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Zhang Hui, Chen Baoquan, Wang Yelong, Qi Feng
2025,48(7):1-8, DOI:
Abstract:
Reconfigurable wideband components play an important role in wireless communication systems, however, designing a straightforward wideband 1-bit unit remains a significant challenge. This design is based on the principle of polarization conversion. By controlling the on-off state of the PIN diodes, the polarization of the incident wave is rotated by ±90°, provide dual-polarization regulation and a 180° phase shift. The unit operates from 25.58 to 31.87 GHz, with a relative bandwidth of 21.9% and the cross-polarization reflection coefficient is greater than -2 dB. Meanwhile, the unit demonstrates excellent angular stability, supporting an oblique incidence angle of up to 30°. Utilize this unit to construct a phase-reconfigurable reflectarray with an aperture size of 70 mm × 70 mm. The gain is 20.6 dB at 30 GHz, which can realize ±60° full-space beam scanning capability. This wideband tunable design can be utilized in wireless communication, radar detection, and other fields.
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Duo Meng, Tao Aihua, Yao Shunyu, Mei Jianing, Yang Guang
2025,48(7):9-15, DOI:
Abstract:
During the wind tunnel testing, the balance will be subjected to aerodynamic dynamic loads for a long time, which may lead to fatigue failure of the balance. In severe cases, cracks or fractures may occur, which can not only cause damage to the balance, but also catastrophic situations such as test pieces falling off and being blown away along the surface of the wind tunnel. These unexpected situations often delay the entire development cycle of the aircraft. To reduce the risk of fatigue failure of the balance and ensure the safety of wind tunnel testing, the FL-9 wind tunnel pressurization test balance was taken as the research object. Combined with the finite element analysis results of the balance, high-risk stress nodes of the balance structure were extracted, and fatigue monitoring was carried out throughout the calibration and wind tunnel testing process of the balance. The test results indicate that the fatigue stress monitoring technology of the balance can ensure the safety of the balance. Based on this, the load range of each unit of the balance has been expanded by 1.5 to 2 times. The wind tunnel test results show that the balance still has sufficient safety margin to obtain a wider range of test data.
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Li Shuwei, Liu Guozheng, Fang Shuyu, Liu Xiaotong, Lyu Jinyang
2025,48(7):16-27, DOI:
Abstract:
A method based on the DR-IFMM is proposed for inpainting damaged rock art images. This method determines two optimal repair radii based on the pixel density of the damaged region, and is applied to the IFMM algorithm to generate the repaired image respectively. The IFMM algorithm improves the weight calculation rules based on the FMM algorithm, and then fuses and restructures the two images into the optimal repaired image. The experimental results show that the DR-IFMM method outperforms the MSMM, IK-means, COTR, STDecomposition, SFIIM, AutoFill and ICriminisi methods in inpainting rock art images with various types of damages, and effectively addresses the issues such as color loss and texture clutter. Compared with the LaMa method, the advantage of the proposed approach is that it still can achieve better inpainting results without model training and high-performance computer. Inpainting damaged rock art images can inherit and develop the rock art through the form of digital. and provide cultural relic researchers with a complete record of China′s ′history′ etched on stone walls.
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2025,48(7):28-35, DOI:
Abstract:
Cloud phase is not only an important parameter in meteorological and climatological research but also a key element in cloud parameter inversion. Accurate identification of cloud phase is crucial for weather monitoring and forecasting. Traditional cloud phase recognition methods often rely on threshold setting, which is highly subjective and not very reliable. Therefore, this paper proposes a semi-supervised adaptive possibility C-means algorithm that enhances the processing capability of multi-dimensional data and the robustness of classification through semi-supervised learning combined with an adaptive feature weighting mechanism and regularization techniques. By applying this method to Raman lidar and millimeter-wave cloud radar data, it is possible to accurately classify ice clouds, water-dominated mixed clouds, ice-dominated mixed clouds, and supercooled water clouds. Compared with the algorithm before improvement, the classification accuracy has been significantly increased from 0.699 to 0.967, greatly improving the accuracy of cloud phase classification.
