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Wang Xiaofei, Gong Kexian, Wang Wei, Sun Peng
2024,47(19):1-8, DOI:
Abstract:
With the development of satellite communication technology, the traditional spectrum monitoring mode is difficult to meet the monitoring needs. In order to meet the urgent needs of modern satellite signal receivers for larger bandwidth, higher resolution and stronger real-time processing, an automatic broadband spectrum monitoring system was developed. The system realizes the large-bandwidth and high-resolution display of the satellite signal spectrum through RF direct sampling technology and channelization processing. Experimental results show that the system can monitor the L-band signal in real time and generate the panoramic spectrum of the L-band signal with a spectral resolution of less than 1 kHz. Through the in-depth analysis of spectrum data, the system can automatically identify and store signal parameters, and its portability and flexible design make the system flexible to deploy a variety of application scenarios, which perfectly fits the development direction of modern satellite signal receiver technology.
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Li Xiaotian, Ji Xuejing, Ma Jinlong
2024,47(19):9-16, DOI:
Abstract:
In sparse code division multiple access (SCMA) systems, because multiple user data are superimposed on the same subcarrier and the channel conditions of different users are different, accurate channel estimation cannot be performed directly. To solve this problem, a channel estimation method for SCMA systems based on orthogonal superposition is proposed. This method uses the sparsity of SCMA codebook to design a new pilot structure with multi-user timeslot multiplexing, which can ensure the orthogonal subcarrier resources and reduce the timeslot overhead of pilot resources after the same timeslot is superimposed on the pilot data of different users. On this basis, a pilot structure of local orthogonal superposition is proposed, and the channel fading coefficients of adjacent subcarriers are estimated by using the correlation between subcarriers to further reduce the time slot overhead of pilot resources. The simulation results show that the channel estimation MSE performance of orthogonal superposition method is improved by 3 dB and the local orthogonal superposition performance is further improved by 1.7 dB compared with the traditional method under the same total time cost.
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2024,47(19):17-23, DOI:
Abstract:
A copper wire laying monitoring system based on STM32 was designed to address the industrial production issues caused by the deviation and tilting of copper wires during the laying process of flat copper wires wrapped on I-shaped wheels in industry. The system matched the color of copper wire through the corrosion center algorithm, tracked the copper wire in real-time, determined the position of the copper wire and its width on the LCD screen, and detected the offset or tilt of the copper wire, providing data for subsequent copper wire correction. The system used MT9V034 camera as the image acquisition module, LCD display screen as the image display module, STM32H743 and serial port screen as the control module and human-machine interaction module, respectively. The experimental results show that the system can effectively detect the offset and tilt of copper wires while tracking them and the measurement error for line width is within 5 px.
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Luo Zhijie, Wang Zeyu, Cen Piao, Liu Wenjing, Guo Jianjun
2024,47(19):24-33, DOI:
Abstract:
In response to the shortcomings of existing motion pose recognition algorithms in terms of accuracy and efficiency in human pose detection, this paper proposes an efficient detection algorithm based on an improved YOLOv8pose. This algorithm optimizes the occlusion scenario of key points by introducing the RL-SEAM module, enhances the utilization of contextual information through the C2f-Context mechanism, enhances the model′s ability to recognize complex poses, and uses the Pose_SA lightweight detection head to improve the model′s effectiveness and efficiency in recognizing motion poses. The experimental results show that the improved YOLOv8pose algorithm has achieved significant improvement in human motion pose recognition tasks. Its number of parameters and model size have been reduced by 14.24 and 10.94 percentage points respectively compared to the original YOLOv8n benchmark model. At the same time, accuracy, recall, and mean average precision have been improved by 7.60、7.60 and 10.54 percentage points respectively compared to the original model. Therefore, the YOLOv8-LSP model proposed in this article helps to solve the challenges faced in human motion pose recognition tasks, such as key point occlusion and complex and variable postures.
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2024,47(19):34-43, DOI:
Abstract:
The integration of the integrated communications/navigation/identification (ICNI) system for unmanned aerial vehicles necessitates that the master control module of the ICNI performs functions such as system control and data forwarding via multiple bus communications. This requirement imposes higher demands on the processing performance and interface resources of the core processor. Consequently, there is a need for a more lightweight design for the health control system within the module. This paper presents a fully domestic CAN bus-based health management system designed using a CPU+MCU distributed architecture within the main control module. The FT-2000/4 serves as the core processor, while the MCU acts as the coprocessor to implement the CAN bus interface unit. The two components facilitate application layer data exchange over the CAN bus through SPI full-duplex communication, enabling the CPU to query the status and issue commands to each module/unit in the system, thereby achieving effective health management. The system is capable of real-time monitoring of the health status of each module. Notably, the circuit design utilizes only the SPI and GPIO interfaces of the core processor, without occupying additional interface resources or programmable logic (FPGA) resources. This approach simplifies the hardware design and alleviates the challenges associated with circuit layout and structural design. Furthermore, the cost of the health management circuitry is at least 60% lower than that of other typical designs in the industry, addressing the need for cost-effective solutions.
