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Wang Yi, Qin Chao, Xu Zhenbang
2025,48(9):1-8, DOI:
Abstract:
To meet the demand for non-contact synchronous measurement of the large-range six-degree-of-freedom pose of a parallel robot′s moving platform, a pose measurement system composed of a laser and visual components is proposed. The system utilizes image processing technology to output the two-dimensional coordinates of light spots on the detection surface, achieving precise pose measurement of parallel robot platforms through the cooperation of lasers and vision components. Subsequently, the solution method of the measurement system is studied, and the pose of the target is derived based on analytical geometry methods. Finally, experimental tests are conducted using a high-precision parallel robot. The results indicate that the system achieves a root mean square error of less than 0.1 mm in position measurement and less than 0.07° in attitude measurement. The average relative errors of position and attitude measurements are 0.76% and 2.56%, respectively. This research provides a novel and effective solution for large-stroke pose measurement technology, with broad application prospects in robotic motion control, precision manufacturing, and scientific experiments.
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Lu Zhaoqing, Wang Hongwei, He Li, Si Panzhao, Chen Yaohua
2025,48(9):9-18, DOI:
Abstract:
In unknown or hazardous environments (such as emergency rescue and disaster relief), traditional navigation methods struggle to achieve specific target navigation due to the inability to obtain prior maps and location information. This paper proposes a target-driven autonomous navigation method for mobile robots based on dueling double deep Q network (D3QN). The cross-modal fusion module of this method dynamically weights and integrates features from different modalities, effectively consolidating observational data while fully capturing environmental information. This significantly enhances the capability to perceive the environment.Building on this, a general target-driven navigation approach is designed, where YOLOv5 is used to recognize specific targets (such as flames or smoke) and obtain their locations. The identified target locations are used to replace predefined waypoints in deep reinforcement learning-based navigation, enabling autonomous navigation to specific targets. Simulation results show that the proposed method has significant advantages in the navigation success rate and other indicators. In simple, complex, and dynamic scenarios, the success rates increased by 9%, 27%, and 38%, respectively. Moreover, the model trained in a simple simulation environment can be directly deployed in more complex simulation environments and real-world scenarios, exhibiting strong generalization capability.
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2025,48(9):19-26, DOI:
Abstract:
Aiming at the characteristics of complex features in bearing fault signals, a bearing fault determination method combining the attention mechanism and multi-scale residual convolutional network is proposed. The model combines the powerful feature extraction capability of convolutional neural network (CNN) and the adaptive weighting capability of the attention mechanism, which can effectively deal with the complex features in the bearing fault signal. The model employs a multi-scale convolutional layer, which captures the multi-scale features of the signal through different sizes of convolutional kernels, which helps to recognize different types and severities of faults. Meanwhile, the residual structure is introduced to effectively integrate the features extracted by multilayer convolution through the cooperative decision-making mechanism of high-dimensional and low-dimensional features, which enhances the model′s ability to perceive the key information and reduces the problems of gradient vanishing and feature redundancy in the training of the deep network, so as to ensure the stability and accuracy of the model. The fusion of attention mechanisms (e.g., SEBlock and ECABlock) enables the model to adaptively focus on more important feature channels, which further improves the diagnostic performance. The experimental results show that the model can achieve high-precision diagnosis under various fault modes, demonstrating its potential application in intelligent maintenance and fault warning systems.
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Wang Yu, Peng Xincun, Zou Jijun, Deng Wenjuan, Jiang Linlin
2025,48(9):27-35, DOI:
Abstract:
The positive electron affinity potassium cesium antimony photocathode has driving laser wavelength is longer (532 nm), high quantum efficiency, long life, fast response time, in the field of electronic source has broad application potential, but limited by the photoelectric characteristics of the material itself, effective transport distance is very short, not enough to absorb all the incident light driven laser energy, and quantum efficiency is also affected. Therefore, this article introduced the Fabry-Perot cavity structure into the photocathode, to improve the effective absorption of incident driven laser, through the time domain finite difference method simulation study, make the active layer potassium cesium antimony light absorption reached 90%, and the reflective layer silver and dielectric layer silicon nitride were prepared and verified, predicted the quantum efficiency is 11.17%, to meet the requirements of high brightness, high frequency electron source in the future.
