- Current Issue
- Online First
- Most Downloaded Archive
-
Li Hao, Ma Zhenzhe, Yang Yunyun, Li Zhirui, Xu Xinying
2026,49(13):1-12, DOI:
Abstract:
With the rapid development of power system intelligence and unattended substations, intelligent inspection robot play an increasingly important role in equipment operation state perception and safety monitoring. Aiming at the problems of missed detection and false detection in the meter detection process of the inspection robot in the substation environment with complex lighting conditions and changing target scales, this study proposes a pointer meter detection model of substation intelligent inspection robot (SRPMNet). By constructing the average pooling down sampling (ADown) module, the computational complexity is reduced and the detection accuracy of the small target meter is improved. A lightweight attention mechanism is designed to highlight key feature information and suppress irrelevant background interference. In order to further improve the accuracy and feature expression ability of pointer meter detection in complex environment of substation, this study constructs an auxiliary detection head strategy. At the same time, the wise complete intersection over union (Wise-CIoU） is proposed in the boundary frame regression process to improve the positioning accuracy and convergence stability of the model in the complex background of the substation. This study builds a substation intelligent inspection robot platform for data collection. The experimental results show that the mAP@0.5 index of the model on the self-built substation pointer meter dataset and the public industrial meter dataset reaches 91.7% and 90.4%, respectively, which is 3.0% and 5.3% higher than the baseline model. It effectively solves the problem of missed detection and false detection, and provides effective technical support for substation intelligent inspection tasks.
-
Chen Qidong, Qian Jiahao, Wu Hao, Ye Guangfei, Zhang Songyang
2026,49(13):13-26, DOI:
Abstract:
For volatile multi-metric application performance time series in cloud-native environments, we propose an integrated forecasting and monitoring method and system based on an enhanced Informer. At the encoder side, context-aware reversible normalization (CA-RevIN) is introduced to mitigate distribution shifts, while multi-frequency band convolutional enhancement (MFBCEM) and long-term dependency aggregation (LTA) are used for multi-scale modeling. A generative decoder produces multi-step forecasts. On the system side, a closed-loop architecture is built for data acquisition, feature governance, inference and alerting. Peaks-over-threshold (POT) and adaptive residual thresholds enable hierarchical anomaly detection and early warning. Experiments on public datasetsand real production data show that the proposed approach outperforms LSTM, GRU and the vanilla Informer in multi-step prediction accuracy and stability, while reducing false positives and missed detections.
-
Yang Sihan, He Qing, Yang Shiqi
2026,49(13):27-35, DOI:
Abstract:
With the rapid development of intelligent driving technology, driver attention monitoring has become a crucial research focus for ensuring road safety. Traditional distraction detection methods rely on discrete behavior classification, which fails to capture the continuous variation of driver attention and often neglects individual differences in physiological structures and gaze habits.To address these limitations, this paper proposes a gaze estimation-based distraction detection method that continuously identifies distraction states by evaluating the deviation of gaze Euler angles from a predefined safe attention region. The proposed model adopts a ResNeXt-Transformer hybrid architecture that integrates local detail extraction with global dependency modeling, and incorporates a feature enhancement module to strengthen task-relevant representations and improve gaze feature extraction. Furthermore, a multi-level personalized calibration mechanism performs bias compensation at both the feature and output layers, enabling rapid individual adaptation under few-shot conditions.Experimental results on the MPIIGaze dataset demonstrate that the proposed method significantly outperforms existing approaches in cross-user gaze estimation, reducing the average angular error to 2.94°. Visualization and validation in real-world driving scenarios further confirm that the method exhibits strong interpretability and high practical applicability.
