Wind speed and direction are critical meteorological elements. Multi-rotor unmanned aerial vehicles UAVs are widely used as a premium payload platform in meteorological monitoring. The meteorological UAV is able to improve the spatial and temporal resolution of the elements collected. However, during wind measurement missions, the installed anemometers are susceptible to interference caused by rotor turbulence. This paper puts forward a wind pressure orthogonal decomposition (WPOD) strategy to overcome this limitation in three ways: the location of the sensors, a new wind measurement method, and supporting equipment. A weak turbulence zone (WTZ) is found around the airframe, where the turbulence strength decays rapidly and is more suitable for installing wind measurement sensors. For the sensors to match the spatial structure of this area, a WPOD wind measurement method is proposed. An anemometer based on this principle was mounted on a quadrotor UAV to build a wind measurement system. Compared with a standard anemometer, this system has satisfactory performance. Analysis of the resulting data indicates that the error of the system is ±0.3 m/s and ±2° under hovering conditions and ±0.7 m/s and ±5° under moving conditions. In summary, WPOD points to a new orientation for wind measurement under a small spatial–temporal scale.
Marine sensors are highly vulnerable to illegal access network attacks. Moreover, the nation’s meteorological and hydrological information is at ever-increasing risk, which calls for a prompt and in depth analysis of the network behavior and traffic to detect network attacks. Network attacks are becoming more diverse, with a large number of rare and even unknown types of attacks appearing. This results in traditional-machine-learning (ML)-based network intrusion detection (NID) methods performing weakly due to the lack of training samples. This paper proposes an NID method combining the log-cosh conditional variational autoencoder (LCVAE) with convolutional the bi-directional long short-term memory neural network (LCVAE-CBiLSTM) based on deep learning (DL). It can generate virtual samples with specific labels and extract more significant attack features from the monitored traffic data. A reconstructed loss term based on the log-cosh model is introduced into the conditional autoencoder. From it, the virtual samples are able to inherit the discrete attack data and enhance the potential features of the imbalance attack type. Then, a hybrid feature extraction model is proposed by combining the CNN and BiLSTM to tackle the attack’s spatial and temporal features. The following experiments evaluated the proposed method’s performance on the NSL-KDD dataset. The results demonstrated that the LCVAE-CBiLSTM obtained better results than state-of-the-art works, where the accuracy, F1-score, recall, and FAR were 87.30%, 87.89%, 80.89%, and 4.36%. The LCVAE-CBiLSTM effectively improves the detection rate of a few classes of samples and enhances the NID performance.
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