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Wang Xinfeng, Jiang Xinjie, Zhang Pi, Zhao Siqin
2025,48(7):36-45, DOI:
Abstract:
To address the issue of traditional algorithms being prone to local optima during the maximum power point tracking (MPPT) process due to the multi peak characteristic of photovoltaic array output power curves under partial shading conditions, this paper proposes an MPPT control strategy combining the improved Black-winged kite algorithm (TBKA) and the Perturb and observe method (P&O), referred to as TBKA-P&O. In the global search phase, the population is first initialized using the Tent-Logistic-Cosine chaotic mapping. Then, a tangent flight strategy is introduced to enhance the search efficiency and convergence accuracy of the TBKA. Additionally, a dynamic lens imaging reverse learning strategy based on a greedy approach is designed to improve search diversity and prevent local optima. In the local search phase, the P&O method is incorporated to achieve rapid localization and high-precision tracking of the maximum power point. To verify the effectiveness of the proposed algorithm, a photovoltaic power generation system simulation model was constructed, incorporating the traditional P&O algorithm, the BKA-P&O algorithm, the quantum CS-P&O algorithm, and the TBKA-P&O algorithm. Experimental results demonstrate that the TBKA-P&O algorithm achieved tracking accuracies of 100%, 99.97%, 99.96% and 99.96% under four operating conditions, with corresponding tracking times of 0.093, 0.090, 0.077 and 0.047 s. Compared to other algorithms, the TBKA-P&O algorithm exhibited significant advantages in terms of dynamic tracking speed, steady-state tracking accuracy, and power oscillation control.
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Zhang Ruifang, Liu Zhanzhan, Cheng Xiaohui, Zhao Hong
2025,48(7):46-54, DOI:
Abstract:
In order to solve the problem of difficulty in target detection caused by noise interference, illumination fluctuation and complex background in UAV aerial infrared images, an infrared target detection model for UAV based on YOLOv8 was proposed. Firstly, the SCDown module in YOLOv10 was introduced to maximize the preservation of contextual semantic information for each scale. Secondly, the dynamic upsampler DySample was introduced to improve the sensitivity of the model to image details. At the same time, the triplet attention mechanism is introduced to improve C2f to strengthen the model′s understanding of the relationship between spatial and channel dimensions and the processing ability of complex data. Finally, a lightweight decoupling head Efficient_Head module is designed to ensure the detection accuracy and greatly reduce the model parameters. Experimental results show that the improved algorithm mAP50 reaches 83.7%, which is 4.2% higher than YOLOv8n, the accuracy is increased by 1.2%, the recall rate is increased by 3.8%, the number of floating point operations isreduced by 2.5%, and the FPS reaches the detection speed of 323.17 fps, which fully shows that the overall performance of the improved algorithm is better than that of other mainstream algorithms, and it can better complete the task of UAV infrared target detection.
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He Yin, Kong Lingling, Zheng Zheming
2025,48(7):55-65, DOI:
Abstract:
To address the issues of low convergence accuracy and susceptibility to local optima in the PKO algorithm, this paper proposes a multi-strategy improved IPKO algorithm. First, Latin hypercube sampling is used to avoid over concentration or neglect of potentially beneficial areas in high-dimensional problems, thus reducing the risk of local optima. Secondly, the positioning fishing mechanism from the OOA algorithm is introduced to enhance exploration of the optimal region and improve the ability to escape from local optima. Finally, a new falling mechanism is integrated to improve search stability and prevent premature convergence. An adaptive mutation rate termination condition is also applied to dynamically balance global exploration and local exploitation, optimizing solution quality and search efficiency. The training-testing accuracy and runtime under different feature dimensions are compared, and the impact of population size and iteration count on the algorithm′s performance is analyzed. Experimental results on 12 benchmark test functions show that IPKO outperforms other comparison algorithms in terms of convergence speed, solution accuracy, stability, and the Friedman test. When applied to the microgrid scheduling problem, IPKO demonstrates lower costs compared to other algorithms, with a reduction of 1.92% over the original PKO, confirming its effectiveness and reliability in practical applications.
