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Liang Lunwei, Zhang Xiaodong, Hu Yuzhe, Tao Qing
2025,48(18):1-12, DOI:
Abstract:
Based on the YOLOv8n model, this paper proposes a improved hazard detection model CML-YOLO for power tower components. It aims to solve the problems of low accuracy, large number of parameters, high computational complexity and large model weight of multi-scale power tower components hazard detection model under complex background. It is mainly used for the detection of targets such as damaged insulators, rusted dampers and bird nests. Firstly, the C2f-HEFE module is designed to enhance the ability to distinguish between background and target by enhancing the edge information. Secondly, the MSFFPN module is designed, and the multi-scale feature fusion is used to enhance the adaptability of the model to multi-scale targets. Finally, the lightweight LSBDH module is designed to reduce the number of parameters and calculation amount of the model. Experimental results show that compared with the baseline model YOLOv8n, the mean average precision of CML-YOLO is improved by 4.4%, and the number of parameters, calculation amount and model weight are reduced by 33.9%, 20.9% and 26.4% respectively. This model improves detection performance while maintaining its lightweight characteristics, achieving a good balance between model detection accuracy and model weight.
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2025,48(18):13-19, DOI:
Abstract:
To address the issues of local optima, path oscillations, and goal unreachability in traditional artificial potential field (APF) methods for multi vehicle collaborative formation obstacle avoidance, this paper proposes an improved APF approach. By implementing four key enhancements—defining the minimum potential energy for gravitational fields, incorporating Euclidean distance into repulsive fields, constructing road boundary repulsive potential fields, and establishing nonlinear formation stabilization force potential fields—the dynamic equilibrium mechanism between attraction and repulsion forces is optimized, thereby improving formation obstacle avoidance capability and driving stability. Numerical simulations demonstrate that in triangular formation obstacle avoidance scenarios, the improved algorithm achieves a 37.7% reduction in arrival time (22.3 s), 23.2% shorter total path length (55.7 m), and 61.5% faster formation recovery (2.5 s), while eliminating local optima and reducing goal unreachability rate from 25% to 2%. Physical prototype experiments further validate the algorithm′s robustness in dynamic environments, showing rapid restoration of triangular formations post-obstacle avoidance. This method provides an efficient and stable solution for multi-vehicle collaborative obstacle avoidance, demonstrating significant application value for intelligent transportation systems.
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Cheng Jiangzhou, Luo Yingquan, Bao Gang, Lyu Aobo, Yang Jingyi
2025,48(18):20-28, DOI:
Abstract:
Aiming at the problems of insufficient range, high maintenance cost and insufficient inspection frequency of online monitoring equipment in transmission line inspection, this paper designs a U-type triple-coil wireless power supply system using a U-type triple-coil wireless power supply system and at the same time proposes a parameter design optimization method based on a genetic algorithm in order to realize wireless power supply of online monitoring equipment for 110 kV medium and long-distance transmission towers. On this basis, the circuit model of the U-type triple-coil wireless power transmission system is firstly established, and the relational equations between the output power, transmission efficiency and the mutual inductance of the coil, coupling coefficient, and load impedance are deduced, and then the genetic algorithm is used to search for the optimization of the parameters and obtain the optimal solution and its corresponding system parameter values. Finally, an experimental platform is built according to the simulation data, and the experimental results show that the wireless power supply system for on-line monitoring equipment of U-type triple-coil transmission towers achieves an output power of 81.19 W at an operating frequency of 380 kHz and a transmission distance of 1.2 m, which verifies that it can meet the power supply requirements of on-line monitoring equipment.
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2025,48(18):29-40, DOI:
Abstract:
In order to solve the problems of low accuracy and poor effect of tomato leaf disease detection in natural environment, a tomato leaf disease detection model based on optimized YOLOv8 was proposed, namely GDDL-YOLOv8n. In this model, the original backbone network is improved by using GhostHGNetV2, C2f-DWR-DRB is used to improve the neck network feature fusion, and the Lightweight Shared-Convolutional detection head (LSCG) is innovatively introduced. The lightweight and high-precision detection effect of the model has been successfully realized. Experimental results show that the GDDL-YOLOv8n model decreases by 49.13% in the number of parameters, 37.04% in the amount of computation, and 46.67% in the memory occupation of the model, while maintaining the high-precision detection performance, with the mAP@0.5 reaching 98.4% and the mAP@0.5-0.95 reaching 92.3%. In addition, a user-friendly interface system based on PyQt5 was developed, which supports image and video detection and camera real-time tracking and recognition functions, and the intelligent management of agriculture and the identification technology of tomato leaf disease have been significantly enhanced, and the model is lighter, which greatly promotes the application of these technologies in actual production.
