Current Issue
15 November 2022, Volume 35 Issue 11
  • Quantitative Analysis and Evaluation of the Disturbance Degree of Vehicle-Mounted VHF Radio
    WANG Wen,LU Hongmin,ZHANG Guangshuo,CHEN Chongchon...
    2022, 35(11):  1-6.  doi:10.16180/j.cnki.issn1007-7820.2022.11.001
    Abstract ( 35 )   HTML( 30 )   PDF (827KB) ( 30 )  

    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.

    Design and Analysis of Spaceborne 5G Miniaturized Antenna
    LI Kai,ZUO Wencheng,ZHAO Ziwen,TAN Kangbo
    2022, 35(11):  7-12.  doi:10.16180/j.cnki.issn1007-7820.2022.11.002
    Abstract ( 23 )   HTML( 14 )   PDF (3124KB) ( 14 )  

    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.

    Design and Implement of a High-Performance RLWE Cryptoprocessor
    WANG Chunhua,LI Bin,DU Gaoming,LI Zhenmin
    2022, 35(11):  13-20.  doi:10.16180/j.cnki.issn1007-7820.2022.11.003
    Abstract ( 16 )   HTML( 8 )   PDF (1262KB) ( 8 )  

    The RLWE encryption scheme is one of the most potential candidates in the lattice cryptosystem in the post-quantum era. In view of the problem of high latency and low throughput in RLWE cryptoprocessor, this study proposes a high-performance RLWE cryptoprocessor hardware architecture. The parallel circuit structure of two NTT modules and four butterfly modules are adopted in the proposed architecture. In the pre-calculation and post-calculation process, the multipliers in the four butterfly modules are used for parallel calculation. In the encryption process, NTT calculation and ciphertext calculation are performed in parallel. In the processing of NTT and INTT operations, the data read and write process and calculation process are ping-pong operations, thereby hiding the data read and write cycle, reducing the delay of the RLWE encryption processor, and improving the throughput of the RLWE encryption processor. A hardware architecture is designed for resource reuse, the multiplier and adder are reused in the butterfly module during the encryption and decryption process, and the circuit structure of NTT is reused by INTT, thereby reducing the hardware resource consumption of the encryption processor. The cryptoprocessor with parameters of n=256 and q=65 537 is implemented on the Spartan-6 FPGA development platform. The results indicate that the encryption time is only 12.18 μs, the throughput is 21.01 Mbit·s-1, the decryption time is only 8.65 μs, and the throughput is 29.60 Mbit·s-1. Compared with other cryptoprocessor, the proposed design has improved the delay and throughput of the cryptoprocessor.

    Design of Matrix Decomposer Based on Improved QR Algorithm
    CHEN Wenjie,SONG Yukun,ZHANG Duoli
    2022, 35(11):  21-28.  doi:10.16180/j.cnki.issn1007-7820.2022.11.004
    Abstract ( 18 )   HTML( 10 )   PDF (2007KB) ( 10 )  

    Matrix decomposition is one of the important operations in matrix inversion, which is widely used in neural networks, digital signal processing, wireless communication technology and other fields. Based on a column-vector optimized QR decomposition algorithm, this study proposes a one-dimensional linear matrix decomposition structure and completes the ASIC implementation of the structure to address the shortcomings of the traditional decomposition algorithm operations that are not conducive to hardware implementation. The matrix decomposer supports matrix decomposition operations of order 2~32 and operates at 700 MHz at TSMC 28 nm process. Simulation and FPGA test results show that the relative error between the decomposer and MATLAB results is less than 10-12. When performing matrix decomposition of more than 12-orders, the operation cycle of the decomposer has a speedup ratio of 2.3 times compared with the traditional one-dimensional linear structure. When performing 32-order matrix decomposition, the operation cycle of the decomposer has a speedup ratio of 22.8 times compared with NIVIDA RTX2070.