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Chen Jiayun, Xiao Genfu, Zhang Xiangming
2025,48(7):66-74, DOI:
Abstract:
Timely detection of Self-exploding insulators during the inspection process can effectively prevent power grid accidents. In response to the problems of large memory and slow detection speed required for convolutional neural network training, which do not have advantages in real-time detection on mobile devices, self-explosion fault detection algorithm for Insulator based on improved YOLOv7-tiny is proposed. Firstly, deformable convolution and dynamic snake convolution is introduced into the YOLOv7 tinyIncorporating dynamic serpentine convolution into the YOLOv7-tiny algorithm and designing a more efficient layer network to enhance perception; then, the Gold-YOLO network is introduced to enhance the information fusion of the intermediate layer; subsequently, the MPDIoU loss function is used to reduce the redundancy of the prediction boundary; finally, designing a Self-exploding insulator detection system to enable staff to quickly identify self-exploding insulators in a massive collection of images. The research results show that the mean average precision of the improved algorithm is 96.3%, which is 1.1% higher than the original YOLOv7-tiny algorithm. The average precision of the improved algorithm is 99.5% for identifying Self-exploding insulators, which is 0.2% higher than the YOLOv7-tiny algorithm and 0.1% higher than the YOLOv7 algorithm. Moreover, the scale of the improved algorithm is only 28% of that of the YOLOv7 algorithm, and the FPS has increased by 11.3, reaching 60.6. The improved algorithm can meet the requirements of real-time detection while ensuring recognition accuracy.
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Wang Zheng, Zhao Xinhui, Wang Xiaowei
2025,48(7):75-85, DOI:
Abstract:
Accurately localizing and recognizing human moions in both spatial and temporal dimensions is of significant importance for applications such as intelligent sports analysis. However, existing step-by-step human motion recognition methods are often limited by the fixed receptive field of RoI features, making it difficult to achieve effective modeling and semantic representation in complex scenarios. To address this issue, this paper proposes a fine-grained motion & situation fusion (FMSF) network that integrates human representation features and global spatiotemporal situation features through parallel semantic modeling and motion proposal units. The semantic modeling unit employs a human localization model to generate fine grained human candidate features from key frames and leverages a 3D video backbone network to extract global spatiotemporal features. The motion proposal unit then uses a shared Transformer framework to jointly model these multi-modal features, capturing complex interactions between humans and their surroundings, resulting in highly discriminative motion predictions. Furthermore, a weighted score aggregation strategy is introduced to integrate the motion classification results of multiple key frames and short video segments for long-video motion recognition. On the AVA-60 v2.2 dataset, the FMSF model achieved a frame-level mAP of 30.01%, while the long-video strategy-based FMSF-Prolonged reached 30.74%. On the Charades dataset, the mAP of FMSF increased to 30.68%, and that of FMSF-Prolonged increased to 31.29%.
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Yang Qian, Xiong Wei, Meng Shengzhe, Huang Yuqian
2025,48(7):86-97, DOI:
Abstract:
Accurately detecting insulator defects is one of the main tasks of power grid maintenance. In response to the problems of low recognition accuracy of current insulator defect detection algorithms and large models that are difficult to deploy to mobile devices such as drones, a method based on YOLOv8 is proposed to improve the detection accuracy and lightweight the model. This method uses the feature fusion mode in a bi directional feature pyramid network BiFPN to fully fuse multi-scale features, and then integrates a deformable attention mechanism DAttention into the original algorithm to extract features with lower complexity. In addition, it introduces a fusion of average pooling and maximum pooling coordinate attention DAF-CA to enhance key information, and finally uses the minimum point distance based Intersection over Union MPDIoU as the loss function to improve the training effect of bounding box regression, thereby improving the accuracy of the algorithm. Multiple comparative experiments were conducted on the dataset, and the results showed that the proposed method achieved an average accuracy of about 91.0%. The model had a floating point count of 7.2 G and a parameter count of 2.07 M, respectively, and all performance indicators were superior to commonly used detection algorithms. This method can provide reference for intelligent inspection of power grids.
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2025,48(7):98-106, DOI:
Abstract:
In order to ensure the safe travel of the blind, an obstacle avoidance method combining object detection and binocular stereo vision was proposed to solve the problems of blind path occupation, damage and absence. Firstly, the sidewalk information is collected by binocular camera, and the obstacles on the sidewalk are detected by the improved YOLOv8s model. Then, an improved stereo matching algorithm is used to match the obstacles, which uses FAST algorithm with adaptive threshold to find the feature points on the scale space, and uses least square method to obtain the sub-pixel coordinates of the feature points and reduce the dimension of the feature descriptors. Finally, the two-dimensional pixel coordinates are converted into three-dimensional spatial coordinates by the ideal binocular model, and the depth value of the obstacle is obtained. Experiments show that the obstacle avoidance system can accurately identify the types of obstacles in the range of 10 meters, and the FPS can reach 149.1; when measuring the depth of obstacles, the maximum error is controlled within 5.6%, and the FPS can reach 3.8, which meets the requirements of real-time and distance accuracy required by the blind to avoid obstacles.
