Table of Content

15 February 2022 Volume 35 Issue 2

  

Design and Implementation of High Integration and Miniaturization IF Filter Module

BAI Rui,XU Da,YANG Liang,SUN Congke,WANG Shaodong

Electronic Science and Technology. 2022, 35(2):  1-6.  doi:10.16180/j.cnki.issn1007-7820.2022.02.001

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To meet the demand of large number and high integration of filters in the IF filter modules of radar receivers, a miniaturization LC filter based on the 3D integration technology is proposed in this study. The application and integrating process of 3D LC filter in modules are studied and analyzed. The 3D LC filter adopts two substrates, and uses 3D integration process and BGA technology to realize the three-dimensional integrated assembly of the two substrates. High-density layout can be realized by placing capacitive elements on the bottom substrate. Inductive elements are placed on the top substrate and separated from the capacitive elements, and signal communication is achieved through solder balls. The use of a three-dimensional integration scheme can reduce the size of the LC filter circuit by nearly 50%, and the filter index is consistent with the performance of the planar filter circuit. Compared with the traditional IF filter component design method, this solution is more conducive to achieve high integration and miniaturization.

Research on Scheduling Method of Layered Heterogeneous Signal Processing Platform

LI Na,GAO Bo,XIE Zongfu

Electronic Science and Technology. 2022, 35(2):  7-13.  doi:10.16180/j.cnki.issn1007-7820.2022.02.002

Abstract ( 159 )   HTML ( 5 )   PDF (1125KB) ( 33 )  

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The efficiency and reliability of heterogeneous multi-processors can meet the requirements of increasingly complex signal processing tasks. Therefore, layered heterogeneous system has become the new research trend of signal processing platform. In order to improve the real-time performance of the platform and solve the problem of high throughput, the software and hardware modules and architecture of the hierarchical heterogeneous signal processing platform are studied, and a directed acyclic graph is used to model component tasks and hardware resources. The proposed scheduling algorithms are classified according to task types, scheduling objectives, scheduling processes and research methods, and the concept of combinatorial optimization algorithms is proposed according to the latest research progress of task scheduling. By comparing and analyzing the performance of classical heuristic algorithm, intelligent search algorithm, machine learning algorithm and combinatorial optimization algorithm, it is found that the combined optimization algorithm can meet the requirements of task scheduling of the platform.

Underwater Laser Communication Coding Technology Based on QC-LDPC

GUAN Jingkai,CAI Wenyu,ZHU Zhangfeng,TANG Guodong

Electronic Science and Technology. 2022, 35(2):  14-19.  doi:10.16180/j.cnki.issn1007-7820.2022.02.003

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In response to the need for long-distance and reliable communication with underwater blue-green lasers, a modulation and coding communication method combining PPM and QC-LDPC is proposed in this study. By simulating combinations of different code lengths, code rates, and PPM modulation orders, the optimal combination of QC-LDPC coding and PPM modulation under different SNR conditions is found. The principle of the classic PPM signal and the basic structure of the QC-LDPC code are introduced, and the generation of the PPM signal is realized through MATLAB software simulation.The check matrix of QC-LDPC is constructed, and the BER performance curves of different combinations schemes obtained are by simulating different code words. The simulation results show that the performance of QC-LDPC encoding is improved, and QC-LDPC codes with different code lengths and code rates have large differences in BER performance under different SNR conditions. For the same codes under the same SNR conditions, the higher the PPM modulation order, the lower the bit error rate. The BER performance curves of codes with the same code length and different code rates are quite different under the same modulation order.

Design of FPGA-Based SqueezeNet Inference Accelerator

CHU Ping,NI Wei

Electronic Science and Technology. 2022, 35(2):  20-26.  doi:10.16180/j.cnki.issn1007-7820.2022.02.004

Abstract ( 120 )   HTML ( 7 )   PDF (3949KB) ( 42 )  

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In view of the problems of the lightweight deep neural network SqueezeNet, such as large amount of intermediate data and long consumption calculation cycle,this study proposes to divide the entire network with a process block structure to speed up the calculation. Each process block is composed of Expand layer and Squeeze layer. The processing block structure ending with the Squeeze layer reduces the amount of intermediate data flowing between the computing module and the memory, and reduces the read and write consumption. The core calculation module introduces the early termination of the convolution calculation technology using the characteristics of the activation function. The effective index survival unit, the effective index control value unit and the convolution judgment unit are designed to skip the calculation amount and calculation cycle occupied by invalid values in the convolution calculation. Experimental results show that the data flow of the accelerator is reduced by 55.38%, and the calculation amount and calculation period occupied by invalid values are reduced by 14.68%.