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Cai Yonghui, Zhou Lingke, Li Sheng, An Yuxing
2024,47(19):44-53, DOI:
Abstract:
Aiming at the problem of uncertainty of GNSS measurement noise variance parameter in the traditional combined navigation filtering algorithm, this paper, based on the principle of SINS/GNSS dynamic differential sequence, improves the traditional Sage-Husa adaptive extended Kalman filtering algorithm (AEKF) method of estimating the measurement variance array based on the information of residual sequences, utilizes the high-precision characteristics of short-term positioning of SINS and combines with the smoothing of bounded layers to isolate the abnormal observation information of GNSS, so that the improved adaptive filtering algorithm can maintain a high level of positioning accuracy under different noise environments of GNSS. The fault detection algorithm isolates the abnormal observation information of GNSS, so that the improved adaptive filtering algorithm can maintain high positioning accuracy under different noise environments of GNSS. The experimental results of the actual sports car show that in the low-density anomaly noise environment in GNSS work, the algorithm in this paper improves the average positioning accuracy by 39.9% and 7.9% compared with the EKF algorithm and the traditional Sage-Husa algorithm, and in the high-density anomaly environment, the overall positioning accuracy is improved by 64.5% and 29.1%. Therefore, the algorithm in this paper effectively improves the anti-interference ability of the combined navigation system against different measurement noises.
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Xu Wu, Tian Hanmin, Liu Weilong, Wang Jiwei, Cao Rui
2024,47(19):54-62, DOI:
Abstract:
Errors can occur in the 2D projection contact angle measurements of droplets on uneven material surfaces, and currently, only a few international equipment manufacturers offer 3D contact angle measurement solutions. This paper proposes a 3D droplet contact angle measurement method aimed at providing a complete and practical 3D contact angle measurement solution. The system employs an array of circular structured light for 3D detection, suitable for liquids with contact angles less than 90° and good diffuse reflection properties.The main steps include: feature region selection, 3D point cloud acquisition, droplet surface fitting, and contact angle measurement. In the image preprocessing stage, a method based on HSV space combined with morphological operations is proposed. For feature region selection, an area filtering method is introduced. Geometric calculations are used to convert 2D point clouds to 3D point clouds, and a point cloud matching algorithm is proposed to optimize point cloud fusion.Compared to 2D measurement methods, experimental results show that the proposed method significantly improves measurement accuracy and reliability, achieving a maximum error of less than 1°, with most errors being less than 0.5°. Additionally, it allows for further analysis of material physical properties and surface uniformity.
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Hu Yuanyuan, Zhang Minjuan, Wei Chenbo, Cui Yifan, Gao Yunfeng
2024,47(19):63-69, DOI:
Abstract:
Time domain reflection technology, as a technique that can intuitively reflect changes in transmission line impedance, is widely used in the field of impedance detection. However, there are problems such as inaccurate calibration and low impedance testing accuracy in the process of measuring the characteristic impedance of transmission lines. In response to these issues, this article designs a time-domain measurement and calibration system for characteristic impedance. The system uses the shortest interval estimation algorithm to measure the voltage on the transmission line, achieving the measurement of the characteristic impedance of the transmission line. By measuring four coaxial cables with different lengths and characteristic impedances, the measurement error is controlled within ±1%, meets the requirements of impedance measurement. The actual results show that the system can accurately measure the impedance of transmission lines. This method simplifies the operation process, avoids subjective bias caused by manual selection, and provides a new method for the development of transmission line impedance detection technology.
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Liu Jiale, Guo Zhitao, Wang Hong, Zhang Sen
2024,47(19):70-78, DOI:
Abstract:
Specific emitter identification is playing an increasingly important role in the military and civilian sectors. With the rapid development of deep learning technology, the recognition performance of SEI method has been significantly improved. However, these methods often rely on a large number of radiation source sample data, and perform poorly in practical application scenarios with limited sample size. To solve this problem, this study proposes a novel deep learning network model CRCPA-GCN to achieve SEI in small-sample scenarios. The model integrates CPCA and GCNet attention modules in a multi-layer complex convolutional neural network, and the recognition performance in small-shot scenarios is significantly improved by using the methods of class reconstruction and adversarial training. In this study, a series of experiments were carried out on public datasets and compared with the current mainstream SEI networks. Experimental results show that the proposed CRCPA-GCN network model achieves an accuracy of 95.81% under the learning condition of 20 shot, which is better than other mainstream SEI networks and performs well in robustness.
