Junpeng Dang

To address the difficulty of effectively sensing weakly distorted electric fields generated during the early stage of insulation degradation in power equipment, this paper proposes a miniaturized high-sensitivity multilayer flexible electric field sensor optimized using an improved GOOSE algorithm. First, based on the electric field coupling mechanism, the enhancement effect of the flexible arc-shaped structure on local distorted electric fields is analyzed, and an equivalent model that discretizes the flexible curved surface into parallel-plate micro-elements is established. Furthermore, a multilayer sensor equivalent circuit model considering the effects of parasitic capacitance and conductive ink electrode resistance is developed, and the relationship between sensor sensitivity and structural parameters is derived. Subsequently, an electric field–circuit-coupled simulation model is established in COMSOL Multiphysics to systematically investigate the influence of parameters such as electrode layer number, electrode thickness, dielectric layer thickness, and electrode side length on the output response. The results show that the multilayer structure can effectively improve the equivalent sensing capacitance and electric field coupling capability, while the electrode thickness and dielectric layer thickness exhibit significant nonlinear effects on the output voltage. To achieve the coordinated optimization of high sensitivity and miniaturization, Tent chaotic mapping initialization and nonlinear dynamic adaptive inertia weight are introduced to improve the traditional GOOSE algorithm, and a multi-objective optimization model is constructed with the objectives of maximizing the output voltage and minimizing the electrode area. The optimization results indicate that the improved GOOSE algorithm achieves faster convergence and better optimization stability. Among the Pareto solution sets with different electrode layer numbers, the seven-layer structure exhibits the best size–performance trade-off. The final optimized design achieves an electrode area of only 1.746 mm² and an output voltage of 2.565 × 10⁻⁶ V. These results demonstrate that the proposed multilayer flexible structure and improved GOOSE optimization method can significantly enhance the response capability to weak electric fields while maintaining device miniaturization, providing a new sensor design concept and optimization approach for non-contact detection of early-stage insulation degradation in power equipment.

This study proposes a fusion method of infrared and visible images based on feature interaction. Existing fusion methods can be classified into two categories based on a single-branch network and a two-branch network. Generative adversarial networks are widely used in single-branch-based fusion methods, which ignore the difference in feature extraction caused by different input images. Most two-branch-based fusion methods use convolutional neural networks, which do not take into account the inverse promotion of fusion results and lack the interaction between different input features. To remedy the shortcomings of these fusion methods and better utilize the feature from source images, this study proposes a two-branch feature interactions method based on a generative adversarial network for visible and infrared image fusion. In the generator part, a two-branch feature interaction approach was designed to extract features from different inputs and realize feature interaction through the network connection of different branches. In the discriminator part, a double-classification discriminator was used for visible images and infrared images. Extensive comparison experiments with state-of-the-art methods have demonstrated the advantages of this proposed generative adversarial network based on two-branch feature interaction, which can enhance the texture details of objects in fusion results and reduce the interference of noise information from source inputs. In addition, the above advantages were also confirmed in generalization experiments of object detection.

Ensuring personal safety and preventing accidents are critical aspects of power construction safety supervision. However, current monitoring methods are inefficient and unreliable as most of them rely on manual monitoring and transmission, which results in slow detection and delayed warnings regarding violations. To overcome these challenges, we propose an intelligent detection system that can accurately identify instances of illegal wearing of power construction workers in real-time. Firstly, we integrated the squeeze-and-excitation (SE) module into our convolutional neural network to enhance detection accuracy. This module effectively prioritizes informative features while suppressing less relevant ones, resulting in improved overall performance. Secondly, we present an embedded real-time detection system that utilizes Jetson Xavier NX and Edge-YOLO. This system promptly detects and alerts power construction workers of instances of illegal wearing behavior. To ensure a lightweight implementation, we design appropriate detection heads based on target size and distribution, reducing model parameters while enhancing detection speed and minimizing accuracy loss. Additionally, we employed data augmentation to enhance the system’s robustness. Our experimental results demonstrate that our improved Edge-YOLO model achieves high detection precision and recall rates of 0.964 and 0.966, respectively, with a frame rate of 35.36 frames per second when deployed on Jetson Xavier NX. Therefore, Edge-YOLO proves to be an ideal choice for intelligent real-time detection systems, providing superior accuracy and speed performance compared to the original YOLOv5s model and other models in the YOLO series for safety monitoring at construction sites.