Current Issue
15 March 2023, Volume 36 Issue 3
  • Design of Multifunctional Array Signal Processing System Based on FPGA
    LUO Xin,FENG Wu,SUN Weijie,LIU Maliang
    2023, 36(3):  1-6.  doi:10.16180/j.cnki.issn1007-7820.2023.03.001
    Abstract ( 19 )   HTML( 3 )   PDF (2270KB) ( 3 )  

    In order to adapt to the characteristics of high real-time performance and large computation of the array signal processing system, this study proposes a multifunctional array signal processing system based on FPGA. By using advanced large-scale high-performance FPGA and multichannel high-precision ADC chips, the synchronous acquisition and digital down conversion processing of 40 intermediate frequency signals can be accomplished, and 36 sets of beam data can be obtained by digital beamforming. By configuring various types of external interfaces, the network data interaction, serial port control, beam control and MGT high-speed data transmission can be achieved. In this study, the hardware and software architecture of the system is presented, and the chip models, peripheral interface and the realization method of each software function module are introduced in detail. The measurement results indicate that the system meets the design requirements, has a strong array signal processing capabilities and good versatility and scalability.

    Text Keyword Extraction Method Based on BERT and LightGBM
    HE Chuanpeng,YIN Ling,HUANG Bo,WANG Mingsheng,GUO ...
    2023, 36(3):  7-13.  doi:10.16180/j.cnki.issn1007-7820.2023.03.002
    Abstract ( 14 )   HTML( 4 )   PDF (1624KB) ( 4 )  

    Traditional text keyword extraction methods ignore the contextual semantic information and cannot solve the problem of ambiguity of a word, so the extraction effect is not ideal. Based on the LDA and BERT models, this study proposes the LDA-BERT-LightGBM (LB-LightGBM) model. The LDA topic model is selected to obtain the topic of each review and its word distribution, candidate keywords are filtered out according to the threshold, and the filtered words and the original review text are spliced and input into the BERT model. The word vector training is performed to obtain the word vector containing the text topic, so the text keyword extraction problem is converted into a two-classification problem through the LightGBM algorithm. The textrank algorithm, LDA algorithm, LightGBM algorithm and the proposed LB-LightGBM model are compared through experiments on the accuracy rate P, recall rate R and F1 of text keyword extraction in the present study. The results show that when TopN takes 3~6, the average value of F1 is 3.5% higher than that of the optimal method, indicating that the extraction effect of this method is generally better than that of the comparison method selected in the experiment, and the text keywords can be found more accurately.

    Path Planning and Smoothing for Unmanned Surface Vehicle Based on Improved Ant Colony Optimization
    SUN Pengna,ZHANG Zhongmin
    2023, 36(3):  14-20.  doi:10.16180/j.cnki.issn1007-7820.2023.03.003
    Abstract ( 17 )   HTML( 6 )   PDF (910KB) ( 6 )  

    In view of the problems of USV path planning in complex environment, such as large steering angle, many turning points, and high energy consumption, a path planning and smoothing method based on improved ant colony optimization is proposed. The method adopts the grid method for environmental modeling, and improves the path optimization and static obstacle avoidance ability by introducing the path smoothness and distance heuristic factor into the heuristic function and introducing the obstacle heuristic factor into the path transition probability. Combined with heuristic factors, the pheromone update standard is improved, and the adaptability of the algorithm to increase the volatile factor of pheromone can be adjusted. And then the key nodes of the optimal path are extracted and smoothed to further guarantee path smoothness and security. According to the simulation results of obstacle avoidance under different grid map, compared with the traditional ACO, the path optimization speed of improved ACO is increased by 45%~62%, and the steering times of path is reduced by 25%~44 %. Moreover, the path security and feasibility after smoothing are improved. The above results show that the autonomous path planning of USV in different environments is realized.