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Yuan Haoren, Zhou Fuqiang, Sun Jianghong
2025,48(9):36-43, DOI:
Abstract:
In practical applications, the expansion state observation in self disturbance rejection controllers faces the problem of inability to guarantee estimation and compensation accuracy when faced with significantly changing load disturbances. A scheme is proposed to improve the estimation and compensation accuracy by compensating the load disturbance observed by the load torque observer to the extended state observer. The load torque observer is designed using angle data that can be directly measured in the motor as a known quantity, avoiding the problem of introducing differential errors by using the speed as a known quantity. The simulation and experimental results show that the improved linear active disturbance rejection controller reduces the maximum speed adjustment by 7.24% compared to the traditional linear active disturbance rejection controller, and has better anti load disturbance performance while ensuring the adjustment speed.
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Wang Jiawei, Fu Shengwei, Huang Haisong
2025,48(9):44-55, DOI:
Abstract:
The rime optimization algorithm (RIME) is an intelligent optimization algorithm inspired by the natural growth process of rime. It demonstrates strong optimization capability by employing a soft rime strategy for global search and a hard rime strategy for local exploitation. However, RIME suffers from slow convergence and a tendency to fall into local optima during applications. To address these issues, this paper proposes an improved rime optimization algorithm (IRIME). First, a dynamic centroid guidance strategy is introduced in the early stages of the algorithm, significantly enhancing convergence speed. Second, an improved differential mutation operator is incorporated into the later iterations to effectively reduce the risk of the algorithm becoming trapped in local optima. Additionally, a novel centroid boundary adjustment strategy is designed to enable collaborative optimization of accuracy and efficiency by deeply exploiting population information. Experiments conducted using the CEC2017 benchmark set demonstrate that IRIME outperforms PPSO, AGWO, HPHHO, RIME, and SRIME in optimization performance. Furthermore, IRIME is applied to the three-dimensional path planning problem for UAVs. The results indicate that IRIME provides substantial improvements in solution quality, convergence stability, and computational efficiency, offering an effective solution for complex engineering optimization problems.
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Qin Lunming, Zhang Yunqi, Cui Haoyang, Bian Houqin, Wang Xi
2025,48(9):56-64, DOI:
Abstract:
To address the issues of decreased detection accuracy and difficulty in small object recognition caused by blurred traffic signs in extreme weather conditions such as rain, fog, and snow, this paper proposes a traffic sign detection algorithm based on an improved RT-DETR. First, data augmentation is applied to the TT100K dataset under simulated extreme weather conditions to enhance the model′s ability to recognize traffic signs in these environments. Second, the Ortho attention mechanism is introduced into the backbone network, which uses orthogonal filters to reduce feature redundancy and prioritize essential channel information, thereby improving the model′s detection accuracy for small objects. Additionally, a high-level screening-feature pyramid network (HS-FPN) replaces the cross-scale contextual feature mixer (CCFM) in the original model. HS-FPN filters and merges low-level feature information using high-level features, enhancing the model′s detection accuracy for low-contrast and blurred targets in extreme weather conditions. Experimental results show that the proposed improved algorithm achieves an average detection accuracy of 87.84%, an improvement of 2.37% over the original RT-DETR model, while reducing the parameter count to 18.22 M, an 8.4% reduction compared to the original model. The model demonstrates higher accuracy in recognizing small objects and targets under extreme weather, contributing significantly to passenger safety.
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Han Dongsheng, Lang Yuhang, Huang Liyan
2025,48(9):65-74, DOI:
Abstract:
In distributed IoT application scenarios such as environmental monitoring, nodes often have different priorities due to the different regional importance of node monitoring and the amount of data collected. The dynamic change of node priority will make the UAV frequently replace the target node of data acquisition, resulting in prolonged task completion time and unwarranted waste of energy. Therefore, we propose a joint optimization algorithm of UAV task completion time and energy consumption based on DDQN for distributed IoT application scenarios with dynamic priority of nodes. During the training process, the UAV learns the optimal strategy under the constraints of task completion time, energy consumption and avoiding node data overflow. The simulation results show that compared with the maximum priority strategy and greedy strategy, the task completion time of the proposed algorithm is reduced by 9.2% and 15.1% respectively, and the energy consumption is reduced by 10% and 16.3% respectively. Compared with the DQN method, the proposed algorithm converges faster and the training process is more stable.
