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Zhang Tuanlong, Sun Lizhen, Zhang Rongxing, Lin Muhua, Zhang Xiaojia
2026,49(1):1-8, DOI:
Abstract:
To address the performance distortion issue in the light-load region faced by the state-trajectory-model-based synchronous rectification method for CLLC resonant converters, this paper proposes an optimized synchronous rectification strategy based on the state trajectory model. Through an in-depth analysis of the DCM of the converter under light-load conditions, the boundary conditions for current DCM are derived, enabling accurate identification of the current discontinuous mode and effectively suppressing circulating current losses. The proposed method requires no additional detection devices and relies solely on basic input and output electrical parameters to achieve control, significantly reducing hardware complexity and cost. Experimental validation on an 800 W platform demonstrates that the proposed scheme achieves a peak conversion efficiency of 97.38% across the entire operating frequency range, with notable improvements in dynamic response and overall efficiency compared to traditional methods.
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Bai Chuanqing, Peng Yangyang, Zhou Jian, Pan Ruru
2026,49(1):9-20, DOI:
Abstract:
To enhance the development efficiency and real-time signal processing capabilities of flexible sensors, this paper presents an integrated signal processing platform specifically designed for resistive flexible sensors. The platform enables rapid acquisition and continuous processing of sensor signals while addressing issues such as nonlinear output and poor stability. It consists of a resistance acquisition system and a signal processing system: the acquisition module, based on an STM32 microcontroller, supports real-time acquisition from eight channels of flexible sensors; the processing module integrates a segmented linear mapping algorithm based on calibration points and supports 11 filtering methods—including hybrid adaptive filtering—as well as real-time waveform visualization. Experimental results demonstrate that the platform achieves precise acquisition of flexible sensor signals, significantly improves signal linearity and output stability, and supports fully integrated real-time operation across the acquisition and processing stages. This platform provides a reliable data foundation and methodological support for the development and application of flexible sensors.
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Tian Yilong, Ma Youchun, Guo Xin
2026,49(1):21-29, DOI:
Abstract:
To address the limitations of traditional data acquisition systems in air-to-air missile testing, such as insufficient multi-channel synchronous transmission, high-speed storage, and anti-interference capabilities, this study designs an anti-interference real-time data acquisition system based on the ZYNQ-7000 series and an EMMC 5-1-compliant storage module. The system enhances signal robustness through isolated LVDS interfaces and RS422 transceivers, achieves synchronous multi-source data acquisition via a tunable multi-channel sampling rate/baud rate architecture and hybrid framing technology, and ensures timing stability for high-speed EMMC read/write operations through dynamic calibration of data sampling points. Additionally, DDR3 cache is integrated to optimize burst data processing. Experimental results demonstrate that the single EMMC system achieves a write speed of 157.6 MB/s, a read speed of 180.1 MB/s, and a host computer data transmission rate of 40.78 Mb/s. By synergizing hardware isolation, framing optimization, and high-speed storage design, significantly enhancing real-time performance and anti-interference capabilities for multi-source data acquisition in high-dynamic environments. This system provides high-reliability data support for evaluating air-to-air missile performance.
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Nie Junxi, Kang Jianjun, Jing Jialu, Li Hulin, Liu Chaoran
2026,49(1):30-39, DOI:
Abstract:
The dynamic marine environment induces platform motions that compromise the reliability of ocean observation platforms and measurement instruments, leading to errors in the measurement of ocean dynamic parameters such as wind, waves and currents. Throughout its deployment and operational cycle, an ocean observation platform experiences irregular motions. Conventional attitude estimation algorithms based on a nine-axis Micro Inertial Measurement Unit (MIMU), such as Kalman filtering or complementary filtering, often fail to achieve timely convergence, resulting in significant errors in attitude computation. To address this issue, this study proposes a two-stage extended Kalman filtering system for MIMU-based attitude estimation. In the first stage, the system utilizes acceleration data to estimate the initial attitude angles, thereby improving filter convergence speed. In the second stage, it employs hierarchical processing of roll, pitch, and yaw angles to enhance system robustness. Laboratory and field experiments at sea demonstrate that the proposed method achieves rapid convergence and high-accuracy attitude estimation for ocean observation platforms operating under non-stationary motion conditions.
