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Wang Weijian, Li Xin, Zhang Wenya
2024,47(24):1-11, DOI:
Abstract:
A parameter design method for a full bridge LLC resonant converter is proposed, which includes magnetic inductance, resonant inductance, resonant capacitance and transformer primary to secondary side turn ratio. Through this design method, zero voltage switching (ZVS) of the primary side switching tube can be achieved effectively while obtaining a larger magnetic inductance,which means the reduced losses of the switching devices and higher converter efficiency. Moreover, the proposed method of drawing voltage gain characteristic curve clusters can intuitively and efficiently determine the inductance coefficient K and quality factor Q, optimizing parameter design. Meanwhile, theoretical analysis and experimental research are conducted on the influence of inductance coefficient K on the efficiency of the converter. Finally, the correctness and validity of this parameter design method and related theoretical research are verified by the 200 W experimental results of on a full bridge LLC prototype.
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Cheng Yinan, Luo Zhaoxu, Yu Kang, Cao Yunzhou
2024,47(24):12-20, DOI:
Abstract:
In order to solve the problems of large voltage fluctuation and long switching time when switching between grid-connected and off-grid modes under the traditional control strategy, and the need to boost the voltage when the parallel structure is connected to the medium and high voltage power grid, a series photovoltaic DC microgrid control strategy with unified grid-connected mode was proposed. Firstly, the on-grid and off-grid structure model of the tandem photovoltaic system is established. Secondly, the DC bus voltage is regulated by droop control and PI control, and the on-grid and off-grid mode is unified through the difference between the reference power and the maximum output power, so as to realize the bus voltage stability and maximum power tracking during the switching process. Finally, the stability of the system is proved by the small signal method, and the feasibility and stability of the proposed control strategy are verified by experimental results.
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Li Shiyuan, Yang Hengzhan, Qian Fucai, Tan Bo
2024,47(24):21-29, DOI:
Abstract:
Aiming at the problem of parameter changes and coupling errors during the operation of permanent magnet synchronous motor, this paper proposes an online multi-parameter identification model based on the interconnected adaptive extended Kalman observer. First, by establishing an interconnected multi-parameter coupling compensation identification model to reduce the impact of measurement noise and parameter coupling errors on identification accuracy, high-precision identification results are obtained. Secondly, the adaptive method is used to dynamically adjust the extended Kalman observer to ensure the speed and accuracy of motor parameter identification after working conditions change, and the Lyapunov function is used to analyze the convergence of the observer when there is a model error. Finally, simulation and semi-simulation experiments were conducted on Matlab and RT-LAB semi-simulation physical system platforms. The results show that the method in this paper effectively reduces the measurement noise error and parameter coupling error, and significantly improves the anti-disturbance performance of the observer.
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Teng Jun, Zhao Fei, Wei Hao, Wei Heng, Tang Luyao
2024,47(24):30-38, DOI:
Abstract:
In this paper, two miniaturized SIW filters in the Ku-band are investigated, and the equivalent magnetic wall cutting method is adopted to obtain the quarter mode substrate integrated waveguide in order to meet the required miniaturization specifications. Two distinct QMSIW configurations, designated as triangular QMSIW and rectangular QMSIW, are developed based on the distinct equivalent magnetic wall cuts. Subsequently, the QMSIW filters with two distinct structural configurations are subjected to investigation. However, the out-of-band rejection capability of the designed QMSIW filters is not high, and therefore the complementary split ring resonators are loaded in the QMSIW resonant cavity in order to improve the out-of-band rejection. Then we employ the electromagnetic simulation software to simulate the proposed QMSIW filters with the two different structural configurations, and obtain the ensuing results. The passband range of the rectangular QMSIW filter loading the CSRRs is from 14.94 to 16.02 GHz, with a relative bandwidth of 6.97%. The insertion loss is better than 0.6 dB, the return loss is better than 15 dB, and the out-of-band rejection is better than 35 dB@19~20 GHz, with a size of 0.2×0.314λ2g. The triangular QMSIW structure filter loading the CSRRs exhibits a passband range of 14.89~16.11 GHz, with a relative bandwidth of 7.87%. The insertion loss is better than 0.6 dB, the return loss is better than 17.2 dB, and the out-of-band rejection is better than 40 dB@19~20 GHz with a size of 0.27×0.27λ2g. Both modified QMSIW filters are processed using an alumina oxide thin film and tested with GSG probes and subsequent to the actual test, it is determined that the test results are essentially consistent with the simulation results and met the expectations.
