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Yu Xin, Miu Jiaxin, Long Haochen, Hao Yanzhe, Liu Xiaoyu
2026,49(10):1-11, DOI:
Abstract:
Wafer defect detection is a critical step in ensuring product quality within semiconductor manufacturing processes. Analyzing the distribution areas and presentation patterns of defects enables precise tracing of weak links in production processes, providing essential evidence for optimizing manufacturing technologies. To address the demands for high precision, real-time processing, and lightweight solutions in identifying hybrid wafer defects under complex scenarios, this paper proposes a lightweight wafer map defect detection algorithm based on an improved YOLOv8s-seg. The algorithm first introduces a context-guided mechanism within the backbone feature extraction network to optimize the C2f module. It then proposes a shift-enhanced multi-branch & scale fusion feature pyramid network (SEMF-FPN) to enhance the model′s feature fusion capabilities. Finally, it incorporates lightweight asymmetric detector head concepts to optimize the head network. Experimental results demonstrate that the improved model achieves mean average precision (mAP@0.5:0.95) of 90.9% for bounding boxes and 84.5% for masks, with a detection speed of 333.3 fps. It significantly reduces parameters and computational load by 54.84% and 37.18%, respectively, while maintaining accuracy loss below 0.5%. This lightweight, high-precision and fast-detection model meets industrial production requirements and holds considerable engineering application value.
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Xu Shanyong, Li Xuesong, Huang Yourui
2026,49(10):12-20, DOI:
Abstract:
A new five level inverter circuit is designed to address the issues of a large number of components and modulation difficulty in MLIs. This circuit uses a single DC source, eight switching transistors, two capacitors, two diodes and one inductor, reducing the number of components and simplifying the modulation complexity of the system. Compared with existing multilevel inverters(MLIs) circuits, this circuit has the ability to autonomously boost voltage, eliminate leakage current, self balance capacitor voltage, and suppress capacitor peak current. This circuit adopts the SVPWM modulation strategy, which reduces the harmonic content of the output voltage compared to the SPWM modulation strategy. Firstly, the circuit structure and working principle are introduced in the article, and the expression for device selection is established. Then, the SPWM and SVPWM modulation strategies are analyzed. Finally, a simulation model of the circuit and an experimental platform are built for verification, and the results prove the feasibility of the inverter circuit and modulation strategy.
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Liu Baoyu, Song Jialing, Zhou Xu
2026,49(10):21-34, DOI:
Abstract:
Coal and gas outburst is one of the highly destructive geological hazards in coal mining operations, and efficient outburst early warning is crucial for ensuring the safe production of mining areas. To resolve traditional Stacking models′ issues of inefficient hyperparameter optimization, local optima traps, and manual base learner selection, this paper develops an improved Stacking early warning model with modified artificial rabbit optimization （ARO） and differential evolution （DE） algorithms. The AMSARO algorithm—incorporating an elite memory pool and a multi-strategy adaptive selection mechanism—is applied for parameter optimization. The DE algorithm dynamically optimizes weights to realize base learner selection while performing feature selection synchronously. Combining these two algorithms with the Stacking ensemble method, the DE-AMSARO-Stacking coal and gas outburst early warning model is constructed. Eight different benchmark functions are adopted for testing, and experimental results show that the AMSARO algorithm achieves faster convergence speed and higher optimization accuracy compared with the ARO algorithm and its improved variants. Experiments are conducted on a dataset of 500 samples expanded from 50 sets of original data collected from a coal mine in Shanxi Province. The results indicate that the DE-AMSARO-Stacking model outperforms single models and comparative models based on different optimization algorithms in prediction performance. This research provides a more efficient approach for coal and gas outburst early warning.
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2026,49(10):35-42, DOI:
Abstract:
To enhance the technological feel and interactivity of exhibition halls such as museums, and to improve the service level of these halls, this study designs an recognition system based on computer vision technology, featuring functions such as navigation and positioning, as well as intelligent audio guide for exhibits. The system is mainly controlled by Raspberry Pi, with external cameras, infrared remote sensors and other hardware modules. After the image is collected by the camera of the guide vehicle, the path information is identified by the image processing algorithm to control the movement of the vehicle body. The test results showed that when the channel retention rate was 40%, the model parameters were reduced by 18.09%, the floating-point operations were reduced by 12.35%, and the test set accuracy still reached 98.5%. The designed system can accurately identify guide lines, cross lines, and exhibit information, and is easy to deploy and has great promotional value.