-
Li Liangchuan, Guo Longyi, Sun Meng, Zhang Xiaodong, Zhong Shan
2026,49(13):36-44, DOI:
Abstract:
To address the challenges of low accuracy and high model complexity in tunnel blast hole recognition caused by poor lighting and high dust levels, this study proposes a lightweight blast hole detection algorithm that integrates infrared thermal imaging with deep learning technology. Using YOLOv11n as the baseline model, the lightweight StarNet network is first incorporated to enhance multi-scale feature extraction capabilities and optimize computational efficiency. Subsequently, the Star Block module is introduced to construct the C3k2_BlockStar module, improving the fusion of features at different scales. Finally, a lightweight detection head is designed to further reduce computational complexity. Experimental results demonstrate that the improved model reduces the number of parameters by 54% and the model size to 32% of the original, achieving effective recognition of tunnel infrared blast holes. This research provides a technical foundation for robotic automated charging in tunnel construction, offering significant practical value and application prospects.
-
Li Yin, Han Jitai, Zhang Kaiwen, Yu Min, Liu Xiaoqing
2026,49(13):45-55, DOI:
Abstract:
Dermoscopic image segmentation faces significant challenges due to complex lesion morphology, blurred boundaries and noise interference. This study proposes a deep learning model named MelanoFusionNet, which employs a dual-branch encoder composed of ResNet50 and an improved Mamba VSS module. A multi-scale attention fusion decoder (MSAFD) is introduced to integrate both local and global information. Within the decoder, the extended kernel grouped gate (EKGG) and the hierarchical scale-aware attention module (HSAM) enhance feature fusion and boundary precision, while the depthwise upsample refinement block (DURB) improves reconstruction efficiency. The model is trained with a weighted combination of Dice and boundary losses. Experiments on the ISIC2018 dataset demonstrate that MelanoFusionNet outperforms mainstream methods across multiple metrics and provides reliable support for computer-aided skin cancer diagnosis.
-
Lu Zijia, Tian Hanmin, Tian Xuemin, Wang Yuhao
2026,49(13):56-62, DOI:
Abstract:
This study develops a three-dimensional finite-element model of a dense-electrode electric capacitance tomography （ECT） sensor under a quasi-static electrostatic field. How electrode-plate axial height, radial angle and interlayer spacing affect the capacitance dynamic range and the uniformity of sensitivity are investigated, and feasible design intervals are final determined. A composite performance objective function is constructed from the image relative error, image correlation coefficient and spatial image error, and the sensor geometry is optimized via response surface methodology (RSM). Results show that the optimized 8×8 dense electrode array markedly improves oil-water two-phase flow imaging: The image correlation coefficient is increased by 9.4%, and the image relative error and spatial image error are reduced by 13.4% and 15.2% respectively. Interaction effects among structural parameters are further analyzed to ensure robustness. The optimized dense array delivers higher resolution and accuracy, demonstrating strong potential of this ECT sensor for multiphase-flow measurement.
-
Lu Xiaojie, Wang Wencheng, Yu Zhike, Xu Shilin, Zheng Shihan
2026,49(13):63-74, DOI:
Abstract:
Aiming at the problems of difficult parameter tuning, poor anti-interference ability and low trajectory tracking accuracy of automatic weeding control systems in complex field operating environments, which lead to difficulties in mechanical seedling avoidance and weeding between soybean plants and a high seedling damage rate, a soybean inter-plant seedling avoidance and weeding control system optimized by (multi-strategyimproved whale optimization algorithm,MSWOA） is proposed. This system controls the angular velocity of the stepper motor to achieve precise seedling avoidance and weeding with the weeding knife. Firstly, a mathematical model for inter-plant seedling avoidance and weeding is established, and a fuzzy controller is combined with a linear active disturbance rejection controller (LADRC) to improve the anti-disturbance performance of the system. Secondly, the MSWOA is used to tune the key parameters of the fuzzy-linear active disturbance rejection controller (Fuzzy-LADRC). Comparative tests with benchmark functions prove that MSWOA has better optimization performance and stability. Finally, simulation experiments are conducted on the inter-plant weeding control system model to determine the optimal control method. The simulation results show that the improved algorithm-based control method exhibits faster convergence speed and higher convergence accuracy compared with other control methods, effectively enhancing the system′s control performance. Specifically, the Fuzzy-LADRC control system optimized by MSWOA reduces the disturbance recovery time by 86.0% and 10.9% respectively in contrast to the traditional PID and LADRC control systems. Additionally, by comparing the parameter tuning effects of MSWOA with other algorithms, the superiority of the improved algorithm in this control system is further verified. This control system possesses distinct advantages in anti disturbance capability and response speed, enabling it to adapt to complex field working environments and meet the control requirements for precise seedling avoidance and weeding operations.
