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    Design Analysis and Modeling Simulation of Brushless DC Motor
    Xuanfeng SHANGGUAN,Tingyu YANG,Jinsong WEI,Yongjian LIU
    Electronic Science and Technology    2022, 35 (3): 71-78.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.011
    Abstract204)   HTML9)    PDF(pc) (1749KB)(33)       Save

    Research on the structure design and driving mode of single-phase brushless DC motor is carried out in this study. The basic equation of the motor under ideal working conditions is derived, and the design scheme of the motor is determined according to the actual needs of the project and combined with the design principles of the brushless DC motor. A single phase brushless DC motor with rated power of 38 W and rated speed of 750 rpm is designed using the summarized design scheme. The effect of the gradient air gap on the starting performance and the torque of the gear groove is analyzed, and the optimal air gap length is determined. By comparing the advantages and disadvantages of unipolar winding and bipolar winding form, the winding form is determined, and the number of turns is determined by combining the traditional motor design formula. The rationality of the design scheme is verified by the finite element method. According to the dynamic mathematical model of the motor, the model of the motor system is established in the Simulink environment, and the curves of the motor speed and torque are obtained by simulation. The simulation results are consistent with the theoretical analysis, which verifies the rationality of the motor design scheme and the validity of the motor model.

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    Research on Adaptive Backstepping Control of Quadrotor UAV
    Xinge SHEN,Hai JIN,Liang GUO
    Electronic Science and Technology    2022, 35 (3): 32-37.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.005
    Abstract146)   HTML7)    PDF(pc) (730KB)(22)       Save

    An adaptive controller based on backstepping is designed to solve the problem of attitude and position control stability of UAVs under external interference. The dynamic modeling for the "X" UAV is conducted, and then converted into a strict feedback form with external interference. The system is divided into a position subsystem and an attitude subsystem. Combined with backstepping control, Lyapunov function is used to recursively deduce the adaptive law and the control law of each channel to make the system stable. Then the desired attitude angle can be inversely obtained through the channel control law, so that the system forms a closed loop. The simulation results of MATLAB-Simulink simulation model show that the controller designed in this study can effectively track the attitude and position of the UAV well in the presence of external interference.

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    Design of Ternary Logic-in-Memory Based on RRAM Dual-Crossbars
    Weiyi LIU,Yanan SUN,Weifeng HE
    Electronic Science and Technology    2022, 35 (4): 8-13.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.002
    Abstract139)   HTML12)    PDF(pc) (989KB)(33)       Save

    Implementing logic within RRAM crossbar is an attractive approach to overcome the memory wall in conventional Von Neumann architecture. Ternary logic can reduce the number of logic operations and enhance the computation speed compared to binary logic. In this study, a ternary logic-in-memory scheme is proposed based on the RRAM dual-crossbar structure, in which the inputs and outputs are represented by the multi-level cells of RRAMs. Two ternary logic gates and one binary logic gate are supported in the proposed structure to effectively increase the computation speed. Experimental results show that the operation steps of the ternary logic-in-memory adder are reduced by up to 68.84%, as compared with previously published binary logic-in-memory designs. The energy consumed by the ternary logic-in-memory adder is also reduced by 33.05% when compared with previously published IMPLY-based design.

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    EMI Margin Assessment of Vehicular Communication System
    Jianpeng WAN,Hongmin LU,Guohua LIU,Min LI
    Electronic Science and Technology    2022, 35 (4): 1-7.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.001
    Abstract125)   HTML127)    PDF(pc) (1570KB)(127)       Save

    With the development of science and technology, the electromagnetic interference in the vehicular communication system becomes more and more obvious. In view of the interference margin assessment of the vehicular communication system, this study proposes a novel evaluation method of electromagnetic interference margin of the vehicular communication system based on four-level screening method. The working conditions, frequency, signal power and communication performance of the vehicular receiver are evaluated respectively to display the disturbed situation and interference margin of the vehicular receiver quantitatively. Aiming at the influence of interference source on receiver sensitivity, the communication distance, signal-to-noise ratio and bit error rate are selected to evaluate the performance of vehicular communication system by this method. The results show that compared with the traditional four-level screening method, this method can more comprehensively reflect the performance changes of communication system with interference.

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    Simulation of Dynamic Process of Micro-Cutting Single Crystal Germanium Based on SPH Method
    Xiaojing YANG,Hongxiu YANG
    Electronic Science and Technology    2022, 35 (4): 67-71.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.011
    Abstract124)   HTML5)    PDF(pc) (2989KB)(11)       Save

    In view of the difficulty of observing chip formation in flying cutting of single crystal germanium, a meshless simulation method (SPH method) is adopted in this study. The influence of cutting depth and cutting speed on cutting force and chip formation during plastic removal is studied by establishing a micro- cutting simulation model of single crystal germanium (111). The results show that when the cutting speed is 4 μm·μs-1 and the cutting depth is 0.5 μm, 1 μm, 2 μm and 5 μm, the tangential force and normal force gradually increase and then decrease to a gentle fluctuation trend. It is concluded that the larger the cutting depth is, the greater the stable fluctuation value of cutting force is, and the critical cutting depth for chip generation is between 0.5 μm and 1μm. When the cutting depth of single crystal germanium is 1 μm and the cutting speed is 2 μm·μs-1, 4 μm·μs-1, 6 μm·μs-1 and 8 μm·μs-1, it is found that the tangential force and normal force are not affected by the cutting speed, and the critical cutting speed for chip formation is between 2 μm·μs-1 and 4 μm·μs-1.

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    Arrhythmia Recognition Based on GAN-CNN
    Peng CHEN,Zilong LIU
    Electronic Science and Technology    2022, 35 (3): 45-50.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.007
    Abstract121)   HTML4)    PDF(pc) (1573KB)(24)       Save

    ECG analysis is an important basis for doctors to diagnose arrhythmia. The judgment of arrhythmia helps patients understand their physical conditions in time and find potential diseases. However, ECG analysis is not only time-consuming and labor-intensive, but also relies on clinical experience. Therefore, the efficiency of ECG analysis has always been limited by the number of doctors and work efficiency. The development of deep learning technology provides a foundation for the development of computer-aided diagnosis systems. In this study, a one-dimensional ECG signal is converted into a two-dimensional gray image, and a GAN-CNN network is used to solve the problem of ECG data imbalance, which can simultaneously realize the recognition of 7 types of arrhythmia and normal heartbeat. The experiment is verified by the MIT-BIH arrhythmia database. The average accuracy rate reaches 99.32%, and the sensitivity and specificity are 99.69% and 98.91%, respectively.

