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15 November 2020 Volume 33 Issue 11
  
    Design of MCU Innovation Experiment System Based on Internet of Things
    LOU Shuntian,PANG Siqi,LI Mingyu
    Electronic Science and Technology. 2020, 33(11):  1-6.  doi:10.16180/j.cnki.issn1007-7820.2020.11.001
    Abstract ( 340 )   HTML ( 22 )   PDF (5310KB) ( 48 )  
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    In view of the problems that the traditional single-chip microcomputer experiment can only see the experimental results through the hardware circuit board in a fixed place, and the experimental data cannot be reproduced and analyzed, a set of innovative single-chip microcomputer experiment system based on the Internet of Things is designed in the proposed study. The system consists of Internet of Things experimental platform, experimental server and experimental board. The experimental platform uses STM32F205 chip to process and display the data of the experimental board, and uses NB-IoT technology to upload the data. The experimental server is based on B/S architecture and built on Alibaba Cloud to realize the forwarding processing and reproduction of experimental data. The experimental board uses MSP430G2553 chip and sensor chip to realize data collection and communication. The system realizes the remote display of data by designing the function processing, communication protocol and interface of each part, so that students can analyze the historical data at any time, to better find out the problems in the experiment, and teachers can give guidance more conveniently.

    Radar Pulse Compression Method Based on LASSO
    SHAO Yu'e,WANG Jianlai,ZHOU Shenghua,LIU Hongwei,ZHANG Yuehong
    Electronic Science and Technology. 2020, 33(11):  7-10.  doi:10.16180/j.cnki.issn1007-7820.2020.11.002
    Abstract ( 268 )   HTML ( 8 )   PDF (3458KB) ( 23 )  
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    The anti-jamming performance of radar is an important indicator to measure the pros and cons of a radar, which directly determines the performance of radar combat. Common anti-interference measures include sidelobe cancellation, pulse compression, moving target detection, constant false alarm processing. In this paper, a LASSO-based impulse compression anti-jamming measure is proposed. The method use the characteristics of sparsity of LASSO regression, and combines radar echo signals to set matching dictionary. Then, the data set is constructed by using redundant predictors. The LASSO model is constructed by cross validation, and predictors are identified to achieve the target detection. Compared with the pulse compression method, these simulation results show that the LASSO algorithm can obtain better target resolution without considering the influence of side lobes, and the target detection effect is better under the condition of smaller SNR.

    Investigation on the Crystal Defects of CdZnTe by AC Impedance Spectrum
    MU Hao,XIE Jinghui,LIU Yucong,WANG Ding,SHEN Yue,WANG Linjun
    Electronic Science and Technology. 2020, 33(11):  11-15.  doi:10.16180/j.cnki.issn1007-7820.2020.11.003
    Abstract ( 313 )   HTML ( 3 )   PDF (1665KB) ( 41 )  
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    Tellurium cadmium zinc (CdZnTe) is a kind of II - VI wide band-gap semiconductor compound, which is a promising material to fabricate the X- or γ-ray detectors. The crystalline defects of CdZnTe has a significant influence on the detector performance, which has been paid more and more attention. In this work, CdZnTe crystalline wafer is grown by vertical Bridgman method, and XRD measurement shows that the wafer is (111) oriented. The impedance characteristics of crystal defects are studied by measuring the AC impedance spectra of the samples at different temperatures. The results show that the prepared CdZnTe single crystal exhibits a negative temperature coefficient effect, and Ohmic contact has been formed between the Au electrode prepared by chemical method and the wafer. The effect of electrode interface and grain boundary is not observed, and the mechanism of grain conduction is dominant. The fitted activation energy of the defect is about 0.48 eV determined by fitting the Arrhenius equation, indicating that Cd vacancy is the main crystal defect.

    Simulation Study of Photovoltaic MPPT Based on Variable Universe Fuzzy Control
    JIANG Pengcheng,TANG Zhanjun,LIU Pinglan
    Electronic Science and Technology. 2020, 33(11):  16-23.  doi:10.16180/j.cnki.issn1007-7820.2020.11.004
    Abstract ( 307 )   HTML ( 8 )   PDF (1970KB) ( 39 )  
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    Generally, the controller designed by the fuzzy control algorithm is targeted to realize the MPPT control of the photovoltaic system. When the PV system or controller parameters change, there will be problems such as slow tracking speed, poor stability, and even tracking failure. In view of this, an MPPT algorithm based on variable universe fuzzy control is adopted in this paper. The fuzzy rule of controller is designed by using the tracking principle of conductance increment method to improve the universality of the controller. In addition, the algorithm introduces a scaling factor to implement the design of the variable universe to reduce the impact of changes in controller parameters on the tracking effect. The simulation model is built in MATLAB/Simulink, and the feasibility of the algorithm is verified. The simulation results show that the algorithm is nearly doubled in tracking speed compared with the conductance increment method. The simulation results show that the variable-domain fuzzy control method is nearly doubled in tracking speed compared with the conductance increment method. Besides, under the mismatch of the domain mismatch and the PV system model, the new method can better realize the MPPT control of the photovoltaic system compared with the conventional fuzzy control.

