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Chen Boxuan, Huang Junyi, Gong Pingping
2026,49(6):1-9, DOI:
Abstract:
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.
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Zhao Dongdong, Li Yudong, Li Xuejuan, Zhao Wenzhe, Wang Fuhao
2026,49(6):10-19, DOI:
Abstract:
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.
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2026,49(6):20-28, DOI:
Abstract:
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.
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Ling Rui, Yan Kun, Liang Hongyu, Wei Zhuoqi, Hao Hangbo
2026,49(6):29-38, DOI:
Abstract:
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.
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Liu Ning, Han Jiasheng, Wang Tao, Feng Shuyi
2026,49(6):39-46, DOI:
Abstract:
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.
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2026,49(6):47-55, DOI:
Abstract:
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.
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2026,49(6):56-66, DOI:
Abstract:
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.
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Hua Yan, Li Peng, Yan Dong, Zhang Xiangkai
2026,49(6):67-75, DOI:
Abstract:
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.
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Zhang Zhenli, Hu Zhiqiang, Song Chenglin, Li Yongqun
2026,49(6):76-85, DOI:
Abstract:
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.
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Jiao Huailiang, Liu Liqun, He Junqiang, Zhang Zheng, Wu Qingfeng
2026,49(6):86-97, DOI:
Abstract:
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.
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Liu Jie, Li Zhiwen, Zhang Tengqing, Xie Mingshan
2026,49(6):98-109, DOI:
Abstract:
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.
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Jiang Junchao, Wang Yonglan, Fang Jiandong, Zhu Jin
2026,49(6):110-122, DOI:
Abstract:
In recent years, the application of 3D Gaussian splatting technology in simultaneous localization and mapping systems has made it possible to perform high-quality image rendering using explicit 3D Gaussian models, significantly improving the fidelity of environmental reconstruction. However, the existing methods based on 3DGS have problems such as limited tracking accuracy and lack of global consistency in the 3D reconstruction of complex indoor environments. For this purpose, this paper proposes a dense SLAM algorithm based on 3D Gaussian splatting—SNGO-SLAM. This algorithm combines the advantages of both frame-to-model and frame-to-frame tracking methods, and uses surface normal perception to obtain richer geometric information, significantly improving the tracking accuracy. To address the tracking error that occurs over time, the algorithm introduces a loop closure process and optimizes the 3D Gaussian point representation problem, further enhancing the tracking accuracy. In addition, this algorithm also introduces a dual Gaussian pruning strategy, optimizing memory usage and ensuring precise camera tracking. Experiments on the Replica, ScanNet and TUM RGBD datasets show that while maintaining high rendering quality, the absolute root mean square error of the trajectory of this algorithm on the Replica dataset reaches 0.27 cm. Compared with NICE SLAM, Vox-Fusion, Gaussian SLAM and SplaTAM, the tracking accuracy has increased by 74.53%, 91.26%, 12.90% and 28.95% respectively, providing new ideas for SLAM technology.
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Tursun Mamat, Liu Xiangshuo, He Chunguang, Yang Qiuju, Duan Ting
2026,49(6):123-134, DOI:
Abstract:
To address the low search efficiency, slow convergence speed, and limited path expansion diversity of the RRT family of algorithms, an adaptive multi-strategy dynamic step-size algorithm, AMDS-Bi-RRT*, is proposed. Based on the Bi-RRT* framework, the algorithm enhances convergence efficiency through a dynamic goal-directed extension strategy and an adaptive step-size evaluation function. A multi-directional emergency maneuver strategy is designed to improve adaptability in complex environments. Meanwhile, node sampling is optimized using an improved artificial potential field method, and a three-stage path smoothing approach is introduced to ensure path feasibility. Comparative experiments conducted in four simulation environments of varying complexity against five benchmark algorithms—Bi-APF-RRT*, Bi-RRT*, APF-RRT*, RRT*, and goal-biased RRT*—demonstrate that AMDS-Bi-RRT* reduces average planning time by 12.22%~23.45%, shortens average path length by 0.88%~1.89%, and decreases the average number of nodes by 6.69%~22.85%. The results verify that AMDS-Bi-RRT* outperforms the comparison algorithms in planning efficiency, path quality, and convergence speed, confirming its superior performance across diverse environments.