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Lu Hongrui, Zhang Yingjun, Wang Shaobo, Zhang Haoze, Chen Zezheng
2026,49(10):43-50, DOI:
Abstract:
The GNSS has been widely adopted in maritime ships, however, with the increasingly complex global situation, ports have assumed greater strategic importance during military exercises and other special periods, making them more susceptible to external interference. This can reduce the reliability of ships that rely solely on a single navigation system and may even result in GNSS receiver failure. Therefore, the problem of continuous localization and accurate target ranging for intelligent ships in ports under satellite-denied conditions has become a critical challenge restricting the development of autonomous navigation technologies. To address the practical requirements of intelligent ship navigation in port scenarios, this paper proposes an auxiliary localization approach based on prior port maps. On this basis, a target measurement algorithm is developed by constructing an adaptive ROI partitioning strategy for the shipborne LiDAR according to the ship′s navigation state. The proposed method enables rapid re-localization of intelligent ships and real-time, high-precision measurement of targets within navigable waters under satellite-denied conditions. Field experiments demonstrate that the proposed method achieves satisfactory performance in both auxiliary localization and ranging stability, with an APE of 0.058 m, STD of 0.044 m, and RMSE of 0.073 m. These results indicate a significant improvement in the robustness of intelligent ship localization in complex port environments.
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Ding Linhao, Dong Yu, Huang Xing, Hua Guoxiang, Wang Shengxu
2026,49(10):51-59, DOI:
Abstract:
Aiming at the key issues of speed cooperative control of dual permanent magnet synchronous motors for transmission line robots, an improved control strategy is proposed. Firstly, in terms of rotational speed loop control, a non-singular terminal sliding mode controller based on the new approach law was designed, and combined with the expanded state observer to conduct real-time observation and compensation of uncertain disturbances in the system, thereby improving the speed tracking performance of the motor. Secondly, in terms of synchronous control, a cross-coupling control architecture was adopted, and a fuzzy control error compensator based on the improved particle swarm optimization （PSO） algorithm was designed. The fuzzy control parameters were tuned through the optimization algorithm, thereby reducing the synchronous error of the dual-motor rotational speed under external disturbances. The simulation experiment results show that the proposed control strategy exhibits significant advantages in terms of robustness, rotational speed tracking accuracy and synchronization performance, providing an effective solution for the high-precision control of live-line operation robots.
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Research on reliable dispatch of wind farm power based on wind turbine nacellen vibration prediction
2026,49(10):60-68, DOI:
Abstract:
The traditional implementation of power scheduling in wind farms mainly adopts yaw control methods. However, whether the yaw process of wind turbine generators can be actually carried out depends on the assessment effect of the safety status of the units (nacelle vibration measurement), which leads to inaccurate reliable power scheduling in actual wind farms. For this purpose, this paper proposes a reliable power scheduling method for wind farms based on the prediction of the vibration acceleration of the nacelle of wind turbine generators. Firstly, aiming at the difficulty in modeling the nacelle vibration mechanism during the yaw process of the unit, a deep neural network is utilized to construct the nacelle vibration acceleration model. Then, under the predictive control framework, combined with the prediction of nacelle vibration acceleration, a reliable power scheduling method for wind farms is proposed to improve the accuracy of power safety scheduling in wind farms by 3%. Finally, a wind farm with 12 wind turbine generators was constructed using Fast.Farm to verify the effectiveness of the method proposed in this paper.
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Hong Xulong, Li Wentao, Yang Jiyuan, Xie Zhidong
2026,49(10):69-78, DOI:
Abstract:
Aiming at the problem that traditional non-destructive testing methods are difficult to detect rail head damage under different radii, a phased array ultrasonic testing method based on the complex curved surface damage of rail heads is proposed. For the complex structural features of the rail head, a phased array ultrasonic static focusing detection scheme is formulated; based on Fermat′s principle, a calculation method for the delay time of the phased array ultrasonic synthetic beam under different radii of the rail head is proposed; a finite element simulation model is established to analyze the propagation characteristics of the phased array beam in the rail head; based on the discontinuous Galerkintime-domain(DGTD) method, a phased array ultrasonic dynamic response simulation model of the rail head is established and the A-type signal characteristics of different defect positions in the rail head are analyzed. Finally, phased array ultrasonic experiments were conducted on defects pre-embedded at different positions of the rail head and defects with a diameter of 1 mm can be accurately detected. The results show that the optimal average signal-to-noise ratio for defect detection in the central area is 22.97 dB and for the near-surface area with a larger rail head curvature, it is 17.16 dB. Compared with the traditional single-probe ultrasonic testing method, this method increases the testing speed by approximately 40%. Compared with the conventional phased array method that has not been optimized for the rail head′s curved surface, the average defect detection signal-to-noise ratio at the center of the rail head and in the large curvature area near the surface has been increased by approximately 10 and 12 dB respectively.