Research on Track Structure Damage Identification Based on Support Vector Machine

WU Weijia,YANG Jian,YUAN Tianchen,SHAO Zhihui

Electronic Science and Technology. 2022, 35(2):  27-33.  doi:10.16180/j.cnki.issn1007-7820.2022.02.005

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The track structure is a key component that carries the load of the train. Once a disease occurs, it will directly affect the safety of the train. To solve this problem, a method for identifying the track structure disease based on a support vector machine is proposed. This method uses time-domain statistics and discrete wavelet transform to perform joint feature extraction on the vibration acceleration data of the sleeper under different working conditions of the track structure, such as normal state, unsupported sleeper and cement hardening, which reduces the dimensionality of data and provides the possibility for disease identification. The method also uses the support vector machine algorithm to identify the feature vector, and uses the grid search method to select the parameters of the support vector machine, so that the recognition accuracy rate reaches about 85%. The experimental results show that the proposed method can better identify different degrees of unsupported sleeper and cement hardening, and provide a technical basis for online early warning of track structure failure.

Fingerprint Location Method of Metro Station Based on GAWK-means

JIN Xiao,WU Fei,YAN Song,LU Wenxia,ZHANG Zhongyi

Electronic Science and Technology. 2022, 35(2):  34-39.  doi:10.16180/j.cnki.issn1007-7820.2022.02.006

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In order to solve the problem of low matching efficiency and poor positioning accuracy when using iBeacon technology for fingerprint location in urban rail transit stations, a metro station fingerprint location method based on GAWK-means is proposed in this study. In the offline stage, the K-means Euclidean distance weight is optimized according to the discreteness of the fingerprint data to better reflect the intra-class similarity. Then, the improved K-means is combined with the genetic algorithm to optimize the clustering results to reduce the clustering results from falling into the local optimum. In the online stage, the K-nearest neighbor method is used to match the signal vector with the nearest sub-fingerprint database to get the location result, and the overall performance of the method is evaluated by the average positioning error. The experimental results show that the average positioning error of the GAWK-means algorithm is 1.52 m in the offline phase of the subway station. Compared with the un-clustered and traditional K-means clustering, the positioning error of the proposed method is reduced by more than 0.41 m.

Optimization of Firefly Algorithm Based on Continuous Space

LIU Chenmin,WANG Yagang

Electronic Science and Technology. 2022, 35(2):  40-45.  doi:10.16180/j.cnki.issn1007-7820.2022.02.007

Abstract ( 129 )   HTML ( 4 )   PDF (1676KB) ( 29 )  

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In view of the problem that the firefly algorithm has poor accuracy in global optimization and is easy to fall into local optimum, an optimized firefly algorithm is proposed in this study. The discrete continuous method is used to make the traditional firefly algorithm space continuous. On the basis of the traditional firefly algorithm, a new attractiveness calculation formula and corresponding update strategy are defined to realize the spatial continuity of the discrete problems to be sought and improve the corresponding movement mode of the firefly monomer. Experimental simulation results prove the effectiveness of the improved algorithm. Besides, the improved firefly algorithm and its application scope are summarized, and the future research direction is pointed out in the proposed study.

CNNCIFG-Attention Model for Text Sentiment Classifcation

LI Hui,WANG Yicheng

Electronic Science and Technology. 2022, 35(2):  46-51.  doi:10.16180/j.cnki.issn1007-7820.2022.02.008

Abstract ( 94 )   HTML ( 3 )   PDF (996KB) ( 37 )  

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Neural networks are weak in text salient feature extraction and have relatively slow learning rate in processing Chinese text sentiment classification tasks. To solve this problem, this study proposes a hybrid network model based on attention mechanism. This study preprocesses the text corpus, uses the traditional convolutional neural network to extract the feature of the local information of the sample vector. Then, extracted features are input into the coupled input and forget gate network model to learn the connection between the preceding and following words and sentences. Subsequently, the attention mechanism layer is added to assign weights to deep-level text information to improve the intensity of the influence of important information on text sentiment classification. Finally, the proposed hybrid network model is tested on the crawled JD product review collection. The test results show that the accuracy of the proposed method reaches 92.13%, and the F-Score value is 92.06%, which proves the feasibility of the CNNCIFG-Attention model.

