In order to maintain the high efficiency operation of the LLC resonant converter, the LLC resonant converter usually works near the resonant frequency, which makes the converter gain range narrow. To address this problem, this paper proposes a topology of primary-side Buck-LLC cascade converter and secondary-side special full-bridge rectifier, which is capable of realizing a wide range of voltage gains. The primary side of this topology adopts a synergistic control strategy of the front-stage Buck unit control and the back-stage LLC resonant converter, Namely, the front-stage realizes the closed-loop voltage stabilization function by PWM modulation, and the back-stage adopts the open-loop of the LLC to work at the point of resonance frequency. The overlapping conduction control method is introduced at the vice-side, and the voltage gain is adjusted by adjusting the overlapping duty cycle of the rectifier bridge switching tubes, so that the system can automatically switch the operation mode according to the output voltage, and the system can realize a 3-fold gain extension range. Theoretical derivation shows that all switching tubes of the system realize soft switching in a wide gain range. Combined with the state plane trajectory diagram, the voltage gain equation and soft-switching boundary conditions are derived. To validate the proposed scheme, an experimental prototype with DC300 V input and DC20-60 V/500 W output is built, and the experimental results and analysis verify the correctness and effectiveness of the system topology and control strategy.

To address the issues of low detection accuracy, high missed detection rate, and poor real-time performance in complex indoor and outdoor scenarios, where the instrument area occupies a small pixel ratio due to the long shooting distance, this paper proposes an improved pointer instrument detection algorithm based on YOLOv8, named GRCP-YOLOv8. First, a C2f_CGA module, integrated with the CGA attention mechanism, is designed to enhance the model's ability to express features at different scales and replace all C2f modules in the backbone network. Secondly, RFAConv is introduced to replace the conventional convolution layers, addressing the insufficient feature representation caused by parameter sharing in standard convolution modules. Subsequently, a new neck network structure, CCFPN is designed. By incorporating high-resolution feature maps extracted from the backbone network, it improves the model's capability to detect small targets, while reducing the number of channels in convolution layers via 1×1 convolutions, thus reducing the model's parameter count and computational complexity. Finally, a new detection head, RepHead, based on reparameterized convolution (RepConv), is introduced to reduce computational load and memory consumption during inference. Experimental results show that the proposed algorithm achieves accuracy, recall rate, and mAP@50 of 94.3%, 91.6%, and 92.5%, respectively, with recall and mAP@50 improving by 1.3% and 1.2% compared to the YOLOv8n model. The algorithm also reduces computational complexity and parameter count by 39% and 27%, respectively, while the model size is only 4.22 MB. These results demonstrate that the proposed algorithm not only improves detection accuracy but is also more suitable for deployment on edge devices.

Medium and long-term power load forecasting is a core link to ensure the stability and economy of power system planning and operation.Some studies convert the input data to the frequency domain through Fourier transform to obtain different signal components, thereby reducing the interference of noise. However, existing studies often indiscriminately handle all frequency-domain signals, causing the key frequency-domain components and irrelevant frequency-domain components to mix, which makes it difficult for the model to fully capture the features contained in the frequency-domain signals. Therefore, a multivariable long-term prediction model FTAformer that integrates frequency-domain analysis and attention mechanism is proposed. This model integrates time-domain and frequency-domain information and conducts collaborative modeling to enhance the model's ability to capture global features. Firstly, the input sequence is transformed into a frequency-domain signal by using the fast Fourier transform. A hierarchical filtering and isolation strategy is adopted to isolate the key frequency-domain components and suppress the noise. Then, the correlations among different variables are captured in the time domain through the multi-head attention mechanism, and the global representation of the sequence is modeled by using layer normalization and the feedforward network module. The experimental results show that on two public power load datasets, the predictive performance of this model is significantly higher than that of other benchmark models. Compared with the existing optimal model iTransformer, the mean square error and mean absolute error of the proposed method are reduced by 15.26% and 8.76% respectively in the multi-step prediction scenario, fully verifying the effectiveness and superiority of the collaborative modeling of frequency domain analysis and multi-head attention mechanism in medium and long-term power load forecasting.