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Gang Mingxu, Yan Bingjun, Hu Bo
2026,49(6):135-145, DOI:
Abstract:
In order to meet the requirements of large-scale infrastructure and supporting equipment for corrosion monitoring in atmospheric environment, a multi-channel atmospheric corrosion monitoring system based on STM32F407ZGT6 is designed and implemented, aiming at the shortcomings of poor real-time performance and low monitoring accuracy in the existing corrosion monitoring system. The system combines the electrochemical impedance spectroscopy measurement technology and the theory of the equivalent circuit model of the galvanic probe of the double electrode primary battery, uses the time division multiplexing method to control the multi-channel excitation signal generation module to generate the excitation signal to act on the electrode system of each channel, uses the multi-channel response signal acquisition module to collect the response voltage data generated by the electrode system of each channel in real time, and transmits the calculated and processed electrochemical impedance spectroscopy data to the upper computer software deployed in the cloud server through the wireless communication module, and finally analyzes and processes the electrochemical impedance spectroscopy data to obtain the corrosion state information. The experimental results show that the system can realize multi-point corrosion status analysis and monitoring in atmospheric environment, the accuracy of corrosion rate is more than 90%, and the monitoring data can be accurately transmitted to the monitoring platform in real time.
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Zhang Wenze, Wang Zaijun, Jiang Yuheng, Yang Ruizhe
2026,49(6):146-155, DOI:
Abstract:
Accurate assessment of pilot cognitive states is critical for ensuring flight safety, yet existing methods exhibit limitations in fusing multimodal physiological signals. To address this, this paper proposes a dual-stream deep learning network based on bidirectional cross-modal attention. The model adopts a parallel dual-branch architecture: The electroencephalography (EEG) branch quantifies brain functional connectivity through phase locking value (PLV) features and employs a densely connected network enhanced with squeeze-and-excitation (SE) modules for deep feature extraction; the electrocardiogram （ECG） branch extracts heart rate variability (HRV) and waveform features, processed by a residual-connected multilayer perceptron to characterize autonomic nervous system activity. Building upon this, an innovatively designed bidirectional cross-modal attention module dynamically weights and fuses the dual-path deep features to achieve precise classification of three states—concentrated attention, distracted attention, and startle/surprise. Experimental results on the NASA public dataset demonstrate an overall recognition accuracy of 97.44%. Ablation and comparative analyses confirm that the fusion strategy significantly outperforms single-modality analysis and simple feature concatenation methods. The study reveals that deep integration of EEG functional connectivity and ECG physiological information via attention mechanisms effectively enhances cognitive state recognition performance. This approach provides reliable technical support for developing objective and efficient pilot state monitoring systems, holding significant application value for improving flight safety.
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Luo Pengyang, Zhu Wenzhong, Wang Wen
2026,49(6):156-166, DOI:
Abstract:
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.
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Wu Jiaying, Yang Xiaowen, Han Xie, Han Huiyan, Zhang Yuan, Zhao Rong
2026,49(6):167-176, DOI:
Abstract:
To address the limitations of existing weakly supervised semantic segmentation models for point clouds，which struggle to balance local feature correlation, generalization, and feature utilization. This paper proposes WS-MLF, a weakly supervised point cloud semantic segmentation model via multi-scale local feature fusion, based on the RAC-Net baseline. Firstly, the raw point cloud data is taken as input, and a multi-scale spherical sampling methods (MSSM) is employed to capture hierarchical features across varying spatial radii. Secondly, a multi-local feature aggregation enhancement module (MFA) is designed to refine geometric context within neighborhoods. Thirdly, a spatial-channel-fused hybrid attention module (SCH-Att) is proposed to prioritize discriminative channels and key points. Finally, a decoder is utilized for upsampling to generate point-level semantic labels, thereby completing the semantic segmentation task. The proposed model is evaluated on large-scale indoor scene datasets, S3DIS and ScanNet-v2. Experimental results demonstrate that on the S3DIS dataset, when the label ratios are 0.02% and 0.06%, the mIoU surpasses RAC-Net by 2.71% and 0.54%, respectively. On the ScanNet-v2 dataset, with a label ratio of 20 pt, the mIoU increases by 1.55% compared with RAC-Net. These results validate WS-MLF′s effectiveness in extracting key features under weak supervision, enhancing segmentation accuracy.
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Hou Linjie, Lu Chengfang, Cui Yanrong
2026,49(6):177-191, DOI:
Abstract:
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.
