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Ning Zihao, He Li, Wang Hongwei, Yan Wenlong
2025,48(6):1-9, DOI:
Abstract:
In order to solve the problems that it is difficult for service robots to accurately understand pedestrian intentions and unreasonable obstacle avoidance path selection in dynamic pedestrian environment, a pedestrian openness comfort model was proposed. Firstly, by extending the traditional two-dimensional symmetric Gaussian function to an asymmetric Gaussian function, the dynamic comfort space of pedestrians can be modeled more accurately. Secondly, combined with the pedestrian′s head posture and pedestrian′s openness characteristics, the robot′s ability to understand the pedestrian′s movement intention and social interaction relationship is enhanced, so as to improve the friendliness and rationality of navigation. Finally, through the comparison and verification of simulation and experiments in the real environment, the service robot using the pedestrian openness comfort model is more optimized in path selection, and can actively avoid the interactive space of pedestrian groups, which not only reduces the possibility of conflict with pedestrians, but also enhances the smoothness and naturalness of navigation, and shortens the movement time by 1.15 and 2.58 s respectively in the simulation environment of different scenes. In the real environment of different scenes, the exercise time was shortened by 1.14, 2.30 and 0.12 s, respectively. Experimental results show that the model can effectively adapt the robot to complex pedestrian dynamic scenes, improve the efficiency of obstacle avoidance, and significantly improve the social friendliness and navigation quality of the robot in the human-machine integration scene.
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Zhang Lufeng, Ma Jiaqing, Chen Changsheng, He Zhiqin, Wu Qinmu
2025,48(6):10-19, DOI:
Abstract:
In order to solve the problems of power and frequency fluctuations and harmonic content in the output voltage occurring in the grid-connected PV under the traditional virtual synchronous generator control, a VSG rotational inertia adaptive control method and a modulation scheme with stochastic excitation are introduced in the grid-connected. A voltage control loop with virtual impedance is introduced in the VSG control and combined with a current control loop based on a quasi-proportional resonant controller to construct a VSG control strategy for grid-connected inverters of PV power systems. With this strategy, the THD of the three-phase voltages A, B and C decreased by 15.17%, 15.37% and 13.10%, respectively, and the active power overshoot decreased by 7.42% in simulation results, and the THD of the three-phase voltages A, B and C decreased by 1.92%, 4.61% and 2.44%, respectively, in experimental results, and the frequency was stabilized at 50.07 Hz. The simulation and experimental results demonstrated that the proposed method can effectively suppress the power and frequency oscillations and reduce the THD of output voltage, which verifies the feasibility of the proposed method.
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2025,48(6):20-27, DOI:
Abstract:
In the logistics robot transportation process, path planning is the core link, facing challenges such as insufficiently smooth paths and low algorithm search efficiency. The A* algorithm, as a widely used global path planning method, has problems such as ineffective path smoothing when applied to logistics robots. To this end, the traditional A* algorithm has been improved by dynamically weighting the heuristic function and using the Floyd algorithm to remove redundant points in the path, while introducing a safe distance mechanism to prevent collisions. In addition, the path has been smoothed and optimized to better adapt to the actual movement needs of logistics robots. The MATLAB simulation results show that the improved A* algorithm reduces the average number of turning points by 58.5%, shortens the path length by 3.19%, and reduces the number of traversal points by 59.9% compared to traditional algorithms. Further combining with DWA algorithm for local path planning, obstacle avoidance function has been achieved. The effectiveness of the fusion algorithm has been verified through simulation and real vehicle experiments.
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2025,48(6):28-37, DOI:
Abstract:
In order to address the issues of missed detections, false positives, and low accuracy in small traffic sign detection, this paper proposes a detection model for small traffic signs, named YOLOv8-Faster-Ghost-GAM. The algorithm introduces a global attention mechanism (GAM) into the last C2f module of the backbone network, enhancing key features and suppressing irrelevant information to significantly improve the detection of small targets and the recognition capability in complex scenarios. Additionally, each C2f module in the backbone network is replaced with FasterNet to reduce the number of model parameters, and standard convolutions are replaced with Ghost convolutions, which use inexpensive linear transformations to reduce computational effort. Finally, the WiOU loss function is employed to effectively improve the recognition of low-quality samples, resulting in a 1.6% increase in precision and a 3.2% increase in recall, thereby demonstrating the effectiveness of the proposed improvements.
