The effects of electron cyclotron current drive (ECCD) on the double tearing mode (DTM) in slab geometry are investigated by using two-dimensional compressible magnetohydrodynamics equations. It is found that, mainly, the double tearing mode is suppressed by the emergence of the secondary island, due to the deposition of driven current on the X-point of magnetic island at one rational surface, which forms a new non-complete symmetric magnetic topology structure (defined as a non-complete symmetric structure, NSS). The effects of driven current with different parameters (magnitude, initial time of deposition, duration time, and location of deposition) on the evolution of DTM are analyzed elaborately. The optimal magnitude or optimal deposition duration of driven current is the one which makes the duration of NSS the longest, which depends on the mutual effect between ECCD and the background plasma. Moreover, driven current introduced at the early Sweet-Parker phase has the best suppression effect; and the optimal moment also exists, depending on the duration of the NSS. Finally, the effects varied by the driven current disposition location are studied. It is verified that the favorable location of driven current is the X-point which is completely different from the result of single tearing mode.

This paper presents an autonomous unmanned-aerial-vehicle (UAV) tracking system based on an improved long and short-term memory (LSTM) Kalman filter (KF) model. The system can estimate the three-dimensional (3D) attitude and precisely track the target object without manual intervention. Specifically, the YOLOX algorithm is employed to track and recognize the target object, which is then combined with the improved KF model for precise tracking and recognition. In the LSTM-KF model, three different LSTM networks (f, Q, and R) are adopted to model a nonlinear transfer function to enable the model to learn rich and dynamic Kalman components from the data. The experimental results disclose that the improved LSTM-KF model exhibits higher recognition accuracy than the standard LSTM and the independent KF model. It verifies the robustness, effectiveness, and reliability of the autonomous UAV tracking system based on the improved LSTM-KF model in object recognition and tracking and 3D attitude estimation.

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It is a challenging and meaningful task to carry out drone-based livestock monitoring in high-altitude and cold regions. The purpose of AI is to execute automated tasks and to solve practical problems in actual applications by combining the software technology with the hardware carrier to create integrated advanced devices. Only in this way, the maximum value of AI could be realized. In this paper, a real-time tracking system with dynamic target tracking ability is proposed. It is developed based on the tracking-by-detection architecture using YOLOv7 and DeepSORT algorithms for target detection and tracking, respectively. To address the existing problems of the DeepSORT algorithm, the following two optimizations are made: (1) Optical flow is used to compensate the Kalman filter for improvement of the prediction accuracy; (2) A low-confidence trajectory filtering method is adopted to reduce the influence of unreliable detection on target tracking. In addition, an visual servo controller for the UAV is designed to enable the automated tracking task. Finally, the system is tested using the Tibetan yaks living in the Tibetan Plateau as the tracking targets, and the results reveal the real-time multiple tracking ability and the ideal visual servo effect of the proposed system.