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Ren Feng, Xiao Hui, Feng Yixuan, Wu Yujie, Ai Yujie
2026,49(1):40-49, DOI:
Abstract:
Aiming at the lack of multi-step prediction methods for soil landslide displacement and the issue of significant prediction errors over extended time horizons, this paper proposes a multi-step landslide displacement prediction method based on a parallel model with a multi-level attention mechanism. The method employs a multi-input multi-output prediction strategy. Utilizing a Transformer encoder branch incorporating a multi-head attention mechanism and a bidirectional gated recurrent unit (BiGRU) branch optimized with a global attention mechanism (GAM), the two parallel network branches process historical landslide monitoring data. The landslide feature information extracted by the parallel networks is then fused via a cross attention mechanism (CAM), subsequently outputting the predicted multi-step displacement values. Experimental results demonstrate that the multi-level attention mechanism model achieves a mean absolute error (MAE) of 2.17 mm, a root mean square error (RMSE) of 3.05 mm, and a coefficient of determination (R2) of 0.968 9 in multi-step landslide displacement prediction. Compared to other models, it yields the lowest errors and the optimal R2 result. The model exhibits more stable prediction performance over long time horizons, facilitating the early anticipation of landslide development trends. This provides crucial technical support for landslide prevention and mitigation.
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Sheng Lina, Xu Yao, Li Yan, Yang Yang, Fu Nan
2026,49(1):50-60, DOI:
Abstract:
In Vehicle-to-Everything scenarios, the mobility of devices and the complexity of the environment render them more vulnerable to malicious attacks, necessitating a secure and efficient authentication mechanism. Radio Frequency Fingerprinting (RFF) offers a novel approach to identity authentication in V2X networks. However, as device fingerprints are extracted directly from wireless signals, their stability is highly susceptible to channel variations. The combined effects of the wireless channel and receiver noise cause distortion in the received signal, making it challenging to directly isolate the authentic features of the transmitted signal. To address these issues, this paper proposes an RFF extraction method based on an improved Linear Minimum Mean Square Error channel estimation for the Physical Sidelink Broadcast Channel. First, a channel estimator based on the LMMSE criterion is constructed. By exploiting the time-frequency two-dimensional statistical properties of the channel, a corresponding 2D correlation matrix is established, which effectively captures the intrinsic coupling relationship between time-selective and frequency-selective fading. Based on this matrix, the random channel response can be optimally separated from the received signal. Subsequently, a channel equalization operation is performed to recover the hardware fingerprints contaminated by the channel, restoring their original feature information. Finally, a structurally optimized dual-branch heterogeneous neural network is employed for deep representation learning and high-precision classification of these hardware fingerprints. Experimental results demonstrate that, under low signal-to-noise ratio conditions, the proposed method achieves classification accuracy of 95.46% and 92.05% in static and mobile scenarios, respectively.
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2026,49(1):61-69, DOI:
Abstract:
To address the navigation accuracy challenges of low-cost unmanned vehicles in complex motion environments, this paper proposes an integrated navigation filtering algorithm based on multi-modal motion characteristic decomposition. The method combines Kalman Filter and Cubature Kalman Filter dynamically selecting the optimal filtering strategy according to the motion characteristics of the vehicle. In low-dynamic environments, the Kalman Filter is used to improve computational efficiency, while in medium-dynamic environments, the Cubature Kalman Filter is applied to enhance nonlinear state estimation capabilities. The proposed method is validated through simulations using the Precise Strapdown Inertial Navigation System toolbox, analyzing UAV and UGV trajectories. Experimental results show that compared to traditional filtering methods, the proposed algorithm reduces position estimation errors by 25% in UAV scenarios and improves computational efficiency by 50% in UGV scenarios.