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Liu Zefeng, Ran Teng, Xiao Wendong, Yuan Liang
2025,48(6):38-44, DOI:
Abstract:
Most existing dynamic simultaneous localization and mapping (SLAM) algorithms simply remove dynamic objects, resulting in the loss of dynamic object motion information that aids in the system′s own localization and navigation, and have limitations for complex and ever-changing industrial environments. In this paper, we propose an improved visual SLAM algorithm for target tracking that performs localization while obtaining a more accurate estimate of the object′s pose. The algorithm uses background points for its own localization, uses refined optical flow information to reduce the effect of noise for accurate localization, and then combines the scene flow information with polynomial residuals to obtain accurate dynamic object sensing results and to reduce the algorithm′s error in estimating the object′s pose. Finally, the proposed algorithm is evaluated on the publicly available KITTI Tracking dataset and real scenes. The experimental results show that on the public dataset, the proposed algorithm has an average rotation error (RPER) of 0.027° and an average displacement error (RPET) of 0.069 m. The average rotation error of object pose estimation is 0.686 97°, and the average displacement error is 0.103 50 m. The proposed algorithm is able to have a better performance of self-localization and dynamic object tracking. The proposed algorithm also shows excellent localization and tracking performance in real scenarios.
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Wang Fenghua, Xu Zhicheng, Zhao Lengrui
2025,48(6):45-52, DOI:
Abstract:
In order to solve the large pose estimation error of radiant field Visual SLAM algorithm and poor robustness in the process of fusion with inertial measurement unit, this paper proposes a radiance field visual inertial SLAM algorithm based on tightly coupled IMU. The algorithm uses an improved pre-integration module to implement a tightly coupled framework, the improved initialization strategy to deal with the robustness problem, combined with radiation field loss to optimize pose and bias. The proposed algorithm is applied to the positioning modules of NICE-SLAM and MonoGS, and is experimentally tested on the IMU-RGBD dataset OpenLORIS, and the tight-coupled module can improve the positioning accuracy by 34.3% and 14.8% respectively. Compared with MM3DGS, the proposed algorithm has higher robustness, which can effectively improve the positioning accuracy and has a good generalization ability to improve the SLAM performance of the radiance field.
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2025,48(6):53-64, DOI:
Abstract:
A lightweight and efficient bearing defect detection algorithm DWA-YOLO is proposed to address the challenges of large scale variation, similar texture, and dense distribution of defects in the surface defect detection of bearing outer rings, as well as the complexity of existing detection model structures, poor computational complexity, and detection accuracy. Firstly, a plug and play lightweight dual bottleneck structure module DBM was designed to effectively reduce model complexity and enhance the model′s ability to extract features at different scales. Secondly, the wavelet convolution WTConv with multi-scale characteristics is introduced as a downsampling operator in the network backbone. By expanding the receptive field of the model and utilizing the multi-scale analysis characteristics to capture the details and texture information of the image, the model′s anti-interference ability against texture and noise and its ability to understand contextual information are enhanced, thereby improving the overall detection accuracy. In addition, this article designs a joint loss function Alpha-MPDIOU, which utilizes power transformation mechanism to improve the localization accuracy of bounding boxes and solve the problem of detecting multiple boxes. Finally, the use of auxiliary detection head training strategy accelerates the convergence speed of the model and enhances its detection capability. The experimental results show that DWA-YOLO improves mAP accuracy by 3.5% compared to the baseline model, with a model parameter size of 2.6 M and a computational complexity of 7.4 GFLOPs. The improved model not only enhances the ability to identify bearing defects, but also reduces network complexity, making it more suitable for the detection needs of bearing outer ring surface defects in industrial sites.
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Liu Yuhong, Xu Peng, Shu Wei, Yu Xirui, Cai Weiwei
2025,48(6):65-72, DOI:
Abstract:
In order to solve the problems of the traditional three vector model predictive control strategy, such as large calculation amount of vector selection, complicated calculation of operation time of each vector and large common mode voltage, a multi-vector model predictive current control strategy is proposed. Firstly, to solve the problem of large common-mode voltage, it is proposed to replace traditional zero vector with effective voltage vector synthesis, and use voltage vector selection table and voltage vector position angle to quickly select vectors and reduce the calculation amount of vector selection. Secondly, voltage error duty cycle is adopted to simplify the calculation of the operation time of each vector. Finally, its effectiveness is verified by simulation and physical platform. It is proved that the control algorithm can improve the steady-state performance of the system and restrain the influence of large common mode voltage on the motor.