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2024,47(19):79-87, DOI:
Abstract:
To address the limitations of conventional pulsed remote field eddy current testing (PRFECT) in accurately determining the circumferential position of defects, we propose an innovative array-based PRFECT probe. This method involves increasing the number of receiving coils and modifying the relative positions of the receiving and excitation coils to enhance defect localization capabilities. To address the issue of weak signals detected, a hybrid signal denoising technique combining successive variational mode decomposition (SVMD) and singular value decomposition (SVD) is introduced. Initially, the signal is decomposed into a series of modal functions using SVMD. Subsequently, components for reconstruction are selected based on the Pearson correlation coefficient. The retained components are then denoised using the SVD method, and these denoised components are superimposed to reconstruct the signal. Both simulations and experimental results demonstrate that the proposed novel probe effectively locates defect positions, The proposed algorithm can improve the Denoising Signal-to-Noise Ratio of the measured key signal to 9.30, better than traditional algorithm.
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Xie Fei, Meng Zewei, Xu Xiaobin
2024,47(19):88-95, DOI:
Abstract:
Aiming at the problems of weak anti-interference ability, short transmission distance and complex cable in the traditional analog signal transmission mode of strain balance, an integrated digital balance based on micro high-precision data collector was developed and applied. By embedding the micro high-precision data collector into the strain balance and integrating the microcontroller unit (MCU) with floating-point operation ability in the micro high-precision integrated data collector, the real-time calculation and output of the actual force/torque of the balance were realized by combining the static calibration formula of the balance, and the transformation of the measurement signal of the balance from analog mode to digital mode was realized, thus improving the anti-interference ability of the test system. The anti-interference test in the wind tunnel showed that the signal transmission of the integrated balance was almost zero interference in the complex electromagnetic environment of the conventional hypersonic wind tunnel; the wind tunnel test of high lifting body model showed that the integrated balance test has good repeatability (high precision), and the data rules were consistent with the measured results of conventional balance. The maximum deviations of axial force coefficient, normal force coefficient and pitching moment coefficient were 2.1%, 1.8% and 3.6%, respectively.
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2024,47(19):96-103, DOI:
Abstract:
Acoustic emission detection methods are widely used in the defect detection of equipment, for most of the equipment generated acoustic emission signal amplitude is small, large noise and features are difficult to extract the problem, this paper proposes a signal processing method: the CCSO algorithm based on the Pearson correlation coefficient-envelope entropy minimum principle optimizes the processing method of parameters in VMD. In this method, the cross algorithm is integrated on top of the classical flock optimization algorithm, and the key parameters in the VMD, namely the modal number K and the penalty factor α, are accurately optimized by the improved CCSO algorithm. By using the CCSO-VMD method based on the new fitness function, the analog signal was analyzed, and the signal-to-noise ratio reached 25.814 1 dB. This result proves that the CCSO-VMD algorithm based on the new objective function can significantly reduce the noise level while retaining the valid information in the signal to the greatest extent. In addition, this paper proposes a comprehensive spectral difference index, and the CSDI value can effectively distinguish the acoustic emission signals in different states.
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Huang Xin, Li Yuezhong, Li Xiaoli, Bian Nannan
2024,47(19):104-113, DOI:
Abstract:
Major depression is characterized by low mood and slow thinking, and emotion is the attitude experience of object and the corresponding behavioral response of human brain neurons. Many articles have designed classification network of depression and emotion classification, but the network function is single, can only complete a single classification task, and does not combine mental illness with human emotions, language expression, blinking and other behaviors well. The article explores the correlation of index characteristics between emotion classification and depression diagnosis, then designs a network to verify the feasibility of diagnosing depression through emotion across categories and datasets. The differential entropy is extracted as the input feature of the network, and the convolutional neural network is used to study the emotion classification of SEED-IV and the MODMA emotion proportion. Analyze the microstate parameters of the two datasets, samples with the same microstate type are analyzed and the correlation between the two microstate is explored. The difference of α and γ rhythm classification results and microstate correlation coefficients can be used to classify emotions and diagnose depression. After verifying that parameters in both α and γ rhythms show the correlation between emotion and depression, the design experiment proves that abnormal brain characteristics of patients with depression can be captured by adding microstate features, and the diagnosis of depression can be completed by adding correlated microstate parameters to CNN used for emotion recognition.
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2024,47(19):114-122, DOI:
Abstract:
In order to improve the accuracy of pressure classification method. realize the deep mining of multi-modal information interaction and multi-dimensional three-dimensional fusion features, a multi-modal pressure identification method based on model classification is proposed. A new psychological stress index model is constructed based on the amplitude characteristics of speech signals and the amplitude characteristics of each frequency band of EEG signals, and a psychological stress classification method for the model is proposed to solve the problems of limited subjective assessment accuracy and unclear stress classification basis. The labels of MAHNOB-HCI data set are reconstructed based on the model classification, and the multi-dimensional stereo fusion features containing EEG time-frequency-space information and speech time-frequency information are constructed to solve the problem of missing pressure information caused by the single feature research method. Compared with the single modal method, the recognition accuracy of the proposed method is increased by 10.72% and 3.36%, respectively.Compared with the conventional dual-modal method, the recognition accuracy is increased by 7.51%. To sum up, the proposed method can more accurately reveal the relationship between the full-band information of heterogeneous data and psychological stress, and effectively improve the recognition performance.