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Liu Fengchun, Wang Zihe, Yang Aimin, Yuan Shujuan, Kong Shanshan
2026,49(1):70-79, DOI:
Abstract:
With the diversification of network attack means and the complication of traffic characteristics, the detection of network malicious traffic is facing increasingly severe challenges. Traditional traffic detection methods gradually fail to meet the needs of modern network environments in terms of accuracy and reliability, especially in the case of high-dimensional data and complex attack patterns. To address the above issues, this paper proposes a network malicious traffic detection model based on the Crested Porcupine Optimization Algorithm, Bidirectional Long Short-Term Memory Network, and Kolmogorov-Arnold Network. The model uses the Bidirectional Long Short-Term Memory Network to capture the bidirectional temporal features of traffic data, combines the nonlinear mapping of the Kolmogorov-Arnold Network to enhance feature expression capabilities, and optimizes hyperparameters through the Crested Porcupine Optimization Algorithm to improve model performance. Experiments are conducted using the CIC UNSW-NB15 enhanced dataset. The experimental results show that the model achieves accuracies of 99.12% and 94.15% in binary classification and multi-classification tasks, respectively, significantly outperforming other models. In addition, when dealing with class imbalance, the model particularly enhances the detection capability for minority class samples such as Backdoor and Worms.
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Guan Yanpeng, Fu Pengbo, Yao Huijuan
2026,49(1):80-89, DOI:
Abstract:
Aiming at the three problems of complex background, inconsistent target size and small proportion of defective areas to be inspected in aerial insulator images taken by UAVs during transmission line inspection, a lightweight insulator defect detection algorithm, MHD-YOLO, is proposed. Firstly, a feature extraction network MAFNet is introduced into the backbone network of YOLOv8, and hybrid convolution is used to enhance the feature extraction capability of the network under complex background. Second, a feature fusion network, HS-FPN, is used to realize feature fusion at different scales, and combined with a lightweight dynamic up-sampling method, DySample, to improve the quality and efficiency of up-sampling. Then, a lightweight detection head CSH is designed, which significantly reduces the number of parameters in the detection layer and the computation amount by using the shared convolution method. Finally, the NWD loss function is introduced to improve the localization accuracy of the model for small targets. The experimental results demonstrate that the MHD-YOLO target detection algorithm reduces the number of parameters by 43.8% compared with YOLOv8, and improves the detection accuracy by 5.1% on the insulator defect detection dataset. The improved algorithm is significantly more effective in detecting insulator defects, and the model complexity is greatly reduced, providing a more effective method for deployment on embedded devices.
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2026,49(1):90-99, DOI:
Abstract:
To address the issue of performance degradation in rolling bearing fault diagnosis under varying operating conditions caused by distribution discrepancies between the source and target domains, this paper proposes a novel fault diagnosis method that integrates a multi-head self-attention mechanism with dynamic joint distribution adaptation. Firstly, a multi-head self-attention mechanism is incorporated into the feature extraction module to extract more discriminative and domain-invariant features from raw vibration signals. Secondly, maximum mean discrepancy and local maximum mean discrepancy are employed to align the marginal and conditional distributions, thereby reducing the distribution difference between the source and target domains. Finally, a dynamic weighting factor is designed to adaptively adjust the importance of marginal and conditional distribution alignment, enhancing the robustness and generalization ability of cross-domain fault diagnosis. The experimental results demonstrate that the proposed method achieved classification accuracies of 99.84% and 98.97% on two public datasets, significantly outperforming other approaches. Moreover, it maintained strong stability and robustness under severe noise interference, providing an effective solution for rolling bearing fault diagnosis.
