerospace9070390
  37. Wang, X.-L., Zhong, Y., Li, L., Xie, W.*, & Ye, Z.-S. (2022). Warranty reserve management: demand learning and funds pooling. Manufacturing & Service Operations Management, 24(4), 2221–2239. https://doi.org/10.1287/msom.2022.1086
  38. Zhu, X., Lin, Y., He, Y., Tsui, K.-L., Chan, P. W., & Li, L*. (2022). Short-term nationwide airport throughput prediction with graph attention recurrent neural network. Frontiers in Artificial Intelligence, 5, Article 884485. https://doi.org/10.3389/frai.2022.884485
  39. Chang, H., Huang, J., Yao, W., Zhao, W., & Li, L.* (2022). How do new transit stations affect people’s sentiment and activity? A case study based on social media data in Hong Kong. Transport Policy, 120, 139-155. https://doi.org/10.1016/j.tranpol.2022.03.011
  40. Chang, H.*, Li, L., Huang, J., Zhang, Q., & Chin, K. S. (2022). Tracking traffic congestion and accidents using social media data: A case study of Shanghai. Accident Analysis and Prevention, 169, Article 106618. https://doi.org/10.1016/j.aap.2022.106618
  41. Zhao, W., Li, L.*, Alam, S., & Wang, Y. (2021). An incremental clustering method for anomaly detection in flight data. Transportation Research Part C: Emerging Technologies, 132, Article 103406. https://doi.org/10.1016/j.trc.2021.103406
  42. Lin, Y., Li, L.*, Ren, P., Wang, Y., & Szeto, W. Y. (2021). From aircraft tracking data to network delay model: A data-driven approach considering en-route congestion. Transportation Research Part C: Emerging Technologies, 131, 103329. https://doi.org/10.1016/j.trc.2021.103329
  43. Zhu, X., & Li, L.* (2021). Flight time prediction for fuel loading decisions with a deep learning approach. Transportation Research Part C: Emerging Technologies, 128, Article 103179. https://doi.org/10.1016/j.trc.2021.103179
  44. Fei, Z., Yang, F.*, Tsui, K.-L., Li, L., & Zhang, Z. (2021). Early prediction of battery lifetime via a machine learning based framework. Energy, 225, Article 120205. https://doi.org/10.1016/j.energy.2021.120205
  45. Huang, J., Obracht-Prondzynska, H., Kamrowska-Zaluska, D., Sun, Y., & Li, L.* (2021). The image of the City on social media: A comparative study using “Big Data” and “Small Data” methods in the Tri-City Region in Poland. Landscape and Urban Planning, 206, 103977. https://doi.org/10.1016/j.landurbplan.2020.103977
  46. Hong, N., Li, L*., Yao, W., Zhao, Y., Yi, C., Lin, J., & Tsui, K. L. (2020). High-speed rail suspension system health monitoring using multi-location vibration data. IEEE Transactions on Intelligent Transportation Systems, 21(7), 2943-2955. https://doi.org/10.1109/TITS.2019.2921785
  47. Wang, X.*, Li, L., & Xie, M. (2020). An unpunctual preventive maintenance policy under two-dimensional warranty. European Journal of Operational Research, 282(1), 304-318. https://doi.org/10.1016/j.ejor.2019.09.025
  48. Li, C., Wang, X.*, Li, L., Xie, M., & Wang, X. (2020). On dynamically monitoring aggregate warranty claims for early detection of reliability problems. IISE Transactions, 52(5), 568-587. https://doi.org/10.1080/24725854.2019.1647477
  49. Wang, Y.*, Zhan, J., Xu, X., Li, L., Chen, P., & Hansen, M. (2019). Measuring the resilience of an airport network. Chinese Journal of Aeronautics, 32(12), 2694-2705. https://doi.org/10.1016/j.cja.2019.08.023
  50. Wang, X.*, He, K., He, Z., Li, L., & Xie, M. (2019). Cost analysis of a piece-wise renewing free replacement warranty policy. Computers & Industrial Engineering, 135, 1047-1062. https://doi.org/10.1016/j.cie.2019.07.015
  51. Wang, X.*, Li, L., & Xie, M. (2019). Optimal preventive maintenance strategy for leased equipment under successive usage-based contracts. International Journal of Production Research, 57(18), 5705–5724. https://doi.org/10.1080/00207543.2018.1542181
  52. Wang, Y.*, Li, L., & Dang, C. (2019). Calibrating classification probabilities with shape-restricted polynomial regression. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8), 1813-1827. https://doi.org/10.1109/TPAMI.2019.2895794
  53. Guleria, Y., Cai, Q., Alam, S.*, & Li, L. (2019). A multi-agent approach for reactionary delay prediction of flights. IEEE Access, 7, 181565-181579. Article 8924720. https://doi.org/10.1109/ACCESS.2019.2957874
  54. Wang, X.*, Xie, M., & Li, L. (2019). On optimal upgrade strategy for second-hand multi-component systems sold with warranty. International Journal of Production Research, 57(3), 847-864. https://doi.org/10.1080/00207543.2018.1488087
  55. Murça, M. C. R.*, Hansman, R. J., Li, L., & Ren, P. (2018). Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo. Transportation Research Part C: Emerging Technologies, 97, 324-347. https://doi.org/10.1016/j.trc.2018.10.021
  56. Ren, P., & Li, L.* (2018). Characterizing air traffic networks via large-scale aircraft tracking data: A comparison between China and the US networks. Journal of Air Transport Management, 67, 181-196. https://doi.org/10.1016/j.jairtraman.2017.12.005
  57. Li, L.*, Hansman, R. J., Palacios, R., & Welsch, R. (2016). Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring. Transportation Research Part C: Emerging Technologies, 64, 45-57. https://doi.org/10.1016/j.trc.2016.01.007
  58. Pei, Y.*, Wang, W., & Li, L. (2016). Ranking the vulnerable components of aircraft by considering performance degradations. Journal of Aircraft, 53(5), 1400-1410. https://doi.org/10.2514/1.C033683
  59. Charruaud, F., & Li, L.* (2015). Flight operations monitoring through cluster analysis: A case study. IEEE Intelligent Systems, 30(6), 24-29.
  60. Li, L.*, Das, S., Hansman, R. J., Palacios, R., & Srivastava, A. N. (2015). Analysis of flight data using clustering techniques for detecting abnormal operations. Journal of Aerospace Information Systems, 12(9), 587-598. https://doi.org/10.2514/1.I010329