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Lin Qiuhong, Gao Cheng, Li Haokun, Nian Shanshan
2026,49(10):79-87, DOI:
Abstract:
To address the issues of environmental interference and high cost of force sensors in electro-mechanical brake (EMB) systems, this paper proposes a sensorless clamping force estimation method and a composite control strategy. An electromechanical model of the EMB system is established and an improved sliding mode observer using a saturation function is designed to reduce chattering. The finite-time convergence of the observer is proven via Lyapunov theory. An extended state observer is also designed to estimate and compensate for unmodeled disturbances via feedforward control. Furthermore, a novel reaching law is developed by modifying the dual-power reaching law with a variable-gain function to suppress chattering while maintaining convergence speed. A controller based on a nonsingular terminal sliding mode and the improved reaching law is constructed. Bench tests show that the proposed method outperforms dual-power, exponential reaching law and PID methods, offering faster force tracking, higher precision, and enhanced anti-interference capability under various braking conditions.
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Chen Zijing, Li Rui, Zhu Mingda, Fu Kuan, Yang Guangyong
2026,49(10):88-96, DOI:
Abstract:
AC electromagnetic field stress detection technology can achieve rapid screening of stress concentration areas in pipelines, but current AC electromagnetic field stress detection technology is limited to uniaxial stress measurement and cannot adapt to the identification of complex stress states in pipelines. This study integrates magneto-anisotropic stress detection technology to establish a theoretical pipeline plane stress model and a finite element simulation model. It designs an orthogonal stress detection structure with dual coil sets. Simulation results verify that under axial stress, the variation amplitude of the longitudinal magnetic flux density reaches 2.61 times that of the transverse direction. Verified through universal testing machine loading tests, within the tensile range of 200 MPa, the sensor′s output voltage exhibits a quantitative relationship with the angle between the stress gradient and stress direction, expressed as ΔU=K*εl-εhcos2θ. The conclusion confirms that this method breaks through the uniaxial detection limitation of traditional ACSM technology, achieves biaxial stress decoupling and principal stress direction determination, providing a new means for pipeline stress state detection.
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2026,49(10):97-106, DOI:
Abstract:
To effectively suppress the influence of time-varying loads and insufficient model accuracy on the control performance of vtol UAV power systems, an ISTSM-ADRC strategy is proposed. By leveraging the characteristic that ADRC is not completely dependent on the accurate mathematical model of the system, a speed-loop expansion state observer（ESO） is designed to online estimate and compensate for the comprehensive system disturbances. The sliding mode surface function with strong robustness is adopted to replace the nonlinear error feedback link in active disturbance rejection control(ADRC), thus enhancing the robustness of the speed-loop controller. A novel super-twisting sliding mode （STSM） reaching law is developed to achieve adaptive gain, reduce chattering and strengthen robustness against disturbances. Step response tests, load mutation tests and parameter perturbation experiments are conducted. The results indicate that compared with STSM-ADRC, the steady-state error of the proposed strategy is reduced by 50% and the steady-state fluctuation range after parameter perturbation is decreased by 46%. These findings verify that ISTSM-ADRC exhibits better dynamic performance and stronger robustness than STSM-ADRC and PI control strategies.