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Sacral-iliac joint segmentation network integrating large kernel gated mechanisms and dual attention
Yan Wujun, Jing Ying, Xu Yingchen, Zhang Xiaoli, Wang Cheng
2026,49(1):100-109, DOI:
Abstract:
Ankylosing spondylitis is a chronic inflammatory disease whose early diagnosis depends on the accurate identification of pathological features in the sacroiliac joint. However, due to the complex anatomical structure of the sacroiliac joint, the multiscale heterogeneity of lesions, as well as interference from partial volume effects and noise in CT imaging, the accuracy of traditional segmentation methods often fails to meet clinical demands. To address these challenges, this study proposes a Multiscale Attention-Guided U-Net (MAG-UNet). The model enhances local-global feature representation through a Multiscale Feature Fusion (MFF) module, integrates spatial-channel adaptive weighting via a Dual-path Attention (DA) mechanism, and introduces a Large-kernel Grouped Attention Gate (LGAG) to resolve cross-scale feature coupling issues. Experiments conducted on a dataset provided by Shanxi Bethune Hospital demonstrate that MAG-UNet achieves significant performance improvements in sacroiliac joint CT segmentation, with a Dice coefficient of 92.4% and an Intersection over Union (IoU) of 86.0%, surpassing the baseline U-Net model by 3.4% in IoU. This study provides a reliable technical solution for the early diagnosis of AS, offering substantial clinical value and broad potential for practical application.
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Hao Weihan, Peng Guanyan, Chen Yongwen, Zhan Yuhong, Li Tianhao
2026,49(1):110-120, DOI:
Abstract:
Flexible DC transmission systems based on modular multilevel converters (MMC) typically use traditional sliding mode observation techniques to observe state variables, which can significantly reduce the accuracy of system state reconstruction and transient response speed under sudden changes in operating conditions. A discrete-time logarithmic sliding mode observation technique (DTLSMO) is proposed, which constructs dynamic boundary layer functions of observation error norm and expected amplitude, achieves adaptive nonlinear mapping of observer gain, and avoids the parameter tuning process that relies on empirical trial and error. Establish a discretization model of MMC under parameter mismatch and analyze its observability. Design a logarithmic sliding mode approaching law based on active damping characteristics and inject it into the bridge arm dynamic equation. Use the second harmonic circulating current injection algorithm to achieve the coordinated optimization of capacitor voltage balance control and circulating current suppression. Prove the asymptotic stability and generalized convergence of the proposed observer. Analyze the DC voltage 9.6 kV MMC simulation system on MATLAB/Simulink platform and build a hardware prototype for experimental verification. Introduce evaluation indicators such as ITAE to compare and analyze the dynamic performance differences between DTLSMO and traditional time discrete sliding mode observer (DTSMO) and adaptive observer (AO). It is proved that the proposed observer improves the observation accuracy by 0.935% and 2.535% respectively when the bridge arm inductance is mismatched by 20%. It can still demonstrate its fast response ability under uncertain capacitor values and sudden changes in DC voltage, and has good robustness.
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Li Xu, Zhang Yonghong, Zhu Linglong, Kan Xi
2026,49(1):121-132, DOI:
Abstract:
To address the inevitable limitations of current LiDAR-only 3D detection methods, which are affected by point cloud sparsity—where LiDAR-scanned point clouds exhibit significantly higher sparsity at long range compared to short range, leading to imbalanced positive and negative samples during model training—we propose a novel multi-modal framework named MCA-VoxelNet, based on pseudo-point-cloud fusion.It consists of two key designs: the pseudo-point clouds generated by depth completion are utilized to solve the problem of point cloud sparsity, and a large number of nearby redundant voxels are discarded through the distance-aware sampling module to enhance computational efficiency; a multi-stage cascaded attention detection structure is employed to aggregate the target features of multiple detection stages, balance the number of positive and negative samples, and gradually improve the region proposals output by the Region Proposal Network. Experiments on the authoritative KITTI autonomous driving dataset demonstrate that MCA-VoxelNet achieves an inference speed of 17.54 FPS and attains car detection accuracies of 94.19%, 85.93%, and 86.17% on the easy, moderate, and hard difficulty levels, respectively. These results outperform the second-best method by 2.64%, 1.16%, and 1.91%.
