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.

The present study investigated the role of ERK in the onset of mechanical and cold allodynia in a rat model of compression of the trigeminal ganglion by examining changes in the air-puff thresholds and number of scratches following the intracisternal injection of PD98059, a MEK inhibitor. Male Sprague Dawley rats weighing between 250 and 260 g were used. Under anesthesia, the rats were mounted onto a stereotaxic frame and received 4% agar (10μℓ) solution to compress the trigeminal ganglion. In the control group, the animals were given a sham operation without the application of agar. Changes in behavior were examined at 3 days before and at 3, 7, 10, 14, 17, 21, 24, 30, and 40 days after surgery. Compression of the trigeminal ganglion significantly decreased the air-puff thresholds. Mechanical allodynia was established within 3 days and persisted over postoperative day 24. To evaluate cold allodynia, nociceptive scratching behavior was monitored after acetone application on the vibrissa pad of the rats. Compression of the trigeminal ganglion was found to produce significant cold allodynia, which persisted for more than 40 days after surgery. On postoperative day 14, the intracisternal administration of 1 μg or 10 μg of PD98059 in the rat model significantly decreased the air-puff thresholds on both the ipsilateral and contralateral side. The intracisternal administration of 10 μg of PD98059 also significantly alleviated the cold allodynia, compared with the vehicle-treated group. These results suggest that central ERK plays an important role in the development of mechanical and cold allodynia in rats with compression of the trigeminal ganglion and that a targeted blockade of this pathway is a potential future treatment strategy for trigeminal neuralgia-like nociception.

As the transmission of 3D shape models through Internet becomes more important, the compression issue of shape models gets more critical. The issues for normal vectors have not yet been explored as much as it deserves, even though the size of the data for normal vectors can be significantly larger than its counterparts of topology and geometry. Presented in this paper is an approach to compress the normal vectors of a shape model represented in a mesh using the concept of clustering. It turns out that the proposed approach has a significant compression ratio without a serious sacrifice of the visual quality of the model.