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15 July 2025 Volume 38 Issue 7
  
    Calculation Method of TT&C Communication Link Based on Probability Statistics Model
    PANG Yuefeng, MA Zhanshun, SHAN Jing, LI Ke
    Electronic Science and Technology. 2025, 38(7):  1-6.  doi:10.16180/j.cnki.issn1007-7820.2025.07.001
    Abstract ( 206 )   HTML ( 7 )   PDF (941KB) ( 44 )  
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    In view of the low confidence of the current measurement and control communication link calculation method, this study analyzes the traditional analysis method of measurement and control communication link, and puts forward a calculation method of measurement and control communication link based on probability statistical model. According to the characteristic parameters of triangular, uniform and Gaussian distribution and probability density function, the forward and reverse tolerances of measurement and control communication links are calculated, and the calculated values under different confidence degrees are obtained. The calculation process is illustrated by taking the estimation of the operating distance of the measurement and control equipment and the calculation of the EIRP(Effective Isotropic Radiated Power)of the synchronous orbit satellite as examples. The calculation error of EIRP is reduced from 14.8% to 1.47%, and the estimation error of action distance is reduced from 12.35% to 1.84%, which proves that the link calculation method can effectively improve the accuracy of link prediction.

    Fuzzy PID Decoupling Control for Improved Particle Swarm Compression Refrigeration System
    WU Dong, DING Xudong, SUN Hao, MA Haoxiang, YANG Yuanxing
    Electronic Science and Technology. 2025, 38(7):  7-14.  doi:10.16180/j.cnki.issn1007-7820.2025.07.002
    Abstract ( 220 )   HTML ( 6 )   PDF (1232KB) ( 37 )  
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    In view of the complex conditions of high coupling, nonlinearity and external interference in the actual operation of compressive refrigeration system, this study proposes a fuzzy PID(Proportional Integration Differentiation) decoupling control strategy based on particle swarm optimization algorithm. The coupling effect between the evaporation temperature and superheat of the compression refrigeration system is eliminated by the series pre-feedback decoupler, and the dual-input and dual-output system is decoupled into two single-input single-output systems. The inertia weights are dynamically nonlinearly descended, and the control parameters of the fuzzy PID controller are optimized by the improved particle swarm algorithm, and the simulation experiments are carried out by MATLAB. The simulation results show that the overshoot of superheat and evaporation temperature is reduced by 30.6% and 42.7%, respectively, and the adjustment time is shortened by 225 s and 275 s after the fuzzy PID controller is optimized by the series decoupling controller and the improved PSO (Particle Swarm Optimization) algorithm. The above results show that the proposed method effectively suppresses the oscillation of the system, and the dynamic performance of the system is significantly improved.

    A Fuzzy Double C-Means Clustering Method Based on NonlinearCharacterization and Centroid Fusion
    ZHAO Dan, SONG Yan
    Electronic Science and Technology. 2025, 38(7):  15-23.  doi:10.16180/j.cnki.issn1007-7820.2025.07.003
    Abstract ( 146 )   HTML ( 7 )   PDF (3016KB) ( 24 )  
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    In view of the problem of high-precision clustering of non-negative incomplete data, an innovative fuzzy clustering method is proposed in this study. By introducing nonlinear function, case frequency regularization term and knowledge transfer to traditional latent factor model, the model representation ability and data filling accuracy are improved, and a nonlinear latent factor model is formed. Combining sparse self-representation and centroid fusion term, the optimal cluster number is determined automatically while considering the global features, and a fuzzy bicentric clustering model is constructed. The experimental results on real data sets and pictures verify the effectiveness of the fuzzy bicentric clustering method based on nonlinear characterization and centroid fusion in dealing with the clustering problem of non-negative incomplete data.

