The high-speed maglev train is a new type of transportation. The long stator track plays a critical role in the levitation guidance and traction system. Therefore, its condition directly affects the operation of maglev trains. It is extremely important to detect the abnormal condition of high-speed maglev tracks to ensure the stable, safe, and reliable operation of the train. In this article, an onboard image detection system is designed for high-speed maglev tracks, which can accurately obtain the image of long stator tracks under the harsh conditions of limited installation space, insufficient illumination, and rapid operation of vehicles. High-speed maglev trains are not yet in widespread use. In China, there is currently only one demonstration operating line located in Shanghai, and the length of the track test line is limited. Therefore, the number of track images that can be acquired is extremely limited. In view of the lack of defective samples of high-speed maglev tracks, this article proposes a data enhancement method based on sample generation and image fusion to augment the dataset of defective samples. To improve the quality of generated high-speed maglev track defect images, a joint attention layer (JEA) combining squeeze-and-exception (SE) block and spatial attention module (SAM) is designed and introduced into the generative adversarial network (GAN). This work provides a data basis for the study of track defect detection of high-speed maglev trains. In addition, this article detects the defects of high-speed maglev tracks via deep learning-based target detection algorithms, which can automatically detect, accurately classify and locate the defects of stator surface and cables, filling the gap in the field of high-speed maglev track defect detection.

Abstract– –In this paper, a collaborative relay tracking method based on information fusion is proposed for UAVs. Target matching is performed by combining the target's spatio-temporal information with its appearance and other auxiliary cues. Only the motion information and a compressed image of the target are used for target handover. Specifically, to determine the spatio-temporal transfer probability of the target after entering the blind area of visual field, this paper proposes a spatio-temporal correlation model construction method to predict the spatio-temporal similarity. Besides, Siam-LGNet is proposed in this paper for target appearance matching to overcome the effects of various view angles, rotations, scale changes and other factors on the images. This effectively ensures the accuracy of target matching and lowers the model complexity. Additionally, similarities obtained from multiple cues are fused by D-S evidence theory to compensate for the uncertainty of single cue. Finally, the effectiveness of the proposed method is verified in simulation experiments and physical experiments. This paper fills a gap in the field of UAVs relay tracking. The proposed scheme is scalable and lays the foundation for subsequent research on UAVs relay tracking problems.

A novel absolute positioning sensor for high-speed maglev train based on eddy current effect is studied in this paper. The sensor is designed with photoelectric switch and four groups of unilateral coplanar code-reading detection coil combination. The photoelectric switch realizes the positioning of the marker plate, and the four groups of detection coils read the mileage code of the mileage sign plate to obtain the absolute mileage information of the vehicle, which effectively reduces the quality and volume of the sensor, and reduces the impact of ice and snow. At the same time, the code-reading reliability and speed adaptability index are proposed. The code-reading reliability of the sensor is analyzed and tested under the fluctuation of levitation guidance, and the positioning error under the speed range of 0–600 km/h is calculated and analyzed. The results show that the novel sensor has the advantages of simple and compact structure. It still satisfies the system’s requirements for absolute vehicle mileage information under the conditions of vehicle operating attitude fluctuations and changes in the full operating speed range.

This paper proposes a novel hybrid data-driven modeling method for missiles. Based on actual flight test data, the missile hybrid model is established by combining neural networks and the mechanism modeling method, considering the uncertainties and nonlinear factors in missiles. This method can avoid the problems in missile mechanism modeling and traditional data-driven modeling, and can also provide a solution for nonlinear dynamic system modeling problems in offline usage scenarios. Finally, the feasibility of the proposed method and the credibility of the established model are verified by simulation experiments and statistical analysis.

This paper proposes a double neural network modeling method by studying the modeling problem for small unmanned helicopters. Based on the traditional neural network modeling method, the error compensation model is introduced, and the height control identification model of small unmanned helicopter based on BP network is established. Finally, the effectiveness and feasibility of the proposed method are verified by simulation experiments compared with traditional modeling methods. The simulation results show that the method of double neural network modeling greatly improves the approximation accuracy and generalization ability of the model.

In recent years, correlation filtering has been widely used in the field of UAV target tracking for its high efficiency and good robustness, even on a common CPU. However, the existing correlation filter-based tracking methods still have major problems when dealing with challenges such as fast moving targets, camera shake, and partial occlusion in UAV scenarios. Furthermore, the lack of reasonable attention mechanism for distortion information as well as background information prevents the limited computational resources from being used for the part of the object most severely affected by interference. In this paper, we propose the spatial attention aberration repressed correlation filter, which models the aberrations, makes full use of the spatial information of aberrations and assigns different attentions to them, and can better cope with these challenges. In addition, we propose a mechanism for the intermittent learning of the global context to balance the efficient use of limited computational resources and cope with various complex scenarios. We also tested the mechanism on challenging UAV benchmarks such as UAVDT and Visdrone2018, and the experiments show that SAARCF has better performance than state-of-the-art trackers.