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Liu Bo, Wu Songrong, Fu Cong, Wang Shaowei, Zhang Chi
2025,48(9):75-83, DOI:
Abstract:
The state of charge (SOC) of the battery is one of the core parameters for managing lithium batteries in electric vehicles. This paper proposes a lithium battery SOC estimation model based on an improved white whale algorithm optimized BiTCN-BiGRU. Firstly, a SOC estimation model combining bidirectional time domain convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) is constructed. Then, the beluga whale optimization (BWO) is used to optimize the hyperparameters of the BiTCN-BiGRU model to fully leverage the advantages of the combined network model. Improvement strategies are introduced in the exploration and whale fall stages of traditional BWO to solve the problem of traditional BWO easily falling into local optima and slow convergence speed. Finally, the performance of the improved SOC estimation model was verified based on the open-source lithium battery charging and discharging dataset. The results showed that under standardized urban cycling conditions at three temperatures, the improved white whale algorithm optimized the BiTCN-BiGRU model SOC estimation with an average absolute error of 0.428% and a root mean square error of 0.38%, which can be well applied to lithium battery SOC estimation.
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Lian Xiaoqin, Liu Chunquan, Gao Chao, Deng Ziqian, Wu Yelan
2025,48(9):84-93, DOI:
Abstract:
In recent years, motor imagery (MI) has attracted significant attention in the fields of assistive healthcare and human-computer interaction. However, the classical common spatial pattern (CSP) feature extraction method is mainly based on calculating covariance matrices (CM) from time-domain signals, making it susceptible to noise and artifacts while failing to fully exploit the spectral information of electroencephalogram (EEG) signals. This limitation reduces classification accuracy and stability. To address this problem, this study proposes a MI-EEG classification algorithm based on regularized spectral covariance matrix (RSCM) and Riemannian space. Firstly, the preprocessed EEG signals undergo fast Fourier transform (FFT) to compute spectral covariance matrices, followed by ridge regularization. Then, the regularized matrices are mapped into the tangent space for geodesic filtering and projected back to the Riemannian space for CSP feature extraction. Finally, classification is performed using SVM. Experimental results demonstrate that, on BCI Competition IV datasets 1 and 2a, the proposed method achieves an average binary classification accuracy of 86.95% and 81.48%, respectively, outperforming traditional CSP by 7.44% and 9.57%. In the four-class classification task on BCI Competition IV dataset 2a, it reaches 74.23%, representing a 14.10% improvement over traditional CSP. These findings indicate the effectiveness of the proposed method in MI-EEG classification.
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Chen Yinchao, Wang Tao, Wang Kai, Liang Zhaoxin, Wang Rui
2025,48(9):94-99, DOI:
Abstract:
Brushless dc motors (BLDCs) are widely used in various industrial fields. Failures in critical applications can lead to significant economic losses or even casualties, highlighting the importance of fault diagnosis research. BLDCs often operate under varying conditions, leading to the differences between the source and target domains used in data-driven models. While domain adaptation methods are commonly used to address this problem, they require access to the target domain during training, complicating model deployment. To overcome this, we propose the angular domain data mixed domain generalization network (ADMDG). This method leverages multiple source domains from different BLDC operating conditions to learn domain-generalized knowledge, enabling effective generalization to unseen target domains and allowing for a single training process to support multiple deployment scenarios. ADMDG employs an angular domain current resampling technique to convert time domain currents into angular-domain currents, mitigating the impact of varying conditions. A convolutional neural network-based fault diagnosis model is constructed, and advanced data augmentation techniques, Mixup, are used to enhance model generalization. Extensive BLDC fault experiments demonstrate the superior domain generalization performance of the proposed method compared to other state-of-the-art approaches.