-
Yin Xiaohu, Li Yuxuan, Ma Zikai, Peng Shuai
2026,49(13):75-81, DOI:
Abstract:
In cognitive radio systems, to address the problem of inaccurate noise power estimation in traditional energy detection algorithms, a spectrum sensing optimization approach based on dynamic sub-band partitioning is proposed. Two methods are designed in this study. The first method employs the fluctuation degree of the signal envelope to divide the spectrum into multiple sub-bands and introduces an adaptive threshold coefficient to form an adaptive spectrum sensing scheme. The second method utilizes a sliding window to dynamically partition sub-bands and incorporates a single optimal threshold detection mechanism, referred to as the optimal threshold method, to achieve enhanced sub-band division and spectrum sensing.Simulation results demonstrate that, compared with the conventional single optimal threshold detection, the proposed optimal threshold method achieves up to a 10% improvement in detection performance within the SNR range of -11.7 to 0 dB, and exhibits significantly higher detection probability than the adaptive spectrum sensing method over the range of -20 to 0 dB. Overall, the optimal threshold method shows superior spectrum sensing capability and robustness.
-
Xiao Junjie, Wang Guanling, Cheng Jun, Wang Buyun, Xu Dezhang
2026,49(13):82-89, DOI:
Abstract:
To address the cumulative drift issue commonly encountered during long-term autonomous navigation of unmanned forklifts in warehouses, this paper presents a localization system based on the LeGO-LOAM framework, which incorporates ground-mounted Aruco markers. The system utilizes LiDAR to provide continuous odometry constraints, while the Aruco markers offer global pose anchors upon detection. These two sources of information are fused through back-end factor graph optimization. For practical engineering deployment, this paper details the marker arrangement strategy, sensor extrinsic calibration process and the method for setting graph optimization weights. Experiments conducted in a real indoor corridor environment demonstrate that the root mean square error (RMSE) of localization is reduced from 0.635 1 m to 0.272 8 m (a decrease of 57.0%). The system maintains stable performance even under partial marker occlusion. The results indicate that this solution offers significant engineering advantages, including low implementation cost, ease of deployment and strong maintainability, making it suitable for autonomous navigation tasks in warehouse logistics scenarios.
-
Xu Yunxia, Lu Qishuai, Yao Fujin, Han Shuo
2026,49(13):90-99, DOI:
Abstract:
Aiming at the problems such as the closed encoder protocol of the displacement actuator of the spliced mirror telescope and the high power consumption and large volume of the existing decoding schemes, a high-precision position feedback system based on SoC-FPGA technology is proposed to realize the open decoding of the EnDat2.2 protocol, in order to meet the requirements of miniaturization, low power consumption and high integration for domestic substitution. The PS-PL collaborative processing architecture is constructed using the Zynq-7000 platform. The PS end uses the dual-core Cortex-A9 core to complete system control and communication scheduling. The PL end implements Manchester codec and CRC-5 verification through hardware logic. Design a driver interface circuit centered on ADM1485 and ADuM1301 to enhance the anti-interference capability. And the IDELAYE2 module is utilized for dynamic timing compensation to solve the problem of data phase offset caused by physical link differences. Experiments show that the position feedback accuracy of the system reaches 368 nm. Large-scale error injection tests demonstrate that the CRC-5 verification achieves a detection success rate of 99.99% at a 0.2% error rate, while the PCB volume is reduced by approximately 31.6% and the power consumption is lowered by more than 11 times. This solution successfully realizes high-reliability decoding of the EnDat2.2 protocol, providing crucial technical support for the miniaturization, low power consumption and high integration of displacement control systems in China′s astronomical telescopes and high-precision numerical control equipment. It holds significant engineering application value and potential for independent promotion. Future work may further optimize algorithms to enhance stability under extreme conditions.
