Defect detection of a workpiece surface is a basic and essential task in the production of products. Although significant progress has been made in workpiece surface defect detection, traditional methods still find it difficult to detect small defects efficiently. To deal with this problem, we propose an efficient small defect detection network with a novel parallel convolution module, serial convolution module and feature fusion module. First, a lightweight backbone network is used to extract the preliminary defect features. Second, the parallel convolution module and serial convolution module are used to obtain the abundant defect features. Then, the feature fusion module is used to fuse the shallow features with deep features, to enhance the features of the small defects. Finally, the obtained features are put into the corresponding detection head to get the final prediction results. The experimental results on a local cable dataset and a public printed circuit board dataset show that our method achieves a remarkable performance in detecting small defects and achieves a favorable trade-off between accuracy, speed and model size, which meets the requirements of industrial applications.

In multi-UAV collaborative power grid inspection, the system efficiency of existing methods is limited by the performance of both task assignment and path planning, which is critical in large-scale task scenarios, resulting in a huge computational cost and a high possibility to local optimality. To address these challenges, a bilevel optimization framework based on GA-NSGA-II and task segmentation is proposed to balance the total inspection distance and the distance standard deviation of UAVs, where the outer optimization employs the NSGA-II to assign task units to each UAV evenly, while the inner optimization deploys an adaptive genetic algorithm with an elite retention strategy to optimize the inspection direction and order in each task domain to obtain a Pareto-optimal solution set under constraints. To avoid the dimensionality disaster, the massive inspection points are combined into task units based on the UAV’s endurance. In scenarios with 284 tower task points, the proposed algorithm has reduced the standard deviation of UAV flight distances by 41.91% to 84.63% and the longest flight distance by 29.41% to 43.98% compared to the GA-GA bilevel optimization. Against task-adaptive clustering optimization, it decreased the standard deviation by 18.25% to 94.93% and the longest flight distance by 15.97% to 37.33%. Applying it to 406 tower task points also confirmed the GA-NSGA-II bilevel optimization’s effectiveness in minimizing the total inspection distance and balancing UAV workloads.

Vertical Axis Wind Turbines (VAWTs) offer several advantages over horizontal axis wind turbines (HAWTs), including quieter operation, ease of maintenance, and simplified construction. Surprisingly, despite the prevailing beli... | Find, read and cite all the research you need on Tech Science Press

In dynamic and unstructured environments, the obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs) are crucial for mission success. Traditional methods struggle with adaptability and effectiveness in unknown or changing scenes. In contrast, the commonly used deep reinforcement learning (DRL) ones suffer from slow convergence, reduced accuracy, and inadequate robustness due to the loss of sensitivity to outliers and parameter rigidity. To address these challenges, we propose an enhanced DRL framework that leverages a Dynamic Huber loss function tailored for UAV autonomous obstacle avoidance. By incorporating Soft updates for target network and dynamically tuning the Huber loss, the proposed method facilitates faster model convergence, superior control precision, and improved robustness. Both theoretical analysis and experimental simulation verify its effectiveness with superior planning success rate, shorter average path length, and faster model convergence over traditional approaches. Specifically, in static environments, the Dynamic Huber-loss-based DRL framework achieves a 98.85% success rate with an optimized average path length of 10.73; in dynamic environments, it attains a 74.20% success rate with an average path length of 37.04; adding wind disturbances in a dynamic environment, it attains a 70.95% success rate with an average path length of 40.40, highlighting its enhanced performance and adaptability.

Modern composite materials (CM) have wide applications in airframe structures of transport category aircraft. Their application has an important place in providing the minimum mass of an aircraft structure. The successful implementation of CM in airframe structures is directly related to the creation and improvement of computational methods for assessing their strength and stiffness. The most complex task of CM application is to provide the required level of strength and stiffness of separate structural elements and a whole unit. Currently, many methods have been developed to evaluate the strength of CM structural elements. Much attention is paid to the problems of fracture mechanics of CM. Numerous different criteria for fractures of a composite product have been developed, but the question of their unambiguous selection still remains open. In this situation, in order to make a reasonable choice of this or that criterion, additional computational-experimental research in relation to a specific structure is necessary. In this chapter, some aspects of strength determination of a CM aircraft wing have been analyzed. Some aspects of such evaluation have been considered.