For the problem of abnormal opening and closing states of high-voltage isolation switches in substations due to factors such as mechanical wear and electrical quantity changes，an atom search optimization（ASO）algorithm is proposed to identify the abnormal opening and closing status of high-voltage disconnectors in substations.Infrared and visible light cameras are used to capture the status images of high-voltage isolation switches，and the mapping relationship between image feature points is established through bilateral filtering and image registration.The joint weighted average method is used to achieve decision level fusion of images.The optimal segmentation threshold is determined by combining the gradient size and attribute vector of the centroid pixel neighborhood points of the image，and the high-voltage isolation switch feature area is extracted accordingly.Support vector machine algorithm is adopted to construct an abnormal state recognition model，and ASO algorithm is introduced to obtain model parameters，and to optimize model recognition performance，and identify the opening and closing abnormal states of the isolation switch by inputting the pixel values of the isolation switch feature area.Experiment results show that under the application of the studied method，the false positive rate of the obtained recognition results is less than 2%，and the recognition accuracy is relatively high.

In order to delve into the relationships of information flow among various brain regions within causal brain networks，a causal network emotion recognition methodology grounded in electroencephalogram（EEG）signals across communities is proposed.Firstly，time-frequency domain features are extracted from the preprocessed EEG signals.The partial directed coherence（PDC）method is adopted to build the casual brain network，and the Infomap community detection algorithm is used to divide the communities of the brain network.Next，a graph representation of the brain network is formulated，in which the causal interactions，quantified by PDC values between different communities，serve as the edge features，while the node features are defined by the weighted average differential entropy computed for each respective community. Finally，this constructed graph data is fed into a graph convolutional neural network for the ultimate task of emotion classification and recognition.Experimental results demonstrate that compared with the conventional full-channel causal emotion recognition approaches，the proposed method decreases the computational complexity by leveraging the directed causal information between the brain sections，and successfully maintains a high level of emotion recognition accuracy.

For the low detection accuracy problem of small targets and low-contrast defects on the surface of latex gloves，an improved YOLOv（you only look once version）8n algorithm for defects detection on the surface of latex gloves is proposed.The receptive field attention convolution module is introduced in the feature extraction network to dynamically adjust the spatial feature weights within the receptive field，and to enhance the network's focus on defect features. The C2f module is redesigned based on the proposed multi-scale convolution，which captures the contextual information from shallow features through multi-scale convolutional kernels，and improves the network's ability to extract shallow features.The context and the spatial feature calibration network are added to the feature fusion network，where feature calibration refines and aligns contextual information and spatial features，and further enhances the representation of defect features.Experimental results show that on the homemade dataset，the mean average precision（mAP）of the improved algorithm reaches 93.2%，which is 3.1%higher than that of YOLOv8n.It effectively improves the surface defect detection accuracy of latex gloves.In addition，on the VisDrone2019Det and steel defect detection datasets，the mAP reaches 36.1%and 79.8%，respectively，which are 1.1%and 2.7%higher than that of YOLOv8n，and further verifies the effectiveness of the improved algorithm.

To address the issues of missed detection and low detection accuracy in X-ray weld defect detection，an improved YOLOv8-based detection method is proposed.Firstly，the efficient multi-scale attention（EMA）mechanism is improved by replacing the 3×3 convolutional kernel with a 5×5 kernel to expand the receptive field，and replacing the average pooling with the multi-scale pooling to extract multi-scale features.The improved EMA module is embedded into the backbone network to enhance the model's ability to detect defects at various scales.Then the spatial pyramid pooling fast module is improved by introducing adaptive average pooling and max pooling layers，to improve the perception of weld edge information.Finally，in the neck part，Dual convolution is used to replace traditional convolution，to reduce the parameter number of the model.The WIoU（wise intersection over union）loss function is adopted to replace the CIoU（complete intersection over union）loss function to improve the convergence speed of the model. Experimental results show that，compared to YOLOv8n，the proposed algorithm reduces the number of parameters by 4.02%and increases the mean average precision by 5.9%，which is well-suited for X-ray weld defect detection tasks.