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2026,49(1):133-145, DOI:
Abstract:
The deadbeat predictive current control for permanent magnet synchronous motor features fast dynamic response, excellent steady-state performance, and easy digital implementation. However, traditional deadbeat predictive current control exhibits significant steady-state current errors when motor parameters are mismatched. Therefore, this paper proposes an adaptive deadbeat predictive current control method for permanent magnet synchronous motor, which reduces the impact of parameter mismatches on current control performance. Firstly, the proposed method uniformly treats resistance, inductance, and flux linkage parameter perturbations as disturbances and designs adaptive disturbance laws for the dq-axis by incorporating adaptive control. On the basis, the disturbance is estimated online through the disturbance adaptive law, and disturbance compensation is carried out in the traditional deadbeat predictive current controller. This approach effectively reduces the adverse effects of mismatched parameters on the traditional deadbeat predictive current control. Secondly, the stability of the proposed control method was proven using discrete Lyapunov stability theory, and the values of the disturbance adaptive gains were determined through classical control theory. Finally, simulation and experimental results demonstrate that the designed disturbance adaptive law can rapidly and accurately estimate disturbances. Compared with traditional deadbeat predictive current control, the proposed method effectively suppresses the impact of mismatched parameters on current dynamic performance and steady-state error，while reducing the total harmonic distortion of the current.
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Yang Changchang, Zhang Duzhen, Wang Sihao
2026,49(1):146-156, DOI:
Abstract:
In the task of small target defect detection on steel cables, there are common problems such as low detection accuracy, high missed detection rate and frequent false detection, which are particularly obvious in the detection scenario with more small sizes. The main reasons for such problems include: insufficient feature extraction capability of traditional detection algorithms, lack of effective multi-scale information fusion mechanism, and insensitivity of existing loss functions to small targets. To address the above problems, a steel cable defect detection method based on improved RT-DETR is proposed. The BasicStar feature extraction module was designed in the backbone network to improve the semantic representation ability of the model in high-dimensional space; at the same time, a new multi-scale feature fusion strategy small object pyramid network(SOPN) is designed to strengthen the attention and expression ability of small targets; in terms of loss function, a focal enhancement Focaler-SIoU loss function is proposed to improve the positioning accuracy of small targets and the stability of training convergence. Experimental results on the steel cable defect dataset show that the improved model improves the average detection accuracy mAP50 by 2.1% compared with the original RT-DETR. The comprehensive performance is better than the existing mainstream target detection algorithms, which verifies the effectiveness and practicality of the proposed method for small target defect detection tasks in industrial scenarios.
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Wang Wen, Zhu Wenzhong, Cheng Rong
2026,49(1):157-165, DOI:
Abstract:
Aiming at the existing multivariate long time series prediction model in the medium and long term prediction of photovoltaic (PV) power, which has the problem of poor prediction results due to insufficient feature extraction, a multivariate long time series prediction model FFTEMixer based on learning in both frequency and time domains is proposed, which is capable of accurately predicting the PV power while maintaining a high operational efficiency. The model first uses the fast Fourier transform to project time-series data into the frequency domain. It then selectively enhances or suppresses specific frequency components through learnable frequency filters to extract global features and inter-variable correlation features. Next, an interactive convolution module is used to learn local dependencies, further enhancing feature expression capabilities. Subsequently, a feature fusion module is employed to further integrate periodic features, and establishes associations between feature variables and time stamp covariates. Finally, a multi-head self-attention mechanism is employed to comprehensively model the long-term dependencies and temporal dependencies of the sequence, thereby achieving comprehensive feature extraction from time-series data. Experimental results show that on two publicly available photovoltaic power generation datasets, the model′s predictive performance significantly outperforms the baseline model, with mean squared error (MSE) and mean absolute error (MAE) consistently achieving the lowest values. Compared to the current mainstream second-best model, its MSE and MAE are reduced by 12.6% and 15.8%, respectively, validating the model′s effectiveness.