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    VSLAM for Indoor Dynamic Scenes
    Hongjun SAN,Wanglin WANG,Jiupeng CHEN,Feiya XIE,Yangyang XU,Jia CHEN
    Electronic Science and Technology    2022, 35 (4): 14-19.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.003
    Abstract121)   HTML13)    PDF(pc) (1530KB)(36)       Save

    The traditional VSLAM algorithm is implemented based on static scenes, and the positioning accuracy is degraded in indoor dynamic scenes, and the 3D sparse point cloud map has problems such as mismatching of dynamic feature points. In this study, the ORB-SLAM2 framework is improved, which is combined with Mask R-CNN to perform semantic segmentation of images to remove dynamic feature points located on dynamic objects, optimize the camera pose, and obtain a static 3D sparse point cloud map. The experimental results on the public TUM dataset show that ORB-SLAM2 combined with Mask R-CNN effectively improves the pose estimation accuracy of intelligent mobile robots. The root mean square error of the absolute trajectory can be increased by 96.3%. The root mean square error of relative translation trajectory can be increased by 41.2%, and the relative rotation trajectory error has also been significantly improved. Compared with ORB-SLAM2, the proposed method can more accurately establish a 3D sparse point cloud map without the interference of dynamic object feature points.

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    Video Retrieval Algorithm Based on 3D Convolution and Hash Method
    Hanqing CHEN,Feifei LI,Qiu CHEN
    Electronic Science and Technology    2022, 35 (4): 35-39.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.006
    Abstract111)   HTML5)    PDF(pc) (1707KB)(14)       Save

    Different from other multimedia information retrieval, video retrieval requires a large amount of computation in similarity calculation due to the large amount of information contained in videos. In addition, the temporal correlation between video frames is often ignored in feature extraction, which leads to insufficient feature extraction and affects the accuracy of video retrieval. For this problem, this study proposes a video retrieval method based on 3D convolution and Hash method. This method constructs an end-to-end framework, uses a 3D convolutional neural network to extract the features of the representative frames selected from the video, and then maps the features to the low-dimensional Hamming space to calculate the similarity in the Hamming space. Experimental results on two video data sets show that compared with the latest video retrieval algorithms, the proposed method has a greater improvement in accuracy.

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    Research and Implementation of APFC Low Voltage System
    Fuzhuan WU,Haoyang LI,Sheng PENG,Mengna CHEN
    Electronic Science and Technology    2022, 35 (4): 72-77.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.012
    Abstract109)   HTML7)    PDF(pc) (1377KB)(49)       Save

    In view of the harmonic pollution caused by nonlinear rectifier devices in low-voltage application scenarios such as low-voltage micro-grid, lathe lighting, teaching experiment and temporary residence at construction sites, this study investigates the low-voltage APFC system using single-cycle control. The control principle of single-cycle controller UCC28180 is analyzed in detail, and the parameters of main circuit and control circuit are calculated. Based on the analysis of the average current loop and the voltage loop, the loop compensation of the zero-pole in the loop is carried out by combining the nonlinear current loop gain factor M1 of the low voltage system, the PWM slope compensation slope M2 of the voltage loop and the nonlinear gain M3, and the corresponding Bode diagram is given. Finally, the prototype system is established and the system efficiency, power factor, and total harmonic distortion rate of input current are analyzed. The results show that the low-voltage system meets the design requirements.

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    Design of Biological Behavior Analysis System Based on Vision and IMU Sensors
    Sunyun YANG,Xiu KAN
    Electronic Science and Technology    2022, 35 (4): 28-34.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.005
    Abstract109)   HTML13)    PDF(pc) (1211KB)(22)       Save

    In order to solve the problem that the digital image processing technology alone cannot detect and analyze the behavior of mice in behavioral experiments, a biological behavior detection method based on the combination of image and IMU sensor is proposed in this study. In Python programming environment, this method uses digital image processing technology to detect and track the motion video of mice, and obtain the motion behavior parameters of mice. Meanwhile, the wireless bluetooth micro IMU sensor designed by the laboratory is used to measure the three-axis acceleration, three-axis angular velocity and three-axis magnetic force of mice. The extended Kalman filter algorithm is used to solve the measured data to obtain the posture and other posture change information of mice. The experimental results show that the system can automatically analyze the motor behavior and posture information of mice, and can display the behavior information on the interface of biological behavior analysis system in real time.

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    CTA Segmentation Algorithm of Abdominal Artery Based on 3D Fully Convolutional Network
    Lingyu JI,Yongbin GAO,Chenglu ZHAO,Xianhua TANG,Kaicheng XU,Jiacheng XU
    Electronic Science and Technology    2022, 35 (3): 38-44.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.006
    Abstract102)   HTML1)    PDF(pc) (1972KB)(13)       Save

    Convolutional neural networks have become a research hotspot in the field of abdominal artery segmentation. The classic convolutional network has the problems of low segmentation accuracy and discontinuous segmentation of blood vessels. In view of these problems, this study proposes an abdominal arterial vessel segmentation algorithm based on an improved 3D full convolutional network. The side input of different scales is constructed on the encoding path of the network, and the convoluted image of side input is fused with the convoluted image of down sampling to extract more feature information. Meanwhile, a new multi-scale feature extraction module is embedded in the network. In this module, the channel attention and dense dilation convolution are introduced to capture the higher-level feature information. The experimental results on abdominal artery segmentation show that compared with other segmentation methods, the proposed method is more intuitive and quantitative, indicating that this method can improve the accuracy of blood vessel segmentation.

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    PSO Algorithm Based on Improved Contraction Factor
    WANG Pengfei,REN Lijia,GAO Yan
    Electronic Science and Technology    2022, 35 (5): 14-19.   DOI: 10.16180/j.cnki.issn1007-7820.2022.05.003
    Abstract98)   HTML7)    PDF(pc) (657KB)(26)       Save

    The performance of PSO algorithm optimization is affected by the speed update formula. Too fast convergence speed may cause the algorithm to miss the global optimal solution, and the slow convergence speed may cause the algorithm to fall into the local optimal solution. To solve this problem, this study proposes a PSO optimization algorithm based on improved compression factor, namely FPSO. By introducing the compression factor equation, the speed iteration formula is improved, and the influence of the improper setting of the learning factor on the algorithm is reduced. The new adjustment mechanism not only guarantees the convergence performance of the PSO algorithm, but also weakens the influence of the speed boundary on the algorithm. Finally, five classic functions are selected to test the performance of the proposed algorithm. The test results show that compared with the traditional PSO algorithm, the proposed algorithm improves the global convergence ability and shortens the convergence time.

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    Amplitude and Phase Error Estimation Algorithm for Sparse Planar Array
    Wanying XIE,Hongjun JIN,Huaisong ZHAO
    Electronic Science and Technology    2022, 35 (3): 58-64.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.009
    Abstract98)   HTML3)    PDF(pc) (2084KB)(12)       Save

    The sparse array has ambiguity characteristics, which makes the traditional ISM method unable to be directly applied when the amplitude and phase errors or position errors of the array elements exist. In view of this defect, the study utilizes the principle of DOA estimation algorithm based on medium defuzzification in linear arrays, extends this algorithm to two-dimensional sparse planar arrays, and deeply studies the influence of medium parameters on the suppression effect by introducing the concept of suppression degree. This study combines the algorithm with the classic ISM method to establish a new sparse planar array amplitude and phase error estimation algorithm. The simulation results show that this proposed method can not only make the blur angle suppression degree up to 17 dB under the condition of reasonable selection of media parameters, but also can estimate the amplitude and phase error under the premise of obtaining the accurate angle, which improves the robustness.