    HEs Segmentation of Fundus Images by Multi-algorithm Fusion
    YANG Zhenyu,FU Yinghua,FU Dongxiang,WANG Yajing
    Electronic Science and Technology. 2020, 33(11):  24-30.  doi:10.16180/j.cnki.issn1007-7820.2020.11.005
    Abstract ( 303 )   HTML ( 14 )   PDF (1796KB) ( 42 )  
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    Based on the luminance and edge characteristics of the exudates in fundus images, a multi-algorithm fusion method for automatic detection of the exudate is adopted in this paper to solve the problems of low sensitivity of the traditional algorithm and interference of dark lesions such as optic disc and other microangiomas in the detection results. In order to improve the segmentation efficiency and accuracy, this study uses top-hat and boottom-hat to enhance the image contrast of the original image, and then a dual threshold segmentation method combining genetic algorithm and optimal histogram entropy method is proposed to preliminarily segment the image. The experimental results show that the sensitivity and PPV of the algorithm are 83.6% and 93.2% at the pixel level, and the SE, specificity and accuracy are 95.2%, 86.2% and 90.8% respectively at the image level. The results obtained by testing on another independent DIARETDB1 database are 82.4%, 93.3%, 93.6%, 96.2%, 89.9%. Compared with other algorithms, this method can distinguish the exudates from other dark lesions, and the detection time is short, accurate and efficient.

    Three-phase Pulse Blocking Type AC-AC Frequency Conversion When Input Voltage is Unbalanced
    LI Yudong,HU Xiaodan,LIAN Haishan
    Electronic Science and Technology. 2020, 33(11):  31-35.  doi:10.16180/j.cnki.issn1007-7820.2020.11.006
    Abstract ( 236 )   HTML ( 3 )   PDF (2699KB) ( 21 )  
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    To fully realize the advantages of AC-AC variable frequency speed regulation performance, based on the principle of pulse blocking AC-AC frequency conversion under the traditional voltage balance, a three-phase pulse blocking type AC-AC is proposed when the input voltage is unbalanced. Through the theoretical analysis of the chopping control after blocking, fully-controlled power devices are used, and the duty cycle expression corresponding to each interval is derived according to the area equivalent principle. The frequency conversion amplitude modulation of the output voltage under the condition of unbalanced input voltage is realized, which make the frequency conversion speed control system achieved the running effect when it is in equilibrium. Finally, the mathematical model of three-phase pulse blocking type variable frequency speed control system with unbalanced input voltage is established, and a semi-physical simulation platform is designed. The simulation results are consistent with the results under equilibrium conditions, which proves the feasibility of theoretical analysis.

    A Transformer Fault Diagnosis Method Integrating Artificial Fish Swarm Algorithm with Least Square Support Vector Machine
    YANG Yu,ZENG Guohui,HUANG Bo
    Electronic Science and Technology. 2020, 33(11):  36-40.  doi:10.16180/j.cnki.issn1007-7820.2020.11.007
    Abstract ( 356 )   HTML ( 9 )   PDF (1426KB) ( 45 )  
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    In view of the information uncertainty of transformer fault data and the low accuracy of traditional diagnostic methods, the combination of artificial fish swarm algorithm and least squares support vector machine is used to diagnose transformer fault. The DGA characteristic gas ratios of IECTC10 database is used as the input vectors, and the fault diagnosis model of transformers is designed based on LS_SVM. Meanwhile, the artificial fish swarm algorithm is utilized to optimize the parameters of the least squares support vector machine. Then, based on the diagnosis result, the multi-ratio characteristic parameter combination with the best classification effect is selected. The experimental verification results show that the accuracy of the proposed diagnostic method was up to 96.67%, and it has a higher accuracy rate of fault diagnosis.

    Comparison of Image Reconstruction Algorithms of Electrical Capacitance Tomography
    ZHU Guangquan,YANG Haima,LI Jun,YANG Ping
    Electronic Science and Technology. 2020, 33(11):  41-45.  doi:10.16180/j.cnki.issn1007-7820.2020.11.008
    Abstract ( 390 )   HTML ( 8 )   PDF (1168KB) ( 58 )  
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    Image reconstruction algorithm plays a vital role in the practical application of electrical capacitance tomography. For the research of image reconstruction algorithm, the LBP, SVD, ART, Landweber algorithm and Tikhonov regularization algorithm are compared in this study. Four kinds of multiphase flow patterns are simulated based on MATLAB simulation platform, and the effects of reconstructed images are compared in three aspects: image error, correlation coefficient and calculation time. The analysis results show that the image reconstruction speed of the direct algorithm is better than the iterative algorithm, while the image reconstruction quality is worse than the iterative algorithm.