Addressing the key challenges of insulator fault detection in drone-based power inspection scenarios, such as high missed detection rate for small targets, significant interference from complex backgrounds, and insufficient real-time performance, this study proposes an improved YOLOv10n detection model based on multi-scale feature collaborative optimization. By constructing a lightweight adaptive feature extraction network and a hierarchical fusion mechanism of multi-scale semantic enhancement architecture, dynamic deformable grouped convolution and channel recalibration strategies are adopted in the shallow network to enhance the sensitivity to micro-defect features, while a multi-branch dilated convolution pyramid and cross-dimensional attention mechanism are established in the deep network to build cross-scale associations, achieving a collaborative optimization of detection accuracy and computational efficiency. A shape-sensitive InSh-IoU loss function is proposed, which dynamically adjusts the weight coefficient of the bounding box shape to reduce the positioning error of targets with abnormal aspect ratios, enabling more accurate localization of insulators. Verified by a self-built insulator fault dataset, this model maintains real-time detection speed while achieving an average detection accuracy (mAP@0.5) of 97.12%, an improvement of 2.82% over the baseline model.

In order to solve the problems of slow convergence speed, low convergence accuracy and easy to fall into local optimization of artificial lemmings algorithm (ALA), a multi strategy improved artificial lemmings algorithm (IALA) is proposed. Firstly, Hammersley sequence is introduced to initialize the population of the algorithm, so that the initial population has better search ability; then the reverse differential mutation mechanism is used to improve the diversity of the population and enhance the ability of the algorithm to escape from the local optimum; finally, through the soft frost ice search mechanism, the algorithm takes into account the local and global characteristics in the optimization process, which improves the optimization ability and convergence speed of the algorithm. In order to verify the effectiveness of the improved algorithm, nine benchmark functions are selected to compare the improved algorithm. The comparison results show that IALA has faster convergence speed and higher convergence accuracy. Finally, the improved algorithm is applied to the simulation experiment of robot path planning on three kinds of complex maps. The results show that compared with the original algorithm ala, the improved algorithm IALA in the first kind of map, the optimal value of path decreases by 0.64%, and the average value decreases by 2.86%; in the second map, the optimal value of path decreased by 10.24%, and the average value decreased by 6.91%; in the last map, the optimal value of the path decreased by 2.6%, and the average value decreased by 1.3%. It is proved that the improved algorithm has better path optimization ability.

Aiming at the problems of small target size, fuzzy edges, and vulnerability to noise and background interference in defect areas of photovoltaic infrared images, an improved algorithm based on YOLOv11 was proposed. Firstly, a guided local-global spatial attention (GLGSA) module is designed to effectively integrate Local salient region information and Global context semantics to improve the discrimination of feature representation. Secondly, the GLGSA module was combined with the bidirectional feature fusion structure BiFPN to construct the GLGSA-BiFPN structure to improve the effect of multi-scale feature fusion. The P2 detection layer was added to enhance the detection ability of minimal targets. Finally, the NWD loss function is introduced to replace the original loss function to enhance the positioning accuracy of small targets. Experimental verification is carried out on the PV-HSD-2025 photovoltaic hot spot data set. The results show that the detection accuracy of the improved algorithm mAP₅₀ and mAP_50-95 is 9.1% and 5.6% higher than that of YOLOv11n. Effectively improve the accuracy of photovoltaic small target defect detection.

Steel defect detection is critical for industrial quality control, yet performance is constrained by multi-scale variations, small targets, and background interference. To enhance the accuracy and efficiency of the detection model, this paper proposes a defect detection network based on an improved version of YOLO11, named LiteSteel-YOLO. First, a Lightweight Multi-Scale Fusion module (C3k2-LMSF) is designed to enhance multi-scale defect perception through fused convolutional kernels and feature guidance mechanisms. Second, a spatial-channel aware upsampling module (SCAM) is proposed, which improves the robustness of small target detection and suppresses noise through channel reorganization and spatial offset operations. Finally, an Efficient-Head detector optimized via structural reconfiguration is introduced to maximize computational efficiency. Experimental results show that the LiteSteel-YOLO receives mAP@50 of 81.7% and 70.7% with inference speed of 338 and 530 FPS on the NEU-DET and GC10-DET datasets (surpassing YOLO11 by 4.0% and 2.3%). The proposed framework enhances the accuracy and efficiency of steel defect detection, providing a solution for industrial inspection scenarios.

