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.

Most domestic pilots are trained at local airfields using propeller aircraft. Training aircraft are mainly trained in the airspace around the aerodrome, and mainly take-off and landing exercises that require a lot of practice among flight control skills. Aircraft noise is a sound that humans do not want. In this study, based on the Rotax 914 engine used in Korea, the propeller blade angle was changed by 1 degree for the 3-leaf “K company” propeller and the 3-leaf GSC wooden propeller, and the engine RPM was changed to examine the noise and thrust changes. The purpose of this study is to check whether noise and thrust loss are the least at the engine's maximum RPM, and to propose an aircraft operation plan in the noisy aerodrome area based on the values.