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2024,47(24):39-48, DOI:
Abstract:
This paper proposes a BPPID control system based on an improved sparrow algorithm to address the problem of getting stuck in local optima when optimizing the initial weights of BPPID using the traditional sparrow algorithm. Improving population diversity by introducing composite chaotic mapping; utilizing the golden ratio and adaptive Levy flight strategy to balance the algorithm′s global search and local development capabilities; using fuzzy logic adaptive reverse learning strategy to improve the algorithm′s global search and adaptability to complex environments. The benchmark functions were tested using standard sparrow algorithm, improved sparrow algorithm, grey wolf optimization algorithm, whale optimization algorithm, improved whale optimization algorithm, particle swarm optimization algorithm, and improved particle swarm optimization algorithm to compare and verify the effectiveness of the improved sparrow algorithm. The experimental results showed that the system efficiency and fairness of the improved sparrow algorithm were superior to other algorithms. Applying the improved sparrow algorithm to solve the initial weights of BPPID in switch mode power supply systems can significantly improve the system′s dynamic response and reduce overshoot.
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2024,47(24):49-56, DOI:
Abstract:
In order to better solve the underdetermined anti-collision problem of RFID system, the separation algorithm is optimized from the perspective of initializing the separation matrix based on the blind source separation method. Since the mixing matrix determines the linear mapping relationship between the source signal and the observed signal, it directly affects the convergence of the separation algorithm and the quality of the separation results. Therefore, the selection of the initial mixing matrix is crucial to the performance and effectiveness of the algorithm. The initial mixing matrix is calculated using the successive nonnegative projection algorithm, which abandons the traditional random initialization and avoids the algorithm from falling into the local optimal solution. Since the tag signals of RFID are bounded, the bounded component analysis algorithm is used in the next step to separate the tag signal from the mixed signal. The simulation results show that the separation similarity of this algorithm is improved by 3.05% compared with the traditional bounded component analysis algorithm at low signal-to-noise ratio, and the separation accuracy is improved by 6.64% compared with the commonly used non-negative matrix factorization algorithm. Its low bit error rate also shows that the system can effectively handle interference and noise during data transmission or reception, thereby reducing the occurrence of data errors.
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Zhang Bingqi, Liu Yunping, Wang Shuang, Cheng Yong
2024,47(24):57-64, DOI:
Abstract:
Traditional path planning algorithms have problems such as low efficiency, easy to fall into local optimal solutions, low convergence accuracy, etc. The subtractive average optimization algorithm has fewer parameters and simpler principles than other algorithms, but it ignores the influence of optimal values during the search process, which causes the algorithm to fall into local optimal solutions. Aiming at this problem, this paper proposes a subtractive average optimization algorithm incorporating multi-strategy improvement for path planning. First of all, Tent chaotic mapping is used to initialize the search agent population to ensure the diversity of the population; an adaptive guidance mechanism is introduced to enable the algorithm to adaptively choose a better update method with the number of iterations; the population update strategy of the sine-cosine algorithm is integrated into the update method of the search agent, and the good fluctuating and oscillating nature of the sine-cosine algorithm is utilized to balance the global and local searches of the algorithm and to better ensure the algorithm′s convergence accuracy. Finally, the proposed algorithm is simulated and tested by choosing seven benchmark test functions and setting different raster map environments. The results show that the proposed algorithm has better convergence accuracy and speed, and the performance index of path planning is better and the planning effect is better.
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Shen Qian, Zhang Lei, Zhang Yuxiang, Li Yi, Liu Shihao
2024,47(24):65-75, DOI:
Abstract:
Distracted driving is one of the main causes of road traffic safety problems. Aiming at the problems of high computational complexity, limited generalization ability and unsatisfactory detection accuracy of existing detection algorithms based on deep learning, this paper constructs a lightweight distracted driving behavior detection algorithm based on improved YOLOv8n. Firstly, the Context Anchor Attention mechanism was fused into StarNet to form StarNet-CAA, and StarNet-CAA was integrated into the backbone network of YOLOv8n to improve the global feature extraction ability of the model and significantly reduce the computational complexity. Subsequently, FasterBlock combined with CGLU is added to the neck network to form the C2f-Faster-CGLU module, which reduces the computational cost. In addition, the shared convolution is introduced into the detection head to further reduce the computational burden and parameter size. Experimental results show that the improved YOLOv8n algorithm significantly improves the efficiency of distracted driving behavior detection, reaching an accuracy of 99.3%on the StateFarm dataset. The number of parameters of the model is reduced by 46.7%, and the amount of calculation is reduced by 41.5%. In addition, the generalization experiment is carried out on the 100-Driver dataset, and the results show that the generalization effect of the proposed scheme is improved compared with YOLOv8n. Therefore, the proposed algorithm significantly reduces the computational burden while maintaining high reliability and generalization ability.