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Sun Xinyu, Xu Jiachuan, Jiao Xuejian, Zhou Yang, Xu Han
2025,48(6):73-82, DOI:
Abstract:
An improved Informed-RRT* algorithm is introduced to tackle issues related to high randomness, a large number of infeasible nodes, and low convergence efficiency in path planning. This algorithm optimizes node usage through global sampling and an adaptive step size. The initial path is generated using a biased bidirectional search and a parent node reselection technique, which offers a more effective starting point for further iterative optimization. During the elliptic iteration, a greedy approach is applied to eliminate unnecessary nodes. Additionally, path backtracking is refined to decrease redundant nodes and improve trajectory smoothness. This study presents two factors: obstacle complexity and map size, to assess the performance of the enhanced algorithm against the original Informed-RRT* algorithm in four different scenarios. Results from 20 experiments show that the improved algorithm decreases the number of trajectory waypoints by 28.6% to 64.3% and reduces trajectory length by 0.3% to 2.7%. These results suggest that our enhanced method enhances node utilization, produces shorter trajectories, and significantly cuts down on computational iterations compared to the Informed-RRT* algorithm.
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Zhan Jiacheng, Chen Zhe, Wei Ruikai, Chen Guoyi
2025,48(6):83-89, DOI:
Abstract:
Sonar detection technology has been widely used in underwater structure detection. Affected by the complex underwater environment, sonar images usually have substantial problems such as low resolution, serious noise interference, fuzzy edge details, and poor texture information. In order to solve these problems, this paper proposes a fusion denoising algorithm based on improved anisotropic guided filtering and Wiener filtering. Firstly, the local structural similarity index was introduced into the traditional AnisGF as a weighting factor to achieve denoising while retaining more edge structure information. Secondly, the Bayesian optimization method was used to determine the SSIM weight of Wiener filtering. Finally, AnisGF and Wiener filtering were combined for joint denoising of sonar images. The experimental results show that the proposed algorithm has 9.5%, 4% and 10% improvements in mean square error, peak signal-to-noise ratio and structural similarity index compared with the traditional algorithm.
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Yin Xiaohu, Zhang Anyi, Zhang Keke, Tian Chong
2025,48(6):90-98, DOI:
Abstract:
Spectrum sensing is one of the key technologies to alleviate spectrum resource shortages, and intelligent spectrum sensing has become a hot research direction. To address the issues of insufficient feature extraction in existing spectrum sensing methods and poor sensing performance under low signal-to-noise (SNR) ratio conditions, a hybrid spectrum sensing model is proposed. The model consists of an Inception module, bidirectional gated recurrent unit, temporal attention mechanism, and fully connected layer network. Firstly, the Inception module extracts multi-scale spatial features from the received I/Q signals. Then, the bidirectional gated recurrent unit is used to capture the temporal sequence features of the signals, while the temporal attention mechanism enhances important temporal features. Finally, the fully connected layer network maps the extracted features to the classification space of spectrum states to complete classification and recognition. The experimental results show that the proposed method significantly improves perception performance compared to several existing spectrum sensing methods. The overall detection accuracy of the model reaches 84.55%, and when the SNR is -20 dB, the perception error of the method is 24%. The proposed method also demonstrates good adaptability to various modulation types of radio signals. It does not rely on any prior information and exhibits strong robustness in low SNR and complex radio environments. This approach achieves an effective balance between perception performance and model complexity, providing a new solution for intelligent spectrum sensing.
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2025,48(6):99-105, DOI:
Abstract:
Intelligent reflecting surface (IRS) is one of the key technologies in the sex generation(6G). However, for multi-user systems, the computational complexity of the system increases greatly with the increase of the number of reflective units and the number of users, and the optimal design of the system faces great challenges. In this paper, we propose a low computational complex transmission rate maximization algorithm based on multi-user reflection unit selection. According to the user′s rate requirements and channel conditions, the algorithm selects the matching reflection unit, considers the phase shift setting and the base station beamforming, and carries out joint optimization to establish a user rate maximization problem. There is a high degree of coupling between the variables in this optimization problem. Therefore, the original problem is divided into two subproblems for solving, and the approximate solution is obtained by using semidefinite relaxation. The simulation results show that the algorithm proposed in this paper can significantly reduce the computational complexity of the system while improving the downlink transmission rate. Compared to a system without IRS assistance, the transmission rate increases by about 50%; compared to a random phase IRS, the transmission rate increases by about 30%.
