With the increasing demand for electricity, the power grid is undergoing significant advancements. Insulators, which serve as protective devices for transmission lines in outdoor high-altitude power systems, are widely employed. However, the detection of defects in insulators captured under challenging conditions, such as rain, snow, fog, sunlight, and fast-moving drones during long-distance photography, remains a major challenge. To address this issue and improve the accuracy of defect detection, this paper presents a novel approach: the Multi-Scale Insulator Defect Detection Approach using Detection Transformer (DETR). In this study, we propose a multi-scale backbone network that effectively captures the features of small objects, enhancing the detection performance. Additionally, we introduce a self-attention upsampling (SAU) module to replace the conventional attention module, enhancing contextual information extraction and facilitating the detection of small objects. Furthermore, we introduce the insulator defect (IDIoU) loss, which mitigates the instability in the matching process caused by small defects. Extensive experiments were conducted on an insulator defect dataset to evaluate the performance of our proposed method. The results demonstrate that our approach achieves outstanding performance, particularly in detecting small defects. Compared to existing methods, our approach exhibits a remarkable 7.47% increase in the average precision, emphasizing its efficacy in insulator defect detection. The proposed method not only enhances the accuracy of defect detection, which is crucial for maintaining the reliability and safety of power transmission systems but also has broader implications for the maintenance and inspection of high-voltage power infrastructure.

Modern teaching has made significant progress, with many advanced equipment and technologies being introduced into the teaching process. Experimental teaching of engineering design courses is important. Due to limited teaching resources, engineering students need effective guidance during limited laboratory time. We will introduce artificial intelligence solutions to engineering education. We will use artificial intelligence technology for classroom behavior analysis to improve engineering design practice courses' teaching effectiveness. In an instructional milieu, image acquisition tools such as cameras are capable of real-time data capture, facilitating the identification and enumeration of students' emotional states. Concurrently, analytical software gauges the students' interaction patterns and performs comprehensive cluster analysis. Such multifaceted information provides valuable insights into the students' educational engagement, allowing educators to tailor their approach, thereby fostering enhanced pedagogical outcomes. The emotion recognition model we have developed, namely ERAM, demonstrates a rapid response rate coupled with dependable accuracy, making it a robust tool for classroom implementation. In contrast to the conventional post-lesson evaluations, our proposed technique furnishes immediate feedback throughout the instructional process. This real-time approach heralds a significant shift in instructional methodology, promoting timely intervention and adaptive teaching strategies. The control group experiment showed that intelligent systems improved teaching effectiveness by 8.44%. Intelligent systems can help teachers understand students' learning status and improve laboratory teaching quality in engineering design courses.

The rapid development of the Internet of Things (IoTs) has driven the progress of intelligent transportation systems (ITS), which provides basic elements, such as vehicles, traffic lights, cameras, roadside units (RSUs) and their interconnected 5G communications, to constitute the Internet of vehicles (IoVs). In the IoVs, an intelligent vehicle can not only share information with the infrastructures like RSUs by vehicle to infrastructure (V2I) communication but also with vehicles on the road through vehicle-to-vehicle (V2V) communications. We thus expect that vehicles can collaborate with other well-resourced and idling vehicles, making full use of the wasted resources. However, existing approaches cannot achieve this goal due to the increasing strict delay constraints and the dynamic characteristics of the IoVs tasks. To improve the utilization of resources and perform better resource management, in this paper, we propose a hybrid task offloading scheme (HyTOS) based on deep reinforcement learning (DRL), which achieves the vehicle-to-edge (V2E) and V2V offloading by jointly considers the delay constraints and resource demand. To perform optimal offloading decision-making, we introduce a dynamic decision-making method, namely deep Q networks (DQN). To verify the effectiveness of this approach, we choose three baseline offloading approaches (one game theory-based and two single-scenario approaches) and perform a series of simulation experiments. The simulation results demonstrate that, compared to the baseline offloading approaches, our approach can effectively reduce task delay and energy consumption, achieving high-efficiency resource management.