To address the lack of real-time monitoring for wind turbine pitch control system power supplies and the cumbersome replacement procedures during failures, this paper proposes a hot-swappable dual-module switching power supply system based on microcontroller control. This solution enables faulted power supply replacement without system shutdown. This solution adopts a modular dual-power-supply redundant architecture. The power supply is designed based on a flyback circuit with modular components, featuring a wide input voltage range of 20~80 V and a stable output of up to 24 V/3 A. It integrates an STM32 microcontroller and TPS2491 hot-swap chip, enabling automatic switching during power failures and supporting rapid hot-swap replacement of faulty units. A monitoring platform developed using the Bootstrap5 framework was established to achieve intelligent power supply monitoring and management. Experimental results demonstrate that the system achieves short switching times (≤10 ms under full load conditions) and minimal voltage dips during power failures. Concurrently, the monitoring platform enables real-time power status monitoring, fault alerts and operational data analysis, thereby enhancing the power supply reliability of the variable pitch system.

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.

Focused on the issue that multimodal emotion recognition in conversation (MERC) is difficult to effectively capture cross-modal semantic associations in conversation rounds and has limited discrimination ability for minority classes and semantically confusing classes of emotions, a new multimodal sentiment analysis model (FuseNet) is proposed. This model adopts the bidirectional attention dialogue encoder (BiDRN) to capture the context dependency of the dialogue, effectively integrates audio and visual cues from different speakers, and realizes dynamic multimodal fusion through the Hi-gated fusion module based on the hierarchical gated mechanism. Meanwhile, class-aware multimodal contrastive (CAMC) loss is introduced to enhance the inter-class discriminability and improve the discrimination ability of minority classes and semantically similar sentiment categories. Experimental results on the two benchmark ERC datasets of IEMOCAP and MELD show that compared with the current advanced model CORECT, the F1 score of the proposed framework has improved by 2.91% and 2.00%, respectively, which are better than the existing baseline model in terms of classification performance in most emotions, especially in identifying a few classes and semantic similar categories of emotions.

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.

For coastal estuary water quality monitoring environments, traditional conductivity sensors suffer from issues such as bulky size and susceptibility to corrosion. This paper proposes a non-contact seawater conductivity measurement method based on single-coil sweep-frequency resonant impedance measurement. A coil equivalent circuit model in seawater environments was established, with in-depth analysis of the mechanism by which seawater eddy current losses affect system resonance characteristics. It elucidates the linear mapping relationship between resonant equivalent impedance and seawater conductivity under resonant conditions. Finite element simulation was employed to perform linear fitting on simulated data, validating the accuracy of theoretical derivations. Building on this, a sweep-frequency-based conductivity measurement system was constructed, achieving precise extraction of resonant point impedance. Experimental results demonstrate that in low-conductivity environments (saltwater intrusion), this method maintains consistent high measurement sensitivity, with a maximum fitting error of merely 0.0417 mS/cm. Compared to existing research, the proposed approach significantly enhances detection precision for subtle conductivity variations while improving anti-contamination capabilities. Furthermore, this method enables pre-calculation of fitting parameters via simulation software, thereby reducing human and material resources required for sensor calibration and optimizing sensor fabrication processes. It offers a novel solution for estuary water quality monitoring characterized by low-cost, high-reliability, and high-sensitivity.

This paper proposes a superconducting parallel-nanowire dual-resolution single-photon detector capable of simultaneously achieving photon-number resolution and spatial-position resolution under a single-output readout scheme. The detector consists of N superconducting nanowire units connected in parallel. Each unit incorporates a uniquely valued marking resistor in parallel to form an asymmetric resistor network, along with a series resistor of identical value. The entire array is biased by a common current source and read out through a single output channel. Taking a four-pixel structure as an example, with gradient-distributed shunt resistors (100, 200, 400, and 800 Ω) and a 50 Ω series resistor, LTspice simulations demonstrate that the superposition of response pulse amplitudes enables simultaneous discrimination of both photon number and spatial location, allowing up to 4-photon events and 15 distinct spatial response patterns to be identified. Further analysis indicates that the proposed structure effectively suppresses current shunting and latching effects commonly found in conventional parallel-nanowire detectors, thereby enhancing operational stability, albeit at the cost of reduced output signal amplitude and signal-to-noise ratio. This study provides a novel and feasible technical pathway for developing dual-resolution PNDs, offering a new perspective for future large-scale, high-count-rate, and low-SWaP-C multifunctional PNDs with full-information acquisition capabilities, thereby broadening potential applications in quantum imaging, lidar, and quantum communication.

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.

Electric vehicle charging load forecasting supports power dispatch decisions by addressing load fluctuations from widespread EV grid integration. A new method for predicting short-term EV charging loads is proposed to enhance power grid stability and reliability by improving load forecasting accuracy. First, historical load data is decomposed into subcomponents using VMD, then combined with temperature data and input into multiple TCN-LSTM branches for feature extraction, simplifying EV load sequence complexity. Secondly, a two-stage attention mechanism enhances the LSTM structure, improving load characteristic capture at specific times and feature dimension fusion, boosting complex load pattern recognition. Finally, a time conversion prediction module integrates results via a fully connected layer to enhance prediction accuracy and reduce errors. Case study analyzes real EV charging station load data from a Shaoxing community. Experimental results show the proposed method reduces MSE by 68%, MAE by 60%, and improves the performance index by 4%, demonstrating strong predictive performance.

Aiming at the problems that the electromagnetic levitation system is easily affected by external disturbances and the inherent contradiction of the integer order PD in the traditional linear active disturbance rejection control, this paper proposes a fractional order linear active disturbance rejection control method. The linear extended state observer is used to estimate the total disturbance of the system in real time, and a fractional order differential operator is introduced into the position loop control law. By utilizing the characteristic that its order can be continuously adjusted within the interval (0, 2), the requirements of phase and amplitude in the frequency domain are flexibly adapted. Theoretical analysis shows that fractional-order linear active disturbance rejection controller can simultaneously enhance the disturbance suppression ability in the low-frequency band and suppress the high-frequency noise amplification effect. Simulation and experimental results show that, compared with linear active disturbance rejection control, fractional-order linear active disturbance rejection controller, reduces the position deviation by 48.72%, shortens the adjustment time by 80.28%, and can effectively deal with stronger disturbances and improve the tracking accuracy, significantly enhancing the anti-interference and tracking performance of the system.

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.