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    Globaland Local Scene Representation Method Based on Deep Convolutional Features
    Chaowei LIN,Feifei LI,Qiu CHEN
    Electronic Science and Technology    2022, 35 (4): 20-27.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.004
    Abstract97)   HTML6)    PDF(pc) (1896KB)(32)       Save

    Scene Recognition is a fundamental task in computer vision. Different from image classification, scene recognition needs to take a comprehensive consideration of factors such as global layout information, local scene features, and object features, which leads to the poor performance of classic convolutional neural network for scene recognition. In order to solve this issue, this study proposes a global and local scene representation method based on deep convolutional features. The proposed method transforms deep convolutional features of scene image to generate a comprehensive representation for each image. Specifically, CAM is used to discovery local key regions, and LSTM is used to encode convolutional features extracted from local key regions to produce the local representation for scene images. Attention mechanism is adopted to fuse scene features and object features to form a global representation for scene images. Finally, the evaluation experiments are conducted on MIT indoor 67 data set and the results show that the test accuracy is up to 87.59% using the proposed method.

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    Crowd Counting Algorithm Based on Residual Dense Connection and Attention Fusion
    SHEN Ningjing,YUAN Jian
    Electronic Science and Technology    2022, 35 (6): 6-12.   DOI: 10.16180/j.cnki.issn1007-7820.2022.06.002
    Abstract95)   HTML11)    PDF(pc) (1722KB)(25)       Save

    The existing crowd counting algorithm uses multi-column fusion structure to solve the multi-scale problem of a single image, which cannot effectively use the low-level feature information, resulting in inaccurate final crowd counting results. In order to improve the accuracy, a crowd counting algorithm based on residual dense connection and attention fusion is proposed. The algorithm uses improved VGG16 network to extract low-level feature information. Based on the residual dense connection structure, the back-end main branch of the proposed algorithm uses the combination of residual network and dense network to capture the feature information between layers and efficiently capture multi-scale information. Side branch introduces the attention mechanism to generate the corresponding scale attention map, which effectively distinguishes the background and prospect of the feature map and reduces the influence of background noise. The algorithm is tested on three mainstream public data sets. The experimental results show that the algorithm is effective in counting and has better counting accuracy than other algorithms.

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    Track Disease Diagnosis Method Based on VMD and BP Neural Network
    Li HUA,Jian YANG,Tianchen YUAN,Ruigang SONG
    Electronic Science and Technology    2022, 35 (4): 40-46.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.007
    Abstract92)   HTML7)    PDF(pc) (972KB)(25)       Save

    In view of the problem of difficulty in extracting disease features from non-linear and unsteady sleeper vibration signals, this study proposes a track disease feature extraction method based on variational modal decomposition and multi-scale permutation entropy, and adopts the BP neural network disease diagnosis model to perform disease identification. The variational modal decomposition method is used to decompose the collected vibration acceleration signals to obtain several eigenmode components. The multi-scale permutation entropy value of these eigenmode components is calculated and used as the high-dimensional feature vector of the track disease to realize the noise reduction of the sleeper vibration signal and the extraction of the disease feature. Through the establishment of a BP neural network disease diagnosis model, high-dimensional feature vectors are input into the BP network for training, fitting, and verification, and compared with the method of combining empirical mode decomposition and BP neural network. The analysis results show that the proposed method has a higher recognition accuracy rate and can effectively diagnose disease.

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    Optical Design of Reflective ADB Module
    Cunsheng YU,Hongbing YAO,Limin HUANG,Xiajun YANG,Wanyu LÜ
    Electronic Science and Technology    2022, 35 (4): 47-52.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.008
    Abstract91)   HTML8)    PDF(pc) (1760KB)(15)       Save

    In order to solve the problem of car driving safety at night under complex road conditions and avoid the dazzling effect of the drivers of the opposing vehicle when meeting cars, the study is based on the non-imaging theory and combined with the law of refraction and reflection to establish the corresponding projection relationship between the emitting light of LED and the far-light distribution point on the receiving surface. The discrete points of the free-form surface are solved by numerical iteration, and are imported into 3D software for fitting modeling. A reflective adaptive driving beam optical system is designed to combine and form different high beam types by controlling the on and off of the corresponding LEDs. The test results of the final sample show that when the optical system is in the maximum working condition, the luminous efficiency reaches 34.3%, and the maximum illuminance value is 134 lx, which demonstrates that the light distribution results meet the requirements of high beam regulations.

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    Wideband Nonlinear Behavior Modeling of Receiver with Neural Network
    LIU Guohua,LU Hongmin,CHEN Chongchong,LI Wanyu,WAN Jianpeng
    Electronic Science and Technology    2022, 35 (8): 1-6.   DOI: 10.16180/j.cnki.issn1007-7820.2022.08.001
    Abstract90)   HTML63)    PDF(pc) (2848KB)(103)       Save

    In order to predict the nonlinear effect of receiver in complex electromagnetic environment, a nonlinear behavior model of receiver with memory effect is constructed based on real-value time-delay radial basis function neural network. The K-means clustering algorithm and the orthogonal least square method are respectively used to select and learn the center of the hidden layer and weight of the model, and the model is trained with the input and output measured data of the receiver. The model is verified by the in-phase and quadrature components of wideband signals. The simulation results are in good agreement with the measured data, and the normalized mean square errors of the model reaches -41.88 dB. The verification results show that the neural network model has fast convergence speed, good modeling accuracy and generalization ability.

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    A Fast Time Series Anomaly Detection Method Based on Template Matching and Membership Analysis
    GONG Hanxin,QI Jinpeng,KONG Fanshu,ZHU Junjun,CAO Yitong
    Electronic Science and Technology    2022, 35 (6): 1-5.   DOI: 10.16180/j.cnki.issn1007-7820.2022.06.001
    Abstract86)   HTML573)    PDF(pc) (1407KB)(171)       Save

    Traditional data analysis techniques have some problems in processing large-scale medical data detection, such as high time consumption and weak anti-jamming ability. In order to solve these problems, this study presents a fast detection and analysis method for abnormal state of time series data which uses template matching and membership degree analysis techniques. This method uses TSTKS algorithm and sliding window theory to realize rapid detection of multiple mutation points in time series data, extract continuous multi-window fluctuation characteristics, construct a normalized fluctuation vector of time series data, and perform abnormal state detection and analysis on large-scale disease signals. Simulation data and EEG signal analysis show that this method is a relatively fast and accurate method for big data analysis and detection.

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    The Point Cloud Coarse Registration Method Based on Boundary Centroid
    Shanghong LU,Wenguo LI
    Electronic Science and Technology    2022, 35 (4): 53-59.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.009
    Abstract85)   HTML1)    PDF(pc) (1627KB)(12)       Save

    The quality of point cloud registration directly affects the quality of 3D reconstruction. To solve the problem that the traditional K-4PC is time-consuming and prone to mismatching, a coarse point cloud registration method based on boundary centroid is proposed. By extracting the boundary of the point cloud, the surface features of the point cloud are preserved and the size of the point cloud data is reduced, which improves the speed of coarse registration. In order to speed up the extraction of boundary points, the K-D tree algorithm is used to search for k nearest neighbors. By registering the centroid of the boundary points, the initial distance of the point cloud is reduced and the degree of overlap is increased, ensuring the accuracy of coarse registration. The experimental results show that the proposed method is better than the traditional K-4PCS algorithm in terms of speed and accuracy. The speed of this method is about twice that of traditional K-4PCS. Both the translation and rotation accuracy are 40% higher than that of traditional K-4PCS. The proposed method has certain reference value for improving the speed and accuracy of point cloud coarse registration.

