Real-time detection and accurate segmentation of chip pads are important tasks to ensure chip alignment and position correction. To address the challenges of small target chip pad detection, segmentation accuracy and model lightweight, this paper proposes a lightweight chip pad instance segmentation algorithm based on an improved YOLOv8-seg, named YOLOv8-seg-CP (chip pad). Firstly, we integrate the next-generation lightweight StarNet into the original backbone network to enhance fine feature capture capabilities while reducing the number of parameters. Then, we construct the C2f-Star module in the neck network, which enhances the feature extraction performance for small chip pad targets. This maintains accuracy, reduces computational load, and improves detection and segmentation speed. On this basis, we introduce a lightweight shared convolution segmentation head (LSCSH), significantly reducing both parameter count and computational load while enhancing segmentation performance. Additionally, we propose a CGCAFusion convolutional attention fusion module. This module uses a content-guided convolutional attention fusion mechanism to dynamically adjust attention weights based on the content of input features, capturing both global and local feature information and enhancing multimodal feature fusion. Experiments on the chip pad dataset demonstrate that our algorithm achieves a detection and segmentation accuracy of 89.8%. The model size, parameters, and FLOPs are 3.7 M, 1.7 M, and 8.2 G respectively, representing reductions of 45.6%, 50%, and 31.7% compared to the baseline model. The FPS is 1399.3, an improvement of 25.8% over the baseline model. The inference time is 0.72ms, which is 0.18ms less than the baseline model. Extensive experimental results on COCO, carparts-seg, and crack-seg datasets further show that the improved YOLOv8n-seg model outperforms many existing advanced methods in terms of performance. This approach holds significant industrial application value for fully automated chip testing and sorting integrated production.

We study the synchronizability of weighted aging scale-free networks with non-normalized and asymmetrical coupling matrices. We found that the synchronizability of such networks is improved when the couplings from older to younger nodes become dominant, where the out degrees of the nodes are heterogeneous and their in degrees are homogeneous, and that the synchronizabilty of the networks is seriously weakened or even lost when the couplings from younger to older nodes become dominant, where the out degrees of the nodes are homogeneous and their in degrees are heterogeneous. We also found that both the heterogeneity of nodes and a smaller average degree can improve the synchronizability of the networks with some appropriately chosen weighting parameter values. We finally show an example of the coupled Lorenz systems for illustration and verification of the theoretical analysis.

研究了带负载电容的DC-DC buck变换器的分岔行为及其混沌控制问题，提到了一些新的有意义的结果.随着负载电容的增加，这种电路系统出现周期叠加序列窗口，且系统混沌运动的参数空间的测度逐渐趋于零，而且系统处在周期运动区时其电压转换效率高于系统处在混沌运动区时的电压转换效率，这些结果对于实际电路的设计具有较好的参考价值.同时，采用外加周期脉冲控制方法，能有效地实现该电路系统中的混沌控制.

Based on the community discovery method in complex network theory, a power grid partition method considering generator nodes and network weightings is proposed. Firstly, the weighted network model of a power system is established, an improved Fast-Newman hierarchical algorithm and a weighted modular Q function index are introduced, and the partitioning algorithm process is practically improved combined with the characteristics of the actual power grid. Then, the partition results of several IEEE test systems with the improved algorithm and with the Fast-Newman algorithm are compared to demonstrate its effectiveness and correctness. Subsequently, on the basis of subnet partition, two kinds of network attack strategies are proposed. One is attacking the maximum degree node of each subnet, and the other is attacking the maximum betweenness node of each subnet. Meanwhile, considering the two traditional intentional attack strategies, that is, attacking the maximum degree nodes or attacking the maximum betweenness nodes of the whole network, the cascading fault survivability of different types of networks under four attack strategies is simulated and analyzed. It was found that the proposed two attack strategies based on subnet partition are better than the two traditional intentional attack strategies.