Table of Content

15 December 2023 Volume 36 Issue 12
    Cooperative Localization of IMMKF and Chan-Taylor Algorithm
    WANG Xinyue,YU Huimin,HU Luning
    Electronic Science and Technology. 2023, 36(12):  1-8.  doi:10.16180/j.cnki.issn1007-7820.2023.12.001
    Abstract ( 111 )   HTML ( 15 )   PDF (1037KB) ( 54 )  
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    TDOA(Time Difference of Arrival) rangmg method is a typical opproach for UWB(Ultra Wide Band) indoor location.A Chan-Taylor-IMMKF(Interacting Multiple Model Kalman Filter) localization technique is suggested in this study to address the unavoidable random error and inaccurate location of targets with changing motion states. With the addition of the adaptive algorithm IMM, the algorithm is made up of the Chan-Taylor weighting algorithm and the Kalman filter algorithm. The Chan-Taylor weighting procedure is used to acquire the target estimated coordinates for the first time. The coordinate value is then used as the measurement value for the Kalman filter of the adaptive algorithm IMM, and the target coordinates are filtered many times. The target's final estimated coordinates are provided by the final weighting. The experimental results reveal that the filtered Chan-Taylor weighting algorithm outperforms both conventional Kalman filtering and the unfiltered Chan-Taylor weighting algorithm. The algorithm successfully lowers the system's random error and fixes the issue that the conventional Kalman filter cannot track the significant error when the target abruptly changes its motion state,and the mean error standard deviation is controlled within 15 cm.

    Workspace Analysis of A Novel 5-DOF Engraving Machine Tool
    LIU Jiaqi,SAN Hongjun,CHEN Jiupeng,DU Mengyan,XIAO Le
    Electronic Science and Technology. 2023, 36(12):  9-15.  doi:10.16180/j.cnki.issn1007-7820.2023.12.002
    Abstract ( 68 )   HTML ( 5 )   PDF (1595KB) ( 35 )  
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    In view of the problem that the traditional three-degree-of-freedom series-parallel engraving machine tool is difficult to process the complex surface in space, this study presents a novel type of five-degree-of-freedom series-parallel engraving machine tool in space. The three-dimensional model of the engraving machine is established in SolidWorks, the mathematical model of position inverse solution and parameter decoupling is established, and the workspace is generated by Monte Carlo method in MATLAB. The analysis of working space shows that the working space of the engraving machine tool is large, which can meet the processing requirements of complex surfaces in the part space to a certain extent. On this basis, through the analysis of the working space of the engraving machine tool, it is found that the suitable method to increase the working space of the new engraving machine tool is to increase the length of the series slide rail.

    Review of the Research on the Core Algorithm of Table Tennis Robot
    SUO Fangfei,JI Yunfeng
    Electronic Science and Technology. 2023, 36(12):  16-24.  doi:10.16180/j.cnki.issn1007-7820.2023.12.003
    Abstract ( 74 )   HTML ( 9 )   PDF (1038KB) ( 33 )  
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    As a comprehensive research platform integrating machine vision and motion control, the research of table tennis robot has always attracted much attention. The complexity and high real-time of its mechanical mechanism, visual algorithm and motion control are important factors hindering the development of table tennis robot. In order to improve the competitive ability of table tennis robot, it is necessary to solve the problems of table tennis rotation measurement, accurate prediction of movement trajectory and selection of hitting strategy, which is also the focus of the research of table tennis robot in recent years. Based on the research progress in this field in recent years, this study focuses on the research results of table tennis robot vision core algorithm and ball return core algorithm, analyzes the advantages and disadvantages of different algorithms and prospects the future research trend, so as to provide a reference for the research and design of table tennis robot.

    An Arithmetic Optimization Algorithm Based Fault Section Location Method for Low Voltage Distribution Networks
    WANG Xinyang,WANG Ruiyang,WEI Yunbing
    Electronic Science and Technology. 2023, 36(12):  25-31.  doi:10.16180/j.cnki.issn1007-7820.2023.12.004
    Abstract ( 77 )   HTML ( 4 )   PDF (1215KB) ( 29 )  
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    In order to improve the accuracy and speed of fault location in low-voltage distribution networks and to ensure the safety of residents' electricity consumption, a method based on arithmetic optimization algorithm is proposed to realise fault section location. The arithmetic optimization algorithm has the advantages of simple structure, fast convergence speed and high accuracy. The IEEE 33-node distribution network model is selected and MATLAB is used to program and simulate the node branches, node switching states and adaptation functions of this model. Simulations are carried out for single-point and multi-point faults, as well as single-multi-point faults with signal distortion, and the simulation results are analysed. The results show that the arithmetic optimization algorithm's feature of separate local and global search is used to perform a local adequate search for the fault section location problem, resulting in an accurate location that can achieve an accuracy of 97%, outperforming the binary particle swarm algorithm, genetic algorithm and improved whale optimisation algorithm.