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    Multi-Line Structured Light Centerline Extraction Method Based on System Space Structure Constraints
    Xueyi MAO,Wenguo LI
    Electronic Science and Technology    2022, 35 (3): 16-24.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.003
    Abstract83)   HTML1)    PDF(pc) (3040KB)(23)       Save

    The linear structured light measurement system is easy to be disturbed by ambient light, which affects the extraction of the center line of the fringe. Based on the analysis of the spatial structure constraints of the system, this study presents a method to extract the center line of multi-line structural light stripes. The extraction method is divided into two stages: calibration and measurement. In the calibration phase, the offset and offset coefficient of the center line are calculated. In the measurement stage, the three-dimensional world coordinates of the stripe center line are obtained with and without ambient light, respectively. The experimental accuracy is measured by plane and surface fitting and compared with Steger algorithm. The experimental results show that in the presence of ambient light, the error of skew plane fitting is less than 0.007 mm, and the percentage relative error of surface fitting is less than 1.2%. In the absence of ambient light, the relative error of cylinder height fitting is less than 0.7%. The above results indicate that the accuracy of the proposed algorithm is better than that of the Steger algorithm.

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    Sparse Quantization Level Method for Scintillation Pulse Based on Feedback Regulation
    Zhenzhou DENG,Qin HU,Wensheng LAI,Chunlei HAN,Ling TAO
    Electronic Science and Technology    2022, 35 (3): 1-7.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.001
    Abstract82)   HTML31)    PDF(pc) (2520KB)(45)       Save

    The PET detector uses SQL sampling circuit to process scintillation pulse and fix the quantization level. The electric flipping caused by zero noise leads to abnormal heat of the voltage comparator for a long time, and the lower quantization level cannot be set, which limits the improvement of the detector's coincidence timing resolution. This study proposes a flicker pulse processing method based on feedback adjustment, which uses the switching characteristics of the transistor in the saturation state to perform feedback adjustment on the SQL quantitative samples. The delay element is used to delay and synchronize the quantized samples to generate a quantized level that can vary with the logic level of the sample, avoiding the influence of zero-point noise on the SQL sampling circuit. The experimental results show that compared with the original SQL sampling circuit, the optimized SQL sampling circuit reduces the amount of samples by 29% in the same time, and coincidence timing resolution is improved by 179.3ps, which reduces the useless power of PET detector and improves the coincidence timing resolution of PET system.

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    Trackside Signal Light Recognition Based on Image Processing
    FENG Junyi,SHEN Tuo,ZHANG Xuanxiong
    Electronic Science and Technology    2022, 35 (8): 53-57.   DOI: 10.16180/j.cnki.issn1007-7820.2022.08.009
    Abstract82)   HTML15)    PDF(pc) (2388KB)(19)       Save

    The trackside signal light is one of the important components for prompt train operation. In order to ensure train operation safety, a method based on image processing technique is proposed to effectively locate the trackside signal light and identify its color information. The trackside signal light ROI is extracted through the empirical value, and then the color segmentation is carried out to the ROI in the RGB color space to avoid the influence of irrelevant background, and remaining noise is removed through morphology processing. Hough-circle transform is performed on the processed image for the extracted candidate region of signal light, and the operating trackside signal light related to the running train is located according to the position characteristics between the signal light and the track. The pixel value information in the signal light area is analyzed for signal color recognition. The experimental results indicate that the method can precisely locate and recognize trackside signals, and the color correction ratio is 91.42% for red, 85.00% for yellow, and 94.29% for green, respectively.

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    A Survey of Intelligent Transportation Path Planning Algorithms
    LU Dongxiang
    Electronic Science and Technology    2022, 35 (7): 22-27.   DOI: 10.16180/j.cnki.issn1007-7820.2022.07.004
    Abstract81)   HTML4)    PDF(pc) (640KB)(21)       Save

    In order to solve problems such as traffic congestion, traffic safety and environmental pollution of urban roads, a variety of technologies including path planning algorithm and vehicle self-organizing network have gradually become important research directions in the field of intelligent transportation and attracted a lot of attention. In recent years, the application and popularization of intelligent transportation provide rich and diverse research topics for the research of path planning algorithm, and the research of path planning algorithm also effectively promotes the development of cutting-edge technology of intelligent transportation. By reviewing the research of multiple path planning algorithms, this study summarizes the existing problems in the research of path planning algorithms. On this basis, this study deeply studies and analyzes the advantages and disadvantages of static and dynamic path planning algorithms, and puts forward the future research trend and methods of path planning algorithms in intelligent transportation.

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    Drift Processing of Gyro While Drilling Based on Synaptic Plasticity Pulsed Neural Network
    Jinxian YANG,Yuxin HAN,Pengwei LIU
    Electronic Science and Technology    2022, 35 (4): 60-66.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.010
    Abstract80)   HTML3)    PDF(pc) (3803KB)(61)       Save

    In view of the data drift problem of MEMS gyroscope caused by vibration while drilling, a spiking neural network algorithm is proposed in this study. First, according to the time characteristics of the drift error of the gyroscope, the pulse time of the spiking neural network is used to encode the information intensity of the gyroscope. Then, the synaptic plasticity of the Izhikevich neuron model is used to adjust the excitatory synaptic conductance and inhibitory synaptic conductance to enhance the robustness of the network, thereby improving the anti-interference ability of the gyroscope signal against noise. Finally, under different vibration frequencies, the correlation between the firing rate of the Gaussian white noise output neuron and the membrane potential is analyzed. Experimental results show that under strong vibrations of different frequencies, noise has little effect on the firing rate of output neurons and the relative change of firing rate of output layer neurons, and has little effect on the membrane potential of output layer neurons, but has a greater impact on the correlation between membrane potentials. These results indicate that the proposed method improves the anti-interference ability of the gyroscope under vibration and noise, and can provide a new idea for the processing of gyroscope drift.

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    Speech Enhancement Method Based on Convolutional Recurrent Network and Non-Local Module
    Hui LI,Hao JING,Kanghua YAN,Lianghao XU
    Electronic Science and Technology    2022, 35 (3): 8-15.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.002
    Abstract80)   HTML1)    PDF(pc) (2463KB)(12)       Save

    The existing deep neural network speech enhancement methods ignore the importance of phase spectrum learning and cause the enhanced speech quality to be unsatisfactory. In view of this problem, a speech enhancement method based on convolutional recurrent network and non-local modules is proposed in the present study. By designing an encoder-decoder network, the time-domain representation of the speech signal is used as the input of the encoding end for deep feature extraction, so as to make full use of the amplitude information and phase information of the speech signal. Non-local modules are added to the convolutional layers of the encoder and decoder to extract key features of the speech sequence while suppressing useless features. A gated loop unit network is introduced to capture the timing correlation information between the speech sequences. The experimental results on the ST-CMDS Chinese speech dataset show that compared with the unprocessed noisy speech, the quality and intelligibility of the enhanced speech are improved by 61% and 7.93% on average.