-
Guo Haijie, Cui Xuehong, Gong Yujie, Wang Xu, Xie Yongqi
2026,49(13):100-109, DOI:
Abstract:
To address the challenges of dense stacking, blurred boundaries and uneven illumination of raw material pellets in the ironmaking process, this paper proposes a cascaded particle size detection and segmentation framework based on an improved RT-DETR and MobileSAM. In the detection stage, a coordinate attention （CA） mechanism is embedded into the backbone network to enhance feature extraction capabilities. Furthermore, an efficient discriminative frequency domain-based feedforward network （EDFFN） is introduced into the AIFI encoder and a multi-scale feature modulation （MFM） fusion module is employed to replace the traditional feature fusion method. Subsequently, the generated detection boxes serve as prompts to guide MobileSAM in generating initial masks. To optimize segmentation quality, a refiner incorporating dynamic receptive fields and efficient multi-scale attention (EMA) is designed for multi-scale adaptive repair of mask edges, which is then combined with geometric fitting to achieve precise particle size measurement. Experimental results on the pellet dataset demonstrate that the proposed method achieves a recall rate of 95.7% and a mIoU of 90.1%, effectively improving segmentation accuracy and mitigating over-segmentation. This study provides a high-precision and robust solution for online particle size monitoring in the industrial ironmaking process.
-
Yang Hongtao, Ke Yuan, Shen Mei, Li Tianzeng
2026,49(13):110-120, DOI:
Abstract:
Aiming at the problem that the measurement system of the articulated arm measuring machine cannot complete complex functions such as trajectory planning and error compensation, a measurement system for the articulated arm coordinate measuring machine based on the Unity platform was developed. The functional requirements of the articulated arm measuring machine were analyzed and the corresponding algorithm model was established. The measurement system of the measuring machine was built on the Unity platform. For the measurement system, the virtual prototype display function, the synchronous movement function of the virtual machine and the actual prototype, the multi-joint synchronous trajectory planning function and the error compensation function were respectively designed. The measurement system was used to complete the measurement experiment of the standard ball diameter. The results show that the average error after compensation is 0.015 5 mm. The integration of the measurement system functions of the articulated arm measuring machine was completed, laying the foundation for the realization of the automatic measurement of the measuring machine.
-
Zhang Xingqi, Yang Keran, Liu Jinjiang, Gong Qingchao, Xie Yonghua
2026,49(13):121-130, DOI:
Abstract:
This study presents a lightweight multi-object detection and tracking system tailored for intelligent wildlife monitoring under complex field conditions. To handle background interference, occlusion and limited edge-computing resources, the proposed framework integrates an improved YOLOv8 with ByteTrack. A global attention mechanism (GAM) enhances feature focus and noise suppression, while a lightweight YOLO-based tracking backbone (L-YOLOTrack) reduces model complexity and supports real-time deployment. In the tracking stage, a structure-aware calibration loss (SCALoss) improves localization under occlusion and an enhanced extended Kalman filter (EKF) with adaptive noise adjustment strengthens trajectory continuity under nonlinear motion. Experiments on a wildlife dataset show that mAP@0.5-0.95 increases from 40.1% to 45.7%, multiple object tracking accuracy （MOTA) rises by 14.3%, and parameters decrease by 9.3%. The results demonstrate that the proposed system achieves higher detection accuracy and tracking robustness, offering an efficient and reliable solution for wildlife monitoring.
