A5064515378- Anomaly Detection Techniques and Applications
- Machine Learning and ELM
- Artificial Immune Systems Applications
- Domain Adaptation and Few-Shot Learning
- Machine Fault Diagnosis Techniques
- Fault Detection and Control Systems
- Video Surveillance and Tracking Methods
Harbin Institute of Technology
2022-2023
Accurate fault detection for unmanned aerial vehicle (UAV) actuators is essential ensuring flight safety and mission completion. Without the requirement of modeling complex physical mechanism, data-driven actuator approaches have attracted much attention. Among them, long short-term memory (LSTM) approach has shown superior performance due to its capability spatial–temporal features. However, uncertainty LSTM actually changeable under different conditions, which not been well considered in...
The actuator is critical for controlling the unmanned aerial vehicle (UAV) attitude. With development of sensing technology, convenience collecting high-quality data by sensors has provided favorable conditions data-driven-based UAV fault detection methods. However, there are less historical on a new type UAV, making it challenging to accurately obtain data-driven models. domain distribution (target domain) different from that existing UAVs (source domains), and basic methods difficult...
With the rapid development of artificial intelligence, unmanned aerial vehicle (UAV) has been widely applied in multiple fields for their convenience, low cost, and powerful function. Compared with single-UAV data, UAV fleet data more rich features, which can provide greater collection information support to achieve accurate anomaly detection. However, flight paths different UAVs vary greatly, brings many challenges fleet-level analysis. And it is difficult get exception labels accurately...