Small object detection in UAV aerial imagery encounters critical challenges including extremely small target sizes, complex background interference, and insufficient feature representation. Addressing the limitations of existing RT-DETR models in small object feature extraction and multi-scale fusion, this paper proposes an adaptive multi-scale gated enhancement fusion DETR (MGEF-DETR). A multi-order cross-stage gated aggregation (MCGA) module is designed to achieve selective enhancement of small object texture features through adaptive gating mechanisms. A Micro-OmniPyramid feature pyramid is constructed by integrating space-to-depth (SPD) convolution sparse encoding and cross-stage enhanced spectral kernel (CESK) modules, establishing lossless transmission pathways for small object features. An enhanced feature correlation (EFC) module is introduced to optimize cross-scale feature fusion through grouped attention and multi-level reconstruction strategies. An inner-modified penalty distance IoU (IMIoU) loss function is designed to enhance boundary regression sensitivity for small objects. Experimental results on the VisDrone2019 dataset demonstrate that MGEF-DETR achieves improvements of 3.9% and 3.1% in mAP@0.5 and mAP@0.5:0.95 metrics respectively compared to the baseline RT-DETR, while reducing parameters by 13.6%. Validation on TinyPerson and CODrone datasets further confirms the generalization capability of the algorithm, indicating significant improvements in both accuracy and efficiency for small object detection in aerial scenarios while maintaining lightweight characteristics.

With the continuous expansion of drone application scenarios, small object detection in aerial images has become a research hotspot in the field of computer vision. In view of the problems that small object features are not obvious, complex backgrounds lead to false detection and missed detection, and the existing algorithms are difficult to balance detection accuracy and real-time performance, this paper proposes an aerial image small object detection algorithm FST-RTDETR based on RT-DETR to solve these problems. First, FasterNet is combined with the EMA attention mechanism, and the structure of the Basic Block module of the original module is redesigned to improve the network operation speed and the accuracy of visual tasks. Secondly, in order to solve the problems of excessive calculation and more time-consuming post-processing after adding the traditional P2 detection layer, this study propose to use the P2 feature layer based on the original CCFM architecture to obtain features rich in small object information through SPDConv and give them to P3 for fusion, and then use the CSP idea and Omni-Kernel to improve CSP-OmniKernel for feature integration, effectively learn the feature performance from global to local, and finally reduce the missed detection rate, false detection rate and improve the detection performance of small objects. Finally, in order to simplify the loss function calculation process, improve regression efficiency and accuracy, and have a more comprehensive loss consideration, this study use inner-MPDIoU to replace the original GIoU. Experiments on the improved algorithm on the VisDrone2019 dataset show that the FST-RTDETR model achieves a detection accuracy of 49.6%, which is 2.1% higher than the original RT-DETR model. The FST-RTDETR model significantly improves the object detection performance of drone images, improves model efficiency, and shows good performance compared to other algorithms.

Existing single-stage deep models for traffic accident detection often suffer from high false alarm rates and computational redundancy in highway scenarios, severely limiting their practical deployment. To address these issues, this paper proposes a two-stage traffic accident detection method tailored for highways, following a "stationary vehicle filtering+appearance-based recognition" strategy. In the first stage, YOLO11 and Bot-SORT are integrated to detect and track vehicles, and inter-frame speed analysis is used to identify stationary vehicles as potential accident candidates. In the second stage, an improved model named YOLO-EA is introduced to perform appearance-based detection exclusively on the stationary vehicles, combined with a multi-frame voting mechanism to enhance stability and robustness. Built upon the YOLO11 architecture, YOLO-EA incorporates an EAS-Stem module and an AWD-Conv module. The former enhances edge and contour extraction in the input stage, while the latter improves downsampling efficiency by retaining critical features and reducing computational cost. Experimental results show that YOLO-EA improves Precision, mAP@0.5 and mAP@0.5:0.95 by 10.9%, 3.4% and 2.8% respectively, while reducing parameter count by 21%. On the constructed accident video dataset, the proposed method achieves an accident recognition rate of 81.25%, with a 24.46% reduction in false alarm rate compared to single-stage detection strategies. This method achieves a favorable balance between accuracy and inference efficiency, demonstrating strong potential for real-world deployment.