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Wang Hu, Xie Jun, Liu Junjie, Hu Bo
2025,48(6):106-113, DOI:
Abstract:
To investigate the effect of different guidance methods on the cortical activation during fine motor imagery, a novel fine motor imagery method combining visual and auditory guidance is proposed. The goal is to explore the enhancement of cortical activation during fine motor imagery using different guidance approaches and to uncover the underlying patterns of cortical activity. An experimental paradigm was designed for fine motor imagery of the wrist, elbow, and shoulder joints, incorporating four guidance methods: simple visual guidance, auditory guidance, dynamic visual guidance, and dynamic visual combined with auditory guidance. ERD and ERS in the time and frequency domains were used as metrics to assess cortical activation. Energy distribution and brain network functional connectivity were utilized to observe the spatial distribution of cortical activity and analyze the degree of cortical activation under different guidance methods. The experimental results indicate that the dynamic visual combined with auditory guidance led to significantly higher ERD and ERS amplitudes compared to the other guidance methods. Additionally, under the visual and auditory combined guidance, the activated cortical regions were more extensive, and stronger synchronization and desynchronization were observed in multiple brain areas. Compared to simple visual guidance, auditory guidance, and single dynamic visual guidance, the dynamic visual combined with auditory guidance significantly enhanced cortical activation during fine motor imagery. This method provides a new guidance strategy for fine motor imagery training, contributing to improved training effectiveness and rehabilitation efficiency, with potential practical applications.
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Cui Haiqing, Guo Jiawei, Wang Kai
2025,48(6):114-120, DOI:
Abstract:
In the BeiDou satellite synchronization system, FPGA-based solutions are typically used. However, using an ARM single-core system during scheduling can lead to resource contention and real-time response deviations. While ARM processors are superior to FPGA in handling business logic, floating-point calculations, and similar tasks, this paper proposes a solution for BeiDou 1PPS synchronization and timing based on ARM processors. The synchronization calculation is implemented using the least squares method combined with a sliding window, while the timing calculation is achieved through a phased growth mechanism. Additionally, a delay correction algorithm is introduced to address cycle boundary acquisition deviations caused by interrupt conflicts during signal processing. When the system detects that the data is about to overflow, the algorithm delays recording the rising edge signal′s cycle value and applies corrections. Experimental results show that this algorithm can achieve synchronization accuracy at the level of 10-8 s, proving its effectiveness in high-precision time synchronization applications.
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2025,48(6):121-129, DOI:
Abstract:
Addressing the challenges in the domain of image inpainting, such as the high computational complexity, loss of information during feature extraction, and the blurring of textures in the inpainting images, this study proposed a image inpainting model that integrates multiscale hierarchical feature fusion with synergetic global-local Transformer. Initially, the multi-scale hierarchical feature fusion block was proposed as a means of effectively fusing deep and shallow features in detail, thereby reducing the loss of key information while expanding the sensory field. Subsequently, synergetic global-local Transformer blocks for global reasoning was proposed, featuring an integrated rectangle-window self-attention mechanism and local feed-forward neural networks. This design reduced computational complexity while enhancing the model′s macroscopic understanding of global context and microscopic grasp of local detail characteristics.The proposed method was validated on the CelebA-HQ and Places2 datasets, and the results demonstrated that it yielded improvements in PSNR by an average of 0.26~6.25 dB, SSIM by an average of 1.4%~19%, and L1 decreased by an average of 0.2%~5.66% compared to commonly used inpainting methods when dealing with 40%~50% masks. The experiments show that the inpainted images resulting from the proposed method exhibit a more realistic and natural visual effect, thereby providing further validation of the method′s effectiveness.