    Fractional Order PIλDμ Control of DC Converter Based on Ant Lion Optimization Algorithm
    XIAO Haifei,ZENG Guohui,DU Tao,HUANG Bo,LIU Jin
    2022, 35(11):  29-35.  doi:10.16180/j.cnki.issn1007-7820.2022.11.005
    Abstract ( 13 )   HTML( 1 )   PDF (1673KB) ( 1 )  

    In order to improve the response speed and stability of the output voltage of the DC converter, a method using ALO algorithm to improve the fractional order PIλDμ controller of the dual active bridge DC converter is proposed in this study. The parameter to be optimized is regarded as the spatial position of the individual ant lion, and the error performance index ITAE of the double active bridge DC converter is used as the objective function. The ALO algorithm is used to search for the global optimal solution of the parameters of the fractional-order PIλDμ controller, and then the control optimization of the output voltage is realized. The output voltage performance of dual active bridge DC converters with traditional engineering experience setting integer-order PID, particle swarm algorithm setting fractional PIλDμ, and ALO algorithm setting fractional-order PIλDμ are simulated and experimentally compared. The results show that the proposed method can shorten the adjustment time, improve the response speed, and enhance the anti-interference ability of the system, which proves the effectiveness and feasibility of the optimization method.

    A Texture Feature Analysis-Based Crack Detection Scheme for Metro Tunnels
    ZHANG Qiuyuan,LIU Zhiquan
    2022, 35(11):  36-41.  doi:10.16180/j.cnki.issn1007-7820.2022.11.006
    Abstract ( 9 )   HTML( 2 )   PDF (1435KB) ( 2 )  

    To realize the automatic detection of cracks during the intelligent patrol in metro tunnels, this study presents a texture feature analysis-based crack detection scheme for metro tunnels. In the proposed scheme, several pre-processing operations such as erosion, contrast stretch and weighted neighborhood are firstly adopted to improve the quality of original scanned images. Then, the images are partitioned and the improved maximum between-cluster variance method is utilized for the partitioned areas so as to separate the cracks from the background images. Next, the texture feature analysis is leveraged to filter out incorrect crack information from the images. Afterwards, the images are thinned so as to obtain the corresponding skeleton images, and finally the automatic detection of cracks for metro tunnels can be realized. Furthermore, the experimental results demonstrate that the proposed scheme can efficiently and accurately detect the crack information for metro tunnels as well as automatically create labels for the crack areas, and the detection effect of the proposed scheme is significantly better than the existing schemes.

    A Measuring System for Frame Size Based on Image Processing Technology
    ZHANG Zhihao,FU Dongxiang,YAN Rui
    2022, 35(11):  42-47.  doi:10.16180/j.cnki.issn1007-7820.2022.11.007
    Abstract ( 13 )   HTML( 4 )   PDF (1830KB) ( 4 )  

    In response to the inapplicability of traditional frame size measurement methods under special circumstances, this study proposes an image-based non-contact measurement method which uses image processing technology to extract and measure the outline of the frame. ERT is used to locate the face and locate the face frame area according to the feature points. In the process of preprocessing and contour detection, an improved Canny algorithm is used to obtain a more complete frame contour. The frame recognition mode is calibrated by the minimum bounding rectangle method, which improves the extraction accuracy of the frame, and facilitates the subsequent size measurement of the frame outline. The actual size information of the outline of the lens frame is obtained with the interpupillary distance as the reference object. The results of the algorithm implementation on the PC side show that the proposed measurement algorithm has low complexity, high accuracy, and fast measurement speed, and can quickly and accurately measure the outline size of the frame on a mobile device.

    A Hyperspectral Image Classification Method Based on Grid Diversity and Active Learning
    SHEN Yihan,YANG Jinghui,WANG Hao
    2022, 35(11):  48-57.  doi:10.16180/j.cnki.issn1007-7820.2022.11.008
    Abstract ( 10 )   HTML( 8 )   PDF (1986KB) ( 8 )  

    In order to solve the problems of low classification accuracy and small number of samples in the process of hyperspectral image classification, an image classification method based on grid diversity and active learning is proposed. In this method, the principal component space is divided into several grids using the grid method. A sample is randomly selected from each grid containing samples, and the original spectral data is included in the training set. Then, using the active learning method, the K-nearest neighbor method is used to select some samples with the largest uncertainty among the remaining samples and incorporate them into the training set, thereby expanding the training set, making the data set representative, and improving the classification accuracy. In addition, in the process of data processing, principal component analysis and linear discriminant analysis are combined to reduce the dimension of spectral data, which further improves the operation speed. The experimental results show that in the Indian Pines hyperspectral data set,and in the case of a small number of training set samples, the proposed method improves the overall classification accuracy by 12.24% and 19.76%, respectively when compared with random diversity and non-active learning.