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Li Xintian, Zhang Xiaoming, Zhang Ge
2024,47(24):76-84, DOI:
Abstract:
To meet the needs of UUV cluster collaborative communication in complex marine environments, a magnetic induction communication system based on BPSK is proposed. A channel transmission model is established to analyze the effects of different transceiver models, seawater, carrier synchronization, and carrier frequency on channel transmission, combined with MATLAB simulations and experiments for validation. Simulation results show that BPSK modulation performs well under complex channel conditions; the one-to-three receive model is insensitive to attitude changes; phase offset is a major factor in the effects of eddies, and carrier synchronization significantly reduces its impact. Ultimately, the results indicate that the bit error rate decreases to the order of 10-3, effectively meeting the collaborative communication needs of UUV clusters,validating the system′s high stability and reliability in complex marine conditions. Finally, hardware experiments further validate the feasibility of the system.
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Sun Yangzhou, Yan Tianfeng, Sun Wenhao, Tang Chunyang, Wang Yingzhi
2024,47(24):85-92, DOI:
Abstract:
Semantic communication is a type of communication designed to convey semantic information, which is characterized by the fact that it can effectively reduce redundancy and the amount of transmitted data. Currently the research on semantic communication is only in its infancy, and more theoretical research can help to promote the real implementation of semantic communication systems. The core technology for realizing semantic communication, end-to-end joint source channel coding, has made great progress in the past few years, and semantic images have also been developed. In order to solve the problems of computational inefficiency and insufficient semantic feature extraction, a new neural network JSCC is designed in this paper.Specifically, inspired by the excellent performance of Swin Transformer in visual tasks, the Swin-Transformer module is combined with residual networks for the first time, and a Swin Transformer-based image semantic communication system. In order to solve the problems such as the poor efficiency of traditional CNN for image feature extraction, the attention residual network module is introduced to extract the image semantic features initially, and then the image semantic features are further extracted by Swin Transformer. Through the verification of the experimental results, compared with the existing schemes, the proposed scheme in this paper achieves higher than 2 dB performance improvement in PSNR and more than 5% performance improvement in MS-SSIM performance
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Zhu Yanming, Liu Wenzhong, Lyu Yuhua, Zhang Junjie, Zhang Qianwu
2024,47(24):93-102, DOI:
Abstract:
With the rapid advancement of remote sensing satellite technology in China, space missions are becoming increasingly complex, posing challenges to traditional spaceborne storage and data transmission systems in terms of high customization and costly migration. This study aims to develop an integrated high-performance storage and data transmission system to address these issues. Leveraging the high flexibility of field-programmable gate arrays (FPGAs), the system design incorporates SATA III solid-state drive read/write access with a file system, multi-channel DDR controllers with data multiplexing control, and data transmission functionality supporting intermediate frequency modulation.With minimal device and software deployment, the system achieves a maximum downlink bandwidth of 900 Megabits per second and offers the most diverse data transmission capabilities. The system has been successfully implemented in a specific Jitian satellite model mission. Ground tests demonstrate that the payload storage link bandwidth remains consistently stable above 2.8 Gigabits per second, with peak bandwidth reaching 3.69 Gigabits per second. Both storage and data transmission operations achieve long-term zero-error code performance. On-orbit verification confirms clear and complete transmission of payload images. The system fully meets the stringent stability and reliability requirements of remote sensing satellites, demonstrating significant application value in the field of remote sensing satellite technology.