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Li Zeyin, Li Dong, Fang Jiandong, Zhao Lei, Zhang Jiahui
2025,48(6):130-142, DOI:
Abstract:
Aiming at the problems of high detection false alarm rate, low detection accuracy, leakage and false detection caused by dense target arrangement, large scale difference and complex background of remote sensing images, a remote sensing image detection algorithm YOLOv8-EP based on YOLOv8n is proposed. Firstly, a feature focus diffusion pyramid network (FFDPN) is constructed to capture multi-scale information through parallel deep convolution, while adding a diffusion mechanism to diffuse the feature information to each detection scale to enhance feature interaction. A lightweight task align dynamic detection head (TADD) is designed to improve the localisation and classification performance of detection through feature sharing and parallel task processing. Then, the SimAM attention mechanism is introduced to capture key information in the image and increase the model sensory field. Finally, the Inner-CIoU loss function is introduced to improve the detrimental effect of low-quality images on the network gradient and accelerate the model convergence. Experimental results on the NWPU VHR-10 dataset and RSOD dataset show that YOLOv8-EP achieves a mAP of 97.6% and 97.9%, respectively, with a 13% decrease in the number of parameters, and improves by 2.2% and 1.5% compared to the YOLOv8n baseline network, which can meet the requirements of industrial deployment and achieve good detection performance overall.
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Pan Chenglong, Liu Licheng, Pan Dan
2025,48(6):143-151, DOI:
Abstract:
Segmentation of coronary arteries is crucial for the rapid diagnosis of cardiovascular diseases. Given the challenges posed by the complex structure of coronary arteries and the interference from other vascular tissues, which often result in fragmented segmentation, ensuring the model′s ability to adapt to segmenting different morphological structures of the coronary artery, a novel 3D coronary artery segmentation network (CA-SegNet) is proposed. This model incorporates a combination of CNN and Transformer as the encoder and decoder, leveraging their advantages and complementarity to fully extract both global and local features of coronary arteries. By proposing a multi-scale feature interaction module, the model simultaneously extracts multi-scale features of coronary arteries while facilitating feature channel interaction. In the decoding stage, an attention weighted feature fusion module is proposed to weight and fuse features from both spatial and channel perspectives, enabling the model to focus more on the coronary artery regions. Experimental results demonstrate that the proposed model achieves DSC, Recall, Precision, and HD95 values of 81.96%, 84.24%, 80.11% and 14.94 respectively, surpassing current popular segmentation models and validating the effectiveness of CA-SegNet.
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Chen Guangqing, Chen Yahui, Zhou Peng, Liu Ziyu, Chen Yulun
2025,48(6):152-160, DOI:
Abstract:
In industrial settings, the acquisition and annotation of defective workpieces pose significant challenges, severely hindering defect detection efforts. While generating a large number of defective samples from limited real-world samples effectively mitigates the issue of sample scarcity, existing defect generation methods are often constrained by suboptimal visual authenticity and poor alignment with defect masks. To address these limitations, this study introduces AnomalyAlign, a novel controllable diffusion model designed to synthesize highly realistic industrial defect images with precise mask alignment. Leveraging the foundational knowledge of the text-to-image model Stable Diffusion, AnomalyAlign incorporates a semantic-aligned text prompt generator to produce text prompts that achieve closer semantic alignment with real images, thereby accelerating model convergence. Furthermore, the model integrates a defect alignment loss function, which enhances the spatial consistency between generated defect images and their corresponding masks. Extensive experimental validation on the MVTec-AD dataset demonstrates that AnomalyAlign generates defect images with superior realism and diversity, while significantly improving the performance of downstream defect detection tasks.
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Wu Pengfei, Li Min, Luo Peng, Zhu Ping
2025,48(6):161-170, DOI:
Abstract:
In the tree barrier clearance project for protecting the safety of the electrical distribution network, the manual calculation of the felling quantity faces problems such as strong subjectivity of the calculation results and management difficulties. The existing algorithms have low accuracy, with many false positives and false negatives, and poor robustness. Therefore, a tree stump detection algorithm for calculating the felling workload in the transmission corridor tree barrier clearance is proposed. In response to the problem of inaccurate felling quantity calculation due to the complexity of the distribution network clearance scene and the difficulty in distinguishing between tree trunks and tree stumps, a feature extraction module based on Context Guide Block is designed. RepGFPN and Dysample structures are introduced to optimize the neck network, effectively integrating environmental context semantic information with local details of tree stumps. Subsequently, the algorithm designs a tree stump detection head based on LW-SEAM, optimizing the detection effect under occlusion. The model′s P, R, and mAP50 indicators on the test set have been improved to 85.5%, 76.4%, and 80.4% respectively, showing good detection performance for tree stump detection in complex backgrounds and occlusion scenarios, and providing technical reference for achieving intelligent engineering calculation.