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Zhang Tian, Cui Bowen, Cui Xinmiao
2026,49(10):107-117, DOI:
Abstract:
To address the problems of missed detections and low detection accuracy of YOLOv11n in photovoltaic (PV) panel defect detection, this paper proposes an improved YOLOv11n-based PV panel defect detection algorithm. A novel SPPF-LDESKA module is designed to enhance the original spatial pyramid pooling fast (SPPF) module. The module integrates the concepts of lightweight detail-enhanced convolution, which combines model lightweighting and fine-grained feature enhancement—with a large separable kernel attention mechanism to expand the receptive field and effectively strengthen the extraction of small-object information.In the feature pyramid network （FPN） stage, inspired by the context-guided feature modulation (CGFM), an efficient shared convolutional module (ESCM) is designed. This module incorporates the efficient channel attention （ECA） mechanism, employing 1D convolution along the channel dimension to perform local inter-channel interaction. Furthermore, shared convolution is utilized to reduce redundant computation and prevent repetitive learning, thereby decreasing the parameter count and enhancing the adaptive adjustment of contextual information in multi-scale feature fusion.Additionally, a lightweight detection head is introduced, leveraging shared convolution to further reduce model parameters. Experimental results demonstrate that, compared with the original YOLOv11n model, the proposed method reduces computational cost by 6.25%, accuracy improved by 4.5%,while mAP@50 improves from 87.5% to 89.5%, achieving a 2.3% performance gain. These results validate that the improved algorithm achieves superior performance and better adaptability for PV panel defect detection tasks.
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2026,49(10):118-129, DOI:
Abstract:
To address the issues of low detection accuracy, high computational cost and deployment difficulties in existing tomato leaf disease detection algorithms, this paper proposes a lightweight improved algorithm, YOLO11-WCL, based on the YOLO11n model. The algorithm introduces lightweight anti-aliasing wavelet pooling in the backbone network to replace traditional downsampling operations, effectively reducing network complexity. In the Neck part, a lightweight CA-HSFPN module is incorporated to enhance multi-scale feature fusion, while the LADH detection head is adopted to improve inference speed. Based on the experimental dataset, comparative analyses of lightweight networks, different detection algorithms, and ablation experiments were conducted. The experimental results show that the YOLO11-WCL model achieves only 50.1% and 60.3% of the parameters and computational complexity of the original YOLO11n model, corresponding to 1.29 MB and 3.8 GFLOPs, respectively, while reaching an mAP of 98.5%. These findings demonstrate that the proposed algorithm significantly improves the detection accuracy of tomato leaf diseases while maintaining model compactness, achieving high detection efficiency and good generalization performance. It is suitable for deployment on UAVs and other mobile devices, with broad application prospects and practical market potential.
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Su Zhihao, Yu Hongfei, Cao Yang
2026,49(10):130-140, DOI:
Abstract:
To address the issue of degraded matching accuracy in reflective regions, a stereo matching algorithm is proposed based on a multi-kernel attention decoder and a cost volume dynamic enhancement mechanism. First, the feature extraction network employs a multi-kernel attention decoder, which utilizes parallel depth-wise convolutions to capture spatial details at different resolutions. This enhances the algorithm′s ability to fuse local details and global information, thereby improving disparity prediction accuracy in reflective regions. Next, the algorithm adopts a cost volume dynamic enhancement mechanism to optimize the cost volume. By selectively fusing multi-scale cost vol-umes based on global contextual information, it avoids redundant information accumulation, thus strengthening the cost volume′s modeling capability in reflective regions and mitigating the accuracy degradation problem. Experi-mental results on the Scene Flow dataset show that the proposed method achieves 0.45 EPE and 2.40% D1, while on reflective regions of the KITTI dataset, it attains 4.59% 3-All error, representing an 8.2% reduction com-pared to baseline methods. These results demonstrate that the proposed approach effectively improves matching accuracy in reflective regions while also reducing model parameters.
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Wang Haiqun, Guan Mei, Yu Haifeng, Pan Chuang
2026,49(10):141-151, DOI:
Abstract:
In order to solve the problem of low detection precision caused by complex background environment, large target scale difference, various categories and uneven distribution, an improved YOLOv11n defect detection algorithm for fan blades is proposed. Firstly, an efficient multi-scale convolution EMSConv was innovatively proposed, and C3k2 was redesigned to enable the model to efficiently capture the input feature map information and enhance the detection accuracy; secondly, introduce the large separable kernel attention mechanism and Residual-Conv design SPPF_LSKR module to replace the original pyramid pooling module, enrich the context information and improve the multi-scale feature extraction and fusion capabilities of the model; thirdly, the re-parameter shared convolution detection head (RSCD) is adopted to reduce the number of parameters and computational load of the head by sharing parameters, thereby enhancing the speed and accuracy of the defect detection task; finally, a loss function Inner-Wise-MPDIoU is proposed based on the ideas of MPDIoU, Inner _IoU and Wise_IoU, which balances the detection of defects of different scales and accelerate the convergence speed of the model. The results of the experiments indicate that the modified YOLOv11n model attains an mAP value of 89.2%, which is 2.9% higher than that of the original model. The results show that the improved model can meet the needs of efficient and accurate detection of fan blade defects.