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Shu Rui, Fu Mingwei, Peng Ting, Li Yongkang, Yang Bo
2025,48(7):107-116, DOI:
Abstract:
With the advancement of artificial intelligence technology, baby monitoring systems have become increasingly prevalent in daily life. This paper presents an AI-based infant behavior monitoring system that utilizes computer vision techniques and deep learning algorithms, integrated with hardware components such as the Raspberry Pi 4B and Camera V2, to achieve real-time monitoring and intelligent analysis of infant behavior. The system employs the Google MediaPipe pose recognition algorithm to extract infant joint features within predefined safety zones and uses an optimized Moondream 2 model for multimodal data inference, significantly enhancing the system′s real-time responsiveness and accuracy. Additionally, the system incorporates a lightweight time-series analysis module to improve sensitivity to behavioral changes and integrates dynamic alert functions to ensure efficient and reliable monitoring. By leveraging the Home Assistant platform, MQTT protocol, and network tunneling technology, the system supports remote access and real-time notification capabilities. Experimental results demonstrate excellent performance in terms of accuracy and stability, making the system widely applicable in home monitoring and intelligent caregiving scenarios, and providing a novel solution for the safety management of infants and young children.
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2025,48(7):117-125, DOI:
Abstract:
Existing security research on microgrid frequency control systems lacks a comprehensive analysis of severe attack scenarios, particularly high-concealment attacks executed by adversaries using internal information. The system vulnerabilities and the extent of their potential impact remain insufficiently assessed. This paper develops a load frequency control model of microgrid that incorporates wind, solar, and storage, and performs a vulnerability analysis of its communication layer to identify potential attack vectors. To address concealment constraints, an optimized attack model is formulated by introducing slack variables, which transforms the nonlinear optimization problem into a linear programming problem, enabling faster solutions and the generation of specific attack sequences. Finally, multiple attack tests are conducted on microgrids in islanded operation mode. Compared to traditional random attack methods, the proposed optimized attack sequence achieves approximately 40% improvement in attack effectiveness while maintaining over 95% stealth. The effects of key microgrid system parameters, different operation modes, and various renewable energy penetration rates on optimal attacks are analyzed. Results show that the proposed optimization based attack can significantly improve attack success rate and effectiveness while maintaining stealthy, indicating that microgrid systems remain potentially vulnerable to well-designed attacks.
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Peng Wenjing, Zheng Di, Cai Hui, Shao Haiming, Wang Jiafu
2025,48(7):126-134, DOI:
Abstract:
To address the challenge of short-term fluctuation of large-scale photovoltaic power generations pose a challenge to accurate energy measurement, this paper proposes a new method for run clustering for short-term fluctuation of photovoltaic based on improved Gaussian mixture model. Firstly, the characteristics of short-term fluctuation signals of photovoltaic output are analyzed based on the run theory. Secondly, to address the issue of excessive run and difficulty in extracting typical features in the power generation of photovoltaic, the clustering method based on the improved Gaussian mixture model is adopted to cluster the massive run. Furthermore, a subjective-objective fusion evaluation method for clustering results is proposed. Finally, the simulation results of on-site recorded waveforms from photovoltaic power stations show that, compared with other methods, the proposed method has an improvement in clustering result scores ranging from 1.1% to 61.4% in different aspects. The proposed method can maintain good clustering effects under different noise and outlier levels, with a decrease in the composite index score that is less than that of other algorithms by 0.92% to 18.24%. The proposed method achieves adaptive clustering of the Gaussian mixture model through deep learning technology and the Bayesian information criterion, enhancing its adaptability and stability to noisy and outlier data, and enabling reasonable clustering of run-lengths of photovoltaic power station short-term fluctuation signals.