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Chen Junkeng, Liu Guixiong, Xie Fangjing
2025,48(6):171-178, DOI:
Abstract:
The installation of photovoltaic generation (PVG) systems in existing office buildings (EOB) is one of the environmental green energy measures. However, the fluctuation of PVG negatively impacts the stable electricity use of EOB, making EOB-PVG power prediction crucial. This paper proposes the EOB-PVG power prediction method using sparrow search algorithm-long short-term memory (SSA-LSTM). The method preprocesses the collected environmental and generation data using multiple imputation + principal component analysis (MI+PCA) and splits the dataset. The LSTM neural network prediction model is designed, and SSA is used to automatically optimize the neural network's hyperparameters to achieve accurate prediction. The experiment selects real environmental and generation data from the EOB, and after preprocessing, the cumulative contribution rate of the principal components of the dataset exceeds 95%. Three evaluation metrics are designed to assess prediction performance. Comparison results show that SSA LSTM outperforms LSTM and SSA-TCN in prediction accuracy and fitting ability, providing good accuracy in predicting EOB-PVG power and contributing to the subsequent realization of intelligent energy management tasks for EOB.
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Wang Lingzi, Liu Guixiong, Zhang Guocai, Zhong Fei
2025,48(6):179-187, DOI:
Abstract:
The tension clamp plays the role of connecting wires and carrying current in the transmission line, and its crimping quality is directly related to the safe and effective operation of the power grid. In order to solve the problems of complex operation and high personnel requirements in the DR defect detection of tension clamp crimping, a DR image defect evaluation method using VA-UNet segmentation technology was proposed. Firstly, the semantic segmentation model VA-UNet for DR image defects in tension clamps is studied, VGG16 with significant image feature extraction and analysis ability is selected as the backbone network, multi-scale feature fusion is enhanced by integrating spatial pyramid pooling structure ASPP, and mixed loss function is introduced to accelerate the model convergence and improve the segmentation accuracy. Then, a grading method combining the model prediction segmentation results and related quantitative analysis was studied to realize the hazard severity assessment of DR defects in tension clamp crimping, which provided a reference for the subsequent wire clamp treatment. Based on the data set preparation and the analysis of test evaluation indicators, the relevant ablation experiments showed that the mIoU and mPA of VA-UNet reached 84.14% and 91.58%, respectively, which were significantly higher than those of the original model. The experiment of assessing the severity of DR defects in tension clamp crimping shows that the method is scientific and practical.
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Li Junfeng, Tan Beihai, Zheng Yufan, Chen Hanjie, Yu Rong
2025,48(6):188-195, DOI:
Abstract:
In semantic communication, image semantic information processing heavily relies on computationally intensive convolutional neural networks, which require higher computational performance, especially when handling high-resolution images. This presents a significant challenge for the application of semantic communication in edge scenarios. To address this, this paper proposes an FPGA-based semantic information processing accelerator, which innovatively integrates the convolutional neural network encoder and rANS encoding in the same hardware accelerator. Specifically, the accelerator adopts a systolic array architecture combined with multiply accumulate units, loop tiling strategy, and a dual-buffer structure to fully leverage the parallel computing capabilities and on-chip storage resources of the FPGA, improving data transmission efficiency and computational performance. Each processing unit integrates multiple multiply-accumulate units, capable of performing two INT8 multiplications and local accumulation in each clock cycle. Finally, rANS is used for 8-way parallel encoding of the output features, further compressing the feature data. Experimental results show that, on the ZCU104 platform, the design achieves a throughput of 300.5 GOPS with a power efficiency of 66.77 GOPS/W when processing 1080P images, providing a processing speed approximately 6 times faster than Intel CPUs and 58 times faster than ARM CPUs. Compared with other FPGA accelerators, the BRAM efficiency improves by approximately 730%, 40%, and 63%, the energy efficiency by approximately 802%, 60% and 3%, and the DSP efficiency by approximately 476%, 70% and 133%. The proposed accelerator demonstrates significant performance advantages and can efficiently process image semantic information, offering broad practical application potential.