    A Miniaturized Broadband Tri-Polarized Antenna for 5G Mobile Communication
    HAN Guodong,FU Ming,CHEN Xi
    Electronic Science and Technology. 2023, 36(12):  32-38.  doi:10.16180/j.cnki.issn1007-7820.2023.12.005
    Abstract ( 64 )   HTML ( 4 )   PDF (5250KB) ( 29 )  
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    A miniaturized broadband tri-polarized antenna for 5G mobile communication is proposed in this study. A pair of hollow annular orthogonal dipoles is adopted as the horizontal dual polarization of the antenna, meanwhile a planar rectangular frame equivalent to a monopole is constructed as the vertical polarization of the antenna, which is fed by a microstrip integrated balun and the four-equivalent power dividing microstrip lines, respectively. The proper choice of the feeding method and the ingenious design of the pointed cone planar rectangular frame achieve the broadband characteristics of the antenna, and the integrated structure layout realizes the miniaturization of the antenna. The antenna structure is compact with a volume of 0.27 λmin×0.27 λmin×0.17 λmin, where λmin is the free space wavelength of the lowest frequency. To verify the design, the prototype antenna is manufactured and measured,and the measured results agree well with the simulation. The measurement results show that the antenna impedance bandwidth is 3.00~4.48 GHz(voltage standing wave ratio is less than 2), the relative bandwidth is 39.6%, and the isolation degree between the three ports is greater than 27 dB. In addition, in the entire frequency band, the horizontal polarization gain of the antenna is greater than 6 dB, the vertical polarization gain is greater than 3 dB, and the antenna has stable pattern characteristics and good orientation, indicating that the antenna has greater practical value in 5G small-pitch arrays and narrow spaces.

    Research on Image Segmentation Algorithm Based on Channel Feature Pyramid
    SUN Hong,YANG Chen,MO Guangping
    Electronic Science and Technology. 2023, 36(12):  39-45.  doi:10.16180/j.cnki.issn1007-7820.2023.12.006
    Abstract ( 73 )   HTML ( 7 )   PDF (1926KB) ( 38 )  
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    In view of the problems of huge parameter calculation cost and redundant parameters in semantic segmentation tasks, this study proposes a channel feature pyramid module to solve this problem. Based on the channel feature pyramid module and a lightweight attention mechanism, a real-time semantic segmentation network is constructed. The channel feature pyramid module creates sufficient receptive field and densely utilizes context information, and gradually combines feature maps with summation operations starting from the second channel, and concatenates them to build the final hierarchical feature map, which is used in regular convolutional layers. The attention mechanism of the convolution module is added later to improve the segmentation accuracy. Without any pre-training and post-processing, the algorithm achieves a segmentation accuracy of 68.1% on the CamVid data set using only 0.75 MB parameters and 5.3 MB memory on a single GTX2080Ti, and 56 frames on the Cityscapes data set. The inference speed achieved an average interaction ratio of 75.7%.

    PID Parameter Tuning Based on Improved Honey Badger Optimization Algorithm
    HU Tao,JIANG Quan
    Electronic Science and Technology. 2023, 36(12):  46-54.  doi:10.16180/j.cnki.issn1007-7820.2023.12.007
    Abstract ( 75 )   HTML ( 9 )   PDF (1487KB) ( 36 )  
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    As a swarm intelligence algorithm simulating the predator-prey behavior of honey badger, honey badger algorithm has many problems, such as easy to fall into local optimal solutions, and the number of iterations required. In view of the shortcomings of honey badger algorithm, a cloud honey badger algorithm (CHBA) combining gravity search algorithm and normal cloud technology is proposed. The density factor of the original honey badger algorithm that controls the individual search range of the honey badger is replaced by the acceleration in the gravitational search algorithm to improve the rationality of the individual search range of the honey badger and accelerate the search iteration speed. The normal cloud algorithm is used to generate a new batch of honey badgers with the expectation of the best position of the honey badger between generations, so as to improve the population diversity and avoid falling into local optimization. At the same time, the generation range of the new honey badger is adaptively adjusted to avoid local optimization. Twenty three benchmark functions are selected to test the proposed algorithm. From the optimization results of single peak, multi peak and fixed dimension multi peak functions, the step response PID(Proportion Integration Differentiation) parameters of first-order delay system, non minimum phase system and first-order minimum delay system are optimized and compared, and the results show that CHBA algorithm has better performance in search efficiency and iteration accuracy.