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Gao Lu, Zhuang Qingze, Zhang Fei, Qin Ling, Wu Xilin
2026,49(1):166-175, DOI:
Abstract:
In light of the significance of wind power within the energy landscape and the challenges posed by its intermittency, this paper proposes an end-to-end, ultra-short-term wind power multi-step prediction model that integrates outlier processing and multi-scale feature fusion. The objective is to enhance the accuracy and stability of ultra-short-term wind power predictions, thereby providing robust support for the reliability of power system scheduling and operation. First, the RobustTSF method is employed to address time series anomalies, providing a strong assurance of the prediction model′s robustness and minimizing the disparity between abnormal time series prediction and noise label learning. Secondly, the integration of the spatial pyramid matching mapping strategy, Levy flight strategy, and adaptive T-distribution mutation strategy enhances the dung beetle optimization algorithm, significantly improving its global search capability and convergence efficiency. Meanwhile, the multi-strategy dung beetle optimization algorithm is utilized to optimize the hyperparameters of the enhanced TimeMixer model, resulting in optimal model performance. Finally, the CATimeMixer model is employed to achieve the fusion and prediction of multi-scale seasonal features and trend features. The experimental results indicate that the MAE, RMSE, and MSE decreased by 49.71%, 41.26%, and 65.50%, respectively, compared to the benchmark model multilayer perceptron, while the R2 value increased by 4.49%. This demonstrates a significant reduction in prediction error and offers a novel approach for the accurate prediction of ultra-short-term wind power.
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Wu Yunjia, Cao Ying, Deng Zeyu, Wang Lihui
2026,49(1):176-187, DOI:
Abstract:
This study proposes a new paradigm of dynamic prior modulation based on degradation kernel decoupling evaluation to address the key challenges of kernel estimation bias and non blind method prior mismatch in blind super resolution reconstruction. By establishing a decoupling evaluation mechanism for degraded kernel window width amplitude, it is revealed that the estimation error of kernel window width has a decisive impact on the generalization performance of non blind reconstruction networks. Based on this, this work innovatively constructs a two-stage optimization framework: introducing a loss function relaxation constraint strategy in the kernel estimation stage, enhancing the compatibility between the estimation kernel and non blind priors by avoiding excessive loss functions affecting the accurate estimation of kernel window width; simultaneously design a dynamic kernel prior modulation network, adopting a dual path feature collaborative optimization mechanism. The sharpening feature module extracts image sharpening prior through high-frequency gradient enhancement, while the fuzzy attenuation feature module suppresses noise interference through mean filtering and extracts fuzzy attenuation prior features with regional degradation differences. The two generate degradation modulation vectors through prior modulation layers to achieve dynamic calibration of the kernel feature space. Experimental verification shows that dynamic kernel prior modulation network improves PSNR by 1.92 dB in Set5 dataset with 2×Gaussian blur scenes and 0.61 dB in BSD100 dataset with 4×strong noise scenes, significantly better than existing optimal methods. This method effectively solves the problem of kernel prior mismatch in complex degraded scenarios, providing an innovative solution for blind super-resolution reconstruction in actual complex degraded scenarios.
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2026,49(1):188-198, DOI:
Abstract:
In the key component detection task of UAV inspection of aerial images for transmission lines, a multimodal multi-scale target detection approach is proposed to address the challenges of accuracy degradation and high miss rates for small targets in single-modal detection methods. This approach integrates visible light and infrared images. First, the network constructs a parallel two-stream feature extraction backbone designed to simultaneously process visible light and infrared images. This design fully utilizes the rich color and texture detail information from the visible light images, along with the superior imaging stability and high contrast characteristics of the infrared images. Next, to facilitate cross-modal information interaction and complementarity, a Multimodal Feature Fusion Interactive Module (MFIFM) is developed. This module dynamically adjusts the fusion weights of features from different modalities, adaptively integrating the most discriminative information and effectively mitigating conflicts arising from modality differences. Additionally, to enhance the perception of small target components, a Hybrid Residual Multi-Scale Transformer (HRMS Transformer) module is incorporated into the dual-stream backbone. By utilizing a multi-head attention mechanism, hierarchical feature reorganization, and a residual-based strategy, the model′s ability to extract global context information is significantly strengthened. Experimental results demonstrate that the model′s mean Average Precision (mAP) at IoU thresholds of 0.50 and 0.50:0.95 improves by 5.35% and 4.48%, respectively, compared to existing single-modal methods. These findings confirm the effectiveness and applicability of multimodal fusion technology in transmission line inspection.