勘误
  1. Hong, N., Li, L.*, Yao, W., Zhao, Y., Yi, C., Lin, J., & Tsui, K. L. (2021). Correction to “High-Speed Rail Suspension System Health Monitoring Using Multi-Location Vibration Data”. IEEE Transactions on Intelligent Transportation Systems, 22(9), 6088. Article 9526275. https://doi.org/10.1109/TITS.2021.3092455
编者序
  1. Yan, W., Jiang, Z.-Q., Li, L., Hisano, R., & Li, J. (2023). Editorial: Machine learning in social complex systems. Frontiers in Physics, 11, Article 1199879. https://doi.org/10.3389/fphy.2023.1199879
著作
  1. Li, L.*, Tsui, K.-L., & Zhao, Y. (2022). An overview and general framework for spatiotemporal modeling and applications in transportation and public health. In A. Steland, & K.-L. Tsui (Eds.), Artificial Intelligence, Big Data and Data Science in Statistics: Challenges and Solutions in Environmetrics, the Natural Sciences and Technology (pp. 195-226). Springer. https://doi.org/10.1007/978-3-031-07155-3_8
会议论文
  1. Zhu, X., Li, L.*, Mo, Y., Dong, Y., Shen, X., Chen, X. & Qin, S. J. * (in press). Data-driven approaches for satellite SADA system health monitoring with limited data. In 2024 IEEE 20th International Conference on Automation Science and Engineering. Bari. Italy. Aug 28 – Sept 1, 2024
  2. Li, B., Wang, Y., Zhao, Y., Li, L., & Dong, Y. (in press). Dynamic Feature Extraction and Prediction for High Dimensional Time Series with Seasonality (I). In 2024 IEEE 20th International Conference on Automation Science and Engineering. Bari. Italy. Aug 28 – Sept 1, 2024
  3. Wu, H., Zhu, X., Li, S., Zhou, Y., Li, L., & Li, M.* (2024). Distributionally Robust Ground Delay Programs with Learning-Driven Airport Capacity Predictions. In the 11th edition of the International Conference on Research in Air Transportation (ICRAT), Singapore. July 1 – 4, 2024.
  4. Vale de Almeida Norte, M., Fulton, M., Harvey, A., Plevier, C., Oosterholt, L., Mendez Garcia, M., Wichers, F., van Vliet, L., Ottens, J., Bootsma, S., & Li, L. (2023). Modelling Urban Air Mobility Demand: the Example of the Île-de-France Region. In AIAA AVIATION 2023 Forum Article AIAA 2023-4105 American Institute of Aeronautics and Astronautics, Inc. https://doi.org/10.2514/6.2023-4105
  5. He, F., Li, L., Zhao, W., & Xiao, G. (2018). Aircraft Mass Estimation using Quick Access Recorder Data. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) Proceedings Article 8569866 (AIAA/IEEE Digital Avionics Systems Conference – Proceedings; Vol. 2018-September). Institute of Electrical and Electronics Engineers, Inc.. https://doi.org/10.1109/DASC.2018.8569866
  6. Hong, N., & Li, L. (2018). A Data-Driven Fuel Consumption Estimation Model for Airspace Redesign Analysis. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) Proceedings Article 8569564 (AIAA/IEEE Digital Avionics Systems Conference – Proceedings; Vol. 2018-September). Institute of Electrical and Electronics Engineers, Inc.. https://doi.org/10.1109/DASC.2018.8569564
  7. Zhao, W., He, F., Li, L., & Xiao, G. (2018). An Adaptive Online Learning Model for Flight Data Cluster Analysis. In 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC) Proceedings Article 8569600 (AIAA/IEEE Digital Avionics Systems Conference – Proceedings; Vol. 2018-September). Institute of Electrical and Electronics Engineers, Inc.. https://doi.org/10.1109/DASC.2018.8569600
  8. Xu, P., Yao, W., Zhao, Y., Yi, C., Li, L., Lin, J., & Tsui, K. L. (2018). Condition monitoring of wheel wear for high-speed trains: A data-driven approach. In 2018 IEEE International Conference on Prognostics and Health Management (ICPHM) Article 8448864 (IEEE International Conference on Prognostics and Health Management, ICPHM). Institute of Electrical and Electronics Engineers, Inc.. https://doi.org/10.1109/ICPHM.2018.8448864
  9. Huang, J., Zhang, Q., Li, L., Yang, Y., Chiaradia, A., Pryor, M., & Webster, C. (2016). Happiness and High-rise Living: Sentiment Analysis of Geo-Located Twitter Data in Hong Kong’s Housing Estates. In Proceedings of the 52nd ISOCARP Congress (pp. 380-387).
  10. Das, S., Li, L., Srivastava, A. N., & Hansman, R. J. (2012). Comparison of algorithms for anomaly detection in flight Recorder data of airline operations. In 12th AIAA Aviation Technology, Integration and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference
  11. Li, L., Gariel, M., Hansman, R. J., & Palacios, R. (2011). Anomaly detection in onboard-recorded flight data using cluster analysis. In AIAA/IEEE Digital Avionics Systems Conference – Proceedings (pp. 4A41-4A411). Article 6096068 https://doi.org/10.1109/DASC.2011.6096068
  12. Li, L., Cho, H., Hansman, R. J., & Palacios, R. (2010). Aircraft-based complexity assessment for radar controllers in the multi-sector planner experiment. In 10th AIAA Aviation Technology, Integration and Operations Conference 2010, ATIO 2010 (Vol. 1) https://doi.org/10.2514/6.2010-9005
  13. Histon, J., Li, L., & Hansman, R. J. (2010). Airspace structure, future ATC systems, and controller complexity reduction. In AIAA/IEEE Digital Avionics Systems Conference – Proceedings (pp. 4.A.41-4.A.414). Article 5655354 https://doi.org/10.1109/DASC.2010.5655354
  14. de Albuquerque Filho, E. A. F., Trabasso, L. G., Scarpel, R., Hansman, R. J., & Li, L. (2010). Combining ATC subjective cognitive complexity evaluation metrics in a single indicator. In 10th AIAA Aviation Technology, Integration and Operations Conference 2010, ATIO 2010 (Vol. 1) https://doi.org/10.2514/6.2010-9006
  15. Donaldson, A. D., Dorbian, C. S., He, C., Li, L., Lovegren, J. A., Pyrgiotis, N., & Simaiakis, I. (2010). Parametric design of low emission hybrid-lift cargo aircraft. In 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition Article 2010-1395