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2024,47(24):103-109, DOI:
Abstract:
There are problems such as feature imbalance and insufficient feature fusion in the visible and infrared image fusion pedestrian detection algorithm. To address the above problems, we propose a multispectral pedestrian detection network MIFNet with phased feature fusion, a dual-stream network that handles both visible and infrared inputs, an intermodal information fusion module that changes the structure of the network to reduce the impact of feature imbalance, and an extraction-injection structure that automatically learns how to extract multimodal global information during the process of feature extraction and injects it into the visible and infrared features efficiently, which improves the robustness of the network and feature fusion effect. The feature enhancement fusion module is designed and embedded to enhance the unique information of the two modalities to further improve the feature fusion effect. The experimental results show that the leakage rate of the algorithm is only 9.74%, which is 6% lower than that of the baseline algorithm, effectively improving the detection performance of the algorithm.
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Zhou Zhiwei, Zhou Jianjiang, Wang Jiabin, Deng Kai
2024,47(24):110-117, DOI:
Abstract:
In response to the problem of poor detection performance of some high-risk moving targets in autonomous driving perception tasks due to complex road environments and insufficient fusion of onboard radar and camera data, this paper designs an object detection network MLFusionNet that integrates radar and visual multi-level information based on Centerfusion. Firstly, data level fusion is added to the input layer, which concatenates the radar echo features with the image in the form of pixel values, and then inputs them into the encoding and decoding network through a secondary residual fusion module, enriching the input information of the network; then, a bottleneck structured context module was designed between the encoder and decoder of the backbone network, which obtains broader contextual information from the feature map through a multi branch convolutional structure and reduces the number of parameters through compression channels; finally, a parallel attention fusion module was designed to solve the problem of insufficient feature level modal fusion. The experimental results on the nuScenes dataset showed that the NDS of MLFusionNet reached 46.6%, which increased the mAP of cars, trucks, and pedestrians by 1.4、3.0 and 1.5 percentage points respectively compared to the multimodal network Centerfusion. This indicates that the network pays more attention to high-risk dynamic targets in the driving environment.
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Chen Guangyao, Chen Tian, Gao Xuehai, Liu Jun
2024,47(24):118-127, DOI:
Abstract:
A lightweight semantic segmentation model named C2LA-U2-Net, equipped with a cross attention mechanism and a residual refinement module, was proposed to address issues such as the inability to recognize fine features, blurry defect boundaries, and large model parameters in the segmentation of surface defects in polycrystalline solar cells. Firstly, a C2LA module with a cross attention mechanism was designed in the external decoding stage to extract multi-scale spatial features, reduce spatial information loss, and capture long-range dependencies, which enhanced the segmentation performance for small defects. Secondly, a lightweight two stage residual refinement module (D-RRM) was introduced to tackle the issue of blurry prediction boundaries by modeling fine-grained features to improve boundary precision. Finally, Ghost convolutions were incorporated to further reduce model complexity. Experimental results indicated that, compared to the baseline model, the C2LA-U2-Net model achieved improvements of 3.1% in mean pixel accuracy (MPA), 4.49% in mean intersection over union (MIoU), 4.39% in mean recall rate (MRecall), and 4.17% in F1 score. At the same time, the model′s parameters and GFLOPs decreased by 89.77% and 56.68%, respectively, while inference speed increased by 76.97%, demonstrating the effectiveness of the proposed method.
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2024,47(24):128-138, DOI:
Abstract:
Blood cell detection is a critical tool for diagnosing various diseases, as changes in blood cell count and morphology often reflect a person′s health condition. However, manual detection is time-consuming and prone to errors and omissions. To address these challenges, this paper presents an improved blood cell detection algorithm based on the YOLOv7 framework, named YOLOv7-SMC. The algorithm integrates spatial and channel reconstruction convolution to reduce feature redundancy and enhance performance. Additionally, a mixed local channel attention is incorporated in the neck network to strengthen the model′s representational capability. The algorithm also replaces the nearest neighbor interpolation upsampling with a content-aware reassembly of features upsampling operator, which adaptively adjusts the upsampling strategy to produce detailed and smooth results. Furthermore, a minimum point distance intersection over union loss function is introduced to simplify the similarity comparison between bounding boxes. Experimental results on the BCCD dataset demonstrate that this algorithm improves the mean average precision at IoU thresholds of 0.5 and 0.5:0.95 by 2.6% and 2.9%, respectively, indicating its high practicality and accuracy.