    Research Progress of Service Composition Based on Machine Learning
    ZHANG Ye,BAO Liang
    2022, 35(11):  58-63.  doi:10.16180/j.cnki.issn1007-7820.2022.11.009
    Abstract ( 15 )   HTML( 3 )   PDF (776KB) ( 3 )  

    Service composition is a classic research problem in the field of service computing, which has received extensive attention in both industrial and academic fields. With the increasing popularity of cloud-native and micro-service technologies, a series of innovative researches have emerged in the field of service composition. With the rapid development of computer technology and artificial intelligence, machine learning algorithms such as deep learning and reinforcement learning are increasingly applied to traditional service composition problems. This study introduces the common classification and challenges of service composition problems, and summarizes the application of machine learning algorithms emerging in service composition problems in recent years. Additionally, the proposed study also summarizes the problems faced in the direction of solving service composition problems based on machine learning algorithms, and looks forward to the future development direction.

    Differential Privacy Fuzzy Clustering Location Protection Method
    LIN Jing,HU Demin,WANG Kuihao
    2022, 35(11):  64-71.  doi:10.16180/j.cnki.issn1007-7820.2022.11.010
    Abstract ( 10 )   HTML( 2 )   PDF (1487KB) ( 2 )  

    In view of the problems of sensitive initial value, inapplicability of discrete data and large error in the existing differential privacy clustering location protection methods, a differential privacy fuzzy clustering location protection method is proposed in this study. Firstly, the points are mapped to the feature space by Gaussian kernel function, and the computational efficiency is significantly improved due to the relatively small amount of computation. Secondly, the differential privacy is combined with the improved fuzzy C-means clustering algorithm, so that each group of input data no longer belongs to a specific class, but to the degree of membership. Finally, the Laplacian noise satisfying the differential privacy constraint is added to the centroid of the clustering set to obtain the disturbance position of each point, and the disturbance position is used to query. The experimental results show that the differential privacy fuzzy clustering location protection method reduces the query error and improves the efficiency of the algorithm under the premise of ensuring the location privacy security.

    Calculation Method of Segmented Borehole Trajectory Based on Magnetic Inertia Measurement
    YANG Jinxian,LIU Pengwei
    2022, 35(11):  72-79.  doi:10.16180/j.cnki.issn1007-7820.2022.11.011
    Abstract ( 9 )   HTML( 3 )   PDF (1169KB) ( 3 )  

    In order to improve the accuracy of well trajectory calculation in measurement while drilling, a segmented well trajectory calculation method based on magnetic inertia measurement is proposed in this study. When the drill bit is stopped, the tool face angle model recognition method is used to identify the borehole trajectory model of the test section, and the sub-measurement points of the test section under the constraints of the model are generated according to the depth change in the real-time measurement data. Then, the magnetic inertia measurement data is used to correct the measured points, and the corrected points are identified by piecewise trajectory model. Finally, the corresponding models are calculated by piecewise model. The experimental results show that the absolute error of the coordinate increment is reduced from 0.840 m of the single model algorithm to 0.146 m after adopting the new method, which shows that compared with the single model of the well trajectory calculation method, the error of the segmented well trajectory calculation method is smaller and the wellbore trajectory calculation method can improve the accuracy of the borehole trajectory calculation.