-
Liu Anqi, Yang Hongbo, Pan Hang, Chen Jinlong, Zhan Yongsong
2026,49(13):131-142, DOI:
Abstract:
In the automated harvesting of cluster tomatoes in greenhouse environments, rapid identification of cluster tomatoes and precise localization of picking points are critical. This work addresses the requirements for rapid cluster recognition and accurate picking point localization in automated cluster tomato harvesting within greenhouses by proposing a visual detection and localization method based on an improved YOLOv11-Pose. The method first constructs a dataset containing simultaneous annotations of cluster tomatoes and picking points; then introduces two modules GhostC3ECA and C3k2_CA at different positions within the original YOLOv11-Pose model to achieve network lightweighting and precise picking point localization, respectively. This ultimately enables end-to-end real-time detection of cluster tomatoes and accurate picking point localization. In the network′s backend, we utilize pose-aware non-maximum suppression to optimize detection of densely clustered fruits and key points localization. Experiments validated the approach on a glasshouse-collected cluster tomato dataset, achieving 96.2% recognition accuracy and 86.5% picking point localization accuracy. The model operates at 67.2 fps, meeting real-time processing demands. Verified by real-word greenhouse harvesting scenarios, the proposed cluster tomatoes recognition and picking point localization method based on an improved YOLOv11-Pose provides an efficient and reliable visual detection solution for automated harvesting robots.
-
Wang Saifei, Liu Wuyang, Zhu Ruipu, Tang Mingtao, Dong Huimin
2026,49(13):143-151, DOI:
Abstract:
As a core electrical parameter of high-speed interconnection links, the measurement accuracy of differential impedance and common mode impedance is critical to ensuring signal integrity. However, the fixture-induced pull effect causes the measured values to deviate significantly from their true values, which has become a prevalent challenge in the high-precision testing of high-speed connectors for Ethernet systems. Most existing studies rely on single-factor analysis methods, which fail to reveal the synergistic mechanism of tolerance and electromagnetic coupling. This paper proposes a dual-factor pull model that integrates tolerance variation and coupling strength. By introducing a tolerance variation coefficient and a coupling coefficient, a quantitative method for the pull factor is established, enabling accurate quantification of the pull effect. Simulation and experimental results indicate that the proposed model can effectively characterize the synergistic interaction mechanism between tolerance variation and electromagnetic coupling. Under the strong coupling condition, the deviation rate of impedance testing for the fixture with 3% tolerance can be controlled within 16.8% after being accurately quantified by the model, and the error between the model prediction value and the measured value is within 3%. This study provides a theoretical model and experimental basis for high-precision impedance testing, and is of great value for improving the reliability of performance evaluation for high-speed connecting equipment.
-
Wei Qiangyu, Tusongjiang·Kari, Zhang Ziwei, Li Zhenen, Huang Rui
2026,49(13):152-162, DOI:
Abstract:
To address low accuracy in transformer fault diagnosis under small-sample conditions, we propose a method based on an improved prototypical network. First, a residual multi-layer perceptron extracts features from transformer oil chromatography data to obtain more discriminative representations. Second, we introduce a dual-layer attention-driven prototype construction mechanism: Task-conditioned attention dynamically adjusts support-set representations, and sample attention weights support samples to emphasize discriminative features. This yields more distinguishable class prototypes and addresses prototypical networks′ limitations in cross-task adaptability and intra-class discriminability. Finally, diagnosis is performed by measuring the Euclidean distance between samples and class prototypes. Experiments on the IEEE Dataport and IEC TC 10 Dataport datasets show significant improvements: On the IEEE Dataport dataset our method outperforms the prototypical network, matching network and relation network by 15.14%, 21.49% and 18.68%, respectively; on the IEC TC 10 Dataport the gains are 6.43%, 6.90% and 8.65%, respectively. The study provide new insights and references for transformer fault diagnosis under small-sample conditions.
