Table of Content

15 February 2025 Volume 38 Issue 2

  

A Transformerless Half-Bridge Lithium Battery Equalizer Based on Phase-Shift Strategy

ZHOU Bin, KAN Jiarong, CHEN Heming, CHEN Weiwei, LI Yan, XU Sudong

Electronic Science and Technology. 2025, 38(2):  1-9.  doi:10.16180/j.cnki.issn1007-7820.2025.02.001

Abstract ( 202 )   HTML ( 39 )   PDF (4454KB) ( 170 )  

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In view of the inconsistency between the cells of lithium battery and the difficulty and high cost of making the traditional half-bridge equalizer multi-winding transformer, a transformerless half-bridge lithium battery equalizer is proposed in this study, and the key factors affecting the energy flow between cells at high frequency are analyzed. Due to the use of phase-shifting control, shortest equalization path, and parallel equalization mode, the equalizer can quickly achieve simultaneous equalization between multiple battery cells. The equalization speed is fast, the equalization efficiency is high, and it is not affected by the number of battery cells in series,which makes the equalizer have the advantages of simple control, high equalization efficiency, high flexibility, and fewer switching devices. Based on the dynamic model of battery charging and discharging, an expression for energy balance between individual cells of the battery is derived, and verified by building a simulation model and experimental prototype. The simulation and experimental results show that the transformer free half bridge lithium battery equalizer based on phase shifting strategy can quickly achieve voltage equalization between battery cells.

An Automatic Test Method for FPGA Interconnect Resource Based on An Improved EK Algorithm

FU Mannan, CHEN Suting, XIE Weikun, LIN Xiaohui

Electronic Science and Technology. 2025, 38(2):  10-16.  doi:10.16180/j.cnki.issn1007-7820.2025.02.002

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In the FPGA(Field Programmable Gate Array) IR(Interconnect Resource) testing, existing testing methods have problems such as multiple test vector configurations, high testing complexity,and low testing efficiency. In order to reduce the number of configurations and improve the efficiency of testing,an automatic test method for FPGA IR based on an improved EK(Edmonds-Karp) algorithm is proposed. This method achieves the goal of reducing the number of configurations by changing the search for the shortest path from the source point s to the endpoint t in the EK algorithm to the search for the longest path from s to t. A model based on the internal underlying IR structure of the FPGA is established,the improved EK algorithm is applied to the Kinex-7 series FPGA for automated routing path search,and the routing path is configured into the FPGA for simulation experiments. The experimental results show that the proposed method can detect the open circuit fault, short circuit fault and fixed fault in FPGA with less configuration times without reducing the fault coverage.

Numerical Simulation of Electrically Assisted Pressure Joining Based on Abaqus

LI Yongfang, YANG Yali

Electronic Science and Technology. 2025, 38(2):  17-22.  doi:10.16180/j.cnki.issn1007-7820.2025.02.003

Abstract ( 148 )   HTML ( 9 )   PDF (2896KB) ( 68 )  

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Based on the principle of conservation of energy and taking into account the influence of heat transfer, a universal theoretical model for heat transfer in EAPJ(Electrically Assisted Pressure Joining) of sheet metal lap joining is established. Taking Grade 1 titanium alloy rolling plate as the research object, according to the actual experimental setup process of EAPJ, simulation and analysis of the electric field, temperature field, and residual stress field during the EAPJ process of titanium alloy plate are completed using Abaqus finite element software. The experimental results show that when the current is applied, the temperature of the workpiece joint rises rapidly to the peak value, and then remains near the peak value until the power is stopped. After 20 s of natural cooling, the joint temperature tends to room temperature, and its residual stress is mainly concentrated near the joint, about 110 MPa. By comparing the experimental and simulation data of the thermal cycle curve at the center point of the EAPJ process joint, it is found that the numerical simulation results are in good agreement with the experimental results, which verifies the accuracy of the numerical simulation method, and provides a theoretical basis for the research of EAPJ mechanism and the actual production process.

Fault Identification of Complex Analog Circuit Based on Deep Learning

HUANG Zehua, BI Guihong, ZHANG Zirui

Electronic Science and Technology. 2025, 38(2):  23-34.  doi:10.16180/j.cnki.issn1007-7820.2025.02.004

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For complex analog circuits with complex fault transfer relationships and complex nonlinear relationships between fault types and fault features, which cause difficulties in feature extraction and fault identification. This study presents a fault diagnosis method for analog circuits based on two measuring points-CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-multi-scale false-color image-ALEXNet network. A new combined fault sample is constructed by connecting the output signals of two measuring points in a complex analog circuit. The combined fault sample data of two measuring points improves the ability to characterize the whole fault state of the complex analog circuit. The combined fault sample signals are decomposed in multi-scale, and the multi-scale data matrix is mapped to a two-dimensional false-color graph to form a multi-scale false-color image with abundant information and obvious features. Based on AlexNet's excellent image feature mining and learning ability, multi-scale false-color images of different fault types were input into AlexNet for model transfer training and fault identification. By comparing and analyzing the single and double faults and mixed faults of simple circuit and complex circuit, it is proved that the proposed method can achieve higher recognition accuracy for different fault types of complex analog circuit.