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Zhao Xuefeng, Ren Yi, Zhong Zhaoman, Zhong Xiaomin
2026,49(6):192-201, DOI:
Abstract:
Underwater litter detection is a crucial technology for maintaining the balance of underwater ecosystems. To address the challenge of significant variations in target scales encountered in underwater litter detection, we propose the YOLO11-MDA based on YOLO11 is proposed.Firstly, a multidomain feature extraction module MFEM is proposed, which is capable of extracting different scales of features from the input feature map by extracting the target features in both spatial and frequency domains, and enhances the ability of expression of the global features and local information. Second, the lightweight dynamic up-sampling DySample module is introduced to integrate contextual information and improve the quality and efficiency of up-sampling. Finally, the adaptive threshold focused classification loss ATFL is introduced to reduce the impact of the uneven distribution of multi-scale samples on the detection results and improve the detection accuracy of multi-scale targets. The experimental results show that compared with the baseline model, the mAP of YOLO11-MDA in TrashCan dataset and Trash_ICRA19 dataset reaches 91.4% and 97% respectively, which is an enhancement of 3.1% and 10.7%, and the FPS reaches the detection speed of 354.3 fps, which fully demonstrates that the overall performance of the improved model outperforms that of other algorithms, and it can provide an effective method for the automated monitoring of underwater environments.
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2026,49(6):202-210, DOI:
Abstract:
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.
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Lu Jingyi, Chen Bo, Wu Yang, Liang Qihao, Wang Peng
2026,49(6):211-219, DOI:
Abstract:
Fire and smoke detection is a critical component of intelligent surveillance and disaster early warning systems, with wide applications in forest fire prevention, industrial safety and other fields. However, existing algorithms often suffer from low detection precision, slow speed, and large model size under natural environments. To address these issues, this paper proposes a fire and smoke detection method based on the lightweight YOLOv8n. The proposed model replaces the original backbone with PP-LCNet to reduce model size, introduces the CARAFE upsampling operator to enhance feature reconstruction, and integrates the EMA attention mechanism to improve target perception capability. Experimental results show that, compared with the original YOLOv8n, the improved model reduces parameters by 1.01 M and computational cost by 2.2 G, while achieving a detection precision of 94.8% and an mAP50 of 93.6%. It outperforms other mainstream lightweight detection models, achieving an excellent balance between precision and real-time performance, and demonstrates strong practical value.
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Guo Xinru, Lyu Weidong, Wang Rui, Zhao Dini
2026,49(6):220-228, DOI:
Abstract:
Brain tumors are highly invasive neurological diseases, and accurate early diagnosis is crucial for developing personalized treatment plans. Computer-aided diagnosis (CAD) based on deep learning techniques has achieved significant progress in medical image analysis, but limitations remain in terms of classification accuracy, computational efficiency, and interpretability. To address these issues, this study proposes an optimized EfficientNet model based on transfer learning and fine-tuning strategies. The model improves certain convolutional and fully connected layers and adds a global average pooling layer and a Dropout layer at the top of the network to enhance feature extraction capability and classification performance. Additionally, gradient-weighted class activation mapping (Grad-CAM) is introduced to visualize the model′s decision-making process, effectively highlighting key discriminative regions of brain tumors, thereby improving interpretability and clinical reliability. Experimental results on the Figshare dataset demonstrate that the proposed model achieves an accuracy of 99.35% on the test set while significantly reducing parameter count and computational complexity, outperforming baseline models including VGG16, ResNet152V2, and Vision Transformer across all major metrics. Furthermore, cross-dataset validation shows that the model attains an accuracy of 92.51%, further demonstrating its robust stability and generalization capability.
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2026,49(6):229-238, DOI:
Abstract:
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 mAP50-95 is 9.1% and 5.6% higher than that of YOLOv11n. Effectively improve the accuracy of photovoltaic small target defect detection.