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Wang Jiali, Tan Mian, Feng Fujian
2024,47(24):139-148, DOI:
Abstract:
Semantic segmentation with image-level annotation has been widely studied for its friendly annotation and satisfactory performance. Aiming at the problem of sparse activation regions and semantic ambiguity between foreground and background of class activation maps, a dual-branch weakly supervised semantic segmentation network based on activation modulation is proposed. The network uses Resnet50 and Vision Transformer as a two-branch feature extraction network, and designs an activation modulation module embedded in the convolutional branch, which forces the model to activate the intermediate fraction of pixels to generate a compact class activation map, thus alleviating the problem of sparse activation regions of class activation maps. Second, a dynamic threshold adjustment strategy based on cosine annealing decay is proposed, which adaptively determines the highest background threshold during the training process, so that more low-confidence foreground pixels are involved in the segmentation training, and complete and accurate segmentation maps are generated. The effectiveness of the network is verified on the PASCAL VOC 2012 as well as MS COCO 2014 datasets. mIou values are 74.2% and 74.0% on the PASCAL VOC 2012 validation and test sets, respectively, and 45.9% on the MS COCO 2014 validation set. The experimental results show that the network can solve the mis-segmentation problem and achieve excellent segmentation performance in scenes with similar front background colours.
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Wang Shuang, Liu Yunping, Zhang Bingqi, Lu Xuchun, Xu Liang
2024,47(24):149-159, DOI:
Abstract:
When the SLAM system estimates the camera position, a large number of feature points of moving objects participate in the feature tracking thread leading to a decrease in the accuracy and robustness of the algorithm, so how to efficiently and accurately reject the dynamic objects in the scene is particularly important. Existing dynamic vision SLAM algorithms may miss detecting or incorrectly recognize static objects as dynamic objects and reject them when dealing with dynamic objects, which triggers the problem of insufficient number of static feature points, thus affecting the stability and accuracy of the SLAM system. Therefore, this paper proposes a visual SLAM method based on panoptic segmentation and multi-view geometry, which uses panoptic segmentation FPN network to accurately recognize all objects in the segmented image, rejects a priori dynamic feature points and retains as many static features as possible, based on which LK optical flow method with fused image pyramid is used to realize optical flow tracking and reject parallel dynamic feature points, and potential dynamic feature points are used to track the dynamic feature points. The potential dynamic feature points are rejected more effectively by the multi-view geometry method based on dynamic probability, which avoids the omission of dynamic feature points and realizes the comprehensive screening of dynamic objects in the scene to improve the accuracy of the system. The construction of semantic map and octree map is realized on the basis of sparse point cloud constructed by the system. The experiments use the TUM RGB-D dataset to verify the system localization accuracy, and the results show that the root mean square error (RMSE) of the absolute trajectory error of this algorithm is reduced by an average of 84.34% in all sequences compared with ORB-SLAM2, which significantly improves the robustness and accuracy of the system,and it is of use to construct two maps that can be used for SLAM upper layer tasks.
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2024,47(24):160-170, DOI:
Abstract:
To address the challenges of multi scale features and complex background processing in plant pest and disease detection in modern agriculture, this paper proposes an efficient and accurate detection model, AgriSwin, to improve the precision and efficiency of agricultural pest and disease detection. The AgriSwin model is based on the Swin Transformer and integrates a dilated feature aggregation module and an adaptive spatial convolution module. The dilated feature aggregation module extracts multi-scale features through convolutional layers with different dilation rates and optimizes feature fusion using an adaptive weighting mechanism for global feature information. The adaptive spatial convolution module generates adaptive weights to dynamically weight the feature maps, enhancing the ability to capture both local and global information in complex backgrounds. Experimental results show that the AgriSwin model achieves detection accuracies of 79.65%、99.90%、and 95.08% on the PlantDoc, PlantVillage, and custom datasets, respectively. Additionally, the model′s parameter count is reduced by 25.63% compared to Swin Transformer-T, significantly lowering memory and computational resource requirements while maintaining high accuracy, demonstrating its broad potential for large-scale agricultural applications.
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Jing Xinlan, Huang Min, Ma Chao
2024,47(24):171-178, DOI:
Abstract:
To solve the problems of complex frequency information, strong time variation and obvious modulation characteristics of planetary gearbox vibration signal, a fault diagnosis method of planetary gearbox based on visual spectral feature fusion was proposed. Initially, Welch′s transformation is applied to planetary gearbox signals to obtain power spectra. Subsequently, a visual graph algorithm is used to construct a graph spectrum, and centrality measures of the graph nodes are calculated to form a feature matrix. Finally, an improved CNN-Inception model is employed to obtain the fault diagnosis results of the planetary gearbox. Experimental results demonstrate that this method can accurately identify faults in planetary gearboxes. In the experimental datasets covering two operational conditions, the model achieves an accuracy of 98.57%, demonstrating its generalization ability. Compared with alternative methods, the proposed approach exhibits higher accuracy and stronger generalization capabilities.