媒体报道

  1. 《新加坡人民日报》5月刊报道了题为“先进交通管理项目”的无人机交通管理研究项目。
  2. 据多家香港媒体和香港城市大学报道，香港城市大学的研究项目在第49届日内瓦国际发明展览会上荣获两项大奖，报道标题为“香港城市大学在日内瓦国际发明展览会上斩获36项大奖”（4月）。
    https://www.cityu.edu.hk/media/news/2024/04/20/cityuhk-triumphs-international-exhibition-of-inventions-geneva-36-awards
  3. 据多家香港媒体和香港城市大学报道，香港城市大学的研究项目在第三届香港亚洲创新与发明展览会（AEII）上荣获两项大奖，标题为“香港城市大学在亚洲创新与发明展览会上大放异彩”（12月）。
    https://www.cityu.edu.hk/media/news/2023/12/11/cityu-shines-asia-exhibition-innovations-inventions
  4. 香港城市大学研究故事栏目刊登研究成果：“数据驱动的铁路系统安全可靠管理”，3月https://www.cityu.edu.hk/research/stories/2021/03/29/data-driven-management-safe-and-reliable-railway-systems
  5. AMLD亮点研究报告：“用于航空公司安全管理的飞行数据异常检测和模式识别”，洛桑联邦理工学院 (EPFL) AMLD。
  6. FlightGlobal 在 Flight International 上发表的研究报告：“聚焦：挖掘数字航空电子设备数据以实现未来安全”，2011 年 11 月 [https://www.flightglobal.com/news/articles/in-focus-mining-digital-avionics-data-for-future-sa-364329/]
  7. 麻省理工学院新闻报道研究成果：“平面视角：新工具分析黑匣子数据以发现飞行异常。” 2011年9月 http://news.mit.edu/2011/black-box-analysis-0912

专利

  1. LI, L., HE, F., HE, X., & ZHANG, L. (2026). Route Network Planning for Drone Logistics in Urban Environment. (Patent No. US12,560,943).
  2. HE, F., LI, L., & ZHANG, L. (2024). Grid Based Path Search Method for UAV Delivery operations in Urban Environment. (Patent No. US11,915,599).
  3. LI, L., CHARRUAUD, F., & ZHAO, W. (2022). Method of Presenting Flight Data of an Aircraft and a Graphical User Interface for Use with the Same. (Patent No. US11,299,288).

完整出版物列表