    Small Signal Modeling of Flexible Interconnected Microgrid
    LU Hongwen,YUAN Xufeng,CHEN Ruijie,LI Yulong
    2023, 36(3):  21-28.  doi:10.16180/j.cnki.issn1007-7820.2023.03.004
    Abstract ( 8 )   HTML( 6 )   PDF (3840KB) ( 6 )  

    In view of the problem that the traditional PCC hard switch cannot actively adjust the power flow, the back-to-back converters are used to replace the traditional PCC hard switch to flexibly interconnect the distribution network and microgrid, so as to facilitate the coordinated control between them. In order to analyze the stability of the system and consider the different number of distributed generators in microgrid of different scales, a microgrid small signal state space model framework with N distributed generators connected through back-to-back converter is established. The eigenvalue analysis is carried out using MATLAB, and the key parameters affecting the stability and dynamic performance of microgrid are determined. Finally, a microgrid flexible grid connection operation model based on droop control is built on PSCAD / EMTDC platform. The established small signal model is combined with the built simulation model, and the simulation is carried out under multiple working conditions to verify the correctness of the modeling, analysis and conclusion.

    Fault-Tolerant Clock Synchronization Algorithm Based on Grey Prediction
    LU Yu,ZHANG Li,ZHANG Fengdeng
    2023, 36(3):  29-35.  doi:10.16180/j.cnki.issn1007-7820.2023.03.005
    Abstract ( 9 )   HTML( 14 )   PDF (842KB) ( 14 )  

    In view of the problem of clock Byzantine failure and node communication link loss failure in the non-master clock synchronization in the distributed real-time system, a fault-tolerant clock synchronization algorithm based on gray prediction is proposed in this study. The proposed algorithm is based on the LL model of the broadcast communication network, and uses the gray prediction method of GM (1,1) to analyze the correction deviation value of the previous round, so as to predict the correction deviation value of the node in the failure order, and then obtain the correction value through calculation. The experimental results show that the gray prediction algorithm proposed in this study can tolerate Byzantine faults, and at the same time, it can overcome the problems caused by the failure of node communication link loss, and improve the universality of the FTA algorithm. The data comparison analysis results show that the clock synchronization precision of this algorithm is improved by 24.3% when compared with Original algorithm. At the same time, the algorithm complexity has certain advantages when compared with other algorithms.

    Task Partitioning Optimization Algorithm Based on MrsP Protocol
    ZHANG Haitao,ZHANG Tong,ZHANG Yuhui,GUAN Yinfeng,Z...
    2023, 36(3):  36-41.  doi:10.16180/j.cnki.issn1007-7820.2023.03.006
    Abstract ( 9 )   HTML( 2 )   PDF (785KB) ( 2 )  

    Scheduling and resource sharing are the core problems in multiprocessor real-time systems, the corresponding scheduling algorithm and shared resource access protocol will directly affect the performance of the system, which requires the scheduling algorithm and resource access protocol to maximize the computing power of the hardware platform on the basis of ensuring real-time performance. However, most existing scheduling algorithms assume that tasks are independent of each other and do not consider resource sharing among tasks. Besides, shared resource access protocols also focus on rules and worst-case response time analysis. In this regard, the whole schedule ability condition of multiprocessor real-time system is obtained by combining P-RM algorithm and MrsP protocol. According to the characteristics of the MrsP protocol, this study proposes a task division algorithm to reduce the blocking time. By improving the calculation method of the task utilization, the proposed method solves the problem of repeated calculation in the critical area. Compared with the previous task partitioning algorithm, the proposed algorithm also solves the key area of double-counting and splits the redistribution after task classification problem. Experiments resalts show that the number of processors required by the algorithm is reduced by 15% to 20%.

    Short-Term Photovoltaic Power Prediction Based on VMD and Improved TCN
    HUANG Yuan,WEI Yunbing,TONG Dongbing,WANG Weigao
    2023, 36(3):  42-49.  doi:10.16180/j.cnki.issn1007-7820.2023.03.007
    Abstract ( 8 )   HTML( 2 )   PDF (1971KB) ( 2 )  