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2024,47(19):123-128, DOI:
Abstract:
In the measurement of environmental parameters of rocket cabin, the traditional wired measurement method will increase the overall weight of the rocket cabin. Therefore, it is of great significance to use wireless sensor network to replace the traditional wired measurement method to reduce the weight of the rocket cabin. Aiming at the above problems, a wireless sensor network based on AD hoc network protocol is proposed, which is based on wireless transceiver chip CC1310 to build a wireless sensor network, selects 915 MHz frequency band as the data transmission channel, and designs an AD hoc network protocol, the wireless sensor nodes in the network only need to keep the same static address as the collector. When the node is powered on, it can automatically join the network, and the collector will assign a unique dynamic address to each node, adds CSMA/CA mechanism to prevent channel data collision, and is successfully applied to this wireless sensor network. The test results show that: when the data transmission rate of the wireless sensor network is 250 Kbps and the signal sampling frequency is 100 SPS, the wireless sensor node can automatically join the network created by the collector after being powered on, and successfully upload the sensor data according to the time interval configured by the collector, and no node data loss within the range of 10 m. It can meet the measurement needs of environmental parameters in the capsule of the launch vehicle.
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Ren Zhibin, Fang Fuliang, Wu Yu, Liu Qiang
2024,47(19):129-136, DOI:
Abstract:
The MTPA control strategy of traditional virtual injection method IPMSM is proposed to solve the problem of slow dynamic response due to the complex coordinate transformation process and the cascade of band-pass filter and low-pass filter in signal processing. Firstly, in the virtual signal injection process, the high frequency signal is directly injected into the id and iq obtained by sampling and filtering, which simplifies the complex root operation process. Secondly, a new MTPA criterion formula is derived according to the improved virtual signal injection method. After demodulation of the high frequency response signal, the optimal current vector angle is obtained through the low-pass filter and integrator in turn. Finally, the effectiveness of the improved virtual injection method was verified by simulation and the 8.4 kW built-in PMSM experimental platform. The experimental results show that the improved virtual injection method can effectively simplify the calculation, and still has strong robustness after eliminating the cascade response. The loading experimental speed overshoot is only 3 r/min (0.375%), and the dynamic response time is improved by about 0.2 s.
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Han Jianfeng, Zhang Jing, Song Lili, Tao Yongzhao
2024,47(19):137-145, DOI:
Abstract:
In order to explore the difficulties in detecting fine cracks and the occurrence of breaks in the aerial view of UAVs, a network called ASE-Net is proposed based on the U-Net architecture. First, an improved VGG-16 is used as the encoder to extract the broken feature information. Second, multi-scale feature fusion block (MSFF) module and channel enhanced strip pooling (CESP) module are introduced at the minimum scale network layer. Finally, the ECA_X attention module is added to the decoding stage. The experimental results indicate that the model presented in this paper achieves a mIoU of 0.820 9, a mPA of 0.930 2, and a mPrecision of 0.865 1 on the self-constructed UAV aerial pavement breakage dataset. These results represent improvements of 15.97%, 12.72%, and 11.02% over the baseline U-Net, respectively. Ultimately, the model in this work has been demonstrated to exhibit better performance and generalization ability than other standard models utilizing the open-source dataset Crack500. The model can realize accurate detection of small cracks, potholes, and repairs on the road surface, effectively solving the fracture problem of crack detection, and enhancing the effect of pavement damage detection in large-size aerial images.
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Zhou Jianxin, Zhang Yuan, Jia Zihan, He Yang
2024,47(19):146-154, DOI:
Abstract:
In order to reduce the influence of background interference on pavement defect detection and solve the problem that the features that can be extracted from small sized slender cracks are very limited, this paper is improved based on the YOLOv8 model. Firstly, the C2f-Dysnake module was designed by fusing the C2f in the network with dynamic serpentine convolution, which improved the sensitivity to the shape and boundary of the target and enhanced the feature extraction ability of the slender cracks. Secondly, the reparameterized generalization feature pyramid network RepGFPN and the dynamic upsampler DySample were combined to form a new neck network RDFPN, which increased the attention to the low-level feature map and improved the detection ability of small targets. Finally, the MPCA attention mechanism is added to the backbone network to capture the position relationship at different scales and improve the feature extraction ability of the backbone network. Experimental results show that the improved algorithm improves mAP50 by 2.3% and reaches 98 fps on the RDD2022 dataset, and the detection speed reaches 98 fps, which has obvious advantages over other algorithms and verifies the effectiveness and superiority of the proposed method.