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2026,49(6):239-246, DOI:
Abstract:
This paper proposes a set of high-precision visual measurement methods to address the challenges of perspective distortion, thickness corner offset, and continuous tracking of multiple workpieces in the dynamic environment of intelligent manufacturing. In the preprocessing stage, the collected images are converted into approximately orthographic projection views through camera calibration and perspective correction. To obtain accurate edge images, this paper proposes an edge detection algorithm based on multi-scale edge fusion. By applying guided filtering to the collected images at different scales and then using dynamic Canny edge detection, the complete contour of the workpiece is obtained. To address the corner offset caused by the thickness of the workpiece, this paper proposes a high-precision corner extraction algorithm based on thickness interference elimination. By fusing sub-pixel corners and fitted corners, precise corner positioning is achieved. In addition, an object tracking algorithm is designed to match and identify the centroids of the workpieces, enabling automatic size recognition and measurement of multiple workpieces in consecutive frames. Experimental results show that this method can measure the sizes of multiple workpieces in arbitrary poses, with a mean error of 0.599 mm and a standard deviation of 0.172 mm, meeting the measurement requirements in industrial production.
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Liu Qingqiang, Zheng Xiaodong, Liu Yuanhong, Qian Kun
2026,49(6):247-256, DOI:
Abstract:
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.
Volume 49, 2026 Issue 6
Research&Design
Theory and Algorithms
Data Acquisition
Information Technology & Image Processing
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Xue Xianbin, Tan Beihai, Yu Rong, Zhong Wuchang
2024,47(6):1-7, DOI:
Abstract:
Urban intersections are accident-prone sections. For intelligent networked vehicles, it is very important to carry out risk detection and collision warning during driving to ensure the safety of driving. This paper proposes a traffic risk field model considering traffic signal constraints for urban intersections with traffic lights, and designs a three-level collision warning method based on this model. Firstly, a functional scenario is constructed according to the potential conflict risk points of urban intersections, and the vehicle risk field model is carried out considering the constraint effect of traffic signal. In order to solve the problem of collision warning, a three-level conflict area is proposed to be divided by the index, and the collision risk of the main vehicle is measured according to the position of the potential energy field around the main vehicle by calculating the corresponding field strength around the main vehicle. The experimental results show that the designed model can accurately warn the interfering vehicles entering the potential energy field of the main vehicle, the warning success rate can reach 100%, and the false alarm rate is only 3.4%, which proves the reliability and effectiveness of the proposed method.
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Zhan Huiqiang, Zhang Qi, Mei Jianing, Sun Xiaoyu, Lin Mu, Yao Shunyu
2024,47(6):123-130, DOI:
Abstract:
Aiming at the force test in low-speed pressurized wind tunnel, the original data source of aerodynamic characteristic curve is analyzed. With the balance signal, flow field state and model attitude as the main objects, combined with the test control process, the abnormal detection methods and strategies of the test data are studied from the dimensions of single point data vector, single test data matrix and multi-test data set in the same period, and an expert system for abnormal data detection is designed and developed based on this core knowledge base. The system inference engine automatically detects online during the test, and realizes the pre-detection and pre-diagnosis of the original data through data identification, rule reasoning, logical reasoning and knowledge iteration. The experimental application results show that the expert system is highly sensitive to the detection of abnormal types such as abnormal bridge pressure, linear segment jump point and zero point detection, which guides the direction of abnormal data analysis and improves the efficiency of problem data investigation.
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Wei Jinwen, Tan Longming, Guo Zhijun, Tan Jingyuan, Hou Yanchen
2024,47(6):8-13, DOI:
Abstract:
To address the issue of low accuracy in indoor static target positioning with existing single-antenna ultra-high frequency RFID technology, this paper proposes a new RFID localization method based on an antenna boresight signal propagation model. The method first determines the height position of the target through vertical antenna scanning; secondly, it adjusts the antenna height to match that of the target and then performs stepwise rotational scanning to identify the target′s azimuth angle; furthermore, it utilizes a Sparrow Search Algorithm optimized back propagation neural network to establish a path loss model for ranging purposes; finally, it integrates the height, azimuth angle, and distance data to complete the target positioning. Experimental results show that in indoor environment testing, the proposed method has an average positioning error of 7.2 cm, which meets the positioning requirements for items in general indoor scenarios.