    Photovoltaic power generation fluctuates, photovoltaic output is easily affected by various meteorological characteristics, and traditional TCN networks tend to over-enhance spatial characteristics and weaken individual characteristics. In view of these problem, a short-term photovoltaic power generation prediction model based on VMD and improved TCN is proposed in this study. The original photovoltaic power generation time series is decomposed into several modal components of different frequencies through VMD, and each modal component and the corresponding meteorological data are input to the improved TCN network for modeling and learning. The center frequency method is used to determine the optimal decomposition modal number of VMD. On the basis of the traditional TCN prediction model, DropBlock regularization is used to replace Dropout regularization to achieve the effect of suppressing information synergy in the convolutional layer, and the attention mechanism is introduced to autonomously mine and highlight the impact of key meteorological input characteristics and quantify the impact of various meteorological factors on photovoltaic power generation to improve forecasting precision. Based on the real data of a photovoltaic power station in Jiangsu, the simulation experiments show that the RMSE of the proposed prediction method is 0.62 MW and the MAPE is 2.03%.

    An Accurate Design Approach for the Folded Cascode Operational Amplifier
    WANG Jiaqi,LÜ Gaochong,GUO Yushun
    2023, 36(3):  50-54.  doi:10.16180/j.cnki.issn1007-7820.2023.03.008
    Abstract ( 17 )   HTML( 3 )   PDF (663KB) ( 3 )  

    The results obtained from the traditional design procedure of the folded cascode amplifier are inaccurate. The optimization method can produce fairly accurate design results, but consumes large amount of computations. This study presents an accurate design method for this kind of amplifier. Through SPICE simulation, the errors caused by the analysis and approximation of various performance indicators in the traditional design process are compensated. At the same time, The device size calculation based on the BSIM model is adopted, and this design process is repeatedly executed, which gradually eliminates the errors existing in the traditional design process and obtains accurate design results. Compared with the traditional manual method, the proposed method is more accurate and avoids repeated debugging during design. When compared with the optimization method, although the proposed design still needs to go through an iterative process, the calculation amount is smaller due to the faster convergence. The circuit design under the actual process library of 0.18 μm and 90 nm is taken as an example, and the simulation experiments proves the correctness and effectiveness of the proposed method.

    An Acoustic Treatment Method for On-Line Fault Monitoring of Electromechanical Systems in Complex Noise Environment
    BAI Xingyu,GOU Yutao,JIANG Yu,LIU Mingyu
    2023, 36(3):  55-61.  doi:10.16180/j.cnki.issn1007-7820.2023.03.009
    Abstract ( 6 )   HTML( 1 )   PDF (2517KB) ( 1 )  

    In view of the problem of electromechanical system fault detection under complex background noise environment, this study proposes a noise suppression and fault monitoring method based on broadband acoustic processing. This method starts from acoustic signal pick-up and processing, and establishes the voiceprint database of the normal operating state of the system by collecting, tracking the data and suppressing the complex background noise of the acoustic signal under the normal operating state of the electromechanical equipment. In addition, the proposed method further realizes the detection and classification of fault signals through the voiceprint signal matching and pattern recognition technology based on broadband acoustic processing, and then realizes the online monitoring of the operating state of the electromechanical system and the autonomous early warning of invisible faults. This processing method organically combines the autocorrelation noise suppression technology based on data tracking and the fault signal detection and classification technology based on broadband acoustic processing, which can monitor the early hidden faults of the electromechanical system and effectively solve the fault detection problem of the electromechanical system in the complex noise environment. The simulation experiment finally proves the effectiveness and good practicability of the proposed method.

    Research on Control Strategy of Single-Phase Grid-Connected Inverter Based on dSPACE
    XIA Zihao,LI Yudong
    2023, 36(3):  62-68.  doi:10.16180/j.cnki.issn1007-7820.2023.03.010
    Abstract ( 8 )   HTML( 1 )   PDF (1907KB) ( 1 )  

    In view of the problem of high harmonic content of grid-connected current caused by voltage distortion, a control strategy combining quasi-proportional resonance control and voltage feed-forward is proposed in this study. Considering the infinite gain of the quasi-proportional resonance controller at the resonant frequency, the steady-state error of grid-connected current is reduced, and the voltage feed-forward is introduced to eliminate the influence of voltage on the system, so as to improve the quality of grid-connected current. The structure of single-phase grid-connected inverter is analyzed, the harmonic component of grid-connected current caused by voltage distortion is suppressed by closed-loop control, and the parameters of quasi-proportional resonance controller are set by control variable method, so as to analyze the influence of parameters of quasi-proportional resonance controller on system performance. Finally, the MATLAB/Simulink simulation model and the dSPACE-DS1104 hardware-in-the-loop simulation platform are established to verify the effectiveness of the proposed strategy under different strategies.