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Li Yixiang, Shao Wenjun, Wei Dongsheng, Li Min, Liu Xiufeng
2025,48(7):135-141, DOI:
Abstract:
The Fabry-Perot demodulation method suffers from low sampling frequency, leading to errors when measuring physical quantities with high frequency variations. To address this issue, this paper proposes a high-performance multi-channel fiber Bragg grating wavelength demodulation system using a tunable laser, designed to meet the application requirements of fiber Bragg grating sensors in high-precision and high-frequency measurements. A fast synchronous refresh program was developed to enhance the demodulation frequency and real-time performance, while a dual-core data processing program was implemented to optimize data processing efficiency. Functional and performance tests were conducted using a motor and an isometric beam. Results showed that the proposed system achieved a scanning frequency of 100 Hz and an average fitting error of 6.23 pm, significantly outperforming the comparison system with an average fitting error of 24.10 pm. The linearity reached R2=0.999 9, higher than the comparison system′s R2=0.999 5, validating its feasibility in high-performance fiber Bragg grating demodulation applications.
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Si Panzhao, He Li, Wang Hongwei, Ran Teng
2025,48(7):142-151, DOI:
Abstract:
Early smoke detection is an effective means to eliminate fire hazards in a timely manner, but the small size and complex diffusion form of smoke in the early stage of a fire make its detection extremely difficult. To address the above problems, this paper proposes a multi-path enhanced feature-based YOLO （MEF-YOLO）early smoke detection algorithm, which adopts QA-ELAN to improve the backbone network and optimise the model complexity and accuracy, and develops FGCA to autonomously enhance the feature differences between the sampling regions to effectively capture the spatial information of the smoke. And the feature fusion path is optimised by the MEFAN, which realises the direct interaction between cross-level features and effectively mitigates the loss of detail information; and a Wise-IOU loss function is introduced, which comprehensively takes into account the position and scaling information through the weight adjustment mechanism to improve the robustness of the model in the complex scene. The experimental results show that the algorithm proposed in this paper has an accuracy of up to 92.5% for early smoke detection in experimental scenarios with different lighting and small-scale smoke and smoke diffusion, and has a lightweight advantage, with the number of parameters and GFLOPs reduced by 27.5% and 30.6%, respectively.
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Qiang Haonan, Zou Yongbo, Ma Lidong, Li Bowen
2025,48(7):152-162, DOI:
Abstract:
Helmet detection often faces challenges in complex road scenarios such as heavy traffic, pedestrian interference, and severe occlusion of targets. These conditions can easily lead to low detection accuracy, false detections, and missed detections. This paper proposes a high-performance helmet recognition model based on the CPM-YOLO algorithm. First, a novel cross-scale feature fusion method, CS-FPN, is proposed to better integrate high-level semantic and low-level geometric feature information. Next, the PCT module is introduced to optimize feature extraction capabilities of the model. Additionally, a bounding box regression loss function based on the minimum point distance is adopted to enhance the model′s convergence speed and accuracy. Furthermore, the 20×20 downsampling layer and 20×20 detection head in the backbone network are removed, and a new 160×160 small-object detection head is introduced. Finally, ablation studies validate the effectiveness of each improved module in enhancing the model′s performance, and comparative experiments demonstrate the superiority and generalizability of the CPM-YOLO model.Experimental results show that compared to the baseline model, the proposed method achieves improvements of 5.5% in mAP@0.5. Additionally, the number of parameters and model size are reduced by 69.9% and 67.2%, respectively. The new model significantly reduces complexity while enhancing helmet detection capabilities in road environments.
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2025,48(7):163-170, DOI:
Abstract:
Detecting the wearing of safety helmets by construction workers is an important method to ensure personnel safety. However, existing safety helmet detection methods are mostly manual, which are not only time-consuming and labor-intensive but also inefficient. Moreover, the existing algorithm has low detection accuracy in the face of complex environment or weather. In response to this phenomenon, an improved safety helmet wearing detection algorithm is proposed based on the YOLOv5s algorithm. Firstly, the SLSKA-POOL module is proposed based on the residual idea and large separable module design, and used in the pooling layer. This module can make the network pay more attention to the target features and further improve the network capability; secondly, the CAKConv convolutional module is proposed, which efficiently extracts features through irregular convolution operation to improve the network performance; finally, EMA modules are added to the backbone to aggregate multi-scale spatial structure information and establish short and short dependencies to achieve better performance. The experimental results show that: the improved YOLOv5 compared with the original algorithm, The detection accuracy increased by 2.2%, mAP@0.5 increased by 3.6%, and mAP@ 0.5:0.95 increased by 6.4%, realizing more accurate and efficient helmet wearing detection.