Sleeper Diseases Diagnosis Based on Permutation Entropy and Support Vector Machine

SHAO Zhihui,YANG Jian,YUAN Tianchen,WU Weijia

Electronic Science and Technology. 2022, 35(2):  52-58.  doi:10.16180/j.cnki.issn1007-7820.2022.02.009

Abstract ( 84 )   HTML ( 4 )   PDF (975KB) ( 27 )  

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This study proposes a sleeper diseases diagnosis method based on permutation entropy and support vector machine. This method obtains the vibration acceleration of the sleeper by establishing a vehicle-track coupled vibration model, uses the permutation entropy algorithm to extract the vibration response characteristic indexes under different diseases of the sleeper, and takes the normalized permutation entropy characteristic index set as input. Based on the support vector machine optimized by genetic algorithm, this method diagnoses and classifies the service status of sleepers, and realizes the diagnosis of different diseases of sleepers. The simulation results show that the accuracy of the method for the identification of sleeper diseases can reach more than 90%, and the recognition accuracy can reach 97.5% for the part of track irregularity spectrum excitation and the service state of the train speed. Therefore, the proposed method can effectively diagnose sleeper diseases and provide a certain method basis for online monitoring and intelligent early warning of track structure service status.

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

Abstract ( 184 )   HTML ( 4 )   PDF (2259KB) ( 37 )  

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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.

Comparative Study of Wind Power System Simulation Based on Back-to-Back Converters

LIU Jianlong,HAO Zhenghang

Electronic Science and Technology. 2022, 35(2):  67-73.  doi:10.16180/j.cnki.issn1007-7820.2022.02.011

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The detailed model of the back-to-back converter of the permanent magnet direct drive wind power system contains multiple power electronic devices, and the use of pulse width modulation technology will inevitably increase the mathematical calculation of the simulation system. In view of the problem, a mean value model is proposed to simplify the system so that the wind power system can operate quickly and stably. For the converter mean value model, the controlled power supply is used to replace the converter in structure, and its higher harmonics are ignored in principle, and only the fundamental wave component is retained. In addition, from the perspective of system control, the control strategy of the system is improved by removing the PWM modulation process. Finally, a permanent magnet direct drive wind power system simulation model is built in MATLAB/Simulink, and the system response of the mean model and the detailed model are compared. The simulation results show that the mean value model can not only meet the system accuracy requirements, but also speed up the system simulation speed, greatly improve the system stability, which verifies the rationality of the control strategy.

Research on EV Modeling in V2G Mode

JIANG Chengcheng,ZHU Jian'an,ZHU Chengming,WANG Ke,ZHANG Wenjun

Electronic Science and Technology. 2022, 35(2):  74-78.  doi:10.16180/j.cnki.issn1007-7820.2022.02.012

Abstract ( 130 )   HTML ( 4 )   PDF (680KB) ( 36 )  

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The continuous expansion of grid-connected wind power has brought considerable challenges to power system's stable operation. In response to the increasing demand for SR, a method using EV as SR scheduling is proposed. This study analyzes and simulates the charging and discharging characteristics of EV, and uses cost-benefit analysis to establish an objective function that minimizes the sum of system power generation costs, backup costs, and EV dispatch costs. The EV in V2G mode is stimulated through Monte Carlo simulation. With consideration of the time-of-use price strategy, the optimal charging and discharging period is determined, and the EV charging and discharging curve in V2G way is drawn. Based on the SR provided by thermal power units, different scenarios are divided based on whether the EV participates in scheduling as an SR. Finally, through the comparative analysis of the total operating cost and the probability of load loss of the units in different scenarios in the calculation example, the scientificity and feasibility of EV as SR scheduling are verified.