    Optimal Design of Permanent Magnet Synchronous Machine Based on Analytic Model
    ZHU Tuo,LI Zheng,ZHANG Kai,LI Zi
    2023, 36(3):  69-75.  doi:10.16180/j.cnki.issn1007-7820.2023.03.011
    Abstract ( 10 )   HTML( 1 )   PDF (1917KB) ( 1 )  

    Electromagnetic torque and eddy current loss are two important indexes of permanent magnet synchronous motor. Under the limited conditions of volume, effectively improving electromagnetic torque and reducing rotor eddy current loss are the key for motor design. In view of this problem, the analytical method is used to calculate the electromagnetic torque and eddy current loss respectively, and the PSO algorithm is used to optimize the size parameters of the motor. The analytical model includes armature reactive magnetic field and no-load magnetic field model. When calculating eddy current loss, eddy current reaction and circumferential segmentation of permanent magnet are considered. The objective function of the optimization algorithm uses weight values to convert multi-objective into single objective. The comparison between the analytical solution and the time-stepping finite element numerical solution shows that the error of the analytical solution is about 2%. The response surface of parameter influence analysis reveals that the correlation between electromagnetic torque and eddy current loss is consistent when the stator winding pitch is 25°. The optimization iteration results show that the optimized design reduces the average eddy current loss by 76%, the average electromagnetic torque by 22%, and the electromagnetic torque ripple by 68%.

    Research Progress of Smart Grid Data Security Based on Internet of Things Technology
    YING Jieyao
    2023, 36(3):  76-80.  doi:10.16180/j.cnki.issn1007-7820.2023.03.012
    Abstract ( 8 )   HTML( 2 )   PDF (639KB) ( 2 )  

    In order to protect the core data in smart grid equipment and users' personal privacy, a number of Internet of things security technologies such as distributed computing and homomorphic encryption have gradually attracted attention. In recent years, the development of internet of things technology has promoted the rapid popularization of intelligent power grid, and the application of smart grid has promoted the renewal of internet of things technology. Through introducing various attack methods faced by smart grid, this study reviews and combs the research background and current situation of smart grid data security. On this basis, the definitions of false data injection attack and personal privacy protection are discussed and analyzed, and the future research direction and ideas of smart grid data security technology are prospected.

    Research Progress of Node Assignment Optimization Strategy in Road Traffic Network
    LU Dongxiang
    2023, 36(3):  81-86.  doi:10.16180/j.cnki.issn1007-7820.2023.03.013
    Abstract ( 8 )   HTML( 2 )   PDF (734KB) ( 2 )  

    In order to further improve the traffic efficiency of urban road traffic network, a variety of intelligent optimization algorithms such as particle swarm optimization algorithm and neural network algorithm have attracted extensive attention. Recently, the popularization and application of deep learning technology has greatly improved the efficiency of node identification of urban traffic network, and the node scheduling of traffic network has expanded the application of deep learning technology. In this study, the key problems of traffic node scheduling are analyzed in detail, and the research status of relevant network node allocation is summarized. On this basis, the proposed study thoroughly discusses and analyzes the application prospect of node scheduling and deep learning in urban transportation network, and prospects the future research direction of node allocation optimization strategy in transportation network.


Monthly,Founded in September 1987
Competent Authorities:
Ministry of Education of the People's Republic of China
Sponsored by:Xidian University
Chief Editor:Liao Guisheng
Executive Editor:Wan Liancheng
Editor:Hei Lei
Editor and Publisher:
The Editorial Department of Electronic Science and Technology
Distribution Abroad:
China Intermational Book Trading Corporation
P.O.BOx 399,Beijing 100044,China
P.O.Box 375,2 Taibai Road(South),Xi'an 710071,China
Unit Price:$20.00