Current Issue
15 June 2022, Volume 35 Issue 6
  • A Fast Time Series Anomaly Detection Method Based on Template Matching and Membership Analysis
    GONG Hanxin,QI Jinpeng,KONG Fanshu,ZHU Junjun,CAO ...
    2022, 35(6):  1-5.  doi:10.16180/j.cnki.issn1007-7820.2022.06.001
    Abstract ( 18 )   HTML( 47 )   PDF (1407KB) ( 47 )  

    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.

    Crowd Counting Algorithm Based on Residual Dense Connection and Attention Fusion
    SHEN Ningjing,YUAN Jian
    2022, 35(6):  6-12.  doi:10.16180/j.cnki.issn1007-7820.2022.06.002
    Abstract ( 19 )   HTML( 9 )   PDF (1722KB) ( 9 )  

    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.

    Maximizing the Rest Time of Mobile Charger in Rechargeable Probabilistic Sensor Networks
    RAN Xianyuan,WANG Ran
    2022, 35(6):  13-20.  doi:10.16180/j.cnki.issn1007-7820.2022.06.003
    Abstract ( 14 )   HTML( 3 )   PDF (2685KB) ( 3 )  

    Under the condition of ensuring the permanent coverage of the target point in the wireless rechargeable sensor network, based on the probabilistic monitoring model and the multi-node charging model, the problem of maximizing the rest time ratio of the mobile charger in the rechargeable sensor network is studied. By relaxing the restriction on the immortality of sensors, the charging selection algorithm based on unit sub-cluster and entire-based re-clustering heuristic algorithm are proposed. The redundant sensors around the target point are constructed into target clusters and sub-clusters are divided according to the greedy idea. By adjusting the distance requirements within the sub-clusters and re-clustering the global request nodes, the anchor points are selected for charging, thereby reducing the hybrid gain of the sub-clusters and reducing the number of anchor points. Simulation experiments show that compared with the single-node charging model, the new algorithm improves the rest time of the mobile charger by 10%~15%.

    Research on Spatio-Temporal Data Fusion Algorithm of Wireless Sensor Network Based on Kalman Filter
    DU Peng,BAO Xiaoan,HU Yifei,CHEN Dirong
    2022, 35(6):  21-27.  doi:10.16180/j.cnki.issn1007-7820.2022.06.004
    Abstract ( 11 )   HTML( 2 )   PDF (981KB) ( 2 )  

    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.

    Semantic Segmentation of Streetscape Based on Improved ExfuseNet
    CHEN Jinhong,CHEN Wei,YIN Zhong
    2022, 35(6):  28-34.  doi:10.16180/j.cnki.issn1007-7820.2022.06.005
    Abstract ( 9 )   HTML( 4 )   PDF (2432KB) ( 4 )  

    When using the ExfuseNet model for streetscape semantic segmentation, due to the high background complexity of the street view image, the area ratio and distribution between the classes of interest are unbalanced. Interesting targets with low area and low density in the image are more likely to be misclassified as they go deeper into the network, which ultimately leads to the degradation of model segmentation performance. To solve this problem, an improved Exfusenet model is proposed. In order to obtain the semantic information of different scales without increasing the amount of model parameters, the multi-monitor module adopts atrous convolution with different rates. After the down-sampling features are fused, the random discarding layer is used immediately to reduce the amount of model parameters and improve the generalization ability. Before the main output, the CBAM attention mechanism module is used to sample the depth semantic information of the target class of interest more efficiently, and the class balance function is used after the multi-supervision module to improve the class imbalance problem of the data set Camvid. The experimental results show that the semantic segmentation effect of the improved ExfuseNet model has been significantly improved, MIOU has increased to 68.32%, and the classification accuracy rate of the Pole class has increased to 38.14%.

    A GA-BP Neural Network for Predicting the Structure of Leaf Tobacco
    ZHANG Chongchong,HUANG Yayu
    2022, 35(6):  35-42.  doi:10.16180/j.cnki.issn1007-7820.2022.06.006
    Abstract ( 8 )   HTML( 3 )   PDF (4982KB) ( 3 )  

    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.

    Electrically Tunable Terahertz Metamaterial Absorber Based on Liquid Crystal
    SHI Tian,XU Junjie,SUN Shuangyuan,YIN Mingjun,YANG...
    2022, 35(6):  43-47.  doi:10.16180/j.cnki.issn1007-7820.2022.06.007
    Abstract ( 8 )   HTML( 1 )   PDF (2619KB) ( 1 )  

    A liquid crystal terahertz electronically controlled metamaterial absorber is designed to change the equivalent dielectric constant of the liquid crystal layer to realize the regulation of the terahertz reflected wave through an external electric field. The transmission performance of the device is calculated, and the surface current distribution and energy loss density at the resonance frequency are analyzed. By using ultraviolet lithography and wet etching technology, a 30×30 unit metamaterial absorber array is fabricated and verified. The results show that when the bias voltage is changed between 0 and 30 V, the resonance frequency of the absorber can be dynamically adjusted in the range of 101.5 to 117.95 GHz, and the comprehensive adjustable rate can reach 13.9%. In the working frequency band, the absorptivity of the proposed absorber is higher than 90%.