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Qiu Jidong, Lin Ze, Yu Jing, Guan Jiaxing, Wang Yabin
2026,49(10):152-161, DOI:
Abstract:
With the extensive application of multi-task learning in complex business scenarios such as intelligent recommendation and autonomous driving, the handling mechanism for heterogeneous task priorities has emerged as a critical bottleneck constraining model performance. Existing approaches predominantly rely on empirical strategies of linear loss function combination, which suffer from dual deficiencies: First, The manual parameter-tuning paradigm for task weighting leads to combinatorial explosion in hyperparameter search space as task quantity increases; second, gradient competition among tasks induces negative transfer effects that significantly erode the performance boundaries of high-priority tasks. To address these challenges, this study proposes an innovative constrained optimization-based progressive multi-task learning method. By encoding task priority structures into inequality constraints, we formulate an optimization paradigm with strict priority guarantees. A constrained optimization framework is established through Lagrangian duality theory to ensure that the performance lower-bound constraints of high-priority tasks remain unaffected by secondary task optimization processes. Meanwhile, a progressive gradient projection algorithm enables dynamic adjustment of constraint spaces. Theoretically, we provide convergence guarantees through non-convex optimization theory. Experimental results on public datasets demonstrate that our method enhances the performance of secondary tasks while ensuring the stability of high-priority tasks, establishing a novel theoretical framework and technical pathway for multi task learning.
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Cai Chunlei, Liu Wei, Yang Diya
2026,49(10):162-173, DOI:
Abstract:
Addressing the limitations of the dung beetle optimization algorithm, such as weak global search capability, slow convergence rate and susceptibility to local optima, this paper innovatively proposes an enhanced dung beetle optimization algorithm based on the golden sine algorithm, named GSDBO algorithm. Firstly, the population initialization is optimized using Tent chaotic mapping and lens imaging reverse learning strategy to generate high-quality initial solutions; secondly, an improved golden sine algorithm is adopted to replace the original position update mechanism of the rolling ball beetle, in order to improve the global search accuracy and convergence speed of the algorithm; finally, by combining the t-distribution mutation strategy and adaptive adjustment mechanism, a dynamic balance is achieved between global exploration and local development capabilities. The experiment used CEC2005 and CEC2020 test functions, combined with Wilcoxon rank sum test, to verify the effectiveness and feasibility of the proposed algorithm. The results showed that the GSDBO algorithm exhibited significant improvements in convergence speed and solution accuracy. In the three engineering optimization problems of cantilever beam design, welding beam design, and robot path planning, this algorithm has obtained the optimal solution, further verifying its effectiveness in solving complex practical problems.
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Guo Shijia, Zhang Baolin, Ma Wanyun, Zhang Yuchu
2026,49(10):174-181, DOI:
Abstract:
Maliciously tampered images pose serious threats to both daily life and society. Although numerous detection models have been developed for image tampering detection, they still suffer from issues such as the loss of fine details and insufficient capability in detecting small targets. To address these challenges, this study proposes a multi-scale cross-layer fusion-based for image tampering localization. Built upon the RRU-Net framework, the model changes the skip connection scheme and enhances the encoder features in skip connections using the MSC-SC structure with atrous spatial pyramid pooling. A cross-layer fusion module is then designed to adaptively fuse the encoder and decoder feature maps. In addition, a triple attention mechanism is introduced to enhance feature perception during the down sampling process. Finally, a joint loss function is utilized to alleviate the imbalance between positive and negative samples. Experiments conducted on the CASIA v2 and COLUMB datasets demonstrate that the proposed method achieves F1 score improvements of 11.33% and 6.84%, respectively, compared with the original RRU-Net, indicating that significant effectiveness is achieved.