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Zhang Xinyang, Wang Keqing, Jia Xinwang, Guo Yongxin, Jiang Liang
2024,47(24):179-187, DOI:
Abstract:
The maintenance and prediction of turbofan engine lifespan are critical to modern aviation, playing a key role in ensuring safety and minimizing operational costs. This study addresses the challenge of predicting the RUL of turbofan engines by proposing a novel hybrid model that integrates Parallel TCN and Bidirectional BiLSTM. Traditional methods often struggle to capture both local features and long-term dependencies simultaneously; the proposed model overcomes this limitation by using TCN to extract short-term local features and BiLSTM to capture bidirectional temporal dependencies. To further improve feature importance recognition, an enhanced SE attention mechanism is introduced, which dynamically adjusts feature weights to better highlight critical information. Experiments conducted on the FD001 and FD003 subsets of the C-MAPSS dataset demonstrated that the proposed model achieved RMSE values of 12.15 and 11.16, and Scores of 230.4 and 209.84, respectively, outperforming other approaches in terms of accuracy.
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Jia Lu, Zhao Lei, Ling Fei, Li Guangya
2024,47(24):188-194, DOI:
Abstract:
In order to solve the lack of accuracy of data feature extraction caused by missing values, nonlinear and non-stationary characteristics in the measurement data of oil well dynamic liquid level, and the problem that the accurate measurement of oil well dynamic liquid level position cannot be achieved, an abnormal identification method of oil well dynamic liquid level measurement data based on normalized RBF neural network is proposed. Through the sensor installed on the oil well to collect data in real time, the multi-source oil normalization processing technology based on expert database is used to complete the data verification and integration. Empirical mode decomposition (EMD) is used to decompose the data into trend and fluctuation terms. After removing the fluctuation terms, the trend data is used as the input of normalized RBF neural network. The experimental results show that this method can effectively complete incomplete data, accurately identify abnormal data through the trend term and provide reasonable alternative values, and the obtained dynamic liquid level position curve is basically consistent with the actual dynamic liquid level position curve, with the maximum error of less than 2 m, which can realize the accurate estimation of the dynamic liquid level position of oil wells. The abnormal identification method of oil well dynamic liquid level measurement data based on normalized RBF neural network solves the challenges brought by data missing, nonlinearity and non stationarity, realizes the accurate estimation of oil well dynamic liquid level position, and provides technical support for real-time monitoring and data analysis of oil well dynamic liquid level.
Volume 47, 2024 Issue 24
Research&Design
Theory and Algorithms
Communications Technology
Information Technology & Image Processing
Data Acquisition
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Radiance field visual inertial SLAM algorithm based on tightly coupled IMU
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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Spectrum sensing method based on Inception-BiGRU and attention mechanism
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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Algorithm for detecting outer ring defects of bearings using a dual bottleneck structure fusion
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 2.6M and a computational complexity of 7.4GFLOPs. 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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Improved YOLOv5 safety helmet detection algorithm for complex environments
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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The single-core scheduling conflicts and time correction algorithm in the Beidou satellite synchronous system
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 $10^{-8}$ seconds, proving its effectiveness in high-precision time synchronization applications.
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Radar echo extrapolation method based on enhanced PredRNN
Abstract:
In response to the problems of insufficient data utilization, 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 (DC-STLSTM) module, 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%, and 16.5% at 25, 35, and 45dBZ, respectively, while reducing false alarm rates by 28.3%, 27.5%, and 26.7%, effectively. This model effectively learned the spatiotemporal variation characteristics of radar data and accurately predicted the echo intensity and location of heavy precipitation.