    Frequency Splitting Characteristics Analysis of Electrical-Field Coupled Wireless Power Transfer
    YANG Rui,SUN Yanzhou,YU Jianghua,ZHANG Mengfei,WEI...
    2022, 35(6):  48-53.  doi:10.16180/j.cnki.issn1007-7820.2022.06.008
    Abstract ( 9 )   HTML( 2 )   PDF (1210KB) ( 2 )  

    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%.

    Research on Adaptability Evaluation of Distribution Network Based on Improved TOPSIS-PSO-SVM
    HUANG Yuansheng,JIANG Yuqing,WANG Jing
    2022, 35(6):  54-63.  doi:10.16180/j.cnki.issn1007-7820.2022.06.009
    Abstract ( 9 )   HTML( 2 )   PDF (1711KB) ( 2 )  

    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.

    Research on Robust Feedforward Control Strategy of Bidirectional AC/DC Converter for DC Microgrid
    WANG Xin,XU Xiang,WU Boning,HUANG Chong
    2022, 35(6):  64-69.  doi:10.16180/j.cnki.issn1007-7820.2022.06.010
    Abstract ( 11 )   HTML( 3 )   PDF (946KB) ( 3 )  

    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.

    Centralized Compensation Self-Optimization Strategy of Bus Voltage Ripple in DC Microgrid
    HUANG Kai,ZHANG Guopeng,LI Zihan
    2022, 35(6):  70-75.  doi:10.16180/j.cnki.issn1007-7820.2022.06.011
    Abstract ( 8 )   HTML( 1 )   PDF (2033KB) ( 1 )  

    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.

    Design of VFTO Measuring System at the Metal Flange Hole
    LIANG Shiqi,ZHANG Wenbin
    2022, 35(6):  76-82.  doi:10.16180/j.cnki.issn1007-7820.2022.06.012
    Abstract ( 9 )   HTML( 1 )   PDF (921KB) ( 1 )  

    The measurement sensor of the built-in sensor for VFTO is large in size and difficult to install, and cannot be applied to GIS in operation. In order to solve these problems, the VFTO measurement system for GIS metal flange holes is designed. The system adopts the scheme of combining FPC capacitor voltage divider and secondary resistance-capacitor voltage divider to realize VFTO measurement with external miniaturization of GIS. According to the size of the metal flange hole and the performance requirements of the measurement system, the structure and size of the FPC are designed, and the capacitance partial pressure probe is developed. In order to improve the performance of the measurement system, a broadband resistance-capacitance voltage divider is designed, and the matching resistance and the parameters of the coaxial cable are optimized through actual measurement. The results show that the bandwidth of VFTO measurement system is 22.45 Hz~111.45 MHz, and voltage division ratio is 112 790, indicating that the proposed system can accurately trace the source waveform and meet the requirements of VFTO measurement.

    Research on Permanent Magnet Synchronous Motor Servo System Based on Fuzzy Neural Network
    WANG Peiyu,MA Lixin
    2022, 35(6):  83-88.  doi:10.16180/j.cnki.issn1007-7820.2022.06.013
    Abstract ( 9 )   HTML( 2 )   PDF (811KB) ( 2 )  

    Permanent magnet synchronous motors have nonlinear and strong coupling characteristics, and it is difficult to accurately control them with conventional vector control methods. Besides, the motor system is susceptible to load disturbances, resulting in speed and electromagnetic torque fluctuations. In view of the problem of slow system response and large overshoot caused by fixed speed loop parameters, this study proposes a fuzzy radial basis function neural network PID control strategy to replace the PID control of speed loop in the vector control system. Based on the incremental PID control method, this strategy combines neural network and fuzzy control, and uses the gradient descent optimization algorithm to dynamically adjust the PID parameters in the speed loop. The simulation results show that the overshoot of the motor system controlled by the fuzzy neural network PID is small. Compared with conventional PID control, the proposed method has reduced the start-up time of low-speed and high-speed operation by 66.7% and 75.9%, respectively, and has faster dynamic response, better robustness and anti-interference ability. The experimental platform is built using DSP, and the experimental results prove the effectiveness of the control method.

    Numerical Analysis of NOx Conversion Efficiency of SCR Catalytic Converter of Diesel Engine
    ZHAO Hao,HE Wei
    2022, 35(6):  89-94.  doi:10.16180/j.cnki.issn1007-7820.2022.06.0014
    Abstract ( 11 )   HTML( 1 )   PDF (3084KB) ( 1 )  

    In order to explore the influence of the temperature of cyclone mixer and urea injection flow rate on NOx conversion efficiency, CFD technology is used to study the SCR catalytic converter for a diesel engine by chemical reaction and steady flow numerical simulation. By changing the temperature of the cyclone mixer and the urea injection flow rate, the concentration and uniformity coefficient of NH3 and the mass fraction of NOx at the outlet are compared under different conditions. The results show that the mass fraction distribution of NOx at the outlet of SCR catalyst presents the law of low center and high edge, and the temperature change of cyclone mixer has little effect on the uniformity coefficient of NH3 velocity. When the temperature is 400 ℃, the NH3 distribution is more uniform, the NH3 generation rate and NOx conversion efficiency are higher. These results indicate that the NOx conversion efficiency and urea utilization rate can be improved by selecting the appropriate urea injection flow rate.


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