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    Adaptive Weighted Data Fusion Algorithm Based on Batch Estimation
    SHI Zhenhua,ZHANG Na,BAO Xiaoan,SONG Jie
    Electronic Science and Technology    2022, 35 (5): 19-25.   DOI: 10.16180/j.cnki.issn1007-7820.2022.05.004
    Abstract79)   HTML6)    PDF(pc) (1979KB)(29)       Save

    In this study, an adaptive weighted data fusion algorithm based on batch estimation is proposed for multi-sensor data fusion. The algorithm uses time series and spatial sequences to find the variance of the collected data in batches, and uses data consistency detection to eliminate noise, and then obtains the adaptive factors. Subsequently, the adaptive weighting method is used to fuse the data to obtain the predicted value. The simulation experiments with IoT data show that the adaptive weighted multi-sensor data fusion technology of batch estimation can improve the accuracy of sensor measurement and the reliability of the system when processing data, and the adaptive weighted average method based on batch estimation is 10% less than the root mean square error of traditional adaptive method and the accuracy is improved by 2.3%.

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    An Improved Obstacle Detection Method for AGV
    YANG Yingying,LIU Xiang,SHI Yunyu
    Electronic Science and Technology    2022, 35 (9): 1-6.   DOI: 10.16180/j.cnki.issn1007-7820.2022.09.001
    Abstract79)   HTML7)    PDF(pc) (1193KB)(26)       Save

    To solve the problem that AGV obstacle detection algorithm performs poorly under the interference of uneven illumination and background texture in smart factories, this study proposes an improved Canny operator for obstacle detection. The method achieves the optimization of AGV obstacle detection in terms of color space, filtering method, gradient direction and adaptive threshold. Through Lab color space conversion, the b component is extracted and then filtered. The improved median filter and bilateral filter are merged to replace the Gaussian filter in the traditional Canny operator, which reduces the loss of edge details while achieving noise reduction, and improves the speed of the algorithm. The edge information is enhanced by increasing the gradient direction, and adaptive thresholding is obtained using Otsu algorithm. Experiments show that the proposed method can improve the accuracy of edge detection and reduce noise interference, thus achieving stable detection of obstacles.

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    Quantitative Analysis and Evaluation of the Disturbance Degree of Vehicle-Mounted VHF Radio
    WANG Wen,LU Hongmin,ZHANG Guangshuo,CHEN Chongchong,ZHANG Shiwei
    Electronic Science and Technology    2022, 35 (11): 1-6.   DOI: 10.16180/j.cnki.issn1007-7820.2022.11.001
    Abstract77)   HTML12)    PDF(pc) (827KB)(42)       Save

    As one of the core components of the in-vehicle communication system, the vehicle-mounted radio is susceptible to interference from the complex electromagnetic environment in the vehicle, resulting in a decrease in communication quality or loss of performance. This study uses experimental data to establish a quantitative model and evaluation method that can describe the degree of interference of the vehicle-mounted radio station, and studies the degree of interference of the vehicle-mounted radio station in the complex electromagnetic environment of the vehicle. By analyzing the receiver desensitization mechanism, based on the test data of a certain model of ultrashort wave radio, a quantitative model of receiver desensitization and antenna port interference voltage is established. And using the established model, an evaluation method to characterize the communication performance of the disturbed vehicle radio is proposed using analytic hierarchy process. The experimental data verification shows that the proposed model has high accuracy, and the quantization error of more than 85% of the test frequency points is within the 6 dB limit, indicating that the proposed evaluation method is effective and feasible.

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    Improved Image Matching Algorithm Based on LK Optical Flow and Grid Motion Statistics
    LIU Qunpo,XI Xiulei,YANG Lingxiao
    Electronic Science and Technology    2022, 35 (5): 1-6.   DOI: 10.16180/j.cnki.issn1007-7820.2022.05.001
    Abstract76)   HTML78)    PDF(pc) (2968KB)(104)       Save

    In order to solve the low accuracy and time-consuming problem of AKAZE algorithm in the image matching of glass-encapsulated electrical connectors, improved image matching algorithm based on LK optical flow and grid motion statistics is proposed in this study. First, the AKAZE algorithm is used to extract feature points, and the M-LDB descriptor is used to describe the features. Then, LK optical flow method is used to calculate the matching area for conditional constraints to obtain matching points, and the FLANN algorithm is adopted for feature matching. Finally, the glass-encapsulated electrical connector image is divided into multiple grids, and the numbers and threshold values of correct matching points of the neighborhood of the feature points that are matched by FLANN are calculated to eliminate the wrong matching points. By using public datasets and glass-encapsulated electrical connector data,the performance of the algorithm is verified and analyzed from both real-time and accuracy aspects. The results show that the improved algorithm has a matching accuracy of more than 93% when dealing with image pairs of glass-encapsulated electrical connectors with blur, brightenss and rotation changes, and the time-consuming is within 0.4 s, which proves the effectiveness of the algorithm.

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    Design and Implementation of GPU Configuration Management System Based on WinForm
    Zhao NIE,Jiawen HE,Chengcheng MA,Hui LIU
    Electronic Science and Technology    2022, 35 (3): 65-70.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.010
    Abstract75)   HTML2)    PDF(pc) (2140KB)(43)       Save

    There are many GPU function configuration modules, the setting process steps are cumbersome, and the relationship between the configuration items is close, which increases the difficulty of GPU application development. In view of this problem, a GPU configuration management system based on WinForm is designed. The system is divided into software data generation and hardware environment configuration, which has the functions of completing startup/alarm screen generation, drive configuration, assembly tool, cursor generation, interface configuration, and font tool. According to the configuration information provided by the users, the algorithm automatically configures the module to initialize the content and sequence of steps. Users do not need to use the hard-coded form of function calls, and only use the mouse and keyboard to complete complex GPU configuration tasks. The function, design and implementation of the system are verified based on the FPGA verification platform. The results show that the system simplifies the configuration process and shortens the configuration time under the premise of satisfying functional correctness, design completeness and implementation robustness.

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    Characteristic Signal Extraction of Non-Ideal Three-Phase Grid
    Qingqing YUAN,Min JIANG,Yumei YANG
    Electronic Science and Technology    2022, 35 (4): 78-86.   DOI: 10.16180/j.cnki.issn1007-7820.2022.04.013
    Abstract73)   HTML9)    PDF(pc) (6337KB)(39)       Save

    When the three-phase grid voltage is distorted or unbalanced, there are many non-ideal characteristic signals in the grid-connected system, such as harmonics and negative sequence components. In view of such problems, this study proposes a harmonic and negative sequence component extraction algorithm based on ISDFT. This algorithm redesigns the transfer function of the SDFT, which retains good signal filtering characteristics while improving the response rate of the system, and has strong flexibility. Experimental results show that ISDFT can quickly and accurately extract characteristic harmonics and negative sequence components, and ISDFT has good dynamic stability when the load changes suddenly. In addition, in terms of the extraction results of the 5th and 7th harmonics, DFT, SDFT, and ISDFT have the same effect of extracting harmonics, but the response rate of ISDFT is 44.56% faster than SDFT and 65.32% faster than DFT.

