To improve the accuracy and real-time performance of detection algorithms in Advanced Driver Assistance Systems (ADAS) under foggy conditions, this paper introduces HR-YOLO, an improved YOLO-based model specifically designed for vehicle and pedestrian detection. To enhance detection performance under complex meteorological conditions, several critical modules have been optimized. First, the Efficient High-Precision Defogging Network (EHPD-Net) is introduced to strengthen feature extraction. Inspired by the Global Attention Mechanism (GAM), the Enhanced Global-Spatial Attention (EGSA) module is incorporated to effectively improve the detection of small targets in foggy conditions. Second, the Depth-Normalized Defogging Network (DND-Net) is applied to enhance image quality. Additionally, the Dynamic Sample (DySample) module is integrated into the neck network, complemented by optimizations to the convolution and C2f modules, which significantly improve feature fusion efficiency. Furthermore, The Wise Intersection over Union (WIoU) loss function is introduced to enhance target localization accuracy. The robustness and accuracy of HR-YOLO were validated through experiments on two foggy weather datasets: RTTS and Foggy Cityscapes. The results indicate that HR-YOLO achieved a mean Average Precision (mAP) of 79.8% on the RTTS dataset, surpassing the baseline by 5.9%. On the Foggy Cityscapes dataset, it achieved an mAP of 49.5%, representing a 9.7% improvement over the baseline. This model serves as an effective solution for target detection tasks under foggy conditions and establishes a foundation for future advancements in this field.

The problem of two-dimensional irregular packing involves the arrangement of objects with diverse shapes and sizes within a given area. This challenge arises across various industrial sectors, where effective packing optimization can yield cost savings, enhanced productivity, and reduced material waste. Existing methods for addressing the two-dimensional irregular packing problem encounter several challenges, such as limited computing resources, a prolonged solving time, and the propensity to converge to local optima. To address this issue, this study proposes a hybrid algorithm called the GA-LP algorithm to optimize the two-dimensional irregular packing problem in the manufacturing industry. The algorithm combines the global search capability of a genetic algorithm with the precise solving characteristics of linear programming. Matheuristics merges the advantages of metaheuristics, such as genetic algorithms, and mathematical programming, such as linear programming. The algorithm employs the no-fit-polygon technique along with the bottom-left and lowest-gravity center mixing placement strategies to acquire an initial solution via the utilization of a genetic algorithm. The algorithm then optimizes the solution obtained by the genetic algorithm using linear programming to obtain the final packing result. Experimental results, drawn from a real case involving the European Special Interest Group on Cutting and Packing (ESICUP) demonstrate that the GA-LP algorithm outperforms two hybrid algorithms from the relevant literature. Compared with recent methods, this algorithm can increase the best and average utilization rates by up to 5.89% and 4.02%, respectively, with important implications for improving work quality in areas such as packing and cutting.

Accurate prediction of bearing failures is crucial for reducing maintenance costs and enhancing production efficiency in rotating machinery. However, the variable speed conditions and complex working environments encountered during operation pose significant challenges to fault diagnosis. Problems such as domain shift and insufficient sample quantity may occur during fault diagnosis under cross-working conditions, which can decrease the accuracy and generalization of deep learning algorithms. In this paper, we introduce a fault diagnosis framework grounded in meta-learning. Centered on a dual-channel feature fusion network and employing a meta-learning training paradigm, the framework not only performs well in cross-condition fault diagnosis tasks but also demonstrates superior performance in few-shot learning scenarios. Firstly, dual-channel network is used to extract the classification features of different domains, and the features are fused. Next, training is conducted using a meta-learning strategy to acquire prior knowledge, enabling rapid model adaptation to cross-working conditions and addressing the challenge of limited training samples. Finally, two public rolling bearing data sets are used to demonstrate the efficacy of the proposed method across different operational conditions. Prior to this, we selected the appropriate sample length and fusion domain through experimental validation. The proposed method also has good fault diagnosis accuracy in cross-device tasks. The experimental results verify the effective classification capability and robustness of the proposed method. Furthermore, comparisons with other meta-learning approaches confirm the superior performance of our method. The ablation experiments validated the importance and irreplaceability of each component of the proposed method.

Cemented carbide circular saw blades are widely used for wood cutting, but they often suffer from vibration and noise issues. This study presents a multi-objective optimization method that integrates ANSYS and MATLAB to optimize the design of noise reduction slots in circular saw blades. A mathematical model was developed to correlate the emitted sound power with the overall vibration intensity. A multi-objective optimization model was then formulated to map the slot shape parameters to the deformation, equivalent stress, and vibration intensity during sawing. The ABAQUS thermal–mechanical coupling analysis was used to determine the sawing force and temperature field. The NSGA-II algorithm was applied on the ANSYS–MATLAB platform to iteratively compute slot shape parameters and conduct optimization searches for a globally optimal solution. Circular saw blades were fabricated based on the optimization results, and experimental results showed a significant reduction in sawing noise by 2.4 dB to 3.0 dB on average. The noise reduction effect within the specified frequency range closely agreed with the simulation results, validating the method’s efficiency. This study provides a feasible and cost-effective solution to the multi-objective optimization design problem of noise reduction slots for circular saw blades.

High-precision noise radiation characterization is essential for designing circular saw blades aimed at vibration and noise reduction. However, previous studies have generally overlooked the effects of thermal stress, centrifugal force, and cutting force on the acoustic performance of saw blades during the cutting process. This paper proposes a multi-physics field coupling analysis method based on FEM/BEM joint simulation technology. By performing thermal-force coupling analysis to obtain the sawing vibration response, the resulting vibration acceleration is introduced into the acoustic–solid coupling model to predict the frequency-domain characteristics and spatial distribution of sawing noise. The validity of the simulation results is verified through sawing noise test experiments. The study shows that the circular saw blade radiates the most noise when sawing in the mid-frequency band from 500 Hz to 8000 Hz, while the noise radiation efficiency is lower in both the low-frequency band and the high-frequency band. The multi-physical field coupling simulation method can significantly improve the calculation accuracy of the frequency-domain characteristics of sawing noise. The vibration noise of the circular saw blade shows clear directional distribution at different excitation frequencies, while the directionality of the experimentally measured noise is less distinct. Furthermore, based on the noise radiation characteristics, this study explores the design strategies of noise reduction slots and sound barriers, which provide references for the noise control and vibration damping design of circular saw blades.