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Cai Bingbin, Huang Danping, Zhang Haotian, Liao Shipeng
2025,48(9):100-110, DOI:
Abstract:
To address the current challenges of difficult image acquisition and slow detection for bearing defects, a single-station online detection system for full surface defects of bearings based on machine vision has been developed. The system utilizes a line-scan camera in conjunction with an optical system to efficiently capture images of the entire surface of the bearings. The defect detection network model constructed is based on ConvNeXt as the feature extraction backbone, employing feature fusion to supplement feature information, it then undergoes lightweight modifications. Additionally, a multi-task learning training approach is adopted, enabling the model to effectively process feature information at various levels, thereby significantly enhancing its performance in bearing surface defect detection tasks. Experimental results show that compared to ConvNeXt-Tiny, the proposed deep learning model achieves a 4.14% improvement in detection accuracy, reaching 99.02%. When relying on CPU for computation, the average detection time per bearing by the single-station full surface defect online detection system is 0.735 seconds. This system is characterized by its compact size, low cost, and ability to meet the requirements for online detection of bearing surface defects, showcasing promising application prospects.
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Li Jingchen, Chen Jian, He Libo
2025,48(9):111-118, DOI:
Abstract:
To meet the requirement of testing whether the 1553B interface function of the single machine of the on-board payload is normal, a software and hardware design scheme of a 3U PXIe peripheral module based on FPGA is proposed. The PXIe control module can configure the 1553B module through the PXIe bus and control and transmit the simulation and test data of the 1553B signal of the payload. This design fully simulates the real circuit and electrical characteristics of the 1553B interface of a certain satellite′s on-board computer, and realizes the BC and BM functions of the bus as required. Through the B-6131 bus protocol chip, two 1553B bus interface lines with the above functions are configured, which are highly consistent with the actual application scenarios. Through the operation of the on-board platform test software, one 1553B channel working in BC mode can send custom messages, and can also start a period of broadcasting, long loopback test and other semi-automated tests to achieve adaptive bus testing. All messages appearing on the 1553B bus will be captured by another bus channel working in BM mode, thereby realizing the monitoring and storage function of all bus data.
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Qin Dong, Ren Xiaoming, Ye Zhou, Chen Jian
2025,48(9):119-128, DOI:
Abstract:
To improve traffic monitoring efficiency, particularly in situations where the field of view of fixed road cameras is limited, a drone-based traffic monitoring system using the PLCnext platform is proposed. This system integrates YOLOv9 deep learning technology, enabling two UAVs equipped with cameras to cooperate with the PLCnext platform. This collaboration provides broader coverage and more flexible monitoring capabilities for real-time vehicle surveillance. To enhance detection accuracy in low-resolution aerial images, a new model, YOLOv9s-SPDADown-LSK, is introduced. The model utilizes a SPD layer to improve image detail retention, optimizes the downsampling process with the ADown module, and incorporates the LSK attention mechanism in the backbone network to enhance feature extraction. Experimental results indicate that the system achieves an image processing delay of approximately 80 ms, with the modified model reaching mAP@0.5 and mAP@0.5:0.95 values of 96.3% and 82.7%, respectively. The detection accuracy stands at 97.2%, significantly reducing false positives and false negatives, thereby validating the system′s feasibility and the effectiveness of the proposed algorithm.
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2025,48(9):129-139, DOI:
Abstract:
As meteorological observations evolve towards socialization and intelligence, the existing ground-based automatic weather stations can no longer meet the new demands due to their split design, large volume, high power consumption, and cost. This paper designs a multi-element modular automatic weather station. This weather station uses modular composition and digital sensor technology to flexibly observe meteorological elements such as temperature and humidity, air pressure, particulate matter 2.5, wind speed, and wind direction. This paper employs a band-pass filter designed with multi-channel negative feedback circuits to address the severe environmental interference affecting ultrasonic anemometers. This approach reduces the scale of the ultrasonic signal conditioning circuit and improves the quality of the ultrasonic pulse signals. Moreover, an adaptive filter algorithm with a sliding window size adjustment factor is introduced to reduce signal environmental interference. The experimental results show that the digital sensors used in the designed automatic weather station meet the observation requirements. Specifically, the wind measurement module has a maximum wind speed error of 14.8% and a maximum wind direction error of 11.78%. These figures indicate that the new weather station achieves high accuracy in wind measurements and demonstrates stable performance.