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Xu Kongchen, Tang Huaifeng, Yang Haiqian, Su Xin, Lu Xiaochun
2025,48(18):41-52, DOI:
Abstract:
Flow cytometry is a high-throughput detection technique widely used in life science research and clinical diagnostics. However, conventional flow cytometers exhibit suboptimal performance when handling complex data dimensions and severe noise interference. To enhance the capability of flow cytometry in processing multi-parameter, high-dimensional data while ensuring timeliness and accuracy, this study proposes an intelligent flow cytometry analysis system. The system encompasses hardware design, software architecture, and algorithmic frameworks for flow cytometry. At the hardware level, a real-time data acquisition system was developed based on the cooperative operation of FPGA and ARM. On the software side, an embedded system with a Linux-based architecture was constructed, incorporating a suite of preprocessing, parsing, and batch normalization methods. For intelligent flow cytometry data analysis, a self-organizing mapping algorithm was introduced for dimensionality reduction, combined with an improved residual network from the field of deep learning, resulting in the development of an SE-ResNet-50 deep convolutional neural network model. Experimental results demonstrate that the SE-ResNet-50 model achieves a 4% improvement in overall accuracy and a 3.8% increase in precision compared to the original ResNet-50. The collaborative workflow integrating SOM and SE-ResNet-50 effectively processes the vast amounts of high-dimensional data acquired by flow cytometry. The findings validate the superiority of the proposed approach.
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Zhang Zhihong, Zhang Liling, Ma Tingting, Zhong Sheng, Huang Feng
2025,48(18):53-72, DOI:
Abstract:
With the rapid development of rail transit, the detection of track defects has become crucial for ensuring safety. This paper systematically reviews common types of track defects, such as fatigue cracks, burns of rails, and fastener looseness. It elaborates in detail on detection technologies including ultrasonic, eddy current, magnetic flux leakage, and machine vision, as well as their principles, applications, and advancements. This encompasses various derivative methods of ultrasonic detection, such as conventional ultrasound, phased array ultrasound, laser ultrasound, and ultrasonic guided waves. Additionally, it covers the innovations in eddy current detection regarding the suppression of the skin effect and combination with thermal imaging; the improvements in magnetic flux leakage detection in terms of signal processing and new lift-off layer design; and the characteristics of traditional image processing and deep learning methods in machine vision detection. Meanwhile, the application achievements of multi-source information fusion technology in track defect detection are expounded. For example, defect identification and localization are realized by collecting data from multiple technologies and integrating deep learning models. Finally, the challenges faced by multi-source technology fusion are analyzed, and suggestions for future research directions are proposed, providing a comprehensive reference for the development of track defect detection technologies.
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Yang Zhiyong, Xu Bo, Xiong Yuhong, Deng Lielei, Yang Shengze
2025,48(18):73-81, DOI:
Abstract:
To address the issues of unstable motion of the robotic manipulator and frequent collisions with line fittings caused by frequent start-stop actions of the joints during obstacle negotiation,this paper proposes a collision-free line grasping trajectory planning method based on blended seventh-order non-uniform B-spline curves. First, an obstacle negotiation model for the robot is established, describing the mapping relationship between the pose of the walking wheel assembly and the joint space coordinates. The collision-free regions in the joint space are determined based on the principles of obstacle negotiation, and selection rules for collision free intermediate path points are developed. Second, seventh-order non-uniform B-spline curves are employed to fit the intermediate path points, constructing a collision-free trajectory with high-order continuity and controllable boundaries. Finally, co-simulation using Adams and Matlab, combined with the Non-dominated Sorting Genetic Algorithm(NSGA-II) algorithm, is performed to optimize the manipulator′s end-effector time and impact for the obstacle negotiation trajectory. The results demonstrate that the proposed method effectively avoids collisions with line fittings during obstacle negotiation and line grasping. Additionally, the acceleration and jerk variation curves of all joints are smooth without sharp peaks or abrupt changes. Compared to the linear-polynomial trajectory interpolation method, the proposed approach reduces the acceleration, jerk, and average jerk of the telescopic joint by 18.1%, 83.01%, and 78.32%, respectively, resulting in smoother robot motion and enhanced safety during obstacle negotiation.