    Layered Network Architecture Based Information Interaction and Collaborative Networking System in UAV Cluster
    LU Cunbo, CHEN Yuanyuan, ZHANG Di
    Electronic Science and Technology. 2025, 38(7):  24-33.  doi:10.16180/j.cnki.issn1007-7820.2025.07.004
    Abstract ( 517 )   HTML ( 23 )   PDF (1240KB) ( 1471 )  
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    In view of the problems of intelligent networking protocol and efficient communication technology in UAV (Unmanned Aerial Vehicles)cluster network communication, a feasible information interaction and collaborative networking method based on hierarchical network architecture is adopted in this study. The overall design scheme of the system is described in detail from the aspects of network coding layer, network layer, link layer, physical layer and wireless communication hardware design, and a network coding communication method based on hierarchical network architecture is proposed. A cluster network with relatively stable performance in the face of cluster node movement is obtained by means of the ground station participating in the initial clustering and the air node self-organizing maintenance. An enhanced TCP (Transmission Control Protocol) protocol based on network coding is designed, which can realize the high throughput and fast transmission of information in the UAV network data link. The simulation results show that the performance of network coding TCP method is better than that of traditional TCP, and it is suitable for the UAV cluster communication environment with large bit error rate. The advantage of network coding can realize the efficient and fast data transmission between nodes and between nodes and ground stations.

    Dual-Band U-Slot Patch Antenna Optimization Using Neural Network Model
    ZHANG Bin, DING Haibing, WANG Jing, XUE Qianzhong
    Electronic Science and Technology. 2025, 38(7):  34-39.  doi:10.16180/j.cnki.issn1007-7820.2025.07.005
    Abstract ( 294 )   HTML ( 8 )   PDF (1853KB) ( 128 )  
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    In order to improve the efficiency of antenna design, a double-frequency U-slot patch antenna based on PSO-BPNN (Particle Swarm Optimization-Back Propagation Neural Network) model is designed using machine learning to assist antenna optimization design. The operating frequency covers IEEE802.11y (3.65 GHz) and IEEE802.11a (5.20 GHz), and is compared with the antenna designed based on PSO algorithm. According to the simulation model, the antenna is fabricated and tested. The results show that at the resonant frequency of 5.20 GHz, the antenna return loss designed by PSO-BPNN model and PSO model algorithm is close. At the resonant frequency of 3.65 GHz, the return loss of the antenna designed based on the PSO-BPNN model is -22.65 dB and the impedance bandwidth is 0.205 GHz, which is 47.85% and 11.41% higher than that designed by the PSO algorithm, respectively. Test results reveal that the radiation characteristics of the antenna designed based on the PSO-BPNN model algorithm are in good agreement with the measured results.

    Research Progress of Relation Extraction Based on Deep Learning
    SHEN Yining, WANG Yiran, WU Cong
    Electronic Science and Technology. 2025, 38(7):  40-49.  doi:10.16180/j.cnki.issn1007-7820.2025.07.006
    Abstract ( 322 )   HTML ( 15 )   PDF (1284KB) ( 92 )  
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    In natural language processing, as the core task, the research direction of entity relation extraction task has gradually shifted from rule-based learning and traditional machine learning to deep learning. At present, deep learning relationship extraction models widely use convolutional neural networks, recurrent neural networks and graph neural networks. This study summarizes the excellent relationship extraction models in each neural network, shows the evolution direction of each model by tracing the development history and trend of the model, and makes a comparative analysis of each method and model. Due to the continuous improvement of attention mechanism and other methods, the semantic analysis ability of relational extraction model has been significantly enhanced. In this study, the relevant improvement methods are reviewed, and the characteristics and experimental results of each method are described. This study introduces the common data sets in the field of relational extraction, and summarizes and compares the models with the best performance on each data set. The challenges in relation extraction are summarized and the solutions are proposed.

    Design of Switched-Capacitor Circuits Based on on-Board Voice Chip
    YU Xin, ZHANG Xuanxiong, LI Wenhong
    Electronic Science and Technology. 2025, 38(7):  50-57.  doi:10.16180/j.cnki.issn1007-7820.2025.07.007
    Abstract ( 204 )   HTML ( 4 )   PDF (2091KB) ( 71 )  
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    In order to cope with the problem of high power consumption of Sigma-Delta AD(Sigma-Delta Analog-to-Digital Converter), a switched capacitor integrator suitable for low-power and high-precision speech recognition chip is proposed. A new modeling idea is proposed in the MATLAB system modeling, and the non-ideal factor coefficients are redefined according to the parasitic parameters of the MOS(Metal-Oxide-Semiconductor) tube, so that the model is closer to the actual circuit. The Sigma-Delta modulator model uses a 3-order single-bit feedforward modulator with an oversampling rate of 128 times. By simulating the Sigma Delta modulator model, the integrator coefficients of all levels can be obtained, which provides guidance for the design of MOS transistor circuit with Cadence software. In the MATLAB system modeling simulation, the simulation results show that the effective bit number of Sigma-Delta modulator can reach 16.95 bits, and the signal-to-noise ratio can reach 103.8 dB. In the process of 0.18 μm, the first-stage integrator circuit of the Sigma-Delta modulator is designed, and the operational amplifier in the first-stage switched-capacitor integrator is simulated and verified. Simulation results show that the DC gain can reach 104 dB, the gain bandwidth product is 72 MHz, the phase margin is 85°, and the DC quiescent power consumption is 915 μW.