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Wu Yuqian, Zhang Xiuzai, Li Jingxuan
2025,48(9):140-148, DOI:
Abstract:
The essence of cloud image prediction is spatiotemporal sequence prediction. Deep learning-based spatiotemporal sequence prediction algorithms can be categorized into three frameworks: SISO, MIMO and MISO. Based on the characteristics of cloud image movement, designed a cloud image prediction algorithm under the MISO framework, combining the features of both MIMO and SISO frameworks, called the implicit autoregressive spatiotemporal channel aggregation prediction (IASCACP) algorithm. To address the issue of image correlation loss in MIMO models and excessive error accumulation in SISO models, introduce an implicit autoregressive encoder-decoder. This encoder-decoder endows the model with certain recursive properties through an implicit autoregressive structure to capture spatiotemporal correlation information in sequences. Additionally, a masking and true mapping module is used to reduce error accumulation from recursion and enhance model robustness. To tackle issues such as instability and nonlinearity in cloud movement, we designed a spatiotemporal channel aggregation predictor. This module effectively aggregates multi-order spatiotemporal interaction information and performs adaptive channel reallocation to reduce feature redundancy. The algorithm was tested on the MovingMNIST dataset and the FY-4A satellite cloud image dataset. Experimental results show that this algorithm effectively improves the shortcomings of MIMO and SISO models and achieves higher prediction accuracy compared to other models, demonstrating its reliability and effectiveness in the field of cloud image prediction.
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Zhou Ruoxuan, Zhang Ruiqian, Chen Yong, Yuan Xuhao, Qin Huijun
2025,48(9):149-155, DOI:
Abstract:
The traditional ORB-SLAM3 system demonstrates excellent performance in static environments, however, the presence of dynamic features introduces unnecessary noise, leading to errors in feature matching and inaccuracies in camera pose estimation. Existing dynamic SLAM algorithms face challenges in comprehensively identifying potential dynamic features, resulting in missed detections or false positives and consequently degrading localization accuracy. To tackle these issues, the semantic segmentation network Deeplabv3+ and the Lucas-Kanade optical flow method are incorporated into the tracking thread of ORB-SLAM3. Specifically, the backbone network of Deeplabv3+ is replaced with Mobilenetv3 to enhance the precision of semantic segmentation. Semantic segmentation is then used to obtain a mask of potential dynamic objects, which is employed to preliminarily filter out dynamic feature points. The remaining feature points undergo LK optical flow calculation, with the average optical flow error serving as a threshold to prevent the insufficient number of static feature points from causing pose estimation failure. In comparison to the original ORB-SLAM3, the improved algorithm in this study achieves an average localization accuracy improvement of 47.92% on the high-dynamic sequences of the TUM dataset. Furthermore, among existing advanced dynamic SLAM algorithms, the proposed method achieved the highest localization accuracy on the Walking_static sequence of the TUM dataset.
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2025,48(9):156-167, DOI:
Abstract:
In response to the issues of missed and false detections in transmission line defect detection tasks caused by varying target scales, complex backgrounds, and occlusion, where existing object detection algorithms struggle to maintain detection accuracy while ensuring real-time performance, an improved YOLOv10-based UAV transmission line defect detection algorithm, TLDDet, is proposed. First, a faster C2F with attention module(FC2FA) incorporating context anchor attention is designed to enhance feature integration capabilities while reducing the model′s parameters. Then, an attention-based intrascale feature interaction module (AIFI) based on multi-head self-attention is used to improve the model′s detection performance for small objects by enhancing the representation of high-level semantic information in the feature map, thereby increasing the detection accuracy. Finally, an occlusion-aware attention detection head (SEAM-Head) is designed to reduce feature loss caused by occlusion. Experimental results show that the proposed TLDDet reduces the parameters by 33% and the computational cost by 30% compared to the original YOLOv10s model, while improving precision, recall, and mean average precisionfor various transmission line defects by 4.3%, 2.4%, and 3.7%, respectively. The detection speed reaches 143 FPS, and comparative experiments with other real-time detection algorithms demonstrate superior model performance, making TLDDet more suitable for transmission line defect detection tasks.
