논문 상세보기
Development and Model Compression of CNN-LSTM-Autoencoder Model for Anomaly Detection of UAVs KCI 등재
무인항공기 이상탐지를 위한 CNN-LSTM-Autoencoder 모델 개발 및 경량화
- 언어KOR
- URLhttps://db.koreascholar.com/Article/Detail/442915
pp.155-162
Anomaly detection technique for the Unmanned Aerial Vehicles (UAVs) is one of the important techniques for ensuring airframe stability. There have been many researches on anomaly detection techniques using deep learning. However, most of research on the anomaly detection techniques are not consider the limited computational processing power and available energy of UAVs. Deep learning model convert to the model compression has significant advantages in terms of computational and energy efficiency for machine learning and deep learning. Therefore, this paper suggests a real-time anomaly detection model for the UAVs, achieved through model compression. The suggested anomaly detection model has three main layers which are a convolutional neural network (CNN) layer, a long short-term memory model (LSTM) layer, and an autoencoder (AE) layer. The suggested anomaly detection model undergoes model compression to increase computational efficiency. The model compression has same level of accuracy to that of the original model while reducing computational processing time of the UAVs. The proposed model can increase the stability of UAVs from a software perspective and is expected to contribute to improving UAVs efficiency through increased available computational capacity from a hardware perspective.
2. Literature Review
2.1 Anomaly detection
2.2 Model compression
3. Anomaly Detection Model for PowerUnit of UAVs
3.1 CNN-LSTM-AE model for anomalydetection
3.2 Model compression of anomaly detectionmodel
4. Experimental Results andComparative Study
4.1 Data acquisition
4.2 Implementation and comparison results
5. Conclusion
Acknowledgement
References
