In order to address the problems of high labor costs and low efficiency in manual inspection for tunnel foreign object detection， a tunnel foreign object detection algorithm based on improved YOLOv8n was proposed. Firstly， C2f_CA module was proposed with the incorporation of Coordinate Attention （CA） mechanism. In the module， by embedding positional information into channel attention， the network’s focus on the spatial distribution of features in the image was enhanced， thereby improving feature extraction capability of the network. Secondly， inspired by the concept of high-resolution network， a new feature fusion module HRNet_Fusion （High Resolution Net） was proposed to take extracted feature maps with different resolutions as four parallel branches and input them into the network， and multiple up-sampling， down-sampling， and fusion operations were performed to obtain comprehensive and accurate feature information. The above enhanced performance in small target detection and feature fusion significantly. Finally， the WIoU （Wise-IoU） loss function was introduced to reduce the harmful gradient effects of low-quality samples on the network， further improving model detection accuracy. Experimental results on a tunnel foreign object detection dataset indicate that the improved algorithm achieves mean Average Precision （mAP@0.5） of 79.9%， with a model size of 6.0 MB. Compared to YOLOv8n， the proposed algorithm has the mAP@0.5 enhanced by 6 percentage points， while the model size decreased by 0.2 MB， and the model parameters reduced by 0.379×10⁶.

The traditional multi-path load balancing algorithms cannot effectively perceive the running state of the network, cannot comprehensively consider the real-time transmission states of the links and most of them lack adaptability. In order to solve these problems, a Software Defined Network(SDN) adaptive multi-path Load Balancing Algorithm based on Spider Monkey Optimization(SMO-LBA) was proposed based on the idea of centralized control and whole network control of SDN. Firstly, the perceptul ability of data center network was used to obtain the multi-path real-time link state information. Then, based on the global exploration and local exploitation ability of spider monkey optimization algorithm, the link idle rate was used as the adaptability value of each path, and the paths were dynamically evaluated and updated by introducing the adaptive weight. Finally, the path with the lowest link occupancy rate in data center network was determined as the optimal forwarding path. The fat tree topology was selected to carry out the simulation experiment on Mininet platform. Experimental results show that SMO-LBA can improve the throughput and average link utilization of data center network, and realize the adaptive load balancing of the network.

Community detection is a useful tool for mining hidden information in social networks. And Label Propagation Algorithm (LPA) is a common algorithm in the community detection algorithm, which does not require any prior knowledge and runs fast. Aiming at the problem of the instability of community detection algorithm results caused by the strong randomness of label propagation algorithm, an improved Label Propagation Algorithm based on Random Walk (LPARW) was proposed. Firstly, according to the random walk on the network, the importance order of nodes was determined, so as to obtain the update order of nodes. Secondly, the update sequence of nodes was traversed, and the similarity calculation between each node and the node before it was performed. If the node and the node before it were neighbor nodes and the similarity between them was greater than the threshold, then the node before it was selected as the seed node. Finally, the label of the seed node was propagated to the rest of the nodes in order to obtain the final division result of the communities. The proposed algorithm was comparatively analyzed with some classic label propagation algorithms on 4 labeled networks and 5 unlabeled real networks. Experimental results show that the proposed algorithm is better than other comparison algorithms on classic evaluation indicators such as Normalized Mutual Information (NMI), Adjusted Rand Index (ARI) and modularity. It can be seen that the proposed algorithm has the good community division effect.

With the improvement of standardization and intelligence level of secondary equipment, a kind of communication network more efficient and smarter is needed in smart substation to meet the substation operation and maintenance requirements, to achieve equipment plug and play, intelligent monitoring, subnet secure isolation and element interchange. For the application needs of substation network unified management, security isolation between subnets and equipment compatibility and interchangeability, a Software Defined Network (SDN)-based substation network architecture was proposed. IEC 61850 and OpenFlow protocols were used for network architecture design. OpenFlow controller was used to control and isolate the individual subnets to implement network device management and subnet secure isolation. The experimental results show that precise traffic control based on service types, and securely data isolation can be implemented with the proposed substation SDN-based network architecture. It has a very important application value for promoting the operation and maintenance level of smart substation.

The issue of time delay estimation of narrowband audio frequency signal was studied, the signal model of time delay estimation was given, the traditional time delay estimation method of generalized correlation and the time delay estimation method based on the Hilbert transformation were studied, and then, a time delay estimation method based on the fractional Hilbert transformation was constructed in this paper. Finally, the mean square error curve of time delay estimation based on different fractional order was given through computer simulation. In the best case, compared with the estimation method of generalized correlation and Hilbert transformation, the simulation results show that the fractional Hilbert transformation method has better performance.