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Zhang Guojing, Li Chengjia, Han Jingwei, Huang Man
2025,48(9):168-176, DOI:
Abstract:
Pupil localization plays a crucial role in human-computer interaction and biomedical computing applications. Currently, many sophisticated pupil localization algorithms are designed to detect and locate the pupil position using one single image. However, pupil movement is a continuous process. Therefore, when the pupil position cannot be accurately detected and located in one current frame, the pupil position can be inferred by combining information of previous frames. This approach can more effectively handle difficult and challenging situations such as reflections, pupil occluded by eyelashes and blinks, as well as off-center pupil positions and motion blur. Consequently, it can significantly improve the accuracy and robustness of pupil detection, decreasing localization errors. To address these challenges, propose a pupil detection algorithm based on deep learning using multiple consecutive images. This algorithm enhances the standard Unet encoder-decoder structure by incorporating multi-frame information from continuous eye tracking scenes for improved pupil detection. By combining convolutional neural networks with convolutional long short-term memory networks and a convolutional block attention module, we introduce a hybrid semantic segmentation network. Experiments on a large-scale dataset demonstrate that the proposed method outperforms existing pupil detection algorithms, achieving a mean intersection over union score of 96.78% and a root mean square error value of 3.83, especially in challenging situations.
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2025,48(9):177-188, DOI:
Abstract:
Aiming at the problems of low accuracy of small target detection, large number of parameters, as well as misdetection and leakage in tile surface defect detection, an improved tile surface defect detection algorithm, YOLOv9s-SEFN, is proposed. Firstly, the SPNet multi-scale feature fusion module is designed in this study to effectively improve the model′s detection of small defects on the tile surface by enhancing the network′s capability of capturing and fusion of multi-scale feature expression; second, the ECG lightweight fusion module is designed to reduce the computational and parametric quantities to achieve lightweighting; then, the frequency adaptive dilation convolution (FADC) is introduced to improve the accuracy of small defects detection on tiles by adaptively adjusting the dilation rate and frequency selection; and lastly, a new loss function, NWD-EIOU, is designed to improve the accuracy of small target localization by combining EIOU and NWD. The experimental results show that compared with the original YOLOv9s detection algorithm, the improved YOLOv9s-SEFN algorithm performs better on the self-built experimental dataset, with the mAP@0.5 raised to 93.2%, an improvement of 3.5%; the recall rate is raised by 4.96%; the amount of parameters is reduced by 2.3%; and the amount of floating-point arithmetic is reduced by 4.0%, which is able to satisfy the needs of tile surface defect detection.
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Zhang Zheng, Zheng Yingqiao, Tian Qing
2025,48(9):189-197, DOI:
Abstract:
Laser range gating technology can break through the limitations of traditional imaging in complex environments such as rain, snow and fog, low light and inverse glare, but the generated gated image is a low quality grayscale map, which lacks color information and is difficult to distinguish between the subject and the background, so super-resolution reconstruction technology is needed to focus on the reconstruction of edge information and spatial details to improve the visual effect. Due to the lack of color and rich texture information in the gated image, the traditional feature extraction method is prone to redundant features, which affects the reconstruction efficiency. In order to solve the above problems, this paper proposes a bi-aggregation deep feature extraction network. Firstly, shallow feature extraction was carried out by spatial and channel reconstruction convolution (SCConv) to improve the information content and solve the redundancy problem. Secondly, a new deep feature extraction module was designed to enhance the capture of the edges and details of the gated image. Finally, continuous nearest neighbor interpolation and convolution operations are used for image reconstruction, which effectively avoids the problem of artifacts. Experiments on the gated image dataset show that compared with the baseline DAT algorithm, the PNSR index of the proposed method is increased by 0.19 dB, 0.12 dB and 0.04 dB under the condition of 2 fold, 3 fold and 4 fold resolution degradation, respectively, and the SSIM is increased by 0.000 5, 0.000 8 and 0.001 0, respectively, and the results show that the proposed method can achieve better visual effects.
