Tianjing Liu

Flight Data-Driven LSTM-Family Models for Resource-Aware Edge Deployment in Aerial Systems: A Review and Methodological Evaluation

Flight data-driven modeling has become an important approach for trajectory prediction, anomaly detection, and risk assessment in unmanned aerial vehicles and other aerial systems. Such data are usually high-dimensional, nonlinear, multirate, and non-stationary, especially during...

QAR Data-Driven Flight Anomaly Detection and Risk Warning: A Review of Statistical Learning and Machine Learning Methods in Aviation Safety

Quick Access Recorder (QAR) data provide high-dimensional onboard flight records that are increasingly used for data-driven aviation safety analysis. As flight operations become more complex, conventional manual monitoring and threshold-based exceedance detection are often insuff...