Face Image Super-Resolution Reconstruction Based on Conditional Priori Swin Transformer

ZHENG Fangliang, WANG Yannian, LIAN Jihong, RUAN Pei

Electronic Science and Technology. 2025, 38(2):  35-41.  doi:10.16180/j.cnki.issn1007-7820.2025.02.005

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In view of the problem that the existing image super resolution models based on Swin Transformer do not preprocess the face image, resulting in poor final super resolution results, this study proposes a face image super resolution reconstruction method based on conditional prior Swin Transformer. The method uses face Parsing Map and Swin Transformer model to preprocess the face image, uses conditional prior to optimize the face hyper-segmentation problem, and uses face parsing map to restrict the process so as to obtain more valuable prior information. In the stage of deep feature extraction, the channel space attention mechanism is integrated with Swin Transformer module to balance the speed and precision of feature group adjustment. Experimental results show that the proposed method achieves a PSNR(Peak Signal-to-Noise Ratio)of 32.21 dB on the test set. Compared with the existing model, this method has a certain improvement. It is proved that the improved model is more suitable for human face, and the generated result is clearer and more real, and more details of face image texture can be restored.

A New Electricity Consumption Measurement System Based on Electricity Usage Identification

TAO Xuepan, WANG Tao, LI Wenjie, ZHANG Bing

Electronic Science and Technology. 2025, 38(2):  42-52.  doi:10.16180/j.cnki.issn1007-7820.2025.02.006

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The detailed user's electricity consumption information is the key to promote energy conservation and emission reduction, and implement energy optimization. In view of the problem of power line interference in the process of obtaining accurate electricity consumption information, a new electricity consumption metering system based on electricity consumption identification is proposed in this study. This metering system is composed of two parts: the electricity identification terminal and the collection and metering terminal. The electrical identification terminal is digitally modulated by ASK(Amplitude Shift Keying) to independently encode the electrical information of each electrical appliance, and is capacitatively coupled to the power line. The acquisition and metering terminal collects independent coded information through power line carrier communication, and analyzes and processes it to realize electricity consumption measurement. The metering scheme is simulated and verified by Multisim and MATLAB simulation software. According to the characteristics of electrical appliances, the electricity information can be encoded independently, and the acquisition and metering side can restore the waveform of electrical appliances by analyzing the independent coding data to achieve better anti-interference.

Nonlinear Representation Latent Factor Decomposition Model Based on Trust Relationship

PAN Tianyi, SONG Yan

Electronic Science and Technology. 2025, 38(2):  53-61.  doi:10.16180/j.cnki.issn1007-7820.2025.02.007

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In view of the problems of weak characterization ability and low computational efficiency of high-dimensional sparse undirected networks for mining potential correlation information among entities, this study proposes a non-negative nonlinear characterization latent factor model based on trust relationship under the framework of social recommendation model. The model shapes the feature space of the latent matrix through nonlinear mapping, which guarantees not only the non-negativity of the target matrix, but also improves the characterization ability of the model. With the introduction of the graph Laplace regularization term in the objective function of the model training, the structural consistency between before and after the mapping of the trust relationship is ensured. The experimental results based on six public data sets show that the proposed model has obvious superiority over other models.

Emotion Recognition Based on Multimodal Fusion of the EEG and Peripheral Physiological Signals

MA Zhuang, GAN Kaiyu, YIN Zhong

Electronic Science and Technology. 2025, 38(2):  62-69.  doi:10.16180/j.cnki.issn1007-7820.2025.02.008

Abstract ( 229 )   HTML ( 7 )   PDF (2024KB) ( 137 )  

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Decoding human internal emotional states based on EEG(Electroencephalogram) and surrounding physiological signals is key in the field of emotional computing, but the performance of machine learning models using EEG or surrounding physiological signal modes may be limited. In this study, a multi-mode fusion strategy is proposed based on the single mode method. The differential entropy, statistical and complexity features are extracted from each EEG fragment, and these features are properly integrated with the surrounding physiological signals. Multiple modal features recorded in the DEAP(Database for Emotion Analysis using Physiological Signals) data set are incorporated in the proposed method. In terms of titer, the experimental accuracy of single EEG feature is 49.21%, the classification accuracy of two types of feature fusion is 56.39%, 55.24% and 56.98%, and the experimental accuracy of three types of mode fusion is 56.98%. In terms of arousal, the experimental accuracy of single EEG feature is 49.34%, the classification accuracy of two types of feature fusion is 54.53%, 54.53% and 59.39%, and the experimental accuracy of three types of feature fusion is 55.48%. The experimental results show that the classification accuracy of multi-modal features after the fusion of EEG features and peripheral physiological features is the highest, and the classification accuracy is improved by 7.77% and 10.05%, respectively, compared with the single EEG features.