Volume 48, 2025 Issue 9
Research&Design
Theory and Algorithms
Test Systems and Modular Components
Information Technology & Image Processing
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Xue Xianbin, Tan Beihai, Yu Rong, Zhong Wuchang
2024,47(6):1-7, DOI:
Abstract:
Urban intersections are accident-prone sections. For intelligent networked vehicles, it is very important to carry out risk detection and collision warning during driving to ensure the safety of driving. This paper proposes a traffic risk field model considering traffic signal constraints for urban intersections with traffic lights, and designs a three-level collision warning method based on this model. Firstly, a functional scenario is constructed according to the potential conflict risk points of urban intersections, and the vehicle risk field model is carried out considering the constraint effect of traffic signal. In order to solve the problem of collision warning, a three-level conflict area is proposed to be divided by the index, and the collision risk of the main vehicle is measured according to the position of the potential energy field around the main vehicle by calculating the corresponding field strength around the main vehicle. The experimental results show that the designed model can accurately warn the interfering vehicles entering the potential energy field of the main vehicle, the warning success rate can reach 100%, and the false alarm rate is only 3.4%, which proves the reliability and effectiveness of the proposed method.
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Wei Jinwen, Tan Longming, Guo Zhijun, Tan Jingyuan, Hou Yanchen
2024,47(6):8-13, DOI:
Abstract:
To address the issue of low accuracy in indoor static target positioning with existing single-antenna ultra-high frequency RFID technology, this paper proposes a new RFID localization method based on an antenna boresight signal propagation model. The method first determines the height position of the target through vertical antenna scanning; secondly, it adjusts the antenna height to match that of the target and then performs stepwise rotational scanning to identify the target′s azimuth angle; furthermore, it utilizes a Sparrow Search Algorithm optimized back propagation neural network to establish a path loss model for ranging purposes; finally, it integrates the height, azimuth angle, and distance data to complete the target positioning. Experimental results show that in indoor environment testing, the proposed method has an average positioning error of 7.2 cm, which meets the positioning requirements for items in general indoor scenarios.
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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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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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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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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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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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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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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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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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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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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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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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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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2024,47(6):28-33, DOI:
Abstract:
With the increasing demand for satellite network, vehicle-connected network, industrial network and other service simulation, this paper proposes a multi-session delay damage simulation method based on delay range strategy to build flexible software network damage simulation, aiming at the problems of small number of analog links, low flexibility and high resource occupation of traditional dedicated channel damage instruments. In this method, the delay damage of each session flow is identified and controlled independently, and the multi-queue merging architecture based on time delay strategy is adopted to reduce the resource consumption. The experimental results show that compared with the traditional dedicated device and simulation software NetEm, the proposed method supports the independent delay configuration of million-level links, increases the number of session streams from ten to one million, and reduces the memory consumption by at least 85% under each bandwidth, which meets the requirements of large scale and accuracy, and greatly reduces the system cost.
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Shi Shujie, Zhao Fengqiang, Wang Bo, Yang Chenhao, Zhou Shuai
2024,47(6):116-122, DOI:
Abstract:
Rolling bearings play an important role in rotating machinery. If a fault occurs, it can cause equipment shutdown, and in severe cases, endanger the safety of on-site personnel. Therefore, it is necessary to diagnose the fault. In response to the difficulty in extracting fault features of rolling bearings and the low accuracy of traditional classification methods, this paper proposes a fault diagnosis method based on Set Empirical Mode Decomposition (EEMD) energy entropy and Golden Jackal Optimization Algorithm (GJO) optimized Kernel Extreme Learning Machine (KELM), achieving the goal of extracting fault features of rolling bearings and correctly classifying them. Through experimental data validation, this method can extract the fault information features hidden in the original signal of rolling bearings, with a diagnostic accuracy of up to 98.47%.
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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%.
