Search Results (3)

In this paper, a fault recovery strategy for a distribution network based on a pigeon-inspired optimization (PIO) algorithm is proposed to improve the recoverability of the network considering the increased proportion of distributed energy resources. First, an improved Kruskal algorithm-based island partitioning scheme is proposed considering the electrical distance and important load level during the island partitioning process. Secondly, a mathematical model of fault recovery is established with the objectives of reducing active power losses and minimizing the number of switching actions. The conventional PIO algorithm is improved using chaos, reverse strategy, and Cauchy perturbation strategy, and the improved pigeon-inspired optimization (IPIO) algorithm is applied to solve the problem of fault recovery of the distribution network. Finally, simulation analysis is carried out to verify the effectiveness of the proposed PIO algorithm considering a network restauration problem after fault. The results show that compared with traditional algorithms, the proposed PIO algorithm has stronger global search capability, effectively improving the node voltage after restauration and reducing circuit loss. Full article

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network’s learning rate to derive the optimal group’s weight information and ultimately improve the model’s detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time. Full article

The laser gyroscope of Laser Inertial Navigation System (LINS) eliminates the influence of the locked zone with mechanical dither. The output information of laser gyroscopes must be filtered before use to eliminate vibration noise. Laser gyroscope filters are designed according to the instrument accuracy, calculation capacity, vibration frequency, system dynamic characteristics, and other indicators. In this paper, a pigeon-inspired optimization (PIO) method is proposed for use in filter design. The PIO method can flexibly design filters with excellent performance according to the indicator requirements. In the method, the constraints and indicators of the amplitude, phase and order of the LINS filter are firstly confirmed according to the application requirements; then, the objective function is established, and the parameters to be optimized of the PIO are set according to the order of the filter; finally, the PIO method is used to obtain filter parameters that can satisfy the constraints and achieve better performance. Referring to the idea of biological evolution mechanisms, we propose a new improved pigeon-inspired optimization method based on natural selection and Gaussian mutation (SMPIO), which can obtain more stable results and higher accuracy. In the SMPIO method, the particle swarm is firstly selected by natural selection, that is, the particles are sorted according to the fitness function, and some particles with poor fitness are replaced by those with better fitness; then, all particles are subjected to Gaussian mutation to obtain a better global optimum. SMPIO method can flexibly design filters according to the comprehensive requirements of laser gyro performance and navigation control indicators, which cannot be achieved by traditional filter design methods; the improvement based on natural selection and Gaussian mutation enables SMPIO to have faster convergence speed, and higher accuracy. Full article