    Study on Fluctuation Smoothing Strategy of Flexible Grid-Connection of Microgrid
    ZHANG Wenjing,CHEN Zhuo,LIU Bolin,ZHU Jiawen,HUANG...
    2022, 35(11):  80-89.  doi:10.16180/j.cnki.issn1007-7820.2022.11.012
    Abstract ( 11 )   HTML( 2 )   PDF (1347KB) ( 2 )  

    Due to the instability of generation power of distributed generation in microgrid, when microgrid and large power grid operate in parallel, it will affect the stability of large power grid system. In order to eliminate the influence of power fluctuation in microgrid on large power grid, it is necessary to improve the grid connection interface. A flexible grid connection interface is proposed to stabilize the power fluctuation in microgrid and connect the distribution network and microgrid flexibly. The interface takes the back-to-back converter as the prototype, and applies the hybrid energy storage technology to the grid connection technology to calculate the power fluctuations in the microgrid. Then, the hybrid energy storage link in the grid-connected interface is used to smooth out the power fluctuations, which eliminates the impact of microgrid power fluctuations on the distribution network power and simplifies the control mode of the micro source in the microgrid. Experiments performed on MATLAB/Simulink simulation platform prove that the flexible grid-connected interface can suppress the power fluctuations of the microgrid, and the grid-connected interface can effectively simplify the control mode in the microgrid when the microgrid operating mode is switched.

    Progress in Electrocatalysis Oxygen Reduction for Hydrogen Peroxide Production over Single-Atom Catalysts
    JIN Tao,HU Xia,YU Lanlan,ZHAO Wenjun,YANG Qi,TUO Y...
    2022, 35(11):  90-97.  doi:10.16180/j.cnki.issn1007-7820.2022.11.013
    Abstract ( 15 )   HTML( 2 )   PDF (3589KB) ( 2 )  

    Hydrogen peroxide production via electrocatalytic two-electron oxygen reduction is an effective alternative to traditional centralized anthraquinone processes. However, this process depends on a catalyst with low cost, high activity and high selectivity. In recent years, SACs have been considered as potential catalysts for oxygen reduction and hydrogen peroxide production due to their nearly 100% atom utilization efficiency, tunable electronic structure, and excellent catalytic performance, but the regulation of SAC activity remains a challenge. Starting from the mechanism of the ORR, the influence of the binding strength of the active metal center and *OOH on the oxygen reduction reaction pathway is elucidated. This study systematically illustrates the effect of adjusting the structure of the SAC on the binding of the metal center to *OOH from three aspects: metal center atom, coordination atom and the surrounding environment of the active center, and reveals the activity and selectivity of H2O2 production.

    Summary of Finite Element Analysis Technology for High Precision Machining
    YANG Yunhui,XU Lianjiang
    2022, 35(11):  98-103.  doi:10.16180/j.cnki.issn1007-7820.2022.11.014
    Abstract ( 12 )   HTML( 2 )   PDF (703KB) ( 2 )  

    In order to further optimize the machining accuracy in the process of mechanical manufacturing, computer simulation, analysis and simulation based on finite element have attracted extensive attention and obtained rich research results since they were put forward. The rapid popularization and popularization of finite element technology effectively improves the machining accuracy of mechanical manufacturing, and the experimental research of mechanical manufacturing process promotes the optimization and improvement of finite element technology. This study combs the research process of residual stress prediction and finite element analysis, and summarizes the strategies and methods to improve machining accuracy. At the same time, the shortcomings and deficiencies of the current finite element analysis technology are described in detail, and the problems and directions of the follow-up research of finite element technology in the field of machining are put forward.

    Joint Modulation Recognition Based on Instantaneous Feature and Power Spectrum Entropy
    XIE Aiping,ZHANG Yusheng,LIU Ying,HE Ziang,GAO Rui
    2022, 35(11):  104-110.  doi:10.16180/j.cnki.issn1007-7820.2022.11.015
    Abstract ( 12 )   HTML( 4 )   PDF (2091KB) ( 4 )  

    To solve the problem that the traditional instantaneous characteristic parameter recognition method has few signal types and low recognition rate under low SNR, a modulation recognition method based on the combination of instantaneous characteristic parameter and power spectrum entropy is proposed in this study. The improved instantaneous amplitude and phase characteristic parameters are used to identify the modulation signals, and the power spectrum entropy characteristic parameters are introduced to further realize the in-class recognition of more signals. The decision tree classification method is used to identify and classify the 9 common digital modulation signals {ASK, 4ASK, 2FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM} with appropriate threshold values. Monte Carlo experiment results show that compared with the existing recognition methods, the proposed method increases the number of signal types, and improves the signal recognition accuracy in the case of low SNR.


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