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Multi-scale hierarchical feature fusion and synergistic global-local Transformer for image inpainting
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 paper 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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Dynamic visual SLAM algorithm based on deeplabv3+ and LK optical flow
Abstract:
The traditional ORB-SLAM3 system demonstrates excellent performance in static environments; however, the presence of dynamic features introduces unnecessary noise, leading to errors in feature matching and inaccuracies in camera pose estimation. Existing dynamic SLAM algorithms face challenges in comprehensively identifying potential dynamic features, resulting in missed detections or false positives and consequently degrading localization accuracy. To tackle these issues, the semantic segmentation network Deeplabv3+ and the Lucas-Kanade optical flow method are incorporated into the tracking thread of ORB-SLAM3. Specifically, the backbone network of Deeplabv3+ is replaced with Mobilenetv3 to enhance the precision of semantic segmentation. Semantic segmentation is then used to obtain a mask of potential dynamic objects, which is employed to preliminarily filter out dynamic feature points. The remaining feature points undergo LK optical flow calculation, with the average optical flow error serving as a threshold to prevent the insufficient number of static feature points from causing pose estimation failure. In comparison to the original ORB-SLAM3, the improved algorithm in this study achieves an average localization accuracy improvement of 47.92% on the high-dynamic sequences of the TUM dataset. Furthermore, among existing advanced dynamic SLAM algorithms, the proposed method achieved the highest localization accuracy on the Walking_static sequence of the TUM dataset.
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Service robot path planning based on pedestrian openness comfort model
Abstract:
In order to solve the problems of unreasonable path selection and low obstacle avoidance efficiency for service robots in dynamic pedestrian environments, a pedestrian openness comfort model is proposed. This model enhances the robot"s understanding of pedestrian intent by incorporating pedestrian head pose features and openness, enabling it to make obstacle avoidance decisions that better align with social norms. Additionally, the traditional two-dimensional symmetric Gaussian function model is extended to an asymmetric Gaussian function model to better capture pedestrians" dynamic characteristics and social interactions. Simulation results show that, compared to models without pedestrian consideration and traditional pedestrian spatial models, the robot using the pedestrian openness comfort model reduces travel time by 2.5% to 12.1%, maintains an effective safe distance from pedestrians, and follows a more reasonable trajectory. Experiments in real-world environments further validate the advantages of using
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Industrial anomaly image generation algorithm based on improved controllable diffusion model
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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Attention-based Multi-scale Residual Convolutional Network for Bearing Fault Diagnosis
Abstract:
Aiming at the characteristics of complex features in bearing fault signals, a bearing fault determination method combining the attention mechanism and multi-scale residual convolutional network is proposed. The model combines the powerful feature extraction capability of convolutional neural network (CNN) and the adaptive weighting capability of the attention mechanism, which can effectively deal with the complex features in the bearing fault signal. The model employs a multi-scale convolutional layer, which captures the multi-scale features of the signal through different sizes of convolutional kernels, which helps to recognize different types and severities of faults. Meanwhile, the residual structure is introduced to effectively integrate the features extracted by multilayer convolution through the cooperative decision-making mechanism of high-dimensional and low-dimensional features, which enhances the model's ability to perceive the key information and reduces the problems of gradient vanishing and feature redundancy in the training of the deep network, so as to ensure the stability and accuracy of the model. The fusion of attention mechanisms (e.g., SEBlock and ECABlock) enables the model to adaptively focus on more important feature channels, which further improves the diagnostic performance. The experimental results show that the model can achieve high-precision diagnosis under various fault modes, demonstrating its potential application in intelligent maintenance and fault warning systems.
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Sonar image denoising with improved AnisGF and Wiener filtering
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 (AnisGF) 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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Residual-based visual SLAM algorithm for dynamic target tracking in scene flow
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 0.68697°, and the average displacement error is 0.10350 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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Improved YOLOv8 lightweight tobacco leaf count detection algorithm
Abstract:
The estimation of tobacco leaf yield is a crucial task, as the number of leaves directly impacts the yield. Traditional manual statistics are inefficient and costly, in order to solve these problems, this research proposes a lightweight YOLOv8-SLSS tobacco leaf counting detection algorithm, which improves on the YOLOv8n methods for the lack of detection accuracy, high computational complexity, and missed detections caused by overlapping tobacco leaves. The algorithm replaces the original model's backbone network with an enhanced ShuffleNetV2light architecture, reducing model parameters and computational load. Integrate the LHCB module into the neck network's C2f module to expand the model's receptive field, enhances detection capabilities and reduces computational load. The introduction of the SEAMDetect module has enhanced the detection capabilities in scenarios involving occlusion by tobacco leaves. Finally, the SPPELAN module is introduced to enhance the model multi-scale feature extraction capability and computational efficiency. Experimental results demonstrate that the modified model significantly reduces model parameters and floating-point operations by 63.3% and 61.7% respectively. The algorithm's average precision improves from 91.8% to 93.1%, achieving a real-time detection speed of 83 frames per second, marking a 5.1% enhancement over the original algorithm, meeting real-time detection demands. The improved algorithm enhances the detection ability of the traditional YOLO model in tobacco leaf occlusion scenarios, realizes the balance of high accuracy, lightweight design, and real-time detection performance, thus providing effective technical support for the digitization of tobacco agriculture.