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Zhang Zequn, Zhang Chuntang, Fan Chunling
2026,49(1):199-206, DOI:
Abstract:
High-quality deep-sea images are essential for the development of marine biology, topography and geology etc. In order to solve the problems of color distortion, image blur and low contrast in deep-sea images, we propose a deep-sea image enhancement network using improved U2-Net as the GAN generator. Firstly, the RSU module is introduced in U-Net to enhance the fusion of high-level and low-level information in the network. Secondly, the DA mechanism is introduced in the skip connection of U2-Net, which is used to enhance the interrelationship between the space and channel of the image, and extract the underwater color and texture details. Then, U2-Net with the DA mechanism, is used as the generator of GAN to enhance the realism of the image in the adversity. In addition, a new loss function with edge loss and perceived loss is reconstructed, called DS-Loss and the mapping relationship between deep-sea images and target images of U2-GAN is guided by DS-Loss from multiple perspectives. Finally, U2-GAN is compared with seven advanced underwater image enhancement algorithms on the self-built dataset DSIED. Compared with the second-place Sea-Pix-GAN, U2-Net improves by 5.6%, 3.9%, 5.2%, 16.0%, 7.1% and 2.4% in PSNR, SSIM, IE, UIQM, UCIQE, and PCQI, demonstrating better underwater image enhancement effects.
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Xie Jiaxing, Li Dongjun, Zhou Xinhong, Mo Handong, Luo Yang, Liu Hongshan
2026,49(1):207-215, DOI:
Abstract:
In response to the limitations in flexibility and the insufficient level of intelligence in existing image processing systems, this study addresses key technical challenges in the dynamic reconfiguration domain of domestic FPGAs by proposing a logic-level dynamically scheduled image processing system. The system is developed based on the PH1A90 FPGA chip from Shanghai Anlogic Technology Co., Ltd., integrated with a Qt-based host platform. By employing logic-level dynamic scheduling principles, the system achieves near-Dynamic Partial Reconfiguration (DPR)-level full-link dynamic scheduling under the control of an intelligent host module. Experimental validation demonstrates successful hardware-software co-design between the Qt host and the PH1A90 FPGA, achieving four-interface heterogeneous data acquisition, dual-sensor collaborative imaging, dynamic configuration of algorithmic processing chains, and real-time execution of twenty-two dynamic ISP algorithms. The system outputs 1 080P@60 fps video streams via HDMI, validating its dynamic scheduling capabilities across interfaces, workflows, and algorithms at the logic level. Furthermore, the results confirm the industrial-grade reliability of domestic FPGA solutions, contributing to the advancement of independent and controllable technologies in fields such as intelligent security and industrial inspection.
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2026,49(1):216-225, DOI:
Abstract:
To address the issues of low detection accuracy and missed or false detections of small defects in aluminum profile production, this paper proposes an improved YOLOv12n-based method, termed YOLO-PCSU, for surface defect detection. First, a novel A2C2f-PConv structure is designed by integrating PartialConv into the A2C2f module of YOLOv12n, enhancing feature extraction while reducing redundant computation and memory access. Second, CoordAttention is introduced into the backbone to improve detection accuracy without increasing computational cost. Third, the SEAM attention module is added to the detection head to mitigate missed and false detections of small targets. Finally, the U-IoU loss replaces the original CIoU loss to accelerate convergence and enhance prediction precision. Experiments on an aluminum profile defect dataset demonstrate a detection accuracy of 90.3%, with a 2.3% mAP@0.5 improvement over the baseline YOLOv12n, a 9% reduction in parameters, and a 14% reduction in computation. Additional evaluations on the VOC2012 and Northeastern University hot-rolled strip steel surface defect datasets confirm the robustness of the proposed approach.