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Zhou Wenbo, Zhang Yonghong, Wang Junfei, Yang Tianxiao
2026,49(10):182-196, DOI:
Abstract:
Addressing the issues of insufficient accuracy and low computational efficiency in quantitative precipitation estimation for dual-polarization radar, this paper proposes a lightweight multi-scale fusion algorithm, DPCR-Net, based on an improved DeepLabV3+. This algorithm employs a lightweight backbone network combined with inverted residual and dilated convolution to reduce parameters while maintaining feature extraction capabilities; enhances feature reuse and sensitivity to small targets through a densely cascaded adaptive feature pyramid; and utilizes a hybrid attention decoder to fuse multi-scale features, suppress noise and preserve details. Experimental results on the NJU-CPOL dataset show that the method achieves a mean absolute error (MAE), root mean squared error (RMSE) and correlation coefficient (CC) of 0.519 7, 4.174 4 and 0.661 0, respectively, with a hit rate of 0.897 4, a parameter count of only 4.11 M and a computational cost of 8.29 G. Compared to mainstream models, the algorithm proposed in this paper improves both estimation accuracy and computational efficiency, enabling high-precision real-time precipitation estimation and deployment on edge devices.
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Zhang Rongkai, Jing Mingli, Jiao Long
2026,49(10):197-205, DOI:
Abstract:
Denoising diffusion probabilistic models（DDPM）have demonstrated powerful image generation capabilities and have been successfully applied to image inpainting. While many recent approaches introduce structural priors to assist the inpainting process and have achieved promising results, they still suffer from limitations in structural integrity and the naturalness of texture details, often resulting in fractured or discontinuous regions. To address these issues, this paper proposes a gradient-guided feature reconstruction algorithm based on diffusion models. Specifically, the gradient map of the corrupted image is first restored to preliminarily reconstruct its structure and texture. The restored gradient map is then used as generation guidance to assist the inpainting of the original image. Furthermore, an efficient channel attention (ECA) module is integrated into the noise prediction network to enhance feature interaction and improve the consistency of the reconstructed images. Experimental results show that compared with state-of-the-art methods, the proposed approach achieves improvements of 3.19% in PSNR and 2.74% in SSIM, and a reduction of 8.82% in LPIPS on the CelebA-HQ dataset. On the Places2 dataset, it achieves gains of 0.42% in PSNR and 2.81% in SSIM, with an 2.75% decrease in LPIPS, demonstrating superior capability in structural restoration and detail preservation for image inpainting tasks.
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Xu Shilin, Wang Wencheng, Lu Xiaojie, Yu Zhike, Zheng Shihan
2026,49(10):206-214, DOI:
Abstract:
Aiming at the problem of defects occurring on the surface of 3D metal printed parts due to technology and materials, a surface defect detection method for 3D metal printed parts based on an improved Canny algorithm is proposed, which processes the defect images of 3D metal printed parts by using the improved algorithm. First, an improved bilateral filter is used to replace Gaussian filtering to handle the surface defect images of metal printed parts under salt- and-pepper noise. Then, a dynamically weighted four-direction Scharr operator is adopted to calculate the gradient amplitude and determine the gradient direction, so as to better highlight the edge information of 3D metal printed parts. Next, an improved non-maximum suppression algorithm is employed to further process the image. Finally, the dynamic threshold and hysteresis edge tracking algorithm are utilized to realize the selection of high and low thresholds and enhance the continuity of edges. By comparing the defect detection results of different algorithms on the surface of 3D metal printed parts, the experimental results show that the improved Canny algorithm performs better in noise smoothing and edge continuity than the traditional algorithm. Specifically, the peak signal-to-noise ratio (PSNR) value is increased by 46.70% compared with the traditional Gaussian filter, the structural similarity index (SSIM) value is improved by 39-93%, and the probability of figure of merit (PFOM) value of the processed image is enhanced by 36.46% compared with that of the traditional Canny algorithm. This method can effectively detect the defects on the surface of 3D metal printed parts and has strong practicability.