Total Jitter Prediction Method of FPGA Embedded High-Speed Interface Based on Optimized BPNN

YE Xiangyu, LIN Xiaohui, DING Jiangqiao, XIE Weikun

Electronic Science and Technology. 2025, 38(2):  70-77.  doi:10.16180/j.cnki.issn1007-7820.2025.02.009

Abstract ( 95 )   HTML ( 3 )   PDF (2858KB) ( 55 )  

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In view of the problem that ATE(Automated Test Equipment) can not measure the total jitter of FPGA(Field-Programmable Gate Array) embedded high-speed interface directly, this study presents a method to predict the total jitter of high-speed interface based on optimized BPNN(Back Propagation Neural Network). The GA-BP neural network is formed to optimize the initial weight and parameter seeking process of BPNN using the strong global search ability of GA(Genetic Algorithm), and improve the accuracy of predicting the total jitter. The GA_BP total jitter prediction model was constructed using MATLAB software to predict and optimize the screened jitter data. The experimental results show that compared with the non-optimized BP neural network and the traditional Elman neural network prediction model, the mean square error of the GA_BP prediction model is declined by 75.5% and 88.0%, and the number of iterations is reduced by 68.0% and 59.8%, respectively. It indicates that the proposed GA_BP model has higher prediction accuracy and iteration efficiency, and can be applied to total jitter production test in ATE.

Research on the Security and Privacy Preserving Method of RFID Systems

SHI Zhicai

Electronic Science and Technology. 2025, 38(2):  78-83.  doi:10.16180/j.cnki.issn1007-7820.2025.02.010

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In view of the problems of privacy leakage and security attack in RFID(Radio Frequency IDentification) system due to the simple structure of radio frequency tag and the wireless transmission of data between reader and RFID system, this study proposes to protect the security and privacy of RFID system through two-way lightweight authentication protocol. The protocol generates session messages by randomizing the secret information of tags and then Hashing them, and adopts secondary mutual authentication between tags and readers, which improves the security of the protocol.The protocol ensures the confidential transmission and integrity of session information in the authentication process through Hash operation. The randomization of each session message sent by the tag side ensures the freshness of the message, and the update of the system secret information ensures the forward security of the protocol. RFID authentication protocol can not only resist attacks such as eavesdropping, tracking, replay, and de-synchronization, but also meet the security and privacy protection needs of RFID systems.

Design of High Speed Encoding and Decoding Circuit for Isolation Driver Chip

XIONG Zhangliang, CHEN Suting, XUAN Zhibin, ZHAO Tingchen, LIU Jiajun, FU Mannan

Electronic Science and Technology. 2025, 38(2):  84-92.  doi:10.16180/j.cnki.issn1007-7820.2025.02.011

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A digital isolator encoding and decoding scheme for isolation drive circuits is designed using the isolation transmission method of stacked micro on-chip transformers. In view of the high radio frequency modulation power consumption, pulse modulation rate limitations, and reliability issues in existing isolator mainstream encoding and decoding schemes, single and double pulse encoding and decoding technology is adopted to reduce power consumption and optimize encoding and decoding methods in this study. Compared with traditional single and double pulse decoding methods, replacing the sampled pulse signal in the decoding circuit with an edge triggered signal improves reliability, increases signal transmission rate by nearly twice, reduces delay time, and combines refresh timing circuit and watchdog circuit to achieve reliable transmission of digital isolators. The experimental results show that the digital signal achieves isolated transmission of DC~90 Mbit·s^-1. The overall static power consumption of encoding and decoding is 0.574 mA, the dynamic power consumption is 0.257 mA·(Mbit·s^-1)^-1, the delay time is less than 18 ns, and the pulse width distortion is less than 2 ns.

Joint State Estimation of Active Distribution Network Based on WLS-AUKF Hybrid Algorithm

MAN Yanlu, LIU Min

Electronic Science and Technology. 2025, 38(2):  93-102.  doi:10.16180/j.cnki.issn1007-7820.2025.02.012

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The randomness and volatility of response load and distributed energy, and the economic requirements of PMU(Phasor Measurement Unit)configuration put forward higher requirements for distribution network state estimation.In this paper, a WLS(Weighted Least Squares)-AUKF(Adaptive Untraced Kalman Filtering) considering PMU configuration optimization is proposed for active distribution network joint state estimation.The PMU is optimized by MHCPSO(Metropolis-Hastings Crossover Particle Swarm Optimization), and combined with WLS and AUKF, the joint state estimation is proposed.In the joint approach, WLS feeds robust measurement data to AUKF, and AUKF provides prior predictive values to WLS and supplements measurement redundancy.The simulation results show that the MHCPSO algorithm has higher estimation accuracy than the GAPSO(Genetic Algorithm Particle Swarm Optimization) under the same PMU quantity.In the case of the same state estimation error, the number of PMUs configured by MHCPSO algorithm can be reduced by up to four when compared with GAPSO algorithm.The WLS-AUKF algorithm has better estimation performance than UKF(Untraced Kalman Filtering) algorithm in the case of random charging and discharging of PV(Photovoltaic)/EV(Electric Vehicles) connected to the grid and sudden load change at a certain time.The high precision, economy, robustness and robustness of WLS-AUKF state estimation are demonstrated in three scenarios: PMU configuration optimization, PV/EV grid-connection and load mutation.