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2026,49(1):226-236, DOI:
Abstract:
Aiming at the feature ambiguity and misdetection problems in bird′s eye view lane line detection caused by environmental disturbances such as sudden changes in illumination and extreme weather, this paper proposes a causal intervention based BEV lane detection framework. First, to enhance the representation of features during BEV spatial transformation, composite positional encoding is designed and fused to front view features to maintain spatial continuity and consistency. Second, the causal intervention module is constructed after acquiring the BEV features. The causal intervention module aims to explicitly decouple the lane line features from the environmental disturbances by generating counterfactual features to improve the stability of the model in extreme environments. Finally, the dynamic calibration of multi-scale features and interference suppression is accomplished by introducing the feature fusion module, and the global attention mechanism is utilized to achieve the enhancement of BEV features. The experimental results show that in the three subsets of the Apollo dataset, the F1 values are improved by 0.8%, 1%, and 3% compared to the model with the 2nd performance, and the F1 values are also optimal in the challenging scenarios within the OpenLane dataset that contain extreme weather, night, and intersections. The explicit decoupling of lane line features and environmental disturbances is successfully realized, providing a highly robust solution for autonomous driving perception in complex environments.
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Jia Liang, Chen Maohui, Wang Qi, Xu Cheng
2026,49(1):237-246, DOI:
Abstract:
To address the challenges of small and densely packed targets in drone aerial images, which are prone to missed and false detections, this paper proposes an improved multi-scale target detection model, UCM-YOLOv8, based on YOLOv8n, for complex backgrounds in drone aerial photography.Initially, a pyramid network structure that integrates aggregation and diffusion mechanisms is designed, enabling features at each scale to capture detailed contextual information. Second, a task dynamic alignment detection head is introduced to learn interactive features from multiple convolutional layers, enhancing detection precision. Furthermore, the effective integration of the convolutional additive self-attention mechanism with the C2f module further strengthens the network′s feature representation capacity. Finally, the Wise-Inner loss function is employed to replace the original CIoU loss function, suppressing harmful gradients caused by low-resolution images.The proposed model was validated through comparative and ablation experiments on the VisDrone2019 dataset. Results show a 10.8% improvement in mAP50 over the baseline model and a 9.6% reduction in parameters. These findings demonstrate the model′s superior performance in detecting small targets from drone perspectives, making it well-suited for drone aerial image applications.
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Ren Menghan, Zhao Haiyan, Song Jiazhi
2026,49(1):247-256, DOI:
Abstract:
With the acceleration of urbanization, the continuous increase in domestic waste has posed a severe challenge to the ecological environment. Therefore, intelligent sorting technology based on target detection has become a key solution. Aiming at the problems of insufficient accuracy and low deployment efficiency of existing detection models in complex scenarios, an improved YOLO11 domestic waste detection model is proposed. By introducing deformable convolution and a self-designed three-branch coordinate attention mechanism, an enhanced deformable convolution module is constructed, which is used to reconstruct C3k2 in the backbone network, significantly improving the model′s feature extraction capability for targets in complex background. In addition, a content-aware feature recombination operator is adopted to replace the upsampling in the neck network, enhancing the feature reconstruction effect. An exponential moving average sliding loss function is introduced to improve detection accuracy effectively and accelerate model convergence. Experiments on the optimized Huawei Cloud domestic waste dataset show that the improved model achieves 76.5% and 64.6% in mAP@0.5 and mAP@0.5:0.95 metrics, respectively, with an increase of 1.8% and 1.7% compared to the baseline model. Compared with other mainstream detection algorithms, the improved model has a parameter count of only 2.8 M, making it more suitable for mobile deployment.
Volume 49, 2026 Issue 1
Research&Design
Intelligent Control & Performance Testing
Theory and Algorithms
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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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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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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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:
Abstract:
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:
Abstract:
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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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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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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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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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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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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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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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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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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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.
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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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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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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%.