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2025,48(18):82-91, DOI:
Abstract:
This paper addresses the issue of oil-water two-phase flow measurement under complex downhole conditions by developing an ultrasonic Doppler flow meter with sound velocity compensation. By establishing a one-dimensional velocity profile measurement model based on the Doppler effect and a sound velocity measurement model based on pulse echo intensity, and combining these with the principle of layered integration, a sound velocity compensated flow measurement model is constructed to achieve adaptive reconstruction of the velocity profile and high-precision flow measurement. On this basis, ultrasonic transducers and high-speed excitation and reception control boards suitable for high-temperature and high-pressure downhole environments are designed, and digital signal processing technology is used to achieve online demodulation of flow. Additionally, to ensure reliable operation under downhole high-temperature and high-pressure conditions, the measuring pipe section has been structurally and sealingly designed. A downhole flow test prototype has ultimately been developed. Experimental results indicate that the flow measurement module has a measurement error of less than 1% in the laboratory environment, can rapidly respond to fluid fluctuations, and can operate stably under extreme downhole conditions of 125℃ and 60 MPa. This technology can be widely applied to downhole measurement and adjustment scenarios and can be integrated into intelligent measurement and control systems, providing technical support for the construction of smart oil fields.
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Liu Lili, Xie Meng, Wang Yan, Yang Chunlei, Gu Mingjian
2025,48(18):92-99, DOI:
Abstract:
Due to the volatility and randomness of photovoltaic power generation, it is difficult for traditional models to accurately predict it. To solve this problem, a prediction model of AW-CNN-LSTM is established based on clustering. First, the photovoltaic power plant historical data set is pre-processed and clustered using the K-means clustering algorithm based on the elbow method; secondly, an adaptive weight is established based on the distance between the training samples and the feature center of test samples of the same clustering category; then, an AW-CNN-LSTM network model suitable for different clustering categories is established based on the clustering results and adaptive weights. CNN are used to capture the relationships between different features, while LSTM are used to capture temporal features. Finally, the forecast results of each model are integrated to get the final forecast results. Experiments on the data set of photovoltaic power stations in the Australian Desert Solar Energy Research Center demonstrate the effectiveness of the proposed method.
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Intelligent prediction and maintenance strategy of turbofan engine RUL based on improved CNN-BiGRU-A
2025,48(18):100-110, DOI:
Abstract:
Aiming at the problem that modern industrial systems tend to focus on their predictive performance while paying little attention to equipment maintenance decision-making, a data-driven dynamic predictive maintenance method is proposed to avoid sudden system failures and ensure safe operation. First, the health status of the turbofan engine is monitored in real-time to obtain operating data, which is used to establish a turbofan engine remaining useful life model based on a convolutional neural networks-bidirectional gated recurrent unit-attention mechanism. The hyperparameters of the CNN-BiGRU-A are optimized using the black hawk optimization algorithm; second, the monitored data is input into the trained integrated network, and a dynamic predictive maintenance strategy with uncertain system task cycle is proposed based on the predicted remaining useful life; finally, the proposed method is verified by using the C-MAPSS data set to show that it can improve equipment predictive performance and perform good predictive maintenance afterward.
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Li Jun, Ding Binbin, Shi Weijuan, Yang Lin
2025,48(18):111-121, DOI:
Abstract:
Aiming at the UAV aerial image detection task, there are problems of tiny target size and complex background environment, which often lead to leakage and misdetection, this paper proposes a small target detection algorithm WT-YOLO based on YOLOv11 for aerial images. First of all, taking into account the problem that UAV aerial images are generally small targets, the structure of the YOLOv11 necking network is adjusted, and the output feature map is changed size, which improves the algorithm's ability to detect small targets. Secondly, the structure of Bottleneck and C3k2 module, named C3k2-WT, is redesigned in combination with WTConv to realize the efficient extraction of features. Again, Focal-Modulation is introduced to replace SPPF, which makes the model more robust in dealing with complex scenes by focusing and modulating the features at different spatial scales; finally, the shared convolution detection head is designed to reduce the number of parameters of the model through the convolution sharing mechanism, while enhancing the global information fusion capability between feature maps. The experiments of the improved algorithm on the VisDrone2019 dataset show that compared with the base YOLOv11s model, the accuracy (P), recall (R), and detection precision (mAP50) are improved by 5.6%, 5.9%, and 7.5%, respectively, and the number of params decreases by about one-fourth, which shows a good performance compared with other algorithms.