    Adaptive Multi-Objective Genetic Algorithm with Ensemble Pruning for Facial Expression Recognition
    CHEN Xing,LI Danyang,HE Qing
    Electronic Science and Technology. 2023, 36(12):  55-63.  doi:10.16180/j.cnki.issn1007-7820.2023.12.008
    Abstract ( 41 )   HTML ( 3 )   PDF (2389KB) ( 25 )  
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    In ensemble pruning,a new genetic algorithm with dynamically adaptive crossover strategies is proposed for the ensemble pruning of classifiers to simultaneously and efficiently select high-quality,independent classifiers.The method dynamically updates the priority of each crossover strategy using roulette wheel blocking and greedy strategies,calculates the probability of each strategy being selected based on the priority, and thus adaptively selecting different crossover strategies during the iteration of the algorithm.The method also considers the dynamic adaptive change of crossover probability and mutation probability and integrates the selected classifiers using majority voting to obtain the final result.The proposed method in the study is compared with some ensemble pruning methods on five real face expression data sets.The experimental results show that the proposed method can select classifiers with better results and lower redundancy,and it has the lowest error of 22.50% on the CK+ data set.

    Short-Term Load Forecasting Based on EMD-Bayes-SVR Combined Model
    WANG Yuqian,WANG Wanxiong
    Electronic Science and Technology. 2023, 36(12):  64-71.  doi:10.16180/j.cnki.issn1007-7820.2023.12.009
    Abstract ( 56 )   HTML ( 9 )   PDF (1760KB) ( 30 )  
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    Short-term power load is the key to the balance of power supply and demand, in view of the short-term power load prediction accuracy problem, the EMD(Empirical Mode Decomposition)-Bayes-SVR(Support Vector Regression) combination prediction model is proposed, that is the original power load sequence is decomposed into several IMF(Intrinsic Mode Function) and a Res(Residual) by EMD method, and each IMF is reconstructed into high frequency components, low frequency components and residual components according to the Hurst index, and the parameters optimization of SVR are optimized by Bayesian optimization algorithm. The optimal parameters obtained by the optimization are brought into the SVR and the reconstructed three components are predicted separately, and the predicted values of the three components are added together to obtain the final prediction result. Taking the historical power load data of Nebraska in the United States as an example, eight single prediction models and seven combined prediction models are established as reference models to predict the power load series in this area. Experimental results show that the combined EMD-Bayes-SVR prediction model can effectively predict the change trend of short-term power load, and the error evaluation indexes of MAE(Mean Absolute Error), RMSE(Root Mean Square Error) and MAPE(Mean Absolute Percentage Error) are decreased by 29.84%, 32.05% and 22%, respectively when compared with the SVR model, which are significantly lower than other reference models.

    Research on Generating News Text Summarization Based on Improved T5 PEGASUS Model
    ZHANG Qi,FAN Yongsheng
    Electronic Science and Technology. 2023, 36(12):  72-78.  doi:10.16180/j.cnki.issn1007-7820.2023.12.010
    Abstract ( 70 )   HTML ( 3 )   PDF (906KB) ( 30 )  
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    The task of generating news text summarizations aims to solve the problems of wasting time and reading fatigue caused by users' inability to quickly grasp the key points of the content when reading news. At present, the best text summarization model for Chinese is the T5 PEGASUS model, but there are few researches on this model. In this study, the Chinese word segmentation of the T5 PEGASUS model is improved, and the Pkuseg word segmentation method, which is more suitable for news field, is used for processing, and its effectiveness is verified on three public datasets with different news lengths: NLPCC2017, LCSTS and SogouCS. It is found that the Pkuseg method is more suitable for the T5 PEGASUS model. The ROUGE value of T5 Pegasus model generated summaries is positively correlated with the length of news text, and the loss value of training set and the decline speed of loss value are negatively correlated with the length of news text. In the face of a small number of training sets, the model can get a high ROUGE score, so the model has a strong few-shot learning ability.