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    A GA-BP Neural Network for Predicting the Structure of Leaf Tobacco
    ZHANG Chongchong,HUANG Yayu
    Electronic Science and Technology    2022, 35 (6): 35-42.   DOI: 10.16180/j.cnki.issn1007-7820.2022.06.006
    Abstract71)   HTML5)    PDF(pc) (4982KB)(10)       Save

    In view of the problem that it is difficult to predict the structure of tobacco during the threshing process of redrying plant, a GA-BP neural network prediction model based on MATLAB image processing is proposed. For the classification of tobacco leaf, the obtained tobacco leaf images are preprocessed using of the MATLAB software. Then, the main characteristic variables that measure the structure of the tobacco are extracted, and industry standards and cluster analysis algorithms are used to classify the data. Through the standard mathematical method of statistics, the BP neural network prediction model optimized by genetic algorithm is constructed to predict and optimize the main influencing parameters. The research results show that the method proposed in this study has high prediction accuracy, and the prediction range is less than 0.059, which indicates that the method can effectively solve the problem of prediction of slices of tobacco in the process of threshing.

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    Prediction of PM2.5 Based on External Influences and Time-Series Factors
    Yanmei YANG,Zongmao CHENG
    Electronic Science and Technology    2022, 35 (3): 51-57.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.008
    Abstract70)   HTML2)    PDF(pc) (2079KB)(15)       Save

    As the haze problem gradually worsens, the prediction of one of its main component PM2.5 has become a widespread concern. The daily concentration of PM2.5 is affected by many factors, and it has the characteristics of non-linear and time-varying, which is difficult to accurately predict.To solve this problem, a prediction method of PM2.5 daily concentration based on external influences and time-series factors is proposed. With this method, the main external factors and time factors of PM2.5 daily concentration are separated, and the BP neural network preliminary prediction model based on the main external factors and the combined residual correction model of EEMD-LSTM neural network based on time factor are established. The daily PM2.5 concentration and other related factors data of Hangzhou from 2014 to 2019 are used for simulation experiments. The results show that compared with other models, the root mean square error of the prediction model proposed in the study is 2.74, and the prediction accuracy is higher.

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    UWB/PDR Integrated Indoor Pedestrian Positioning
    GUO Wei,ZHANG Xuanxiong
    Electronic Science and Technology    2022, 35 (8): 41-46.   DOI: 10.16180/j.cnki.issn1007-7820.2022.08.007
    Abstract68)   HTML3)    PDF(pc) (1091KB)(46)       Save

    In view of the problems of the UWB positioning system's positioning accuracy decline and the accumulated error of the pedestrian trajectory estimation algorithm caused by the NLOS in the complex indoor scene, a UWB/PDR integrated indoor positioning algorithm is proposed in this study. The PDR algorithm is applied to estimate the step length and heading of pedestrian. Then, the UWB’s ranging information is used to calculate the absolute position. Finally, a Kalman Filter is used to fuse measurements from the UWB and PDR. Experimental results show that the proposed combined positioning system can effectively solve the problem of excessive errors caused by UWB NLOS effects, improve positioning accuracy and system robustness, and the overall positioning error is below 12 cm.

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    Design of Reliability Evaluation System of Traction Motor Rolling Bearing Based on MATLAB App Designer
    Meiyi QI,Aihua LIAO
    Electronic Science and Technology    2022, 35 (3): 79-86.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.012
    Abstract66)   HTML2)    PDF(pc) (2681KB)(54)       Save

    Due to the uneven load and harsh operating environment during the operation of rolling bearings of traction motors, it is necessary to evaluate the reliability of rolling bearings of traction motors for metro vehicles. Based on MATLAB App Designer, the reliability evaluation system of traction motor rolling bearing is designed and developed. By extracting the time-domain and frequency-domain feature indexes of the vibration signal, the principal component analysis method is used for feature fusion. The fused characteristic index is used as the response covariate of the Weibull proportional hazard model. The moth-fighting optimization algorithm is used to optimize the Weibull proportional hazard model parameters, and the reliability function is substituted into the reliability function to calculate the reliability at any time. MATLAB App Designer is used to create module interfaces for data entry, data analysis and reliability evaluation. Experimental results show that the designed reliability evaluation system of traction motor rolling bearing has the characteristics of friendly interface, easy operation and good reliability evaluation effect, and can be widely used in different fields.

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    Parametric Fault Characteristics Analysis of Electrolytic Capacitor in NPC Three Level Inverter
    WANG Xin,HUANG Chong,XU Xiang
    Electronic Science and Technology    2022, 35 (5): 81-86.   DOI: 10.16180/j.cnki.issn1007-7820.2022.05.013
    Abstract65)   HTML4)    PDF(pc) (823KB)(27)       Save

    In view of the problem that it is difficult to extract the fault features of electrolytic capacitor in NPC three-level inverter, a fault feature extraction method based on variational mode decomposition and modal energy is proposed. By collecting the output current signal of NPC three-level inverter and combining with the reference current signal, the current deviation signal is obtained. According to the characteristics of frequency distribution of current deviation signal, the decomposition scale of VMD is optimized by modal repetition rate. The VMD is employed to decompose the current deviation signal to obtain the limited bandwidth modal component with the center frequency. According to the information entropy of the modal component, the characteristic component which can represent the capacitor fault is determined. Then the modal energy of the characteristic component is calculated, the characteristic vector is constructed, and the characteristic variation rule is sought and classified. The results show that this method can accurately reflect the working state of electrolytic capacitor.

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    Centralized Compensation Self-Optimization Strategy of Bus Voltage Ripple in DC Microgrid
    HUANG Kai,ZHANG Guopeng,LI Zihan
    Electronic Science and Technology    2022, 35 (6): 70-75.   DOI: 10.16180/j.cnki.issn1007-7820.2022.06.011
    Abstract64)   HTML5)    PDF(pc) (2033KB)(9)       Save

    In order to suppress the secondary ripple of DC microgrid bus voltage, a centralized compensation self-optimization strategy of DC active power filter is proposed in this study. Based on the voltage/current double closed-loop control of bi-directional DC/DC converter, the DC bus voltage ripple control is added. A band-pass filter is introduced to eliminate the phase lag problem when the low-pass filter is used to extract the ripple in the traditional control method. The impedance coefficient K which is an important control parameter, is obtained by iterative self-optimization method to realize the real-time tracking and centralized compensation of DC bus voltage ripple by DC active power filter. In MATLAB/Simulink, simulation model of DC microgrid including an interlinking converter, two distributed generations, a DC load, a single-phase AC load and a DC-APF is built, and the corresponding experimental platform is established. Simulation and experimental results validate the effectiveness of the proposed control strategy.