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Zhang Fubao, Wu Ting, Zhao Chunfeng, Wei Xianliang, Liu Susu
2024,47(6):100-108, DOI:
Abstract:
In real-time detection of saw chain defects based on machine vision, factors like oil contamination and dust impact image brightness and quality, leading to a decrease in the feature extraction capability of the object detection network. In this paper, an automated saw chain defect detection method that combines low-light enhancement and the YOLOv3 algorithm is proposed to ensure the accuracy of saw chain defect detection in complex environments. In the system, the RRDNet network is used to adaptively enhance the brightness of the saw chain image and restore the detailed features in the dark areas of the image. The improved YOLOv3 algorithm is used for defect detection. FPN structure is added with a feature output layer, the a priori bounding box parameters are re-clustered using the K-means clustering algorithm, and the GIoU loss function is introduced to improve the object defect detection accuracy. Experimental results demonstrate that this approach significantly improve image illumination and recover image details. The mAP value of the improved YOLOv3 algorithm is 92.88%, which is a 14% improvement over the original YOLOv3. The overall leakage rate of the system eventually reduces to 3.2%, and the over-detection rate also reduces to 9.1%. The method proposed in this paper enables online detection of saw chain defects in low-light scenarios and exhibits high detection accuracy for various defects.
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Zhang Huimin, Li Feng, Huang Weijia, Peng Shanshan
2024,47(6):86-93, DOI:
Abstract:
A lightweight improved model CAM-YOLOX is designed based on YOLOX to address the issues of false alarms of land targets and missed detections of shore targets encountered in ship target detection in large scene Synthetic Aperture Radar(SAR)images in near-shore scenes. Firstly, embed Coordinate Attention Mechanism in the backbone to enhance ship feature extraction and maintain high detection performance; Secondly, add a shallow branch to the Feature Pyramid Network structure to enhance the ability to extract small target features; Finally, in the feature fusion network, Shuffle unit was used to replace CBS and stacked Bottleneck structures in CSPLayer, achieving model compression. Experiments are carried out on the LS-SSDD-v1.0 remote sensing dataset. The experimental results show that compared with the original algorithm, the improved algorithm in this paper has the precision increased by 5.51%, the recall increased by 3.68%, and the number of model parameters decreased by 16.33% in the near-shore scene ship detection. The proposed algorithm can effectively suppress false alarms on land and reduce the missed detection rate of ships on shore without increasing the number of model parameters.
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Li Ya, Wang Weigang, Zhang Yuan, Liu Ruipeng
2024,47(6):64-70, DOI:
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A task offloading strategy based on Vehicle Edge Computing (VEC) is designed to meet the requirements of complex vehicular tasks in terms of latency, energy consumption, and computational performance, while reducing network resource competition and consumption. The goal is to minimize the long-term cost balancing between task processing latency and energy consumption. The task offloading problem in vehicular networks is modeled as a Markov Decision Process (MDP). An improved algorithm, named LN-TD3, is proposed building upon the traditional Twin Delayed Deep Deterministic Policy Gradient (TD3). This improvement incorporates Long Short-Term Memory (LSTM) networks to approximate the policy and value functions. The system state is normalized to accelerate network convergence and enhance training stability. Simulation results demonstrate that LN-TD3 outperforms both fully local computation and fully offloaded computation by more than two times. In terms of convergence speed, LN-TD3 exhibits approximately a 20% improvement compared to DDPG and TD3.
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Chen Haoan, Li Hui, Huang Rui, Fu Pingbo, Zhang Jian
2024,47(6):182-189, DOI:
Abstract:
Facing the challenges of regulating unmanned aerial vehicles (UAV), and based on an YOLOv5-Lite improved model, this paper incorporates an exponential moving sample weight function that dynamically allocates loss function weights to the model during the training iteration. Through model computations, we achieve real-time UAV tracking using a two-degree-of-freedom servo platform. Furthermore, video capture, model calculations, and servo control are all performed locally on a Raspberry Pi 4B.The optimized model maintains the original model's parameter count while achieving a mAP@.5:.95 score of 70.2%, representing a 1.5% improvement over the baseline model. Real-time inference on the Raspberry Pi yields an average speed of 2.1 frames per second (FPS), demonstrating increased processing efficiency. Simultaneously, the Raspberry Pi controls a servo platform via the I2C protocol to track UAV targets, ensuring real-time dynamic monitoring of UAVs. This optimization enhances system reliability and offers superior practical value.