    A Convolutional Neural Network Optimization Method for Fault Diagnosis of Power Transformer
    WANG Xuyang,YI Yingping,LI Tianfeng
    Electronic Science and Technology. 2023, 36(12):  79-86.  doi:10.16180/j.cnki.issn1007-7820.2023.12.011
    Abstract ( 75 )   HTML ( 6 )   PDF (1971KB) ( 30 )  
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    Traditional fault diagnosis methods have disadvantages such as incomplete coding and overly absolute coding boundaries, which are difficult to meet the actual needs of power grid operation and maintenance. Using the gas generated when a power transformer fails to diagnose the transformer fault is currently a popular research area for smart grid condition detection. However, the frequency of various types of faults in transformers varies greatly, which may result in incomplete fault sample information and insufficient data, and the traditional convolutional neural network models have problems such as unstable training process, low training accuracy and long time. Based on the transformer fault diagnosis technology of one dimensional convolutional neural network, this study proposes a new method of data enhancement while keeping the original data features unchanged, transforming expanded one dimensional data into two dimensional pictures to input into the two dimensional convolutional neural network diagnosis model, and improve the Adam optimization algorithm in the convolutional neural network model architecture. Diagnostic results indicate that the accuracy of network training reaches 96.20%. At the same time, it has higher convergence speed and generalization ability than the traditional one dimensional convolutional neural network fault diagnosis method(92.12%).

    Optimal Control Strategy for Bi-Directional Active Full Bridge DC-DC Converter Based on Dual Phase Shift
    LI Yudong,LUO Xinquan,LI Peifeng,HUANG Xin
    Electronic Science and Technology. 2023, 36(12):  87-94.  doi:10.16180/j.cnki.issn1007-7820.2023.12.012
    Abstract ( 88 )   HTML ( 5 )   PDF (3967KB) ( 44 )  
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    In view of the problem of high backflow power, the hard opening loss of the switching device and poor dynamic performance of bidirectional active full-bridge converter,a control scheme based on dual phase shifting combining backflow power optimization and virtual direct power control is proposed in this study. By analyzing the working principle and working characteristics of the dual phase shift control, the soft switching boundary of zero voltage switching is derived as the constraint condition,with the soft switching as the constraint condition, The blackflow power optimization scheme in the whole power range is obtained by KKT(Karush-Kuhn-Tucker) conditional optimization algorithm.This scheme is combined with virtual direct power method and works simultaneously.When the operation condition changes,it could not only reduced effectively backflow power,but also enhance fast dynamic response.Finally,the MATLAB/Simulink simulation model and a small power converter prototype is built for the comparison experiment to verify the correctness and superiority of the proposed control strategy.

    A Novel Dual-band Conformal Antenna On Unmanned Aerial Vehicle
    XU Ping,MA Tianyi,LIU Fei
    Electronic Science and Technology. 2023, 36(12):  95-98.  doi:10.16180/j.cnki.issn1007-7820.2023.12.013
    Abstract ( 61 )   HTML ( 2 )   PDF (2971KB) ( 29 )  
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    In order to meet the demands of dual-band and integrated conformal antenna for UAV (Unmanned Aerial Vehicle) swarm communication systems, an integrated wing conformal antenna is designed and fabricated. The antenna is realized by combining a broadband dipole antenna and a low-pass and high-pass LC combiner. The antenna element is realized by three pairs of open branch lines to achieve effective choking characteristics and broadband omnidirectional radiation. The combiner consists of K-m LC filters, which are used to realize dual-band operation with low insertion loss and high rejection. The measured Voltage Standing Wave Ratio(VSWR) of antenna is less than 2, the measured gain is more than 0 dBi, and the isolation is better than 30 dBc between L and S frequency band. By mounting only one wing conformal antenna, a dual-band simultaneous operation can be realized which meets the need of UAV swarm data link system for high communication rate. The proposed antenna is whole arranged in the tail wing of UAV to enhance the integration capability of the UAV swarm, and can be widely used in UAV field.

    Application of Intelligent Inspection Robot Technology for Hydropower Station
    SHEN Hao,ZHAO Yifeng,LI Xiao
    Electronic Science and Technology. 2023, 36(12):  99-102.  doi:10.16180/j.cnki.issn1007-7820.2023.12.014
    Abstract ( 78 )   HTML ( 6 )   PDF (1052KB) ( 53 )  
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    In view of the high cost of crack and seepage detection in the traditional artificial pumped storage station and the difficulty of ensuring the detection accuracy, a set of inspection robot system based on machine vision is designed in this study. A convolutional neural network which combines cross entropy and dice cost function is constructed, and an evaluation function based on total pixel accuracy, cross parallel ratio and F1-score is established to ensure the accurate detection of common cracks. In order to verify the effectiveness of the designed robot inspection system, convolutional neural network is tested in this study, and its performance is compared with common computer vision methods and manual detection methods. The comparison results show that the neural network constructed in this study has obvious progress in detection accuracy and detection efficiency.


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