An Anti-UAV Long-Term Tracking Method with Hybrid Attention Mechanism and Hierarchical Discriminator.

To prevent unmanned aerial vehicles (UAVs) from threatening public security, anti-UAV object tracking has become a critical issue in industrial and military applications. However, tracking UAV objects stably is still a challenging issue because the scenarios are complicated and the targets are generally small. In this article, a novel long-term tracking architecture composed of a Siamese network and re-detection (SiamAD) is proposed to efficiently locate UAV targets in diverse surroundings. Specifically, a new hybrid attention mechanism module is exploited to conduct more discriminative feature representation and is incorporated into a Siamese network. At the same time, the attention-based Siamese network fuses multilevel features for accurately tracking the target. We further introduce a hierarchical discriminator for checking the reliability of targeting, and a discriminator-based redetection network is utilized for correcting tracking failures. To effectively catch up with the appearance changes of UAVs, a template updating strategy is developed in long-term tracking tasks. Our model surpasses many state-of-the-art models on the anti-UAV benchmark. In particular, the proposed method can achieve 13.7% and 16.5% improvements in success rate and precision rate, respectively, compared with the strong baseline SiamRPN++.

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds.

When performing fault diagnosis tasks on bearings, the change of any bearing's rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions. Firstly, the order tracking algorithm is used to resample the monitoring data obtained under different rotation speeds. Then, the one-dimensional convolutional neural network is adopted to extract features of the fault data. Finally, the fault type of collected data can be obtained by fully connected networks based on the features extracted. In the time domain, while the proposed algorithm only relies on the fault data collected under one speed as the training dataset, it is capable of doing fault diagnosis under different speed conditions. In the condition with the largest difference in speed with each dataset, the accuracy of the proposed method is higher than the baseline methods by 0.54% and 11.00%-on CWRU dataset and our own dataset respectively. The results show that the proposed method performs well in dealing with the fault diagnosis under the condition of variable speeds.