This review summarizes and discusses the strategies for improving the PTC performance of conductive polymer composites.

For the current problems of low accuracy and poor reliability of defect detection for bearing roller end surfaces in industrial production, this paper proposes a bearing roller end surface defect detection algorithm based on improved YOLOv5n and the fusion of gamma-corrected maps and curvature maps. First, this paper uses photometric stereo vision to reconstruct the three-dimensional shape of the surface and proposes an improved Frankot–Chellappa integration algorithm to solve the problem of reconstructing surface deformation. Secondly, the DenseFuse network is used to fuse gamma-corrected maps and curvature maps to generate an image dataset that combines the strengths of both images to enhance defect features and improve the precision of target detection. Finally, the improved target detection model YOLOv5n is proposed to detect defects in the end surfaces of bearing rollers. The experimental results show that by using fused images for training, detection models with higher mean average precision(mAP) than traditional images can be obtained, and the improved YOLOv5n algorithm maintains the high real-time performance of the original algorithm while the mAP0.5 and mAP0.5:0.95 of improved YOLOv5n are 98.6% and 87.4%, respectively, which are respectively 0.9% and 2.8% higher than YOLOv5n.

To improve the driving performance of vehicles, it is of great significance to study the changes in the driver’s brain cognition during driving and to establish an intelligent driving computational framework based on the cognitive process. Electroencephalogram (EEG) is an effective means to study driver cognition because of its low cost, high temporal resolution, and different cognitive state information. The application of brain-computer interface(BCI) technology based on EEG signals to driver assistance systems has the potential to transform the way humans interact with vehicles. It can also help intelligent vehicles to understand and predict driver’s behavior, and to enhance the cognitive ability of vehicles. This paper reviews the research on theorizing and modeling driver cognitive processes based on cognitive architectures (e.g., ACT-R, QN, Soar, etc.), and proposes an EEG-based driver cognitive architecture. Then according to the relationship between the modules of this proposed driver cognitive architecture, the driver’s perception of stationary and hazardous scenarios in the driving environment, the understanding of the driver’s intention to control the longitudinal and lateral movements of the vehicle, and the influence of driver’s working memory as well as human factors such as fatigue, distraction, and emotion on driving performance based on EEG signals are reviewed. The integration of EEG signals with cognitive modeling has the potential to improve the accuracy of driver perception, intention, and cognitive state prediction, thereby enhancing vehicle safety.

In order to ensure the safe navigation of electric ships, it is especially important to detect the insulation status between the ship’s high-voltage system and the ship’s hull in real time. In this paper, a real-time insulation resistance (IR) detection method based on the double-arm unbalanced bridge method was proposed for the problem that the IR calculation results of electric ships have large errors. The detection principle was described, and an experimental test platform was built. The influence law of each branch resistance and voltage on the IR measurement error in this circuit was studied to provide a reference for the parameter selection of the double-arm unbalanced bridge and the effect of temperature on the measurement results was also considered. Besides, the Extended Kalman filtering (EKF) algorithm was used to reduce the influence of voltage fluctuation on the insulation detection error. The results show that the insulation testing method could accurately measure the IR, and the insulation testing error was less than 2.4%, and the error was about 0.18% after processing with the EKF algorithm. This proves that this paper improves the measurement accuracy of IR from three aspects: circuit design of insulation testing, bridge parameter selection and filtering algorithm.