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Xu Chao, Wang Yunjian, Liu Yang, Lu Xuemei, Ding Yong
2024,47(19):155-163, DOI:
Abstract:
Knee osteoarthritis is a common disease in the elderly population, which is highly disabling. Automatic diagnosis of knee osteoarthritis based on deep learning algorithm has important application value. Therefore, an automatic diagnosis algorithm of knee osteoarthritis based on improved Swin Transformer model is proposed. The transfer learning is protected by replacing the global average pooling layer of the neck network with a two-layer fully connected layer plus ReLU activation function. Adding full connection layer and Tanh activation function to the head network to combine more nonlinear features; in the process of data preprocessing and model training, data enhancement is realized by relying on Albumentations library and adding Mixup module respectively. The experimental results show that the proposed algorithm can effectively improve the classification accuracy of X-ray images of knee osteoarthritis, and the diagnostic accuracy reaches 76.0% on the public data set of Kaggle website. At the same time, the generalization experiments on other X-ray image data sets of knee osteoarthritis and medical image data sets in different fields show that it has good generalization ability, which further proves the effectiveness of the proposed algorithm.
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2024,47(19):164-171, DOI:
Abstract:
Addressing the limitations of the LSTR algorithm in practical applications, particularly its single-scale feature extraction and lack of effective capture of local lane features, this paper introduces the Vit-CoMer backbone network for the first time in lane detection tasks, proposing the LSCoMer lane detection model. Initially, the model employs a MRFP module after the feature extraction network to enrich multi-scale features, thereby enhancing detection accuracy. Additionally, a CTI module is integrated at both the beginning and the end of the Transformer structure to promote effective fusion between CNN′s local features and Transformer′s global features, enhancing the latter’s sensitivity to local details. Experimental results indicate that this method achieves an accuracy of 96.68% on the TuSimple dataset, which is a 0.5% improvement over the original LSTR method and significantly outperforms similar methods like PolyLaneNet. On the CULane dataset, our method improves the F1 score by 3.02% compared to the LSTR method.
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Feng Fujian, Luo Taiwei, Tan Mian, Wang Xiaomei, Wang Yueji
2024,47(19):172-180, DOI:
Abstract:
In addressing the issue of ineffective extraction of rich defect features for small-scale surface defects on steel due to their low contrast and small proportion, this paper proposes a solution for small-target defect detection. Leveraging the relationship between contextual information integration and enhanced feature fusion, we introduce the following approaches: incorporating the sliding window mechanism Swin Transformer, which integrates feature information from different blocks hierarchically and through local windows to enhance the contrast of defect features while reducing convolutional operation density; the model employs Coordinate Attention to obtain more positional information, enhancing the diversity of features related to small-target defects. Additionally, we propose the steel surface small-target defect detection model SFNet based on self-attention feature fusion, integrating features with richer semantic information across different scales using the CSP-FCN feature fusion module. Experimental results demonstrate that SFNet achieves superior detection performance on the NEU-DET and GC10-DET public datasets compared to current classic object detection models. Furthermore, the proposed model achieves an average precision improvement of 3% and 3.7%, respectively, while reducing the parameter count to half of its original size.
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Zhou Zhiyao, Ma Changxia, Yang Lisha, Zhong Zhaoman, Hu Wenbin
2024,47(19):181-189, DOI:
Abstract:
Equipment that operates in harsh and variable underwater environments is essential for conducting underwater research and development. The current underwater target detection models are too large in parameter count and computation, which limits the deployment of underwater equipment with limited resources. In order to solve the problem of excessive parameter count and computational volume of underwater detection models, a lightweight underwater target detection model RCE-YOLO is proposed.Firstly, the spatial attention weights of RFAConv are utilized to improve the ability of CBS to process the information in the receptive domain and to enhance the ability of C2f to fuse spatial feature information, so as to enhance the model′s ability of detecting small and dense targets. Second, the CCFM is fused with the Dysample module, which is able to utilize the different scale information more effectively and reduce the blurring and distortion produced by the original sampling through the internal point sampling method. Finally, the Efficient multi-scale attention mechanism is fused in the SPPF forward propagation process, which makes the model focus on the key information of the underwater target and reduces the false detection rate and misdetection rate. The experimental results show that the improved lightweight model is validated on the dataset DUO, and the mAP50 and mAP50:90 values reach 83.6% and 64.2%, respectively, which are 1.4% and 1.2% higher compared to the mAP50 and mAP50:90 values of the benchmark model of YOLOv8, and the number of parameters and the amount of computation drop by 32.3% and 0.9 G, respectively. compared to other The target detection model meets the needs of underwater target detection in harsh and variable environments, and lays the foundation for lightweight deployment of underwater equipment.