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Zhang Bian, Tian Ruyun, Han Weiru, Peng Yuxin
2024,47(6):109-115, DOI:
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In order to solve the problems that the traditional SPD life alarm characterization method can not clearly correspond to the real life state of SPD, and the remaining life model characterized by a single degradation related parameter has poor predictability, a multi-parameter SPD life remote monitoring system based on STM32 is designed. With STM32 as the main controller, the important parameters such as surge current, leakage current, surface temperature and tripping status of SPD are collected in real time, and the status information is uploaded to the One net cloud platform through the BC20 wireless communication module. The One net cloud platform displays and stores the multi-parameter data of SPD in real time, and provides data management and analysis. The SVM classification model is used to judge whether SPD is damaged and the BO-LSTM prediction model is used to predict the remaining life of SPD. Based on the positioning function of BC20, the real-time geographic location of SPD can be viewed on the host computer. The results show that the root mean square error and average absolute error of the BO-LSTM prediction model are 0.001 3 and 0.001 8, and the system can monitor the SPD status in real time, effectively predict the remaining life value of SPD, and give early warning in time.
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Feng Zhibo, Zhu Yanming, Liu Wenzhong, Zhang Junjie, Li Yingchun
2024,47(6):34-40, DOI:
Abstract:
The data bits and spread spectrum codes of the spaceborne spread-spectrum transponder are asynchronous. Due to the influence of transmission system noise and Doppler frequency shift, it can cause attenuation of peak values related to receiving and transmitting spread spectrum codes, leading to a decrease in capture performance. Traditional capture techniques often have problems such as high algorithm complexity, slow capture speed, and difficulty adapting to the requirements of large frequency offsets of hundreds of kilohertz. This article proposes a spread spectrum sequence search method that truncates the spread spectrum sequence into two segments for correlation operations, and combines the signal squared sum FFT loop for a large frequency offset locking, effectively suppressing the attenuation of correlation peaks and improving pseudocode capture performance. MATLAB simulation and FPGA board level testing show that the proposed spread spectrum signal capture scheme can resist Doppler frequency shifts of up to ±300 kHz, with an average capture time of about 95 ms. In addition, the FPGA implementation of this algorithm saves about 47% of LUT, 43% of Register, and more than half of DSP and BRAM resources compared to traditional structures, making it of great application value in resource limited real-time communication systems.
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Fang Xin, Shen Lan, Li Fei, Lyu Fangxing
2024,47(6):20-27, DOI:
Abstract:
The high-frequency measurement data of underground vibration signals can record more specific details about the dynamic response of drilling tools, which is helpful for analyzing and diagnosing abnormal vibrations underground. However, the high-frequency measurement generates a large amount of measurement data, resulting in significant storage pressure for underground vibration measurement equipment. The proposed method uses compressed sensing technology to selectively collect and store sparse underground vibration data and then recover high-frequency measurement results through a signal reconstruction algorithm. In the process of realizing this method, an innovative method of constructing a layered Fourier dictionary against spectrum leakage is proposed, and an improved OMP signal reconstruction algorithm based on layered tracking is researched and realized, which greatly reduces the time required for signal recovery. Simulation and experimental test results demonstrate the method′s effectiveness, achieving a system compression ratio of 18.9 and a reconstruction error of 52.1 dB. The proposed method may greatly reduce the data storage pressure of the measuring equipment in the underground, and provides a new way to obtain high-frequency measurement data of underground vibration.
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Zhou Jianxin, Zhang Lihong, Sun Tenghao
2024,47(6):79-85, DOI:
Abstract:
Aiming at the problems that the standard honey badger algorithm (HBA) is easy to fall into local optimum, low search accuracy and slow convergence speed, a honey badger algorithm based on elite differential mutation (EDVHBA) is proposed. The elite solution searched by the two optimization strategies in the standard HBA is combined with differential mutation to generate a new elite solution. The use of three elite solutions to guide the next iteration of the population can increase the diversity of the algorithm solution and prevent the algorithm from falling into premature convergence. At the same time, the nonlinear density factor is improved and a new position update strategy is introduced to improve the convergence speed and optimization accuracy of the algorithm. In order to verify the performance of the algorithm, simulation experiments are carried out on eight classical test functions. The results show that compared with other swarm intelligence algorithms and improved HBA, EDVHBA can find the optimal value 0 in the unimodal function, and converge to the ideal optimal value in the multimodal function after about 50 iterations, which verifies that EDVHBA has better optimization performance.