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Li Fan, Zhou Xun, Zhang Yan, Gu Haiming
2026,49(10):215-227, DOI:
Abstract:
Aiming at the challenges of small industrial defect targets, blurred boundaries and complex backgrounds, this paper proposes a lightweight industrial defect detection network named MSCENet based on multi-scale context enhancement. First, a multi-scale attention enhanced feature extraction module （MAFE） is introduced, which employs parallel multi-scale dilated convolutions to capture defect features under varying receptive fields. Next, a residual enhancement fusion （REF） module is designed to adaptively integrate multi-level features using a dual-attention feature enhancement mechanism alongside residual connections, thereby improving the reconstruction quality of defect boundaries and details in the decoder. Furthermore, a global attention aggregation （GAA） module is proposed to focus on defect regions while suppressing background interference, further enhancing detection accuracy and robustness. Experimental results on three industrial defect datasets demonstrate that, compared to the backbone network FasterNet-T1, the proposed method achieves significant improvements in mean intersection over union （IoU）, mean pixel accuracy, and overall accuracy, with only 9.418 million parameters, while improving industrial inspection efficiency.
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2026,49(10):228-235, DOI:
Abstract:
To address the issues of long median search time, complex processing, and poor denoising performance of adaptive median filtering under high-density salt-and-pepper noise, a neighborhood-priority multi-level mean filtering algorithm is proposed. The algorithm uses switch filtering to classify pixels, constructs nine priority regions within a 7×7 neighborhood of noisy pixels, and progressively selects valid pixels for mean filtering. After searching the entire neighborhood, a misjudgment prevention mechanism is applied to restore normal pixels. Experimental results show that the proposed algorithm exhibits good denoising performance and robustness within the noise density range of 0% to 90%. Compared to AFMF, IMAF, NAMD and hybrid filtering algorithms, the proposed method achieves a peak PSNR improvement of over 3 dB at low noise densities, with a 20% improvement in SSIM at 90% noise density. The overall processing speed improves by more than 8 times, and the filtered SSIM remains above 87% even under extreme noise conditions. This algorithm requires no parameter tuning, is simple to implement, incurs low computational overhead, and has strong engineering applicability.
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Feng Yujiao, Zhang Jing, Zhao Wenhui, Wang Yongzai
2026,49(10):236-242, DOI:
Abstract:
Chipping defects on wafer dicing lanes severely impair wafer dicing quality, creating an urgent need for high-precision detection technology to support quality assessment. This paper proposes a sub-pixel edge detection method based on Gaussian fitting. First, image quality is optimized, and dicing lane features are isolated through region of interest (ROI) extraction. Second, accurate initial localization of pixel-level edges is achieved. Finally, edge points are selected with the pixel-level edge point as the center; a sub-pixel edge detection algorithm based on Gaussian peak position estimation is used to calculate sub-pixel coordinates via Gaussian integral curve fitting. Combined with random sample consensus（RANSAC） line fitting and density-based spatialclustering of applications with noise（DBSCAN） clustering, the identification and quantification of abnormal regions are completed. Experiments were conducted using white light interferometer measurements as the reference. Tests on 20 dicing lane samples show that the error range is -1.92~3.73 μm. Compared with the Zernike moment sub-pixel algorithm, interpolation method, and traditional Gaussian fitting algorithm, this method exhibits lower error and better stability, and can provide a reliable technical solution for industrial batch detection.
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Yi Chu, Yuan Jinming, Liang Junhao, Luo Yunhan
2026,49(10):243-247, DOI:
Abstract:
In view of the high requirements for temperature control stability of multi-channel surface plasmon resonance (SPR) sensing systems in the detection process of biomolecular interactions, this study designs and implements a separable local constant temperature system. Taking the thermoelectric cooler (TEC) as the core executive component, this system combines oxygen-free copper clamps, digital temperature control modules, thermistor temperature sensors and air-cooled heat dissipation structures to realize local temperature regulation of the sensing area of SPR microfluidic chips. The experimental results show that the system can achieve stable temperature control in a wide temperature range from 15℃ to 45℃, and the temperature fluctuation range is less than ±0.05℃. After the optimization of external constant temperature compensation, the difference between the actual temperature and the set value in the chip sensing area can be controlled within 0.47℃. After being integrated with the SPR imaging system, the average baseline noise is reduced by 13.3%. The temperature control system designed in this study has the advantages of compact structure, separable form and high temperature control accuracy, which is suitable for multi-channel SPR biosensing systems.
Volume 49, 2026 Issue 10
Research&Design
Intelligent Control & Performance Testing
Sensor and Non-electricity Measurement
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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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:
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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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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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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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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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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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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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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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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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.