    A Fast Fabric Defect Detection Algorithm Based on Gray Gradient Co-Occurrence Matrix
    YE Ruifan, LIU Yu, SHEN Jie, REN Jia, ZHANG Xiaoxiang
    Electronic Science and Technology. 2025, 38(7):  58-65.  doi:10.16180/j.cnki.issn1007-7820.2025.07.008
    Abstract ( 382 )   HTML ( 10 )   PDF (2087KB) ( 62 )  
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    In view of the problems of complex model and slow detection in fabric quality control, a fast fabric defect detection algorithm based on GGCM (Gray-Gradient Co-Occurrence Matrix) is proposed. Based on the traditional GLCM (Gray Level Co-Occurrence Matrix), this algorithm adds feature extraction of image gradient information, and combines with SVM (Support Vector Machine) to detect and classify fabric images quickly and accurately. The eigenvalues extracted from GLCM and GGCM are analyzed and compared, and the fabric defects are detected by SVM classifier. Through the training classification experiment based on the fabric image data set collected from the field of a textile enterprise, the results show that the detection effect is significantly improved after adding gradient information, the accuracy rate is 94.8%, and the accuracy rate is 93.9%. The algorithm is fast for detection, after extracting features, each image detection only takes 0.5 ms, which is suitable for industrial production sites.

    Comparison of Timing Modeling Methods for PTL Full Adder Cell
    YE Ruoshan, WAN Jianghua, CAI Yongqi, LI Zhentao
    Electronic Science and Technology. 2025, 38(7):  66-73.  doi:10.16180/j.cnki.issn1007-7820.2025.07.009
    Abstract ( 165 )   HTML ( 7 )   PDF (3192KB) ( 54 )  
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    Timing verification is an important step in the process of chip verification, and timing model is the basis of timing verification. Different time series modeling methods should be adopted for PTL(Pass-Transistor Logic) full adder units of different structural types, and the modeling methods should be evaluated from the aspects of applicability, accuracy and timeliness. For the conventional structural standard unit, its circuit structure is more regular and there are more units of this type, so it is more efficient to use the mainstream time sequence database extraction tool for time sequence modeling. For special structural units, the circuit structure is complex and changeable, and the mainstream time sequence library extraction tool cannot be applied, but the number of such units in the module is small, and the complete time sequence modeling can be successfully achieved using the circuit simulation manual time sequence modeling method. After the time series modeling is completed, the time series analysis and power consumption analysis of the multiplier composed of different types of full adder units are carried out. The results show that the rise and fall delay of the multiplier based on PTL full adder unit are reduced by 16.2% and 18.1% respectively, and the power consumption is reduced by 10.8%. In the subsequent engineering application, the time series modeling method can be reasonably adjusted according to different unit types, which has important practical application value.

    Prediction of Lymph Node Metastasis in Thyroid Cancer with Missing Information
    ZHU Zhengming, ZENG Ru, SONG Yan
    Electronic Science and Technology. 2025, 38(7):  74-81.  doi:10.16180/j.cnki.issn1007-7820.2025.07.010
    Abstract ( 172 )   HTML ( 8 )   PDF (1117KB) ( 96 )  
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    In the decision-making process for thyroid cancer surgery, the accurate preoperative assessment of lymph node metastasis poses a challenging issue. To minimize unnecessary surgeries and enhance patient quality of life, precise prediction of lymph node metastasis in thyroid cancer is of practical significance. In this study non-negative latent factor model and PEFT(Parameter Efficient Fine Tuning) technique are used to solve the problem of small scale of medical data and missing clinical data. The non-negative latent factor model was used to complete the clinical data to improve the reliability and accuracy of the data. By introducing PEFT technology to fine-tune large pre-trained models, the computational cost is significantly reduced. The results show that the latent factor model is superior to the traditional method under different missing proportions, and the PEFT method has higher training accuracy and lower training time on two different data sets. By comparing the comprehensive performance of local data set and public data set, the effectiveness of the proposed method is verified. The proposed method reduces the computational cost and has higher interpretability while maintaining high prediction accuracy, and provides an efficient and feasible scheme for the application of pre-trained large models in medical tasks.