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    Glasses Virtual Try-on System Based on 3D Frame Modeling
    ZHANG Xindong,FU Dongxiang
    Electronic Science and Technology    2022, 35 (9): 52-57.   DOI: 10.16180/j.cnki.issn1007-7820.2022.09.008
    Abstract64)   HTML2)    PDF(pc) (1493KB)(13)       Save

    In the virtual try-on system, virtual glasses need to be superimposed on the face image accurately, which determines the experience of the virtual try-on system. The key technology is to construct a real glasses model and quickly and accurately estimate the 3D face pose of the image. In response to this technical requirement, a virtual try-on technology combining 3D mirror frame modeling and head space pose estimation based on facial feature detection is proposed and a virtual try-on system is implemented. First, the method uses ERT cascade regression to locate the facial feature points of the face images. Then, the VTK visualization tool is adopted to construct a 3D mirror frame, and the processed mirror frame image is accurately fused to the face image according to the information of the face feature points and the rotation information of the head. Finally, the accuracy of the pose estimation method is tested using the AFLW data set. The results show that the algorithm has high precision and fast speed. It can still quickly and accurately realize three-dimensional and multi-angle virtual try-on under complicated conditions of large angles, many background interferences and poor light conditions, which basically meets the requirements of virtual try-on technology.

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    Design of WeChat Mini Program for Lost and Found Based on Image Recognition
    ZHANG Yangfan,HAO Yuxin,LI Yinfeng,TIAN Xinyu,ZHENG Chunhong,LI Zehao
    Electronic Science and Technology    2022, 35 (10): 33-38.   DOI: 10.16180/j.cnki.issn1007-7820.2022.10.006
    Abstract64)   HTML3)    PDF(pc) (2111KB)(8)       Save

    In view of the scattered distribution of lost and found information on campus, the difficulty of the owner’s inquiry, and the low success rate of item retrieval, this study uses image character recognition technology to develop a set of WeChat-based lost and found applets. This program can recognize the card number, name, and other text information in the lost property picture of the certificate, and fill in the form automatically. When the uploaded lost property information matches the school database personnel information, the system will notify the owner to claim the item via SMS and WeChat. The system has been put into operation on campus, which has improved the release efficiency of lost and found information and increased the success rate of lost property recovery.

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    Research on Spatio-Temporal Data Fusion Algorithm of Wireless Sensor Network Based on Kalman Filter
    DU Peng,BAO Xiaoan,HU Yifei,CHEN Dirong
    Electronic Science and Technology    2022, 35 (6): 21-27.   DOI: 10.16180/j.cnki.issn1007-7820.2022.06.004
    Abstract62)   HTML8)    PDF(pc) (981KB)(18)       Save

    In order to solve the problem that the information collected from wireless sensor network nodes has a great similarity and some errors exist in the data results, this study proposes a data fusion algorithm based on Kalman filter for wireless sensor network, which improves the efficacy and accuracy of uploaded data by filtering invalid data and shrunk data packets. The algorithm uses the Kalman filter algorithm with high real-time performance to integrate the data in the wireless sensor network according to the time series. On the basis of time data fusion, according to the characteristics of spatial distribution, the data fusion of multi-sensor at the gateway layer is further carried out according to the weight. In view of the characteristics of real-time changes of different position errors, the gateway layer uses spatial data as the basis to dynamically adjust the weight of each node using an adaptive weighting algorithm. Simulation experiments show that the algorithm is easy to implement, can effectively remove redundant information, and improve data accuracy and reliability. Compared with the improved batch estimation and adaptive weighting method, the root mean square error is reduced by about 7.9% and the accuracy is improved by 2.1% after using this method.

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    Frequency Splitting Characteristics Analysis of Electrical-Field Coupled Wireless Power Transfer
    YANG Rui,SUN Yanzhou,YU Jianghua,ZHANG Mengfei,WEI Yanfang
    Electronic Science and Technology    2022, 35 (6): 48-53.   DOI: 10.16180/j.cnki.issn1007-7820.2022.06.008
    Abstract61)   HTML4)    PDF(pc) (1210KB)(9)       Save

    In order to solve the problems of frequency splitting and low efficiency in wireless power transfer system, the electrical-field coupled wireless power transfer system with double-side LCLC compensation topology is taken as the research object in this study. The effect laws of variable parameters on the system output are analyzed by system modeling. The relationship between system efficiency and the important parameters is derived using the circuit theory, and the system efficiency is analyzed in detail using MATLAB simulation software. The correctness of simulation results are verified by experiments. The results show that adjusting the capacitance value of the coupling mechanism below the frequency division point can avoid frequency splitting in the system, while fixing the relative position of the coupling plates can improve the system efficiency by increasing the frequency of the system to the critical point of efficiency, and the best efficiency of the system can reach 89.4%.

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    A Research on Distance Measurement Between Trains in Rail Transit Based on Machine Vision
    BI Jiazhen,SHEN Tuo,ZHANG Xuanxiong
    Electronic Science and Technology    2022, 35 (9): 37-43.   DOI: 10.16180/j.cnki.issn1007-7820.2022.09.006
    Abstract61)   HTML4)    PDF(pc) (1587KB)(16)       Save

    A safe distance between two moving trains is an important condition to avoid train rear-end collision. Since the image data obtained by machine vision is rich in information and can be integrated in many aspects based on the collected images, this study proposes a distance measurement method based on monocular machine vision. This method uses the constant distance between the two tracks of the train (1 435 mm) as a benchmark to estimate the distance between trains. The images collected by the monocular camera are processed and analyzed by the convolution neural network to extract the track features. Based on the existing small hole imaging principle, the mapping relationship between the world coordinate system and the pixel coordinate system is derived, so as to optimize the calculation formula of the distance between trains. The experimental results show that the error rate of the system is less than 6%, and the measurement time of the system is within 40 ms, indicating that the method realizes the effective fusion and integration of ranging and other information obtained in the image, and can be used to judge the braking distance of the train.

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    Garbage Detection and Classification Based on YOLO Neural Network
    ZHANG Wei,LIU Na,JIANG Yang,LI Qingdu
    Electronic Science and Technology    2022, 35 (10): 45-50.   DOI: 10.16180/j.cnki.issn1007-7820.2022.10.008
    Abstract61)   HTML3)    PDF(pc) (1231KB)(20)       Save

    In view of the problems of low efficiency of manual garbage sorting, heavy tasks and harsh environment, this study proposes a YOLO-based target detection method to realize garbage detection and classification. The model is adjusted through making a specific dataset, using K-means clustering algorithm and Mish activation function. According to the characteristics of the convolutional neural network, the CBAM attention module is embedded in front of each detection head of the YOLO model, combined with PANet to enhance the feature integration ability to improve the accuracy of small target detection. The experimental results show that the garbage detection and classification method proposed in this study can accurately and quickly identify garbage. Compared with YOLOv4, the map value of the proposed model on the garbage data set has increased by 2.81%. The recognition accuracy of Cans can reach 94.56%, and the accuracy of PlasticBottle has increased by 6.36%.

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    Design and Analysis of Spaceborne 5G Miniaturized Antenna
    LI Kai,ZUO Wencheng,ZHAO Ziwen,TAN Kangbo
    Electronic Science and Technology    2022, 35 (11): 7-12.   DOI: 10.16180/j.cnki.issn1007-7820.2022.11.002
    Abstract61)   HTML7)    PDF(pc) (3124KB)(25)       Save

    In view of the limitation of the installation space and structure size of 5G antenna in satellite communication, this study realizes the miniaturization design of the satellite-borne 5G antenna based on the trapezoidal oscillator structure and L-shaped bending mode. By transforming the vibrator of the log-period antenna from a rectangle to a shorter-length trapezoid, two trapezoids combined with a 90° sector structure are used to further synthesize the antenna, and the vibrator structure is optimized to achieve a horizontal reduction in antenna size and a miniaturized design. The designed antenna is applied to a certain type of satellite platform, and the antenna coupling degree under different placement positions is studied. The results show that compared with the traditional structure, the transverse size of the miniaturized LPDA is reduced from 90 mm to 65 mm, and it has better port impedance matching characteristics and radiation performance, which can better meet the application requirements of compact load, lightweight and frequency band intensification in 5G satellite communication.