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Xie Meng, Liu Lili, Yang Chunlei, Wang Yan, Gu Mingjian
2025,48(7):171-178, DOI:
Abstract:
In response to the problems of imbalanced samples and low prediction accuracy, an enhanced predictive recurrent neural network EN_PredRNN is proposed. Firstly, the radar data is preprocessed and samples are selected to construct a high quality radar echo dataset; then, deep fusion of spatiotemporal long short-term memory units and dynamic convolution is used to design a dynamic convolution combined with spatio temporal long short term memory module DC_STLSTM, which adjusts convolution parameters in real-time to accurately capture the instantaneous changes in radar echoes. Then, stack 5 layers of DC_STLSTM to extract deeper features of radar echoes, and use gradient highways to alleviate gradient vanishing, improving the model′s generalization ability and prediction accuracy. The experimental results showed that EN_PredRNN performed the best, significantly improving the critical success index and reducing false alarm rates. Compared with PredRNN, it increased the critical success index by 19.3%, 17.3%, 16.5% and 14.0% at 25, 35, 45 and 65 dBZ, respectively, while reducing false alarm rates by 28.3%, 27.5%, 26.7% and 24.9%, effectively. This model effectively learned the spatiotemporal variation characteristics of radar data and accurately predicted the radar echo intensity and location.
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2025,48(7):179-191, DOI:
Abstract:
Currently, the results of iris recognition cannot be applied to judicial trials. The forensic science community has begun to focus on quantitative identification method based on the statistical rules of interpretable iris features. For this purpose, it is necessary to achieve automatic segmentation and extraction of iris texture features. A segmentation network for block-shaped iris features in near-infrared iris images is proposed, which combines residual networks, attention mechanisms, and atrous spatial pyramid pooling. First, a block-shaped iris feature annotation dataset was constructed for model training, validation, and testing. Secondly, improvements were made to the UNet framework as follows: the convolutional modules were replaced with residual modules to promote gradient propagation and enhance feature retention and transmission capabilities; attention gate modules were added to the skip connections to improve the model′s perception of block-shaped features; and atrous spatial pyramid pooling modules were employed in the middle and end parts of the model to expand the receptive field and perform multi-scale feature extraction and fusion. Finally, a hybrid loss function combining cross-entropy and Dice coefficient was proposed, and preprocessing techniques such as normalization and histogram equalization were used to highlight block-shaped iris features. Experimental results show that the RAA-UNet outperforms other comparison models on the test set, with F1 score, mIoU, and Dice coefficient reaching 82.64%, 84.21%, and 81.66%, respectively, representing improvements of 4.42%, 3.37%, and 3.87% over UNet. The loss function experiments determined the optimal weight factor, and histogram equalization significantly improved segmentation performance. Ablation experiments verified the effectiveness of the model improvements. The proposed RAA-UNet semantic segmentation model can accurately segment block-shaped iris features, providing technical support for iris identification research.
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2025,48(7):192-197, DOI:
Abstract:
Forest resources are key natural resources, and forestry economy is also an important component of the national economy. However, natural disasters occur frequently in China's forests, and traditional post-disaster forest resource detection methods face challenges such as low efficiency and insufficient accuracy. This article designs and implements a post-disaster all-terrain forest plant resource detection and positioning system based on drone technology. The aim is to enhance detection efficiency and accuracy through artificial intelligence image recognition,Beidou positioning, and remote sensing technology. The system utilizes the DJI Phantom 4 Pro drone as its platform, equipped with high-resolution cameras, WIFI modules, and Beidou positioning modules, enabling rapid identification and precise positioning of post-disaster forest plant resources. Experimental results demonstrate that the system exhibits high reliability in flight performance, data transmission stability, image recognition accuracy, and positioning precision, achieving a recognition rate of nearly 90% and positioning accuracy down to the centimeter level. This system offers efficient and low-cost technical support for post-disaster forest resource management, highlighting significant application value.
Volume 48, 2025 Issue 7
Research&Design
Theory and Algorithms
Application of Artificial Intelligence in Electronic Measurement
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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Zhou Jianxin, Zhang Lihong, Sun Tenghao
2024,47(6):79-85, DOI:
Abstract:
Aiming at the problems that the standard honey badger algorithm (HBA) is easy to fall into local optimum, low search accuracy and slow convergence speed, a honey badger algorithm based on elite differential mutation (EDVHBA) is proposed. The elite solution searched by the two optimization strategies in the standard HBA is combined with differential mutation to generate a new elite solution. The use of three elite solutions to guide the next iteration of the population can increase the diversity of the algorithm solution and prevent the algorithm from falling into premature convergence. At the same time, the nonlinear density factor is improved and a new position update strategy is introduced to improve the convergence speed and optimization accuracy of the algorithm. In order to verify the performance of the algorithm, simulation experiments are carried out on eight classical test functions. The results show that compared with other swarm intelligence algorithms and improved HBA, EDVHBA can find the optimal value 0 in the unimodal function, and converge to the ideal optimal value in the multimodal function after about 50 iterations, which verifies that EDVHBA has better optimization performance.