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Ji Dekui, Li Bingfeng, Yang Yi
2025,48(18):122-129, DOI:
Abstract:
In response to the issues of background interference in fine-grained images and the challenge of identifying the most discriminative features in the target region, this paper proposes an improved CVT-based fine-grained image recognition algorithm. First, a target region localization module is introduced into the CVT model. This module extracts features of the target region using a multi level feature aggregation method and determines the target region via threshold-based decision-making. The original image is then cropped proportionally to reduce the interference of background information. Furthermore, a mechanism called MDCSAIA (Multi-Dimensional Channel Spatial-Aware Interaction) is proposed. This mechanism employs dimensional transformation to facilitate effective interaction between spatial information of adjacent channels and channel information of adjacent spatial positions, thereby enhancing the network′s ability to perceive the local details of the target region. Experimental results show that, compared to baseline algorithms, the proposed method improves recognition accuracy by 2.1%, 1.7%, and 1.5% on the CUB-200-2011, Stanford Cars, and Stanford Dogs datasets, respectively. These results validate the effectiveness of the proposed approach.
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Peng Guofeng, Hu Bin, Zhu Xiaochun
2025,48(18):130-141, DOI:
Abstract:
In the Green Flexible Job Shop Problem (GFJSP), the complexity of production processes leads to low efficiency. Effective scheduling of Automated Guided Vehicles (AGVs) for transportation can ensure both production efficiency and cost control. This paper proposes a Multi-step Deep Double Q-Network (D4QN) algorithm to address the scheduling of green workshops and AGVs. The method first designs a mathematical framework based on the Markov Decision Process (MDP) to enable interaction between AGVs and the workshop. By adjusting the decision-making in real time through state features, action space, and reward functions, the algorithm coordinates job and AGV scheduling. Next, the algorithm for training decision-making is optimized, improving the calculation of Q-values and deep network training to obtain suitable solutions. Finally, two validation experiments are conducted to evaluate the learning performance of the proposed algorithm. The first experiment involves single-objective flexible job shop scheduling with the objective of minimizing makespan. The algorithm is trained on the Brandimarte and Kacem benchmark problems, and the results are compared with those of other deep learning algorithms. The results show that the proposed algorithm reduced the average processing time by 5.1~17.2 s and decreased the average optimal gap ratio by 7.5%~21%, demonstrating the algorithm′s superiority and stability. The second experiment focuses on multi-objective AGV scheduling in a workshop, with the goals of minimizing makespan and AGV energy consumption. The optimal number of AGVs is calculated for the MK01 problem instance, with results showing that four AGVs achieved a 3%~31.8% improvement in the normalized index compared to other quantities, proving its effectiveness in reducing costs and improving efficiency in the workshop.
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Wang Lang, Xu Yun, Li Qi, Gao Liang, Zhang Jiajun
2025,48(18):142-149, DOI:
Abstract:
Currently, defect detection of CNC machine tool bearing seats primarily relies on manual visual inspection, which cannot meet the demands for high precision, high efficiency, and low error rates in industrial production. To address these issues, a defect detection algorithm based on an improved YOLOv5s is proposed for CNC machine tool bearing seats. Firstly, the HardSwish activation function is used to replace the GELU in ConvNeXtv2, and a novel CSCConvNeXtv2-HS structure is introduced, incorporating the CSC module to replace the C3 module in the backbone network. This modification reduces computational complexity while enhancing the feature extraction capability of key information. In the Neck network, a Scale Sequence Feature Fusion module is introduced to improve the model′s ability to extract multi-channel information. Finally, The final proposal of the Focal-Inner Loss loss function not only improves the speed of training convergence but also reduces the impact brought about by the imbalance in class distribution. Experimental results show that the improved model achieves an accuracy of 91.09%, a recall rate of 81.97%, and a mean Average Precision of 84.40%, with a processing speed of 61.73 fps. All evaluation metrics show improvements of 2.52%, 4.47%, 6.7%, and 1.12 fps compared to the original YOLOv5s model, thereby satisfying industrial production requirements.