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Zhu Qiangjun, Cheng Liangliang, Wang Huilan, Wang Yang
2024,47(19):190-196, DOI:
Abstract:
In order to accurately identify the fall posture of the elderly, an improved YOLOv8s fall detection model is proposed. Firstly, the SE attention mechanism module is introduced into the backbone network of the YOLOv8s model, which divides the channel features into multiple subgraph features, and fuses the features of different groups, so that the network can adaptively focus on the key features, suppresses the features that contribute less to the current task, and improves the feature extraction ability; secondly, the CIoU loss function is replaced by EIoU to accelerate the convergence rate and improve the accuracy and stability of the model. Finally, the trained model is verified on URFD+ and other data sets. The experimental results show that the precision rate of the model reaches 99.50%, the recall rate reaches 99.00%, and the mAP50 reaches 99.50%, which is better than the original model. Compared with the YOLOv5s+K-means++ model, the accuracy is increased by 3.22%, the recall rate is increased by 5.32%, and the mAP50 is increased by 2.38%. Compared with the C2D-YOLO model, the accuracy is increased by 10.00%, the recall rate is increased by 11.40%, and the mAP50 is increased by 7.80%. Compared with the YOLOv5s+C3new model, the accuracy is increased by 2.50%, the recall rate is increased by 6.80%, and the mAP50 is increased by 4.1%. The improved model has greater advantages than the original model and the current advanced model.
Volume 47, 2024 Issue 19
Research&Design
Theory and Algorithms
Data Acquisition
Information Technology & Image Processing
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Lightweight improved YOLOv8n model for steel defect detection features
Abstract:
To address the issues of high parameter count, computational complexity, and resource demands on computing platforms in steel surface defect detection models, a lightweight improved algorithm is proposed. First, ShuffleNetV2 is utilized as the backbone of the improved model, significantly reducing model complexity and computational load. Second, a flexible and lightweight Channel Attention (CA) mechanism is introduced after the SPPF module, while a Bi-directional Feature Pyramid Network (BiFPN) is employed to enhance feature fusion and improve the flow efficiency of feature information. Finally, a lightweight Dual Conv kernel replaces the convolutional layers in C2f, achieving a reduction in the parameter count through a grouped convolution strategy. Experimental results indicate that the improved model achieves lightweight design while maintaining detection accuracy compared to the original YOLOv8n. The parameter count is reduced to 56.2% of the original, with the model size and computational load decreasing to 3.6MB and 4.8GFLOPs, respectively, representing reductions of 42.86% and 41.47% compared to the original model. This lightweight design lowers deployment costs, making it suitable for practical deployment and application.
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A Stump Detection Algorithm for Estimating Tree Barrier Felling Workload in Transmission Corridors
Abstract:
In the tree barrier clearance project for protecting the safety of the distribution network, the manual calculation of the felling quantity faces problems such as strong subjectivity of the calculation results and management difficulties. The existing algorithms have low accuracy, with many false positives and false negatives, and poor robustness. Therefore, a tree stump detection algorithm for calculating the felling workload in the transmission corridor tree barrier clearance is proposed. In response to the problem of inaccurate felling quantity calculation due to the complexity of the distribution network clearance scene and the difficulty in distinguishing between tree trunks and tree stumps, a feature extraction module based on Context Guide Block is designed. RepGFPN and Dysample structures are introduced to optimize the neck network, effectively integrating environmental context semantic information with local details of tree stumps. Subsequently, the algorithm designs a tree stump detection head based on LW-SEAM, optimizing the detection effect under occlusion. The model"s P, R, and mAP50 indicators on the test set have been improved to 85.5%, 76.4%, and 80.4% respectively, showing good detection performance for tree stump detection in complex backgrounds and occlusion scenarios, and providing technical reference for achieving intelligent engineering calculation.
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Curve Matching Diffusion Model for LDCT images denoising
Abstract:
The use of Low-Dose CT (LDCT) examinations has significantly reduced the radiation dose of CT scans, but it has also led to increased noise and artifacts in CT images, thereby reducing image quality and accuracy, which affects doctors" diagnostic judgments. In recent years, generative models have demonstrated excellent performance in addressing this issue. However, generative models still face challenges such as generating confusing images and lacking interpretability during the generation process. To address this issue, a conditional diffusion denoising network model was constructed. On this basis, a trainable curve correction module was introduced to correct images with different noise levels, and a joint loss function was incorporated. Experimental results show that the proposed algorithm achieves better denoising results compared to baseline algorithms. Additionally, it demonstrates good generalization across LDCT images with different doses, maintaining excellent denoising performance.
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Trajectory Tracking Control of Quadrotor UAV Based on Predefined Time
Abstract:
Addressing the trajectory tracking problem of a quadrotor UAV, a command filter backstepping control strategy based on predefined time is proposed to mitigate the impact of model uncertainties and unknown external disturbances on system stability. Firstly, a predefined-time disturbance observer is designed to accurately estimate the system uncertainties and unknown external disturbances in real-time. Secondly, to effectively alleviate the "differential explosion" issue in the backstepping control strategy, a predefined-time command filter is designed. Based on this, position and attitude controllers are further designed using the backstepping method, enhancing the system"s control accuracy and response speed. Finally, theLyapunov theory is employed to verify the stability of the proposed control strategy. Simulation experiments validate the effectiveness and superiority of the proposed control strategy.
