时 间：2025年3月14日 13:00-14:30
腾讯会议：586-469-278
报 告 人：张晓革 香港理工大学 助理教授
主 持 人：方方 华东师范大学教授
摘 要：
Landing is generally cited as one of the riskiest phases of a flight, as indicated by the much higher accident rate than other flight phases. In this talk, we focus on the hard landing problem (which is defined as the touchdown vertical speed exceeding a predefined threshold) and build a probabilistic deep learning model to forecast the aircraft’s vertical speed at touchdown using DASHlink data. Previous studies have treated hard landing as a classification problem, in which the vertical speed is represented as a categorical variable based on a predefined threshold. In this talk, we develop a machine learning model to predict the touchdown vertical speed during aircraft landing. Probabilistic forecasting is used to quantify the uncertainty in model prediction to support risk-informed decision-making. A Bayesian neural network approach is leveraged to build the predictive model. The overall methodology consists of five steps. First, a clustering method based on the minimum separation between different airports is developed to identify flights in the dataset that landed at the same airport. Secondly, identifying the touchdown point itself is not straightforward; in this paper, it is determined by comparing the vertical speed distributions derived from different candidate touchdown indicators. Thirdly, a forward and backward filtering approach is used to smooth the data without introducing the phase lag. Next, a minimal-redundancy-maximal-relevance (mRMR) analysis is used to reduce the dimensionality of input variables. Finally, a Bayesian recurrent neural network is trained to predict the touchdown vertical speed and quantify the uncertainty in the prediction. The model is validated using several flights in the test dataset, and computational results demonstrate the satisfactory performance of the proposed approach.
报告人简介：
张晓革，香港理工大学工业与系统工程系助理教授，主要从事AI驱动的智能系统的可靠性、可信性及可控性保障等方面的研究，相关研究成果已在物流管理、航空交通管理等领域得到应用。2016年8月至12月，就职于美国国家航空航天局艾姆斯研究中心（NASA Ames Research Center），担任研究工程师；2020年3月至2021年8月，就职于美国联邦快递总部，担任高级运筹学分析师。曾获得Bravo Zulu Award，国家优秀自费留学生奖学金，陕西省科学技术二等奖1项，日内瓦国际发明展银奖、铜奖各1项（均排第一）。现主持国家自然科学基金青年科学基金项目1项，香港大学教育资助委员会杰出青年学者计划基金项目1项。目前，已在《Nature Communications》、《Risk Analysis》、《IEEE Transactions on Reliability》、《IEEE Transactions on Automation Science and Engineering》、《IEEE Transactions on Cybernetics》、《IEEE Transactions on Artificial Intelligence》、《IEEE Transactions on Cybernetics》、《IEEE Transactions on Intelligent Transportation Systems》、《Reliability Engineering & System Safety》等国际主流权威期刊发表SCI论文80余篇。担任《Machine Learning: Engineering》、《Journal of Reliability Science and Engineering》、《International Journal of Parallel, Emergent and Distributed Systems》等期刊编委。