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2025,48(18):150-158, DOI:
Abstract:
Given the difficulty and low accuracy of small target detection in aerial images, YOLOv8s improved small target detection method, namely YOLOv8-ERD, was proposed. Initially, the YOLOv8s Neck network was optimized using the Efficient Neck feature fusion method to facilitate the efficient merging of high-level semantic information with low-level spatial information. Subsequently, the receptive-field attention convolutional operation RFAConv was incorporated to emphasize the significance of various features within the receptive field slider and to bolster the feature extraction capability. Additionally, the C2f module was replaced with the improved DyC2f module, which employs dynamic convolution DynamicConv, thereby not only reducing computational overhead but also enhancing model performance. Lastly, a small target detection layer was added to refine the recognition accuracy of diminutive targets. Experimental results show that on Visdrone2019 public data set, compared with the benchmark model YOLOv8s, the mAP@0.5 of YOLOv8-ERD has increased by 5.0%, and the accuracy P has increased by 4.0%, and it performs well in comparison with other mainstream target detection methods.
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Xiong Wei, Huang Yuqian, Peng Xinxu
2025,48(18):159-167, DOI:
Abstract:
To address the existing PCB defect detection methods with high miss detection rate,poor generalisation and difficulty in balancing detection accuracy and speed, this paper proposes a PCB defect detection algorithm YOLOv8-CSM based on the improved YOLOv8n model. Firstly, a CoordAttention module is added at the end of the backbone network, which suppresses the influence of the complex background on the defective region of the PCB in order to improve the model′s detection accuracy; second, three SEAM modules are referenced in the detection header to expand the model receptive field and improve the model′s ability to identify small defects to reduce the miss detection rate; finally, MPDIoU is used to replace the traditional CIoU loss to optimise the regression effect of the bounding box and improve the convergence speed of the model. The experimental data show that YOLOv8-CSM can better balance detection accuracy and speed, and it is more generalizable. Compared with the base model, the Recall, Precision, mAP50 and FPS are improved by 4.3%, 1.8%, 2.7% and 42.76, respectively, which significantly enhances the model′s performance in PCB defect detection tasks.
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2025,48(18):168-176, DOI:
Abstract:
In the quality control of cigarette production, achieving precise detection of four types of tobacco shred (tobacco silk, cut stem, expanded tobacco silk, reconstituted tobacco shred) blending ratios has emerged as a critical technical challenge. To address the detection difficulties arising from subtle morphological variations and prevalent overlapped distributions of tobacco shreds, this study proposes a rapid overlapped tobacco shred segmentation algorithm based on an enhanced YOLOv8 framework. The method reconstructs the backbone network using a Res2Net architecture to amplify feature extraction capabilities for minute and complex patterns, while integrating ContextGuidedBlock (CGB) modules into the neck network to enhance boundary recognition accuracy in overlapped regions. Experimental results demonstrate that the improved model achieves notable performance metrics of mAP50 (86.5%), mAP50-95 (67.8%), and recall rate (81.9%) while maintaining real-time processing speed at 67 fps. Through ablation studies and comparative analyses with mainstream segmentation networks, the effectiveness and performance advantages of the proposed architectural modifications are rigorously validated. This algorithm not only improves segmentation precision but also optimizes frame rates, demonstrating superior applicability in practical production line environments.
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Zhang Ao, Liu Wei, Liu Yang, Yang Siyao, Guan Yong
2025,48(18):177-188, DOI:
Abstract:
Blood cell detection is a crucial task in clinical diagnosis. However, due to the diverse cell types, significant size variations, frequent target overlap, and complex backgrounds, existing detection models still face challenges in terms of accuracy and robustness. To address these issues, this paper proposes an improved YOLOv8-based object detection model, YOLO-BioFusion.The model incorporates the ACFN module to enhance the detection of small and overlapping targets. Additionally, the C2f-DPE and SPPF-LSK modules are introduced to strengthen multi-scale feature fusion and extraction, improving the model′s robustness and generalization ability. Meanwhile, the adoption of the Inner-CIoU loss function accelerates model convergence and enhances localization accuracy.Experimental results on the BCCD dataset demonstrate that YOLO-BioFusion achieves an mAP@0.5 of 94.0% and an mAP@0.5:0.95 of 65.2%, outperforming YOLOv8-n by 1.9% and 32%, respectively. Moreover, with a computational cost of only 6.8 GFLOPs, YOLO-BioFusion exhibits great potential for applications in resource-constrained environments. This study provides an efficient and accurate solution for blood cell detection in complex backgrounds.