-
Ge Xiaoliang, Zuo Yunbo, Chen Sai, Wang Shaohong, Miao Zhuoran
2026,49(13):163-170, DOI:
Abstract:
Against the complex detection challenges posed by the multi-scale, multi-category, and dense distribution of objects in the daily item recognition task for intelligent household service robots, an improved YOLOv10-DLC object detection algorithm is proposed: By designing a diverse branch module for feature fusion (C2f-DBB), the algorithm network′s ability to detect objects of different scales is enhanced; By adopting a combined module of fast spatial pyramid pooling and large kernel separable convolution attention (SPPF-LSKA), the algorithm network′s ability to understand the spatial structure of features is improved, and the key features of objects are highlighted; By using the content-aware feature reassembly (CARAFE) operator, the accuracy of detail reconstruction during image upsampling in the network is improved. Experimental analysis shows that the improved YOLOv10-DLC algorithm has improved the accuracy of object detection, with its mAP value reaching 94.3%, an increase of 3.6% compared with the original algorithm network model.
-
Zhang Yang, Qin Changhao, Kong Shuo, Qu Yajun, Zhao Hui
2026,49(13):171-180, DOI:
Abstract:
To address the challenges associated with complex background textures on the inner walls of water pipelines and the difficulty in distinguishing minute cracks from irregular fouling, this paper proposes a high-precision detection algorithm based on an improved YOLOv11n. First, the bidirectional feature pyramid network (BiFPN) is utilized to reconstruct the neck network. By employing a fast normalized fusion mechanism, this approach achieves dynamic aggregation of multi-scale features, thereby enhancing the feature representation capability for minute and variable-scale targets. Second, a global context network (GCNet) module is embedded at the end of the feature fusion stage to establish pixel-level long-range dependencies. This strengthens global background understanding and effectively suppresses noise interference such as limescale. Finally, a Focaler-MPDIoU loss function is constructed by integrating geometric optimization with a dynamic focusing mechanism to improve localization accuracy and balance the training of hard and easy samples. Experimental results demonstrate that the improved algorithm achieves an mAP50 of 91.40% on a self-constructed dataset, representing an improvement of approximately 9.5% over the baseline. The proposed method significantly enhances robustness while satisfying real-time requirements, offering substantial value for engineering applications.
-
Zhong Changbin, Shi Jianping, Zhu Mengyang, Yang Hongkai
2026,49(13):181-189, DOI:
Abstract:
To address the issues in printed circuit board(PCB)surface defect detection networks, such as low sensitivity to small defects and low detection efficiency, a PCB defect detection algorithm CID-YOLO based on the improved YOLOv11 is proposed. First, the C3k2 module is improved by integrating Inception Depthwise Convolution, which, through a multi-branch feature extraction mechanism, reduces the loss of high-level feature information of PCB surface defects caused by increased network depth and decreases the number of model parameters. Second, a dynamic activation mechanism, Dynamic Tanh, is introduced to improve the C2PSA module, enhancing the model′s nonlinear representation capability, allowing it to better adapt to defect features of different scales. Next, the CGBD downsampling module is embedded to improve the recognition capability for special PCB surface defects through an efficient context information extraction mechanism. Finally, the original classification loss function is replaced, and a dynamic weighting strategy is applied to suppress gradient bias caused by data imbalance, effectively improving model generalization performance. Experimental results show that the proposed CID-YOLO model achieves mAP50 and mAP50.95 scores of 96.0% and 52.7%, respectively, on the Peking University public PCB defect dataset, improving 2.4% and 1.6% compared to the baseline model, demonstrating the effectiveness of the proposed algorithm.
