The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms for detecting defective insulators in images face issues such as excessive parameter size, low accuracy, and slow detection speed. To address the aforementioned issues, this article proposes an insulator defect detection model based on the lightweight Faster R-CNN (Faster Region-based Convolutional Network) model (Faster R-CNN-tiny). First, the Faster R-CNN model’s backbone network is turned into a lightweight version of it by substituting EfficientNet for ResNet (Residual Network), greatly decreasing the model parameters while increasing its detection accuracy. The second step is to employ a feature pyramid to build feature maps with various resolutions for feature fusion, which enables the detection of objects at various scales. In addition, replacing ordinary convolutions in the network model with more efficient depth-wise separable convolutions increases detection speed while slightly reducing network detection accuracy. Transfer learning is introduced, and a training method involving freezing and unfreezing the model is employed to enhance the network’s ability to detect small target defects. The proposed model is validated using the insulator self-exploding defect dataset. The experimental results show that Faster R-CNN-tiny significantly outperforms the Faster R-CNN (ResNet) model in terms of mean average precision (mAP), frames per second (FPS), and number of parameters.

The interpolation of Thiele-type continued fractions is thought of as the traditional rational interpolation and plays a significant role in numerical analysis and image interpolation. Different to the classical method, a novel type of bivariate Thiele-like rational interpolation continued fractions with parameters is proposed to efficiently address the interpolation problem. Firstly, the multiplicity of the points is adjusted strategically. Secondly, bivariate Thiele-like rational interpolation continued fractions with parameters is developed. We also discuss the interpolant algorithm, theorem, and dual interpolation of the proposed interpolation method. Many interpolation functions can be gained through adjusting the parameter, which is flexible and convenient. We also demonstrate that the novel interpolation function can deal with the interpolation problems that inverse differences do not exist or that there are unattainable points appearing in classical Thiele-type continued fractions interpolation. Through the selection of proper parameters, the value of the interpolation function can be changed at any point in the interpolant region under unaltered interpolant data. Numerical examples are given to show that the developed methods achieve state-of-the-art performance.

In recent years, extreme weather has occurred frequently, and the risk of heavy rainfall and flooding faced by the people has risen. It is therefore an urgent requirement to carry out applied research on heavy rainfall and flooding risk assessment. We took Henan Province, where a major flood disaster occurred in 2021, as an example to analyze the impact factors of urban flooding and conduct a risk assessment. Indicators were first selected from population, housing, and the economy, and correlation analysis was used to optimize the indicator system. Then, a deep clustering network model based on a stacked denoising autoencoder (SDAE) was constructed, the feature information implied in the disaster indicators was abstracted into potential features through the coding and decoding of the network, and a small number of potential features were used to express the complex relationship between the disaster indicators. The results of the study show that the high-risk areas of flood damage in Henan Province in 2021 account for 2.3%, the medium-risk areas account for 9.4%, and the low-risk areas account for 80.3%. These evaluation results are in line with the actual situation in Henan Province, and the division of the grade in some areas is more reasonable compared with the entropy weighting method, which is a commonly used method of disaster assessment. The new model does not need to calculate weights to cope with changes in indicators and disaster conditions. The research results can provide scientific reference for urban flood risk management, disaster prevention and mitigation, and regional planning.

While MADRL has demonstrated significant potential in Unmanned Aerial Vehicle (UAV) swarm control, traditional architectures often rely on fixed-dimensional observation spaces. This rigid structural constraint severely limits the swarm’s adaptability in dynamic environments, particularly when facing sudden topological changes such as node failures or dynamic reinforcements. To overcome these limitations, this paper proposes an end-to-end UAV swarm motion control framework incorporating a state-modulated Graph Attention Network (GAT). By modeling the swarm as a dynamic interaction graph, the proposed method dynamically aggregates neighbor features using attention weights modulated by the agents’ real-time kinematic states. Furthermore, a virtual structure combined with an auction mechanism is introduced to achieve precise formation planning and target allocation. Evaluated in the Genesis 3D physics engine, the proposed Prioritized Experience Replay (PER)-MADDPG-Graph Attention Network (GAT) algorithm exhibits superior robustness and spatial adaptability. Extensive experiments, including dynamic node reduction and addition scenarios, confirm that the proposed framework seamlessly maintains swarm configurations without catastrophic policy degradation, outperforming baseline MADRL methods in both convergence speed and control precision.

This work addresses coverage formation control for the Multi-Rover System (MRS) in extraplanetary environments such as Mars or the Moon, where Global Navigation Satellite System (GNSS) signals are unavailable and uncertain self-localization significantly degrades coverage performance. Existing coverage strategies predominantly assume perfect localization, which is unrealistic for GNSS-denied planetary surfaces. This paper presents a novel multi-rover coverage formation control algorithm that combines guaranteed Voronoi partitioning with belief space planning. The core contributions are (i) a guaranteed Voronoi partitioning framework that provides deterministic bounds on true coverage cells under localization uncertainty; (ii) a dual-layer optimization architecture integrating Extended Kalman Filter-based self-localization with centroid-error minimization; and (iii) a belief space planning approach that predicts system state evolution and infers optimal control inputs over a receding horizon. Simulation results under multiple density distributions and rover configurations demonstrate faster convergence and lower coverage cost compared to conventional Lloyd-based methods. By extending existing coverage strategies, our approach supports rapid and adaptive deployment of multi-rover networks in GNSS-limited environments, providing a promising solution for large-scale planetary exploration.

Underwater images captured by sensors suffer from low contrast and blurry details due to the interference of light absorption and scattering in underwater scenes. Good visibility restoration is often desired for practical processing applications. Current image enhancement methods often rely on prior assumptions to reconstruct a clear image without considering the inherent correlation of underwater image degradation, introducing unconsiderable enhancement results. Thus, this paper proposes an underwater enhancement method based on multiple enhanced layers fusion and transmission-driven color restoration, named EFCR, which consists of three key modules: a pixel-based transmission computation (PTC), a multiple-enhanced-layers fusion (MELF), and a transmission-driven color restoration (TCR). First, PTC designs a linear transformation to adjust the saturation and estimates the transmission based on the mapping relationship between the transmission, the brightness, and the saturation, preventing the transmission from being under-estimated. Then, MELF extracts the original details from the luminance channel and enhances these desired details based on the estimated transmission. Meanwhile, adaptive histogram equalization is used to improve the global brightness. Finally, TCR further analyzes the inherent correlation between the transmission and the image degradation, and constructs a compensation factor to adaptively correct the attenuated a and b channels of Lab space, producing a good enhancement result with reasonable brightness and natural colors. Extensive experiments on three underwater image datasets demonstrate the effectiveness and robustness of the proposed method in underwater image restoration. Especially, the average $\overline{r}$ and Blur values of our method at most incline and decline by 99.87% and 10.22%, respectively, which shows our method has obvious advantages in edge enhancement and haze removal. Moreover, our method provides helpful support for color restoration and image salient detection, and also shows good generalization capability for enhancing outdoor hazy images.