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Li Songkai, Guan Beibei, Liu Jinhai
2025,48(18):189-196, DOI:
Abstract:
Precision measurement technology has always been the core technology in the fields of biomedical impedance analysis, electrochemistry, power transmission, radio frequency antenna analysis and many other analytical instruments.In modern precision measuring instruments, the number of resistance-capacitance is often hundreds of thousands, and the impedance parameters are very strict, the quality of the resistance-capacitance used and the stability of its own impedance value is an important factor affecting the performance of electronic systems, so the precision resistance-capacitance measurement has become an important part of the design of many electronic systems.Under this background, a high precision resistance-capacitance analyzer based on hardware orthogonal phase-locked structure is proposed in this paper.The design takes hardware orthogonal phase discrimination as the core. Around this part, the orthogonal frequency generator, I-V conversion regulation module, Sallen-Key filter and other hardware structures are designed. At the same time, the MCU system is designed for module control and drive, task allocation and scheduling, and data acquisition and processing. Finally, performance tests were conducted on the key components of the system and the physical complete machine, achieving the judgment of resistance and capacitance as well as the measurement of impedance values. After the actual system test and comparison with the standard bridge test, the average value of the resistance measurement error of this system is 0.032 4%, and the average value of the capacitance measurement error is 0.054 7%. And while achieving a lower measurement error, it ensures the small volume and portability of the overall system, ultimately enabling the design to reach the expected goal.
Volume 48, 2025 Issue 18
Research&Design
Intelligent Instrument and Applications
Theory and Algorithms
Information Technology & Image Processing
Theory and Algorithms
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Xue Xianbin, Tan Beihai, Yu Rong, Zhong Wuchang
2024,47(6):1-7, DOI:
Abstract:
Urban intersections are accident-prone sections. For intelligent networked vehicles, it is very important to carry out risk detection and collision warning during driving to ensure the safety of driving. This paper proposes a traffic risk field model considering traffic signal constraints for urban intersections with traffic lights, and designs a three-level collision warning method based on this model. Firstly, a functional scenario is constructed according to the potential conflict risk points of urban intersections, and the vehicle risk field model is carried out considering the constraint effect of traffic signal. In order to solve the problem of collision warning, a three-level conflict area is proposed to be divided by the index, and the collision risk of the main vehicle is measured according to the position of the potential energy field around the main vehicle by calculating the corresponding field strength around the main vehicle. The experimental results show that the designed model can accurately warn the interfering vehicles entering the potential energy field of the main vehicle, the warning success rate can reach 100%, and the false alarm rate is only 3.4%, which proves the reliability and effectiveness of the proposed method.
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Wei Jinwen, Tan Longming, Guo Zhijun, Tan Jingyuan, Hou Yanchen
2024,47(6):8-13, DOI:
Abstract:
To address the issue of low accuracy in indoor static target positioning with existing single-antenna ultra-high frequency RFID technology, this paper proposes a new RFID localization method based on an antenna boresight signal propagation model. The method first determines the height position of the target through vertical antenna scanning; secondly, it adjusts the antenna height to match that of the target and then performs stepwise rotational scanning to identify the target′s azimuth angle; furthermore, it utilizes a Sparrow Search Algorithm optimized back propagation neural network to establish a path loss model for ranging purposes; finally, it integrates the height, azimuth angle, and distance data to complete the target positioning. Experimental results show that in indoor environment testing, the proposed method has an average positioning error of 7.2 cm, which meets the positioning requirements for items in general indoor scenarios.
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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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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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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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Zhang Bian, Tian Ruyun, Han Weiru, Peng Yuxin
2024,47(6):109-115, DOI:
Abstract:
In order to solve the problems that the traditional SPD life alarm characterization method can not clearly correspond to the real life state of SPD, and the remaining life model characterized by a single degradation related parameter has poor predictability, a multi-parameter SPD life remote monitoring system based on STM32 is designed. With STM32 as the main controller, the important parameters such as surge current, leakage current, surface temperature and tripping status of SPD are collected in real time, and the status information is uploaded to the One net cloud platform through the BC20 wireless communication module. The One net cloud platform displays and stores the multi-parameter data of SPD in real time, and provides data management and analysis. The SVM classification model is used to judge whether SPD is damaged and the BO-LSTM prediction model is used to predict the remaining life of SPD. Based on the positioning function of BC20, the real-time geographic location of SPD can be viewed on the host computer. The results show that the root mean square error and average absolute error of the BO-LSTM prediction model are 0.001 3 and 0.001 8, and the system can monitor the SPD status in real time, effectively predict the remaining life value of SPD, and give early warning in time.