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Lightweight Road Multi-Target Detection Algorithm Combining Asymptotic Feature
Abstract:
In complex road environments, existing algorithms for road multi-target detection suffer from poor recognition performance, large number of parameters, and high computational complexity, making them unsuitable for deployment on resource-limited mobile devices. To address these issues, a lightweight road multi-target detection algorithm combining non-adjacent features is proposed based on YOLOv7-tiny. First, the design of the Tiny-AFPN combines non-adjacent features of different scales, reducing the loss of features caused by scale differences and achieving richer cross-scale information interaction. Secondly, with the introduction of DSConv, the Efficient Layer Aggregation Network was redesigned and named ELAN-DS, improving the expression of features while optimizing the efficient layer aggregation network and reducing the complexity of the model. Finally, the use of the MPDIoU loss function improves the accuracy of bounding box
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Remote sensing image detection algorithm based on improved YOLOv8
Abstract:
Aiming at the limitations of small targets in remote sensing images, such as complex image background, dense distribution of small targets, and diverse target scales, this paper proposes an improved algorithm based on YOLOv8n. Firstly, a multi-scale null attention module is designed to introduce a multi-scale null attention mechanism in the backbone network in combination with the C2f module to effectively capture multi-scale semantic information and reduce the redundancy of the self-attention mechanism; secondly, a residual fast convolution module is designed to reduce the model computation and improve the feature extraction capability; finally, the PIoU v2-Iou loss function is used instead of the CIOU loss function to improve the detection accuracy of the model. The experimental results on DOTA, RSOD and VisDrone2019 datasets show that the improved YOLOv8n model improves the mAP by 2.7%, 3.3% and 3.8%, respectively, and reduces the computation by 0.5GFLOPs compared with the original model YOLOv8n, which validates the effectiveness of the new algorithm.
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Study on Adaptive White Balance Scheme Based on Histogram
Abstract:
Aiming at the problems such as the limited application scenarios commonly existing in the automatic white balance algorithm (Auto White Balance, AWB), and taking into account factors such as real-time hardware processing, an adaptive automatic white balance algorithm is proposed and implemented in hardware using a Field Programmable Gate Array (FPGA). Firstly, the histograms of different color channels of the color image are statistically analyzed. Then, the similarity of histogram patterns among channels is determined by utilizing the histograms of different color channels, and based on this, an adaptive histogram adjustment algorithm is employed for white balance correction of different color images. Experimental results demonstrate that this algorithm exhibits superior adaptability and yields favorable processing effects for images rich in colors and those containing large area color blocks. Both subjective and objective evaluations have improved compared to single algorithms, and it is capable of real-time processing of images with a resolution of 1280*720@30fps on embedded devices, presenting excellent prospects for engineering applications.
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Research on defect detection of drainage pipeline network based on improved YOLOv8
Abstract:
Addressing the issues of urban drainage pipeline defects being susceptible to background interference, the variability of characteristic scales, and the low detection accuracy and high false positive rate of existing detection models, this paper presents an improved defect detection algorithm based on YOLOv8. Initially, the DSK module is designed and embedded within the C2f module of the backbone network to expand the receptive field and improve the ability to extract multi-scale defect features. Subsequently, the Slim-neck network structure is introduced to refine the neck network, effectively utilizing and fusing defect feature information, which also contributes to the lightweightification of the model. Finally, the FocalEIOU loss function is adopted to enhance the detection performance for smaller defect targets and the convergence speed of the model. Experimental results on a pipeline defect dataset indicate that the proposed improved algorithm achieves a mean Average Precision (mAP) of 67.5% at a detection rate of 70.4 frames per second. Compared to the original YOLOv8 algorithm, the mAP value and detection speed are respectively increased by 3.8% and 1.7 frames per second, demonstrating superior detection performance. For the purpose of practical application, this paper has developed a system software capable of real-time detection of pipeline defects based on an improved algorithm. Through actual project detection, the enhanced algorithm proposed in this paper has been validated to meet the requirements of high precision and real-time detection for the task of urban drainage pipeline defect inspection.
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GNSS/UWB/IMU integrated indoor and outdoor seamless positioning method with robustness estimation
Abstract:
In order to solve the problems of low positioning accuracy and poor continuity in the single navigation source positioning system in indoor and outdoor seamless positioning, a GNSS/UWB/IMU integrated indoor and outdoor seamless navigation and posi-tioning algorithm based on robust estimation was proposed. In the face of complex indoor and outdoor scene switching, the robustness estimation algorithm is used to evaluate the confidence level of the two observation signals collected by GNSS and UWB and fuse them, and the fused data is used as the new observation value, and the extended Kalman filter algorithm is used to fuse the new observation value with the data of the inertial system to achieve fusion positioning. In order to evaluate the navi-gation and positioning accuracy of the algorithm in the presence of interference and noise, the inertial navigation positioning module, the satellite positioning module and the ultra-wideband tag were integrated together and field tests were carried out. Experiments show that the root mean square error of the proposed fusion positioning method is 6.40cm in the east direction and 6.73cm in the north direction, and the maximum error is not more than 28.55cm.