    Design of Power Amplifier Control System Based on ARM
    WANG Jiwei
    Electronic Science and Technology. 2020, 33(11):  46-48.  doi:10.16180/j.cnki.issn1007-7820.2020.11.009
    Abstract ( 244 )   HTML ( 8 )   PDF (1831KB) ( 37 )  
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    In order to solve the problem of unstable working state of power amplifier in RF link of transmitter, this paper studies the working principle of RF link and designes the control system of power amplifier. This system uses STM32F103ZG chip based on ARM architecture as the main control chip, which can realize the real-time acquisition of the working state of power amplifier through TTL interface. After the working state of the power amplifier is analyzed by the upper computer program, combined with the indicators of the transmitter, the control instructions of the power amplifier are issued, and the working state of the power amplifier is adjusted. The test results show that the system achieves 1 dB compression point-11.8 dBm at the input power of 15.9 dBm. Additionally,the output third-order node is reached at 25.7 dBm, and the input third-order node is -2.7 dBm, which indicates that the design can meet the requirements of the transmitter.

    Tracking Control Algorithm Based on Dynamic Surface for Nonlinear Systems with Dead-zone Input
    LIU Lulu,LIU Ye
    Electronic Science and Technology. 2020, 33(11):  49-54.  doi:10.16180/j.cnki.issn1007-7820.2020.11.010
    Abstract ( 234 )   HTML ( 6 )   PDF (1005KB) ( 38 )  
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    The adaptive dynamic surface control is proposed for a class of uncertain nonlinear strict-feedback systems proceeded by unknown input dead-zone. Unlike the low-order filters used in CSCD, the nonlinear filters with time-varying integral function are utilized to cope with the virtual controls. The proposed control method not only overcomes the “explosion of terms” and reduces the computational complexity, but also simplify the structure of the controller. Furthermore, it compensates the boundary layer error inherent in the dynamic surface procedure. Theoretical analysis proves that the proposed control scheme can eliminate the influence of dead zone nonlinearity and ensure the stability of the closed-loop system. Taking the simulation of a single-arm manipulator as an example, the transient performance can be improved by the tuning of design parameters, and the asymptotic convergence of the tracking error can be achieved, which verifies the effectiveness of the proposed method.

    Research of Fast Determination Method of On-chip Band-gap Voltage Reference
    ZHENG Xuan,LI Chunjiang,ZHANG Shuping,LI Xiao
    Electronic Science and Technology. 2020, 33(11):  55-58.  doi:10.16180/j.cnki.issn1007-7820.2020.11.011
    Abstract ( 224 )   HTML ( 5 )   PDF (1441KB) ( 25 )  
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    The deviation of the output characteristics of the on-chip bandgap reference causes an error of 0~0.3 V on the output of the power module on the integrated circuit. To investigate the causes of the on-chip power module error, an peripheral circuit design scheme with adjustable output bandgap reference voltage is proposed to realize the accurate output of the on-chip power supply module, and the method of determining the circuit parameters is studied. The circuit accurately adjusts the output of the power module by performing parameter correction on the on-chip bandgap reference voltage source. The mathematical model is established according to the principle of equal current, and the output data of two sets of fixed parameter power modules are measured, and the parameters are quickly determined by substituting the model calculation. The actual test data proves that the circuit can achieve accurate output of the on-chip power module with an error of only ±0.02 V, which meets the requirements of practical applications.

    Air Conditioning Load Forecast of University Students' Dormitory Based on SVD-LSTM
    QI Xin,WANG Fuzhong,ZHANG Li,WANG Rui,WANG Xiaohui
    Electronic Science and Technology. 2020, 33(11):  59-66.  doi:10.16180/j.cnki.issn1007-7820.2020.11.012
    Abstract ( 328 )   HTML ( 8 )   PDF (2834KB) ( 28 )  
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    Accurate prediction of air conditioning load in colleges is the premise and basis to ensure the safe electricity consumption and stable operation of regional distribution network during power peak period. In this paper, the student dormitory air conditioning load of college air conditioning is taken as the research object, and an air conditioning load forecasting model based on SVD-LSTM is established. Based on the characteristics of air conditioning load in college dormitory, this model uses SVD to reduce data noise, and predicts the air conditioning load of college students' dormitory through LSTM. The actual data of a university in Wuhan is taken as a sample to analyze and verify the model. It is proves that the prediction result of SVD-LSTM is better by the comparison with traditional prediction model results. The model improves the prediction accuracy. The analysis of a university in Wuhan shows that the prediction effect and accuracy of the proposed prediction model are better than the traditional prediction method.