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Zhou Jianxin, Zhang Lihong, Sun Tenghao
2024,47(6):79-85, DOI:
Abstract:
Aiming at the problems that the standard honey badger algorithm (HBA) is easy to fall into local optimum, low search accuracy and slow convergence speed, a honey badger algorithm based on elite differential mutation (EDVHBA) is proposed. The elite solution searched by the two optimization strategies in the standard HBA is combined with differential mutation to generate a new elite solution. The use of three elite solutions to guide the next iteration of the population can increase the diversity of the algorithm solution and prevent the algorithm from falling into premature convergence. At the same time, the nonlinear density factor is improved and a new position update strategy is introduced to improve the convergence speed and optimization accuracy of the algorithm. In order to verify the performance of the algorithm, simulation experiments are carried out on eight classical test functions. The results show that compared with other swarm intelligence algorithms and improved HBA, EDVHBA can find the optimal value 0 in the unimodal function, and converge to the ideal optimal value in the multimodal function after about 50 iterations, which verifies that EDVHBA has better optimization performance.
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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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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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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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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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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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Peng Duo, Luo Bei, Chen Jiangxu
2024,47(6):50-57, DOI:
Abstract:
Aiming at the non-range-ranging location problem of multi-storey WSN structures, a three-dimensional indoor multi-storey structure location algorithm IAODV-HOP algorithm based on improved Tianying is proposed in the field of large-scale indoor multi-storey structure location for some large commercial supermarkets, hospitals, teaching buildings and so on. Firstly, the nodes are divided into three types of communication radius to refine the number of hops, and the average hop distance of the nodes is modified by using the minimum mean square error and the weight factor. Secondly, the IAO algorithm is used to optimize the coordinates of unknown nodes, and the population is initialized by the best point set strategy, which solves the problem that the quality and diversity of the population are difficult to guarantee due to the random distribution of the initial population in the Tianying algorithm. In addition, the golden sine search strategy is added to the local search to improve the position update mode of the population, and enhance the local search ability of the algorithm. Through simulation experiments, compared with traditional 3D-DV-Hop, PSO-3DDV-Hop, N3-3DDV-Hop and N3-ACO-3DDV-Hop, the normalized average positioning error of the proposed algorithm IAODV-HOP is reduced by 70.33%, 62.67%, 64% and 53.67%, respectively. It has better performance, better stability and higher positioning accuracy.
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Ma Zhewei, Zhou Fuqiang, Wang Shaohong
2024,47(6):94-99, DOI:
Abstract:
A feature point extraction algorithm based on adaptive threshold and an improved quadtree homogenization strategy are proposed to address the issue of low positioning accuracy or low matching logarithms of the SLAM system caused by the ORB-SLAM2 algorithm extracting fewer feature points in dark environments or environments with fewer textures, resulting in system crashes. Firstly, based on the brightness of the image, FAST (Features from Accelerated Seed Test) feature points are extracted using adaptive thresholds. Then, an improved quadtree homogenization strategy is used to eliminate and compensate the feature points of the image, completing feature point selection. The experimental results show that the improved feature point extraction algorithm increases the number of matching pairs by 17.6% and SLAM trajectory accuracy by 49.8% compared to the original algorithm in dark and textured environments, effectively improving the robustness and accuracy of the SLAM system.
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2024,47(6):28-33, DOI:
Abstract:
With the increasing demand for satellite network, vehicle-connected network, industrial network and other service simulation, this paper proposes a multi-session delay damage simulation method based on delay range strategy to build flexible software network damage simulation, aiming at the problems of small number of analog links, low flexibility and high resource occupation of traditional dedicated channel damage instruments. In this method, the delay damage of each session flow is identified and controlled independently, and the multi-queue merging architecture based on time delay strategy is adopted to reduce the resource consumption. The experimental results show that compared with the traditional dedicated device and simulation software NetEm, the proposed method supports the independent delay configuration of million-level links, increases the number of session streams from ten to one million, and reduces the memory consumption by at least 85% under each bandwidth, which meets the requirements of large scale and accuracy, and greatly reduces the system cost.