Volume 48, 2025 Issue 6
Research&Design
Theory and Algorithms
Communications Technology
Data Acquisition
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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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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Wang Huiquan, Wei Zhipeng, Ma Xin, Xing Haiying
2024,47(6):14-19, DOI:
Abstract:
To solve the problem of low control accuracy of the tidal volume emergency ventilation for lower air pressure at high altitudes, we propose a dual-loop PID tidal volume control system, which utilizes a pressure-compensated PID controller to adjust fan speed, supplemented by an integral-separate PID controller in order to achieve precise control of airflow velocity.Compared with single-loop PID control, the rapid response and no overshooting are observed in the performance tests of the dual-loop control system at an altitude of 4 370 m and atmospheric pressure of 59 kPa, in addition, the output error of the average airflow velocity decrease to 3.19% (the maximum error is 4.1%), which is superior to that of current clinical equipment. Our work offers an effective solution for high-altitude emergency ventilator tidal volume control, and contributes important insights to the development of ventilation control technology in special environments.
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Fang Xin, Shen Lan, Li Fei, Lyu Fangxing
2024,47(6):20-27, DOI:
Abstract:
The high-frequency measurement data of underground vibration signals can record more specific details about the dynamic response of drilling tools, which is helpful for analyzing and diagnosing abnormal vibrations underground. However, the high-frequency measurement generates a large amount of measurement data, resulting in significant storage pressure for underground vibration measurement equipment. The proposed method uses compressed sensing technology to selectively collect and store sparse underground vibration data and then recover high-frequency measurement results through a signal reconstruction algorithm. In the process of realizing this method, an innovative method of constructing a layered Fourier dictionary against spectrum leakage is proposed, and an improved OMP signal reconstruction algorithm based on layered tracking is researched and realized, which greatly reduces the time required for signal recovery. Simulation and experimental test results demonstrate the method′s effectiveness, achieving a system compression ratio of 18.9 and a reconstruction error of 52.1 dB. The proposed method may greatly reduce the data storage pressure of the measuring equipment in the underground, and provides a new way to obtain high-frequency measurement data of underground vibration.
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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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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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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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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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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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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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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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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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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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Zhou Guoliang, Zhang Daohui, Guo Xiaoping
2024,47(6):190-196, DOI:
Abstract:
The gesture recognition method based on surface electromyography and pattern recognition has a broad application prospect in the field of rehabilitation hand. In this paper, a hand gesture recognition method based on surface electromyography (sEMG) is proposed to predict 52 hand movements. In order to solve the problem that surface EMG signals are easily disturbed and improve the classification effect of surface EMG signals, TiCNN-DRSN network is proposed, whose main function is to better identify the noise and reduce the time for filtering the noise. Ti is a TiCNN network, in which convolutional kernel Dropout and minimal batch training are used to introduce training interference to the convolutional neural network and increase the generalization of the model; DRSN is a deep residual shrinkage network, which can effectively eliminate redundant signals in sEMG signals and reduce signal noise interference. TiCNN-DRSN has achieved high anti-noise and adaptive performance without any noise reduction pretreatment. The recognition rate of this model on Ninapro database reaches 97.43% 0.8%.
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Li Hui, Hu Dengfeng, Zhang Kai, Zou Borong, Liu Wei
2024,47(6):164-172, DOI:
Abstract:
In signal generation algorithms, a large number of labeled signal samples are needed for network training, but it is usually difficult to obtain signals carrying message information markers in bulk. To address this problem, this paper proposes a method based on CycleGAN and transfer learning, which realizes the generation of Enhanced LORAN signals without the need for a large number of signals and the corresponding messages as markers and uses migration learning to generate them quickly with a small number of measured signals. The structure of the CycleGAN includes two generators and two discriminators, using the Enhanced LORAN signals and message data sets that do not need to be one-to-one correspondence, so that the generator learns the interconversion relationship between the two data sets, and realises that the input message data can generate the Enhanced LORAN signals corresponding to it, for the characteristics of the Enhanced LORAN signal, the network model is improved using a one-dimensional convolution, residual network, and self-attention mechanism. Experimentally confirmed, it is confirmed that the mean square error of the signal generated by this paper with the measured data is 0.015 3, the average Pearson correlation coefficient is 0.984 3, and the accuracy of the contained message information is 99.02%. To verify the universality of the algorithm, this paper validates the algorithm on PSK, ASK, and FSK datasets, and the experimental results show that the generated signals satisfy the expectations and provide a new idea for signal modulation and demodulation with unknown parameters.
