Unmanned aerial vehicle (UAV) inspection has become the mainstream of transmission line inspection, and the detection of insulator defects is an important part of UAV inspection. On the premise of ensuring high accuracy and detection speed, an improved YOLOv5 model is proposed for defect detection of insulators. The algorithm uses the weights trained on conventional large-scale datasets to improve accuracy through the transfer learning method of feature mapping. The algorithm employs the Focal loss function and proposes a dynamic weight assignment method. Compared with the traditional empirical value method, it is more in line with the distribution law of samples in the data set, improves the accuracy of difficult-to-classify samples, and saves a lot of time. The experimental results show that the average accuracy of the insulator and its defect is 98.3%, 5.7% higher than the original model, while the accuracy and recall rate of insulator defects are improved by 5.7% and 7.9%, respectively. The algorithm improves the accuracy and recall of the model and enables faster detection of insulator defects.

To enhance equalization efficiency and address the issue of traditional equalization methods overlooking temperature factors, this paper proposes a multilayer equalization circuit for both intra-group and inter-group balancing. The traditional Buck-Boost equalization topology between groups is improved by incorporating a two-way interleaved inductor structure, which helps reduce equalization idle time. An adaptive fuzzy control equalization strategy for multiple objectives is applied to the topology. The state of charge (SOC) and temperature of the battery are used as key variables for equalization, with the equalization current dynamically adjusted based on changes in the SOC and temperature. This approach improves the balance between equalization speed and temperature control, reducing equalization time while limiting battery temperature rise. A simulation model is developed using MATLAB/Simulink. The simulation results demonstrate that, compared to the traditional Buck-Boost equalization topology, the proposed topology reduces equalization time by 15.1%. Additionally, under three different operating conditions, the equalization cotnrol strategy designed in this paper improves time efficiency by over 14% compared to traditional methods, while also reducing both the maximum temperature and temperature difference.

In sensor design, electromagnetic field numerical simulation techniques are widely used to investigate the working principles of sensors. These analyses help designers understand how sensors detect and respond to external signals during operation. One popular method for electromagnetic field computation is the meshless radial point interpolation method (RPIM), where the number and distribution of nodes are critical to ensuring both accuracy and efficiency. However, traditional RPIM methods often face challenges in achieving stable and precise results, particularly in complex electromagnetic environments. In order to enhance the stability and accuracy of electromagnetic numerical calculations, a node generation and adaptive refinement algorithm for the meshless RPIM is proposed. The proposed approach includes an initial node-generation method designed to optimize the balance between computational accuracy and efficiency, as well as a dynamic error threshold and hybrid node refinement method to precisely identify and adaptively refine areas requiring additional nodes, ensuring high precision in critical regions. The proposed method was validated through its application to electrostatic fields and multi-media magnetic fields, demonstrating significant improvements in both stability and accuracy compared with conventional RPIM approaches. These findings highlight the potential of the proposed algorithm to enhance the reliability and precision of electromagnetic field simulations in sensor design and related applications.

Due to the high requirements for performance of antennas in the modern communication system, more and more objectives need to be considered, which makes the antenna design unendurable time-consuming and hard to converge. To obtain the Pareto solutions for antenna design effectively and efficiently, a dual reduction method is incorporated in an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D). To obtain information about conflicting objectives on the Pareto front, the size of the MOEA/D neighbourhood is dynamically adjusted; to improve the speed and accuracy of finding redundant objectives, PCA and Pareto impact ratio are used as dual objective-reduction criteria. Numerical results in solving the mathematical benchmark problems, an antenna array problem and Yagi-uda optimal design demonstrate that the proposed method can reduce redundant objectives and increase the convergence speed.

The optimization designs of modern electromagnetic devices are often modeled as high dimensional multi-objective problems due to their high-quality demands in multiple functions and systemization. To identify and remove the redundant objectives, an interactive aggregation trees method embedded into the two-archive algorithm 2 is proposed to solve the high dimensional multi-objective optimizations. To balance the speed and the accuracy in finding the redundant objectives, conflict degree, conflict list and conflict flag are developed in an interactive aggregation trees frame. The two-archive algorithm 2 is modified to adjust the updated model obtained from the objective-reduction frame. Mathematical test functions and TEAM Workshop Problem 22 are used to test the performance of the proposed method. Numerical results demonstrate that the proposed method can remove the redundant objectives effectively and provide qualified solutions for the electromagnetic optimization designs.