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Ma Dongyin, Wang Xinping, Li Weidong
2024,47(6):58-63, DOI:
Abstract:
Aiming at the Automatic Train Operation of high-speed train,an algorithm based on BAS-PSO optimized auto disturbance rejection control (ADRC) is used to design speed tracking controller.The ADRC is designed based on the train dynamics model,ITAE is used as the objective function,and the parameters are tuned by BAS-PSO.CRH380A train parameters are selected, The tracking effect of BAS-PSO, PSO and improved shark optimized ADRC algorithm on the target speed curve of the train is compared by MATLAB simulation,The tracking error of the train target speed curve based on the BAS-PSO optimized ADRC algorithm is kept in the range of ±0.4 km/h,which is closer to the target speed curve than the other two algorithms.The results show that the ADRC based on BAS-PSO optimization has the advantages of small tracking error and strong anti-interference ability.
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Shi Shujie, Zhao Fengqiang, Wang Bo, Yang Chenhao, Zhou Shuai
2024,47(6):116-122, DOI:
Abstract:
Rolling bearings play an important role in rotating machinery. If a fault occurs, it can cause equipment shutdown, and in severe cases, endanger the safety of on-site personnel. Therefore, it is necessary to diagnose the fault. In response to the difficulty in extracting fault features of rolling bearings and the low accuracy of traditional classification methods, this paper proposes a fault diagnosis method based on Set Empirical Mode Decomposition (EEMD) energy entropy and Golden Jackal Optimization Algorithm (GJO) optimized Kernel Extreme Learning Machine (KELM), achieving the goal of extracting fault features of rolling bearings and correctly classifying them. Through experimental data validation, this method can extract the fault information features hidden in the original signal of rolling bearings, with a diagnostic accuracy of up to 98.47%.
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Long Biao, Yang Jun, Chen Huiping, Chen Guangrun, Zhao Peiyang
2024,47(6):157-163, DOI:
Abstract:
In order to solve the problem that the audio signal processing in the voice communication system has a large amount of data, a lot of stray signals, and the received audio signals of the frequency modulation receiver are large and small, a lightweight audio signal processing algorithm is proposed, and based on this algorithm, the audio signal receiving and automatic gain control are realized on the field programmable gate array(FPGA) platform. The algorithm combines digital down conversion technology, multistage extraction filtering technology and automatic gain control technology (AGC) technology, and is applied to the audio signal processing system. The RF analog signal received from the upper antenna is converted into baseband audio signal through analog-to-digital conversion and digital down-conversion, and the stray signal in the baseband signal is filtered through four-stage extraction filtering, reducing the complexity and power consumption of the system. At the same time, the digital AGC controls and adjusts the baseband audio signal to output a more stable audio signal. The experimental results show that the algorithm can effectively reduce the information rate from 102.4 MHz to 32 kHz, reduce the computation burden, improve the signal quality, and reduce the resource utilization of FPGA. And the automatic gain control adjustment of audio signal is realized, and the adjustment time is only 12.8 μs, which meets the power stability time of the receiver.
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Zhou Guoliang, Zhang Daohui, Guo Xiaoping
2024,47(6):190-196, DOI:
Abstract:
The gesture recognition method based on surface electromyography and pattern recognition has a broad application prospect in the field of rehabilitation hand. In this paper, a hand gesture recognition method based on surface electromyography (sEMG) is proposed to predict 52 hand movements. In order to solve the problem that surface EMG signals are easily disturbed and improve the classification effect of surface EMG signals, TiCNN-DRSN network is proposed, whose main function is to better identify the noise and reduce the time for filtering the noise. Ti is a TiCNN network, in which convolutional kernel Dropout and minimal batch training are used to introduce training interference to the convolutional neural network and increase the generalization of the model; DRSN is a deep residual shrinkage network, which can effectively eliminate redundant signals in sEMG signals and reduce signal noise interference. TiCNN-DRSN has achieved high anti-noise and adaptive performance without any noise reduction pretreatment. The recognition rate of this model on Ninapro database reaches 97.43% 0.8%.
