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About On-Board Deep-Learning-Based Unmanned Aerial Vehicle Fault Cause Detection and Identification
With the increase in use of Unmanned Aerial Vehi- cles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone’s software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs
Detailed Information
| Author: | ['vshssvs7'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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