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Wang Huiquan, Wei Zhipeng, Ma Xin, Xing Haiying
2024,47(6):14-19, DOI:
Abstract:
To solve the problem of low control accuracy of the tidal volume emergency ventilation for lower air pressure at high altitudes, we propose a dual-loop PID tidal volume control system, which utilizes a pressure-compensated PID controller to adjust fan speed, supplemented by an integral-separate PID controller in order to achieve precise control of airflow velocity.Compared with single-loop PID control, the rapid response and no overshooting are observed in the performance tests of the dual-loop control system at an altitude of 4 370 m and atmospheric pressure of 59 kPa, in addition, the output error of the average airflow velocity decrease to 3.19% (the maximum error is 4.1%), which is superior to that of current clinical equipment. Our work offers an effective solution for high-altitude emergency ventilator tidal volume control, and contributes important insights to the development of ventilation control technology in special environments.
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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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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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Zhan Huiqiang, Zhang Qi, Mei Jianing, Sun Xiaoyu, Lin Mu, Yao Shunyu
2024,47(6):123-130, DOI:
Abstract:
Aiming at the force test in low-speed pressurized wind tunnel, the original data source of aerodynamic characteristic curve is analyzed. With the balance signal, flow field state and model attitude as the main objects, combined with the test control process, the abnormal detection methods and strategies of the test data are studied from the dimensions of single point data vector, single test data matrix and multi-test data set in the same period, and an expert system for abnormal data detection is designed and developed based on this core knowledge base. The system inference engine automatically detects online during the test, and realizes the pre-detection and pre-diagnosis of the original data through data identification, rule reasoning, logical reasoning and knowledge iteration. The experimental application results show that the expert system is highly sensitive to the detection of abnormal types such as abnormal bridge pressure, linear segment jump point and zero point detection, which guides the direction of abnormal data analysis and improves the efficiency of problem data investigation.
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Li Ya, Wang Weigang, Zhang Yuan, Liu Ruipeng
2024,47(6):64-70, DOI:
Abstract:
A task offloading strategy based on Vehicle Edge Computing (VEC) is designed to meet the requirements of complex vehicular tasks in terms of latency, energy consumption, and computational performance, while reducing network resource competition and consumption. The goal is to minimize the long-term cost balancing between task processing latency and energy consumption. The task offloading problem in vehicular networks is modeled as a Markov Decision Process (MDP). An improved algorithm, named LN-TD3, is proposed building upon the traditional Twin Delayed Deep Deterministic Policy Gradient (TD3). This improvement incorporates Long Short-Term Memory (LSTM) networks to approximate the policy and value functions. The system state is normalized to accelerate network convergence and enhance training stability. Simulation results demonstrate that LN-TD3 outperforms both fully local computation and fully offloaded computation by more than two times. In terms of convergence speed, LN-TD3 exhibits approximately a 20% improvement compared to DDPG and TD3.
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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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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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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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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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Qiu Yanbo, Chu Kaibin, Zhang Ji, Feng Chengtao
2024,47(6):173-181, DOI:
Abstract:
In order to improve the image quality of font generation and reduce the labour cost of font design, a method for few-shot font generation based on multilevel channel attention network is proposed. Firstly, the method acquires important local features through the style-aware attention module; then a multilevel attention mechanism is designed, where shallower layers can only observe the local features of the image, while deeper layers can observe all the features of the image, and new stylistic features are constructed by aggregating the local features of different levels. Finally, a content loss function, a style loss function and a L1 loss function are used to optimise the parameters of the model and stabilise the training of the network so that the generated images are consistent with the target font in terms of content and style. The experimental results show that the method has a strong generalisation to fonts of unknown style and fonts of unknown content. Compared to other methods, the proposed method shows better experimental results that maintain the integrity of the content structure and the accuracy of the font style.