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Peng Duo, Luo Bei, Chen Jiangxu
2024,47(6):50-57, DOI:
Abstract:
Aiming at the non-range-ranging location problem of multi-storey WSN structures, a three-dimensional indoor multi-storey structure location algorithm IAODV-HOP algorithm based on improved Tianying is proposed in the field of large-scale indoor multi-storey structure location for some large commercial supermarkets, hospitals, teaching buildings and so on. Firstly, the nodes are divided into three types of communication radius to refine the number of hops, and the average hop distance of the nodes is modified by using the minimum mean square error and the weight factor. Secondly, the IAO algorithm is used to optimize the coordinates of unknown nodes, and the population is initialized by the best point set strategy, which solves the problem that the quality and diversity of the population are difficult to guarantee due to the random distribution of the initial population in the Tianying algorithm. In addition, the golden sine search strategy is added to the local search to improve the position update mode of the population, and enhance the local search ability of the algorithm. Through simulation experiments, compared with traditional 3D-DV-Hop, PSO-3DDV-Hop, N3-3DDV-Hop and N3-ACO-3DDV-Hop, the normalized average positioning error of the proposed algorithm IAODV-HOP is reduced by 70.33%, 62.67%, 64% and 53.67%, respectively. It has better performance, better stability and higher positioning accuracy.
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Ma Zhewei, Zhou Fuqiang, Wang Shaohong
2024,47(6):94-99, DOI:
Abstract:
A feature point extraction algorithm based on adaptive threshold and an improved quadtree homogenization strategy are proposed to address the issue of low positioning accuracy or low matching logarithms of the SLAM system caused by the ORB-SLAM2 algorithm extracting fewer feature points in dark environments or environments with fewer textures, resulting in system crashes. Firstly, based on the brightness of the image, FAST (Features from Accelerated Seed Test) feature points are extracted using adaptive thresholds. Then, an improved quadtree homogenization strategy is used to eliminate and compensate the feature points of the image, completing feature point selection. The experimental results show that the improved feature point extraction algorithm increases the number of matching pairs by 17.6% and SLAM trajectory accuracy by 49.8% compared to the original algorithm in dark and textured environments, effectively improving the robustness and accuracy of the SLAM system.
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Wang Huiquan, Wei Zhipeng, Ma Xin, Xing Haiying
2024,47(6):14-19, DOI:
Abstract:
To solve the problem of low control accuracy of the tidal volume emergency ventilation for lower air pressure at high altitudes, we propose a dual-loop PID tidal volume control system, which utilizes a pressure-compensated PID controller to adjust fan speed, supplemented by an integral-separate PID controller in order to achieve precise control of airflow velocity.Compared with single-loop PID control, the rapid response and no overshooting are observed in the performance tests of the dual-loop control system at an altitude of 4 370 m and atmospheric pressure of 59 kPa, in addition, the output error of the average airflow velocity decrease to 3.19% (the maximum error is 4.1%), which is superior to that of current clinical equipment. Our work offers an effective solution for high-altitude emergency ventilator tidal volume control, and contributes important insights to the development of ventilation control technology in special environments.
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Long Biao, Yang Jun, Chen Huiping, Chen Guangrun, Zhao Peiyang
2024,47(6):157-163, DOI:
Abstract:
In order to solve the problem that the audio signal processing in the voice communication system has a large amount of data, a lot of stray signals, and the received audio signals of the frequency modulation receiver are large and small, a lightweight audio signal processing algorithm is proposed, and based on this algorithm, the audio signal receiving and automatic gain control are realized on the field programmable gate array(FPGA) platform. The algorithm combines digital down conversion technology, multistage extraction filtering technology and automatic gain control technology (AGC) technology, and is applied to the audio signal processing system. The RF analog signal received from the upper antenna is converted into baseband audio signal through analog-to-digital conversion and digital down-conversion, and the stray signal in the baseband signal is filtered through four-stage extraction filtering, reducing the complexity and power consumption of the system. At the same time, the digital AGC controls and adjusts the baseband audio signal to output a more stable audio signal. The experimental results show that the algorithm can effectively reduce the information rate from 102.4 MHz to 32 kHz, reduce the computation burden, improve the signal quality, and reduce the resource utilization of FPGA. And the automatic gain control adjustment of audio signal is realized, and the adjustment time is only 12.8 μs, which meets the power stability time of the receiver.
