Despite being indispensable devices in the electronic manufacturing industry, printed circuit boards (PCBs) may develop various soldering defects in the production process, which seriously affect the product’s quality. Due to the substantial background interference in the soldering defect image and the small and irregular shapes of the defects, the accurate segmentation of soldering defects is a challenging task. To address this issue, a method to improve the encoder–decoder network structure of UNet is proposed for PCB soldering defect segmentation. To enhance the feature extraction capabilities of the encoder and focus more on deeper features, VGG16 is employed as the network encoder. Moreover, a hybrid attention module called the DHAM, which combines channel attention and dynamic spatial attention, is proposed to reduce the background interference in images and direct the model’s focus more toward defect areas. Additionally, based on GSConv, the RGSM is introduced and applied in the decoder to enhance the model’s feature fusion capabilities and improve the segmentation accuracy. The experiments demonstrate that the proposed method can effectively improve the segmentation accuracy for PCB soldering defects, achieving an mIoU of 81.74% and mPA of 87.33%, while maintaining a relatively low number of model parameters at only 22.13 M and achieving an FPS of 30.16, thus meeting the real-time detection speed requirements.

This paper is concerned with trajectory planning for unmanned aerial vehicle in a three-dimensional complex workspace. Biogeography-based optimization algorithm is widely used in solving practical problems because of its fewer parameters, fast convergence rate, and good global optimization ability. In this paper, some improvements, modifying migration and mutation operations are made on the biogeography-based optimization algorithm to make it suitable for solving the trajectory planning problem. The optimal trajectory obtained by the improved algorithm can be used to generate the reference trajectory. Then, a control scheme of unmanned aerial vehicle based on the Lyapunov theory and radial basis function neural network is formed to track the reference trajectory. The improved trajectory algorithm generates the shortest trajectory and the time consumption is the lowest. Finally, the designed control scheme makes the unmanned aerial vehicle track the different trajectories quite well, the effectiveness of it can be illustrated by algorithm accuracy and electricity consumption.

Accurately predicting PM2.5 concentration can effectively avoid the harm caused by heavy pollution weather to human health. In view of the non-linearity, time series characteristics, and the problem of large multi-step prediction errors in PM2.5 concentration data, a method combining Long Short-term Memory Network and Convolutional Neural Network with Time Pattern Attention mechanism (TPA-CNN-LSTM) is proposed. The method uses historical PM2.5 concentration data, historical meteorological data, and surrounding station data to predict the future 6-hour PM2.5 concentration of air quality monitoring stations. Firstly, CNN is used to obtain the spatial characteristics between multiple stations, secondly, LSTM is added after CNN to extract the temporal changes of non-linear data, and finally, to capture the key features of temporal information, Temporal Pattern Attention mechanism (TPA) is added. TPA can automatically adjust weights based on the input of each time step, and select the most relevant time step for prediction, thereby improving the accuracy of the model. An example analysis is conducted on the measured data of Beijing's air quality stations in 2018, and compared with other mainstream algorithms. The results show that the proposed model has higher prediction accuracy and performance.

In recent years, remote sensing imagery has become indispensable for applications such as environmental monitoring, land use classification, and urban planning. However, the physical constraints of satellite imaging systems frequently limit the spatial resolution of these images, impeding the extraction of fine-grained information critical to downstream tasks. Super-resolution (SR) techniques thus emerge as a pivotal solution to enhance the spatial fidelity of remote sensing images via computational approaches. While deep learning-based SR methods have advanced reconstruction accuracy, their high computational complexity and large parameter counts restrict practical deployment in real-world remote sensing scenarios—particularly on edge or low-power devices. To address this gap, we propose LSANet, a lightweight SR network customized for remote sensing imagery. The core of LSANet is the large separable kernel attention mechanism, which efficiently expands the receptive field while retaining low computational overhead. By integrating this mechanism into an enhanced residual feature distillation module, the network captures long-range dependencies more effectively than traditional shallow residual blocks. Additionally, a residual feature enhancement module, leveraging contrast-aware channel attention and hierarchical skip connections, strengthens the extraction and integration of multi-level discriminative features. This design preserves fine textures and ensures smooth information propagation across the network. Extensive experiments on public datasets such as UC Merced Land Use and NWPU-RESISC45 demonstrate LSANet’s competitive or superior performance compared to state-of-the-art methods. On the UC Merced Land Use dataset, LSANet achieves a PSNR of 34.33, outperforming the best-baseline HSENet with its PSNR of 34.23 by 0.1. For SSIM, LSANet reaches 0.9328, closely matching HSENet’s 0.9332 while demonstrating excellent metric-balancing performance. On the NWPU-RESISC45 dataset, LSANet attains a PSNR of 35.02, marking a significant improvement over prior methods, and an SSIM of 0.9305, maintaining strong competitiveness. These results, combined with the notable reduction in parameters and floating-point operations, highlight the superiority of LSANet in remote sensing image super-resolution tasks.

The expanding market scale of the insulated gate bipolar transistor as a new type of power semiconductor device has higher insulated gate bipolar transistor soldering requirements. However, there are some small bubbles difficult to detect. The accuracy and speed of existing detection algorithms are difficult to meet the requirements of automated quality monitoring. For solving these problems, a detection data set of solder layer images captured by X-ray and labeled was made and an improved algorithm based on YOLOv5 was proposed, which can detect defects accurately and at a fast speed. The main contributions of this research are as follows: (1) a tiny bubble detection layer that further integrates the deep feature information and shallow feature information is added to improve the model’s ability to detect small bubbles; (2) to speed up model convergence by optimizing anchor frame parameters; (3) we change the EIoU loss function as the bounding box loss function to solve the sample imbalance of the dataset; (4) combine the Swin Transformer structure to improve the convolution module and form a new feature extraction module, and introduce it into the backbone layer to improve the detection accuracy. The results of the experiment show that the overall performance of the improved network is better than the original and mainstream detection algorithms. The accuracy of the improved YOLOv5_SEST has reached 94.5% and 5.6% improvement in mAP for common bubble defect detection compared to the original algorithm. Our model size is only 5.3 MB, and the detection speed reaches 110 f/s. Therefore, the improved YOLOv5_SEST can well meet the requirements of automated quality monitoring of insulated gate bipolar transistors.