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A lightweight contraband detection algorithm focusing on edge and multi-scale Features
Abstract:
To address challenges such as complex backgrounds, varying scales, and the difficulty of detecting small objects in X-ray security images, we propose a lightweight contraband detection algorithm named LEM-YOLO, which focuses on enhancing edge and multi-scale features. First, a Lightweight Edge Feature Enhancement module (LEFE) is designed to construct the EFE_C2f, enhancing the model"s capability to extract edge features. Next, we develop an Efficient Multi-level Feature Fusion Pyramid Network (EM-FPN) that utilizes Dynamic Upsampling (Dysample) and the Hierarchical Scale Feature Pyramid Network (HS-FPN) to enhance multi-scale feature fusion and reduce computational redundancy. Additionally, a Dynamic Feature Encoding module (DFE) is employed to preserve global information for small-sized objects. Finally, Shape-IoU is used as the bounding box regression loss function, focusing on the shape and scale of the bounding boxes to improve object localization accuracy. Experimental results on the publicly available SIXray dataset show that LEM-YOLO achieves a mean Average Precision (mAP) of 94.63%, which is a 2.56% improvement over the original algorithm. Furthermore, the model size is reduced by 50.67%, making it better suited for contraband detection scenarios compared to similar algorithms.
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sleep staging based on attention gated multi-layer perceptron mechanisms
Abstract:
Sleep staging has attracted much attention as an important method for studying sleep disorders in recent years. The majority of the current automatic sleep staging methods focus on studying time-domain information and ignore the interrelation between features, resulting in low sleep classification accuracy. To solve these problems, a multi-scale features and attention gated multi-layer perceptron mechanisms named MA-SleepNet is proposed for automatic sleep stage classification, using single-channel electroencephalogram (EEG) signals. The network consists of a multi-scale feature extraction (MFE), squeeze and excitation network (SE), and an attention gated multi-layer perceptron mechanism(aMLP). The MFE module uses convolutional kernels of different sizes to fully extract different scale features from EEG signals. The SE module further optimizes the weight of features and improves the feature expression ability of the network. The aMLP module combines multi-layer perceptron with gating mechanism, adds tiny self-attention mechanism to realize data communication between different dimensions and integrates powerful feature representation.The MA-SleepNet model is evaluated on two public datasets, Sleep-EDF-20 and Sleep-EDF-78. It achieves the accuracy of 86.1% and 83.2% on the Fpz-Cz channel, respectively. Compared with the existing sleep staging methods, our method improves the classification performance.
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Improve the Small Target Detection Algorithm of YOLOv8 UAV Aerial Image
Abstract:
Aiming at the problems of low feature extraction capability and scale diversity in UAV aerial images, an improved YOLOv8 object detection algorithm for UAV aerial images is proposed. Firstly, P2 layer is added to enhance the small target detection capability of the model. Secondly, the bidirectional feature alignment fusion method is designed to improve the neck. Combining the idea of feature alignment module and bidirectional feature pyramid, the multi-scale fusion capability of the model is improved to achieve a more complete feature fusion. Then, bi-level routing-spatial attention module is designed and added to the backbone. By connecting the bi-level routing attention module and spatial attention module, the feature capturing ability of the target is strengthened. Finally, the loss function Focaler-XIoU is designed to solve the influence of sample difficulty distribution on border regression, and enhance the stability and detection effect of the model. The experimental results show that the improved network model has improved the VisDrone dataset mAP50 by 9.2%, which has better detection effect than the current mainstream target detection algorithm, and can well complete the UAV aerial image detection task.
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Research on the prediction of the remaining life of internal corrosion in oilfield water injection pipelines
Abstract:
In order to estimate the remaining safe service life of the pipeline, the extreme gradient boosting algorithm model besed on grey correlating analysis was proposed. Grey Relational Analysis (GRA) was used to calculate and rank the correlation values between each influencing factor and the remaining life, and the data of the influencing factors with high correlation were preferably input into the eXtreme Gradient Boosting (XGBoost) algorithm for the prediction of the remaining life of corroded pipelines. Taking an oilfield water injection pipeline as an example, the results showed that the Root Mean Square Error (RMSE) was 0.012, the Mean Absolute Error (MAE) was 0.068, and the goodness of fit (R2) was 0.999, compared with the other three prediction models, the results showed that the prediction accuracy and generalization performance of the model constructed in this paper were better.
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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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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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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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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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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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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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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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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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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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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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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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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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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.
