Small objects in UAV aerial images have low pixel proportions, are vulnerable to interference from complex backgrounds, and exhibit large scale variations, which often lead to missed detections and insufficient accuracy in existing methods. Therefore, an improved algorithm, YOLO11⁃RHO, is proposed for UAV aerial imagery based on YOLOv11. First, online convolutional re⁃parameterization (OREPA) with frequency⁃prior initialization is introduced into the backbone network to enhance the model's representation of high⁃frequency edge and texture information through spatial⁃frequency selectivity. Second, the RepNCSPELAN4_high module is used to optimize gradient propagation paths and construct a direct gradient pathway, thereby alleviating the attenuation of small⁃object features in deep network layers. Third, a hierarchical feature fusion block (HFFB) is integrated into the feature⁃fusion network. By combining a spatial⁃ and channel⁃decoupled adaptive attention mechanism, the block strengthens small⁃object representation and achieves multiscale information alignment. Finally, the overall architecture is designed for lightweight real⁃time deployment. Comparative and ablation experiments on the VisDrone2019 dataset show that the improved model achieves excellent performance, with mAP@0.5=0.401 and mAP@0.50∶0.95=0.245. It also maintains a lightweight architecture with 6.22 M parameters and 15.1 GFLOPs, effectively balancing high detection accuracy with real⁃time edge⁃device operation and satisfying practical end⁃side deployment requirements.

This paper proposes a distributed coordinated optimization method for multi?microgrid systems based on the Alternating Direction Method of Multipliers (ADMM). The proposed model comprehensively accounts for generation costs, energy storage operation, and inter?microgrid interactions, while employing second?order cone relaxation techniques to address nonlinear power flow constraints. By optimizing the ADMM iteration process and parameter selection, the method significantly improves computational efficiency while protecting data privacy through its distributed architecture. Case studies demonstrate that the method converges within only five iterations, achieves a 76.7% improvement in computational efficiency compared with centralized optimization, and maintains a solution accuracy within a 0.34% deviation from the global optimum. Compared to linear programming methods, the ADMM enhances voltage regulation performance by 40.0% and reduces line losses by 15.5%. The method exhibits excellent scalability with computational complexity increasing linearly with the number of microgrids, is applicable to various network topologies, and requires sharing only boundary interaction information, thus providing effective technical support for multi?microgrid coordination optimization.

An improved ant colony algorithm is proposed to address issues such as susceptibility to local optima and slow convergence speed. Firstly, the relationship between the current target node and the next target node and the normal distribution function are introduced into the heuristic function, enhancing the algorithm's search capability in the early stages. In addition, by introducing an inflection point factor, the diversity of directional selection is enhanced. Secondly, an adaptive dynamic pheromone volatility coefficient is proposed to adjust the pheromone evaporation rate adaptively, modifying the pheromone update rules. Finally, simulation experiments were conducted using Matlab to compare the traditional ant colony algorithm and the improved ant colony algorithm on three different grid maps. The experimental results demonstrate that, compared with the traditional ant colony algorithm, the improved algorithm exhibits advantages such as faster convergence speed, shorter paths, and fewer inflection points.

Aiming at the problems of low positioning accuracy and poor stability in multi?effect and non?line?of?sight conditions, a new indoor positioning system Chan?Taylor?Unscented Kalman Filter (C?T?UKF) combined positioning algorithm is designed based on the time of flight positioning algorithm, combined with the Chan?Taylor (C?T) cooperative positioning algorithm, and fused with the Unscented Kalman Filter (UKF) algorithm. The system mainly consists of positioning base stations, positioning tags, wireless communication systems and upper computers, etc. The Chan algorithm is adopted to calculate the distance measured by the time of flight method, and the calculated coordinates are used as the initial value of the Taylor algorithm for iterative calculation. The iterative results are smoothed by the Unscented Kalman algorithm. The results show that the positioning system based on this algorithm has the characteristics of high accuracy, strong stability and low cost. The average positioning errors in line?of?sight and non?line?of?sight conditions are less than 0.17 m and 0.20 m respectively, and it can be applied to high?precision positioning scenarios.

In view of the huge scale of urban underground pipe network, the traditional manual detection method can no longer meet the needs of the current projects. In this paper, the MobileNetv3?YOLOv7 network model is proposed as the algorithm for target detection of underground pipeline defects to improve the accuracy and speed of detection. First, the pipeline image dataset is preprocessed, and the input image is grayscale and resampled to balance the number of samples. Secondly, the lightweight network MobileNetv3 and YOLOv7 network frameworks are combined to increase the BiFPN feature pyramid structure to improve accuracy. Then, in terms of data processing, Mosaic data augmentation is used to improve the robustness of the model. Finally, a comparative experiment with the YOLOv7 network model is designed to verify the feasibility of the model. In this paper, the MobileNetv3?YOLOv7 network model is verified under the framework of Pytorch experiment, and the experimental results show that the model greatly reduces the amount of parameter calculation and improves the average accuracy.

In order to solve the problems of short life cycle and low data throughput caused by too fast energy consumption in cluster head node selection of LEACH protocol in wireless sensor networks, a leach optimization algorithm based on cluster head node energy balanced selection is proposed. The algorithm selects ordinary nodes with high residual energy in WSNs as cluster head nodes, and considers the distance between ordinary nodes and cluster head nodes and between cluster head nodes and base station, as well as the residual energy and average energy of all nodes to select communication mode and transmission path. The proposed algorithm is simulated by MATLAB. The simulation results show that in the 100 m×100 m small and monitoring area and 200 m×200 m large and monitoring area, compared with LEACH, DEEC and IMP?LEACH algorithms, the algorithm reduces the energy consumption of nodes in WSNs, prolongs the life cycle and improves the data throughput.