During real-time production in industrial Internet of Things systems, equipment changes its operating speed due to changing operating conditions. And dynamic speed changes of rotating machinery under fluctuating workloads often lead to domain changes of vibration signals, which will directly lead to degradation of fault diagnostic model performance. Furthermore, the acquisition of data from multiple domains in real industrial scenarios is challenging due to the expense of collecting data from all possible working conditions. Consequently, applying diagnostic models trained using a single-source domain directly to an unknown target domain is a very challenging single domain generalization problem. Therefore, a generic single-source domain generalization framework via wavelet packet augmentation (WPA) and pseudo-domain generation (PDG) for fault diagnosis under unknown operating conditions is proposed in this article. PDG involves augmenting single-source domain by integrating data generation model, thereby enhancing prediction accuracy. Furthermore, a WPA method is proposed. Initially, the original signal is decomposed to obtain high- and low-frequency information. Subsequently, the high- and low-frequency information within the batch are linearly interpolated, respectively. Consequently, the interpolated high- and low-frequency information is then reconstructed to yield enhanced samples. The experimental results on four datasets show that the proposed framework can effectively improve the robustness of the generalization ability of fault diagnosis under unknown operating environments.

Roadside Unit (RSU) plays an important role in Vehicle-to-Everything (V2X) service on the Internet of Vehicles (IoV). Due to the limitation of the existing network architecture, the RSU management technology either cannot fulfill the time location-varying service demands or consumes a large number of resource to cover the interested area. To mitigate the gap between the stringent V2X requirements and the limited available resource, a cybertwin-based IoV architecture is proposed to facilitate the RSU management and achieve always-on V2X services. Two types of RSUs, i.e., static RSU (sRSU) and mobile RSU (mRSU), are applied in infrastructure-assisted V2X commu-nications. To evaluate the performance of cybertwin-based RSU deployment and scheduling, the utility maximization problem with coverage constraints is formulated. A three-stage hybrid RSU management strategy is proposed considering the different granularity of service loads. First, sRSUs are deployed to satisfy the basic service demands in different areas. Second, mRSUs are flexibly selected and managed to adapt to real-time variations of service loads. Finally, the RSUs are scheduled based on the real-time load prediction. The case study of Wuxi city illustrates that the proposed solution outperforms the existing strategy in terms of the deployment utility, response ratio, and adaptiveness to demand dynamics.

The event camera, as an innovative perception sensor, generates a high-temporal-resolution event stream, filtering out redundant visual information and offering a wide dynamic range for object detection. Nevertheless, it is non-trivial to effectively utilize the advantages of the event stream to enhance the accuracy of object detection. Initially, current event representation methods require hyperparameters tuning to achieve optimal performance for event-based object detection, which is very time-consuming. Moreover, most existing methods rely on large-scale models to enhance detection accuracy. In addition, they fail to leverage the temporal clues provided by the event stream efficiently. In this work, we propose a detection pipeline which better achieves equilibrium between model speed and accuracy. First, we design an event representation without hyperparameter tuning named Dynamic Temporal Representation (DTR), which efficiently utilizes the spatial-temporal asynchronous event stream. Then, a module named Spatial Temporal Focus (STF), capable of extracting the rich temporal information from the DTR tensor, is proposed. Besides, a memory module is implemented, leveraging rich temporal clues named Light Recurrent Convolution (LRC). Finally, experiments demonstrate that our method outperforms state-of-the-art approaches on two classic event camera datasets. Specifically, in the Gen1 Automotive Dataset and the 1Mpx Detection Dataset, mean Average Precision (mAP) increases by 0.5% and 0.4%, respectively, while inference times decrease by 34.3% and 32.5%. Additionally, the parameters of the model are reduced by 50.1%. Therefore, our method demonstrates competitive advantages in terms of accuracy, efficiency, and parameters.

The recent development of advanced trackers, which use nighttime image enhancement technology, has led to marked advances in the performance of visual tracking at night. However, the images recovered by currently available enhancement methods still have some weaknesses, such as blurred target details and obvious image noise. To this end, we propose a novel method for learning multidimensional spatial attention for robust nighttime visual tracking, which is developed over a spatial channel transformer based low light enhancer (SCT), named MSA-SCT. First, a novel multidimensional spatial attention (MSA) is designed. Additional reliable feature responses are generated by aggregating channel and multi-scale spatial information, thus making the model more adaptable to illumination conditions and noise levels in different regions of the image. Second, with optimized skip connections, the effects of redundant information and noise can be limited, which is more useful for the propagation of fine detail features in nighttime images from low to high level features and improves the enhancement effect. Finally, the tracker with enhancers was tested on multiple tracking benchmarks to fully demonstrate the effectiveness and superiority of MSA-SCT.