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    Research on Induction Motor Control Based on Improved Sliding Mode Disturbance Observer
    YUAN Qingqing,QU Hanfei
    Electronic Science and Technology    2022, 35 (2): 59-66.   DOI: 10.16180/j.cnki.issn1007-7820.2022.02.010
    Abstract60)   HTML4)    PDF(pc) (2259KB)(13)       Save

    In view of the poor control performance of traditional PI regulators when the load is drastically changed, and the use of traditional sliding mode observers has serious chattering problems, taking induction motors as the research object, a real-time disturbance compensation scheme based on sliding mode observer and traditional PI regulator is proposed in this study. The system disturbance is observed and estimated in real time through the improved disturbance observer, and the estimated disturbance is fed back to the PI regulator for feedforward compensation, thereby effectively improving the motor control performance and improving the chattering phenomenon. A simulation platform based on MATLAB and an experimental platform of TMS320F28335 DSP are established, and the improved sliding mode observer disturbance feedforward compensation method is compared with conventional single PI adjustment and conventional sliding mode observer disturbance compensation. The experimental results show that the speed overshoot is optimized by 0.5%, and the response time is 30 ms, which verifies the effectiveness and feasibility of the proposed scheme.

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    Research on Adaptability Evaluation of Distribution Network Based on Improved TOPSIS-PSO-SVM
    HUANG Yuansheng,JIANG Yuqing,WANG Jing
    Electronic Science and Technology    2022, 35 (6): 54-63.   DOI: 10.16180/j.cnki.issn1007-7820.2022.06.009
    Abstract60)   HTML5)    PDF(pc) (1711KB)(10)       Save

    With the rapid development of renewable energy, new energy generation has become one of the main forces of power generation in China. In view of the adaptability of distribution network after distributed energy is connected to the grid, this study proposed a new definition of the adaptability of distribution network. A distribution network adaptability evaluation index system with six first-level indexes, including reliability, load rate, current, power quality, service life and new energy utilization rate is established. Through the subjective and objective weighting method, the combined weight is obtained. Combined with TOPSIS, the expected output value of the evaluation model is determined. This study proposes a distribution network adaptability evaluation model based on improved TOPSIS-PSO-SVM. A distribution network adaptability evaluation model based on improved TOPSIS-PSO-SVM is proposed, and the distribution network in 5 regions of Ningxia is used for example analysis. The results show that the evaluation relative error interval of the TOPSIS-PSO-SVM evaluation model is [0.94%, 1.03%], and the average absolute value of the relative error is 0.885 4%, which indicates that the evaluation model has smaller evaluation error and higher evaluation precision in the adaptability evaluation of distribution network.

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    Research on Robust Feedforward Control Strategy of Bidirectional AC/DC Converter for DC Microgrid
    WANG Xin,XU Xiang,WU Boning,HUANG Chong
    Electronic Science and Technology    2022, 35 (6): 64-69.   DOI: 10.16180/j.cnki.issn1007-7820.2022.06.010
    Abstract60)   HTML7)    PDF(pc) (946KB)(9)       Save

    In view of the problem that the two-way AC/DC power converter can effectively ensure the stability of the DC microgrid bus voltage, a feedforward robust control strategy composed of LESO and sliding mode theory is proposed. By establishing the dynamic mathematical model of DC microgrid three-phase AC/DC bidirectional power converter, the third-order linear extended state observer is designed, and the observation values of third-order LESO are used in the design of sliding mode controller. The control strategy can realize feedforward control without additional current sensor and ensure that the system has good dynamic performance. The strategy can effectively reduce the implementation difficulty of sliding mode control, and significantly improve the robustness of the system. Through simulation analysis, the effectiveness of the proposed control strategy is verified.

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    Early Warning and Monitoring Technology of Marine Internal Wave Based on Acoustic Vector Field Processing
    Yu JIANG,Min ZHANG,Xingyu BAI,Shenghui HUA
    Electronic Science and Technology    2022, 35 (3): 25-31.   DOI: 10.16180/j.cnki.issn1007-7820.2022.03.004
    Abstract58)   HTML3)    PDF(pc) (1432KB)(13)       Save

    In practical applications, the current ocean internal wave detection technology has large errors, and is significantly affected by the marine environment, and cannot be identified independently. In view of these problems, this study proposes a monitoring method for ocean internal wave early warning based on vector field processing. This method is based on the combined information processing of sound pressure and vibration velocity, and uses the three-dimensional information of the sound field picked up by the ultra-low frequency vector hydrophone. The time-space-frequency three-dimensional tracking and locking of non-cooperative targets can be carried out in the complex ocean background noise field according to the azimuth estimation algorithm. The arrival of internal wave causes the change of the three-dimensional sound velocity profile. The fluctuation of the sound field will lead to the change of the acoustic energy flow intensity of the target signal source. This method realizes the monitoring and prediction of ocean internal waves based on the abnormal jump of the vertical grazing angle caused by the channel distortion of the target signal in the internal wave space. The simulation results show that the vertical grazing angle fluctuates slightly in a normal environment, and the range of change is small. When the internal wave strikes, the grazing angle changes strongly, and the maximum deflection can be abruptly changed to a negative angle, which proves the effectiveness of the method.

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    Multi-Objective Optimization of Active Distribution Network Based on Particle Swarm Optimization
    SHI Zhenli,WEI Yewen
    Electronic Science and Technology    2022, 35 (9): 7-14.   DOI: 10.16180/j.cnki.issn1007-7820.2022.09.002
    Abstract58)   HTML4)    PDF(pc) (1041KB)(18)       Save

    With the large-scale grid integration of distributed renewable energy, the traditional distribution network has gradually developed from a single flow to a complex two-way flow. In view of the problem that the traditional dispatching method in active distribution network technology cannot be directly applied, this study explores improvement measures from two aspects of intelligent algorithm and optimization model. On the basis of considering the relevance of “source network load and storage”, aiming at improving the effect of peak shaving and valley filling, improving the economy of distribution network, and reducing the loss of distribution network, the forecast of wind and solar output has been carried out to improve the validity of the data, and a two-stage two-layer joint optimal dispatch model has been established. The study analyzes the advantages and disadvantages of the traditional particle swarm algorithm, and proposes to use the improved HE-MOPSO algorithm to solve the model. By solving the ZDT1~4 test function and using the extended IEEE33 node to perform the simulation calculation, the experimental results proved the superiority of the improved algorithm and model.

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Address:
P.O.Box 375,2 Taibai Road(South),Xi'an 710071,China
Tel/Fax:0086-029-88202440
Website:http://www.dianzikeji.org
E-mail:dzkj@mail.xidian.edu.cn
Unit Price:$20.00