-
Pei Xiaofang, Yang Jihai, Zhou Jin, Xu Yongheng
2026,49(13):190-202, DOI:
Abstract:
In response to the problem that background interference leads to insufficient extraction of key features of underwater targets and the attention area cannot cover the entire target, an underwater target detection algorithm with mixed attention and dynamic feature guidance network was proposed. This method first designs a full-dimension dynamic extraction module （FDEM） to fully understand the overall features of the target. Secondly, a weighted feature concatenation module (WFCM) is designed to retain key features from the shallow to the deep layers. The CARAFE operator is used for the feature up-sampling operation, so that the key features of local areas can receive more attention. Finally, a hybrid attention mechanism (HAM) is constructed to effectively combine the channel information and pixel information of the target and retain the key features. Experimental verification shows that this method can fully extract key features, pay more attention to the overall features of the target, thereby reducing underwater background interference and further enhancing the underwater target detection performance.
-
Fu Qiang, Peng Zhangwei, Ji Yuanfa, Ren Fenghua
2026,49(13):203-215, DOI:
Abstract:
To address the problems of difficult feature extraction and low detection accuracy caused by small target scales and severe occlusion from the UAV perspective, this paper proposes an improved RT-DETR-based UAV small target detection algorithm named SwiftHawk-DETR. Firstly, the fusion path of the feature pyramid network (FPN) is reconstructed: A tiny target detection layer P2 is added to enhance fine-grained feature capture, and the redundant large target detection layer P5 is removed to simplify computations. Secondly, a deformable fast multi-scale attention network (Dfaster_net) is designed, which strengthens the capture of small target boundary details through a dynamic sampling mechanism while adopting an efficient feature extraction strategy to reduce computational overhead. Thirdly, wavelet feature upgrade (WFU) is introduced into the neck network to alleviate the distortion of edge high-frequency features caused by upsampling and Concat operations in the original model and the slimneck architecture is combined to optimize cross-scale feature fusion. Finally, a loss function based on the weighted fusion of Focaler EIoU and NWD is constructed to improve the small target modeling ability by balancing localization errors and feature distribution differences.Experimental results on the VisDrone2019 and HIT-UAV datasets show that compared with the baseline RT-DETR, the proposed algorithm increases mAP50 and mAP50:95 by 3.8% and 4.2% respectively, and reduces the number of parameters by 61%.
-
Wang Jincong, Yu Zhichao, Yang Haifeng, Tang Puran, Zhang Zhiwei
2026,49(13):216-223, DOI:
Abstract:
Rice is one of the main grain crops in China and the pest stress on rice restricts the healthy and sustainable development of the rice industry. Therefore, it is of great significance to quickly and accurately identify the types of rice pest infestations. An improved YOLO version 8 nano (YOLOv8n) rice pest identification and detection method was proposed to address the issues of low accuracy, easy missed and false detections in rice pest detection. Firstly, the BiFPN pyramid structure is integrated for the integration of effective feature layers. And introduce an attention mechanism module based on squeeze and excitation (SE) between the backbone network and the neck network to improve the network′s feature fusion ability; introducing the Wise-IoUv2 loss function instead of the IoU loss function to reduce the regression accuracy issue of the original IoU loss function; further enhance the detection performance of the network. Extensive experiments were conducted on the rice pest dataset, and the results showed that the improved model proposed in this paper outperforms current mainstream algorithms in terms of mAP, Precision and Recall. Specifically, this method achieved 89.2% mAP, 92.5% Precision and 83.3% Recall. In contrast, the mAP of YOLOv5 is 84.1%, YOLOv8 is 85.2%, YOLOv9 is 86.4%, YOLOv10 is 85.1% and Faster R-CNN is only 59.75%. In terms of precision, our model outperforms YOLOv5 (88.4%), YOLOv8 (89.6%) and has a higher recall than all other models,demonstrating stronger object detection ability and stability. mAP has increased by 4% compared to the original YOLOv8n. Improving the YOLOv8n algorithm can meet the requirements of rice leaf pest identification and detection accuracy, while improving the detection ability of small and dense targets, thereby reducing missed and false detections. The improved YOLOv8n algorithm has certain advantages in accuracy compared to current mainstream algorithms and provides a better method for rice pest detection, which is of great significance for the prevention and control of rice pests.