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Cheng Dongxu, Wang Ruizhen, Zhou Junyang, Zhang Kai, Zhang Pengfei
2024,47(6):137-142, DOI:
Abstract:
For the tobacco industry, there is currently no detection device and method for detecting the heating temperature and temperature uniformity of heated cigarette smoking sets. In order to solve the temperature measurement needs of micro rod-shaped heating sheets in a narrow space, this article developed a cigarette heating rod thermometer, and designed a new structure suitable for temperature measurement of cigarette heating rods. In order to verify the accuracy and reliability of the measurement results of the cigarette heating rod thermometer, uncertainty analysis of the thermometer was performed. The analysis results are based on the "GB/T 13283-2008 Accuracy Level of Detection Instruments and Display Instruments for Industrial Process Measurement and Control" standard. The measurement range is 100 ℃~400 ℃, meeting the requirements of level 0.1. The final experiment verified that the heating temperature field of different cigarettes can be effectively measured.
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Zhou Guoliang, Zhang Daohui, Guo Xiaoping
2024,47(6):190-196, DOI:
Abstract:
The gesture recognition method based on surface electromyography and pattern recognition has a broad application prospect in the field of rehabilitation hand. In this paper, a hand gesture recognition method based on surface electromyography (sEMG) is proposed to predict 52 hand movements. In order to solve the problem that surface EMG signals are easily disturbed and improve the classification effect of surface EMG signals, TiCNN-DRSN network is proposed, whose main function is to better identify the noise and reduce the time for filtering the noise. Ti is a TiCNN network, in which convolutional kernel Dropout and minimal batch training are used to introduce training interference to the convolutional neural network and increase the generalization of the model; DRSN is a deep residual shrinkage network, which can effectively eliminate redundant signals in sEMG signals and reduce signal noise interference. TiCNN-DRSN has achieved high anti-noise and adaptive performance without any noise reduction pretreatment. The recognition rate of this model on Ninapro database reaches 97.43% 0.8%.
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2024,47(6):28-33, DOI:
Abstract:
With the increasing demand for satellite network, vehicle-connected network, industrial network and other service simulation, this paper proposes a multi-session delay damage simulation method based on delay range strategy to build flexible software network damage simulation, aiming at the problems of small number of analog links, low flexibility and high resource occupation of traditional dedicated channel damage instruments. In this method, the delay damage of each session flow is identified and controlled independently, and the multi-queue merging architecture based on time delay strategy is adopted to reduce the resource consumption. The experimental results show that compared with the traditional dedicated device and simulation software NetEm, the proposed method supports the independent delay configuration of million-level links, increases the number of session streams from ten to one million, and reduces the memory consumption by at least 85% under each bandwidth, which meets the requirements of large scale and accuracy, and greatly reduces the system cost.
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Shi Shujie, Zhao Fengqiang, Wang Bo, Yang Chenhao, Zhou Shuai
2024,47(6):116-122, DOI:
Abstract:
Rolling bearings play an important role in rotating machinery. If a fault occurs, it can cause equipment shutdown, and in severe cases, endanger the safety of on-site personnel. Therefore, it is necessary to diagnose the fault. In response to the difficulty in extracting fault features of rolling bearings and the low accuracy of traditional classification methods, this paper proposes a fault diagnosis method based on Set Empirical Mode Decomposition (EEMD) energy entropy and Golden Jackal Optimization Algorithm (GJO) optimized Kernel Extreme Learning Machine (KELM), achieving the goal of extracting fault features of rolling bearings and correctly classifying them. Through experimental data validation, this method can extract the fault information features hidden in the original signal of rolling bearings, with a diagnostic accuracy of up to 98.47%.
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Ma Dongyin, Wang Xinping, Li Weidong
2024,47(6):58-63, DOI:
Abstract:
Aiming at the Automatic Train Operation of high-speed train,an algorithm based on BAS-PSO optimized auto disturbance rejection control (ADRC) is used to design speed tracking controller.The ADRC is designed based on the train dynamics model,ITAE is used as the objective function,and the parameters are tuned by BAS-PSO.CRH380A train parameters are selected, The tracking effect of BAS-PSO, PSO and improved shark optimized ADRC algorithm on the target speed curve of the train is compared by MATLAB simulation,The tracking error of the train target speed curve based on the BAS-PSO optimized ADRC algorithm is kept in the range of ±0.4 km/h,which is closer to the target speed curve than the other two algorithms.The results show that the ADRC based on BAS-PSO optimization has the advantages of small tracking error and strong anti-interference ability.