    Research on Emotion Recognition of EEG Features Based on the Long Short-term Memory Neural Network
    ZHANG Yue,HU Chunyan
    Electronic Science and Technology. 2020, 33(11):  67-72.  doi:10.16180/j.cnki.issn1007-7820.2020.11.013
    Abstract ( 242 )   HTML ( 6 )   PDF (1312KB) ( 41 )  
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    In order to improve the accuracy rate of the emotional recognition of EEG signals in multi-classification, the SEED dataset published by SJTU is selected as the sample of EEG dataset. The original EEG signal is divided into five frequency bands, and their features are extracted. After the features of the differential entropy, the differential asymmetry and the rational asymmetry of EEG datasets are smoothed by linear dynamic system, the classification effect is compared with the feature of power spectral density. Then, the method of the long short-term memory neural network is used to classify emotion. It is concluded that the classification of the differential entropy feature is effective. Finally, compared with other machine learning methods, the recognition rate is improved, and the average accuracy of emotion recognition reaches 95.045 9%.

    Effect of Slot Wedge Material on Electromagnetic Characteristics of PMLSM
    LIU Zhuli,WANG Xudong,XU Xiaozhuo,JI Shengyang,FAN Lele
    Electronic Science and Technology. 2020, 33(11):  73-78.  doi:10.16180/j.cnki.issn1007-7820.2020.11.014
    Abstract ( 285 )   HTML ( 7 )   PDF (3755KB) ( 31 )  
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    In order to improve the electromagnetic performance of permanent magnet linear synchronous motor and reduce the cogging force and thrust ripple, a solution of installing slot wedge at the slot of the motor is proposed in this paper. Different materials of slot wedge have different optimization effective for the motor. To obtain the best electromagnetic performance, fractional slot permanent magnet linear synchronous motor is taken as the research object, the influence of hard magnetic, soft magnetic and non-magnetic slot wedge on the air gap coefficient of the motor is calculated. Meanwhile, the air gap magnetic field, back electromotive force, cogging force and force ripple of three slot wedge materials are compared and analyzed by finite element method, and the degree of reducing thrust fluctuation under different loads is analyzed. The results show that the hard magnetic material can effectively reduce the cogging force and thrust fluctuation. Finally, the influence of hard magnetic materials on motor performance under different relative permeability is investigated, and the relative permeability value at the best performance of the motor is obtained, which provids a theoretical basis for further improvement of the motor performance.

    Human Skeleton-based Action Recognition Based on Deep Learning
    WU Qian,WU Fei,LUO Lizhi
    Electronic Science and Technology. 2020, 33(11):  79-83.  doi:10.16180/j.cnki.issn1007-7820.2020.11.015
    Abstract ( 518 )   HTML ( 17 )   PDF (1529KB) ( 68 )  
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    Based on the advantages of robustness and view-invariant representation, a skeleton-based action recognition algorithm combining geometric features with LSTM network is proposed to further improve the recognition rate and to break the limitation that the inputs of most methods based on deep learning are human joint coordinates. The geometric features based on the distances between joints and selected lines are selected as the input of the network. Then, time-selective LSTM network is introduced to train. Time selection LSTM network has the ability to select the most recognizable time period features. By using this feature, 99.36% and 99.20% recognition rates are achieved on SBU Interaction dataset and UT Kinect dataset, respectively. The experimental results show that the method is effective for human skeleton-based action recognition.

    Research on Intelligent English Translation Method Based on Improved Attention Mechanism Model
    ZHENG Meng
    Electronic Science and Technology. 2020, 33(11):  84-87.  doi:10.16180/j.cnki.issn1007-7820.2020.11.016
    Abstract ( 331 )   HTML ( 8 )   PDF (955KB) ( 31 )  
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    The use of neural machine algorithms to translate English is a hot topic in current research. The ability to capture long-distance information in English translation using traditional sequence neural frameworks is too poor and has its own limitations. The current improved frameworks, such as recurrent neural network translation effect is not ideal. Aiming at the shortcomings of traditional machine translation algorithms, this paper establishes an attention coding and decoding model, combines the attention mechanism with a neural network framework, and uses TensorFlow to implement the entire English translation system. This method can improve the accuracy of translation. Experiments show that the BLUE value of the algorithm model constructed in the article has different degrees of improvement compared with traditional machine learning algorithms, which further proves the effectiveness of the improved attention mechanism model proposed in this article in English translation.

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