-
Yang Yang, Wei Weimin, Zheng Shuqing, Ma Kainan, Zhang Jinyang
2026,49(13):224-234, DOI:
Abstract:
Aiming at the problems in UAV aerial images, such as complex illumination interference, low resolution of small targets with high susceptibility to missing detection and false detection and blurred target structures, a target detection method for UAV aerial images based on improved RT-DETR is proposed, denoted as IF-DETR. Firstly, a cross-stage illumination gating unit is designed in the backbone network, which enhances the model′s ability to extract targets under complex illumination environments by modeling illumination and reflection components at the feature level. Secondly, a multi-scale feature fusion structure for small targets is constructed in the neck network, effectively improving the feature extraction and recognition capabilities for small targets. In addition, a Gaussian feature enhancement module is proposed, which utilizes Gaussian prior and spatial attention mechanism to enhance target-related structural information while suppressing background noise. Finally, a Focaler-Powerful-IoU loss function is constructed to achieve more stable and accurate target localization. Experimental results show that compared with the RT-DETR model, the improved IF-DETR algorithm on the Visdrone2019 dataset achieves improvements of 3.0%, 2.3%, 3.9% and 2.0% in mAP50, mAP50:95, recall and precision respectively. It can effectively alleviate the problems of missing detection and false detection in UAV aerial image detection, improve detection performance, and has broad application prospects.
-
Chen Yuanyuan, Zhang Duzhen, Wang Sihao, Yang Changchang
2026,49(13):235-246, DOI:
Abstract:
To address the challenges in industrial surface defect detection, such as variable morphology, low contrast and the frequent missing of small targets. A novel framework for semantic segmentation is presented, which leverages an edge-guided encoder-decoder architecture and local-global Mamba (GLMamba) to realize the synergy of "edge enhancement, global-local encoding and dual-path decoding".Firstly, a local-global Mamba encoder is built. Local details and global context are collaboratively modeled through the deployment of local and global Mamba blocks at shallow and deep layers, respectively.Secondly, an edge feature extraction module (EGFM) is designed, where edge feature maps are explicitly generated by fusing Sobel gradients with multi-scale features and are subsequently enhanced by an edge attention module (EAM). To further inject edge information into the backbone network, a multi-scale boundary gating (MSBG) mechanism is introduced to dynamically modulate encoder and decoder features. The decoder employs a dual-path attention module, where spatial details and channel context are fused in parallel to optimize multi-scale feature fusion efficiency and semantic consistency. Experimental results on the NEU-Seg, MT-Defect and FSSD-12 datasets demonstrate the effectiveness and strong generalization capability of the proposed model.
-
2026,49(13):247-259, DOI:
Abstract:
Automatic detection of fabric defects is a critical step for quality assurance in the textile industry. However, current methods often miss defects because of difficult feature extraction. They can also misclassify defects that have little contrast with the fabric background.To address these challenges, we proposed an improved algorithm based on the YOLO version 11 nano model. First, we introduced a parameterized block with dilated convolutions into the main network backbone to enhance its ability to represent features at multiple scales. We also developed a new module to improve small object detection and model the global context. In the network′s neck, we designed a specialized attention module. This module strengthens the capture of defect shapes and textures, which reduces confusion between defects and the background. Finally, we refined the downsampling process using a re-parameterized stem structure. This change expanded the model′s receptive field and added more paths for feature extraction.Experimental results show that our improved algorithm achieves 93.2% precision, 90% recall, and 94.1% mean average precision. These results are 1.3%, 7.53% and 4.21% higher than the original model, respectively. The proposed algorithm effectively improves the accuracy of fabric defect detection and meets the practical demands of industrial production.
Volume 49, 2026 Issue 13
Application of Artificial Intelligence in Electronic Measurement
Research&Design
Precision Measurement
Test Systems and Modular Components
Theory and Algorithms
Information Technology & Image Processing
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
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%.
-
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%.
-
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.
-
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.