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Adaptive VSG PV inverter control strategy based on stochastic excitation
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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Path planning for robots with improved A* algorithm and fused DWA
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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IRS rate maximization algorithm based on multi-user reflection unit selection
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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Research on Path Planning Algorithms Based on an Improved Informed-RRT*
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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Analysis of the Impact of Carrier Signal on Displacement Detection Noise in Gravity Gradiometers
徐恒通, 雷军刚, 王佐磊, 李云鹏, 席东学, 张文艳, 敏健
Abstract:
Addressing the issue of carrier signal impact on the low-frequency detection accuracy of the capacitance displacement detection circuit in the electrostatic gravity gradient instrument, this paper, based on elucidating the detection principle of the displacement detection circuit, theoretically analyzes the effects of carrier amplitude noise, phase noise, frequency noise, and broadband noise factors on displacement detection performance. It proposes a method for assessing the extent of influence of various types of noise on the detection accuracy of the capacitance displacement detection circuit. Using this method, the various noise factors in the carrier are evaluated, and the noise of the capacitance displacement detection circuit is theoretically synthesized. The results indicate that the conformity rate between the theoretically synthesized noise and the total noise at the 100 mHz frequency point ranges from 73% to 106%, with higher conformity rates observed with larger amplitudes. When the carrier amplitude is less than 2.25V, broadband noise accounts for more than 50% of the total noise among these factors. As the amplitude increases, the proportion of phase and frequency noise power to the total noise power gradually increases.
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Integration of CNN and Transformer for Coronary Artery Segmentation
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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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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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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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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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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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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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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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 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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Feng Zhibo, Zhu Yanming, Liu Wenzhong, Zhang Junjie, Li Yingchun
2024,47(6):34-40, DOI:
Abstract:
The data bits and spread spectrum codes of the spaceborne spread-spectrum transponder are asynchronous. Due to the influence of transmission system noise and Doppler frequency shift, it can cause attenuation of peak values related to receiving and transmitting spread spectrum codes, leading to a decrease in capture performance. Traditional capture techniques often have problems such as high algorithm complexity, slow capture speed, and difficulty adapting to the requirements of large frequency offsets of hundreds of kilohertz. This article proposes a spread spectrum sequence search method that truncates the spread spectrum sequence into two segments for correlation operations, and combines the signal squared sum FFT loop for a large frequency offset locking, effectively suppressing the attenuation of correlation peaks and improving pseudocode capture performance. MATLAB simulation and FPGA board level testing show that the proposed spread spectrum signal capture scheme can resist Doppler frequency shifts of up to ±300 kHz, with an average capture time of about 95 ms. In addition, the FPGA implementation of this algorithm saves about 47% of LUT, 43% of Register, and more than half of DSP and BRAM resources compared to traditional structures, making it of great application value in resource limited real-time communication systems.
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Chen Haoan, Li Hui, Huang Rui, Fu Pingbo, Zhang Jian
2024,47(6):182-189, DOI:
Abstract:
Facing the challenges of regulating unmanned aerial vehicles (UAV), and based on an YOLOv5-Lite improved model, this paper incorporates an exponential moving sample weight function that dynamically allocates loss function weights to the model during the training iteration. Through model computations, we achieve real-time UAV tracking using a two-degree-of-freedom servo platform. Furthermore, video capture, model calculations, and servo control are all performed locally on a Raspberry Pi 4B.The optimized model maintains the original model's parameter count while achieving a mAP@.5:.95 score of 70.2%, representing a 1.5% improvement over the baseline model. Real-time inference on the Raspberry Pi yields an average speed of 2.1 frames per second (FPS), demonstrating increased processing efficiency. Simultaneously, the Raspberry Pi controls a servo platform via the I2C protocol to track UAV targets, ensuring real-time dynamic monitoring of UAVs. This optimization enhances system reliability and offers superior practical value.
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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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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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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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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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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.
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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.