    Research on Decoupling Control of Refrigeration System Based on Neural Network Inverse Model
    WANG Junchao, DING Xudong, YANG Yuanxing, LIU Yuting, YANG Yuping
    Electronic Science and Technology. 2025, 38(7):  82-88.  doi:10.16180/j.cnki.issn1007-7820.2025.07.011
    Abstract ( 180 )   HTML ( 5 )   PDF (1126KB) ( 56 )  
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    In view of the nonlinearity and multi-variable coupling of compression refrigeration system, the inverse system control method of α-order neural network is used to decouple it into two first-order subsystems:superheat and evaporation temperature. On this basis, the linear closed-loop controller PID(Proportional Integration Differentiation) is added to realize the high performance decoupling control of the system. The results show that the proposed method is simple in structure and easy to implement, and effectively avoids the shortcomings of the traditional control method which depends on the accuracy of the system model. The step response time for both superheat and evaporation temperature is reduced by 234 s and 360 s,respectively. The overshoot of the evaporation temperature and superheat under step perturbation is decreased by 9.4% and 13.3%,respectively, demonstrating that the proposed method displays better dynamic performance and stability.

    Research on Parameter Identification of Voltage Ride Through in Cascading Faults for Doubly Fed Induction Generator
    YANG Zhi, ZHANG Jing, HE Yu, YE Yongchun, CAO Guoqiang, SUN Qichen, LI Shunyu, WANG Zhiyang
    Electronic Science and Technology. 2025, 38(7):  89-96.  doi:10.16180/j.cnki.issn1007-7820.2025.07.012
    Abstract ( 187 )   HTML ( 5 )   PDF (1428KB) ( 198 )  
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    With the grid-connected operation of large-scale Doubly Fed Induction Generator(DFIG), the nonlinear, impact and unbalanced characteristics of the operation process are easy to cause the chain failures of DFIG off-grid operation. In view of the existing DFIG parameter identification studies based on a single voltage sudden change for transient analysis and control strategy formulation, a single voltage sudden change study is difficult to characterize the applicability of control parameter identification. In this study, a parameter identification method of doubly-fed fan rotor controller based on IMOLA(Improved Multi-ObjectiveLichtenberg Algorithm) is proposed. The PSASP platform is used to build the electromechanical transient model of doubly-fed wind turbine, and the main control mode during steady-state operation and chain fault voltage crossing is determined. The measured data of voltage, active power and reactive power are input into the identification model, and the control parameters are identified based on IMOLA algorithm. The effectiveness and practicability of the proposed method are verified by simulation data and measured data. The results show that compared with traditional methods, IMOLA identification method can effectively improve the identification accuracy of model control parameters.

    Virtual Baseline Direction Finding Technology Based on 5G SSB Signal External Radiation Source Radar
    SONG Yu, TU Gangyi, ZHAO Di, SUN Zhuwei, CHEN Yan
    Electronic Science and Technology. 2025, 38(7):  97-104.  doi:10.16180/j.cnki.issn1007-7820.2025.07.013
    Abstract ( 285 )   HTML ( 6 )   PDF (2257KB) ( 125 )  
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    In view of the phase ambiguity problem that occurs between the phases of 5G SSB(Synchronization Signal Block) during the target detection process of 5G external radiation source radar, this study proposes a virtual baseline direction finding technology for external radiation source radar based on 5G SSB signals. The correlation of the signal structure in the 5G communication signal SSB is utilized to confirm the SSB index, and the phase angle information is extracted at the corresponding SSB index positions. A phase unwrapping algorithm is adopted to reduce the phase ambiguity, and the target angle is obtained through the virtual baseline direction finding algorithm. A 5G toolkit is used to generate simulated signals for simulation analysis. The results show that the proposed method has a better direction finding effect compared with the traditional direction finding method. When the SNR(Signal-to-Noise Ratio) is -15 dB, the ambiguity resolution probability of the proposed method is 88.48%, and the RMSE(Root Mean Square Error) is 1.15°. The experimental results indicate that the proposed method can measure the target angle more accurately and has a better measurement accuracy than the traditional direction finding method.

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Monthly,Founded in September 1987
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Ministry of Education of the People's Republic of China
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