Table of Content

15 September 2024 Volume 37 Issue 9

  

Particle Size Analysis in A Coupled Multi-Physics Models for Lithium-Ion Batteries

YU Runzhou, LI Peichao

Electronic Science and Technology. 2024, 37(9):  1-7.  doi:10.16180/j.cnki.issn1007-7820.2024.09.001

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In order to deeply understand the multi-physical field coupling behavior inside the LIB(Lithium-Ion Battery) and better provide reference for the manufacturing and optimization design of the LIB, a more physically realistic coupled ETM(Electrochemical-Thermal-Mechanical) model of the LIB is established and solved in the finite element simulation software COMSOL Multiphysics by means of numerical simulation in the present study. The model takes into account the stress generation in both electrode and particle scales during battery operation, which solves the problem of difficult calculation of stress at the electrode level in previous models, and better correlates the relationship between stress and electrochemistry by considering the correction of stress on lithium diffusion and overpotential. Based on this model, the effect of different positive electrode particle sizes on the battery performance is discussed in the study. The numerical results show that the performance index of each physical field during the discharge of LIB is better and the energy density of the battery is improved when the positive electrode particle size is small, which proves that the use of smaller positive electrode particle size can improve the performance of LIB.

Irreversible Expansion Model for Lithium-Ion Batteries and its Application

WANG Yahui, LI Peichao, WANG Keyong

Electronic Science and Technology. 2024, 37(9):  8-13.  doi:10.16180/j.cnki.issn1007-7820.2024.09.002

Abstract ( 65 )   HTML ( 6 )   PDF (968KB) ( 29 )  

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In view of the problem of rapid estimation of capacity attenuation after multiple charge and discharge cycles of LIB(lithium-ion battery), a new method based on the capacity attenuation model of lithium-ion battery is proposed to rapidly estimate its internal capacity attenuation using the external expansion displacement of the battery.Based on the capacity attenuation model of LIB, the radial irreversible expansion model is derived, and the cylindrical LIB Sanyo UR18650E is modeled and solved by COMSOL Multiphysics, and the numerical results are compared with the experimental data, so as to verify the proposed model. Based on the above model, the causes of capacity attenuation and irreversible expansion of battery due to side reactions during the charge-discharge cycle are analyzed.The results show that the change of side reaction rate causes the gradient distribution of the concentration of side reaction products in the negative electrode, and the accumulation of side reaction products causes the expansion displacement of the battery to increase linearly with the cycle.The function formula of the cell capacity attenuation and its radial displacement is obtained by using the side reaction product as a bridge, which provides a new method for the rapid estimation of the capacity attenuation.

Antenna Optimal Design Based on Flamingo Search Algorithm

HUANG Zeming, SHAN Zhiyong

Electronic Science and Technology. 2024, 37(9):  14-19.  doi:10.16180/j.cnki.issn1007-7820.2024.09.003

Abstract ( 59 )   HTML ( 1 )   PDF (2700KB) ( 30 )  

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In the field of antenna optimization design, traditional electromagnetic softwares adopt the method of sweeping with parameter, which causes the problems of large amount of calculation and low efficiency. In view of this problem, a joint optimized design based on flamingo search algorithm and HFSS (High Frequency Structural Simulator) is proposed in this study. This method is realized through the co-simulation of MATLAB and HFSS. MATLAB generates a VBS (Visual Basic Script) script for HFSS to call by writing the code of the design and simulation about antenna in MATLAB. HFSS returns the result of electromagnetic simulation to the function about fitness calculation. The flamingo search algorithm optimizes the particle position according to the fitness value until the optimal result is found and the optimal antenna size parameters are determined. In the co-simulation design, the optimal size parameters are found after 23 iterations, so that the fitness value of the optimized antenna in the two working frequency bands is optimized from -483.37 dB to -771.15 dB. The results of simulation show that the proposed algorithm has strong optimization ability and fast speed of convergence, which significantly improves the efficiency of the design of antenna optimization.

Design and Implementation of Distribution Network Online Monitoring System Based on C#

GAO Yuanlong, WU Chengming, ZHANG Lei

Electronic Science and Technology. 2024, 37(9):  20-26.  doi:10.16180/j.cnki.issn1007-7820.2024.09.004

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The medium and low voltage distribution network monitoring technology cannot effectively achieve the sharing and analysis of power grid operation information at all levels, and the monitoring visualization effect is poor. To solve this problem, this study uses the VISIO (Flowchart and vector graphics software) secondary development technology to design a C# based distribution network operation online monitoring system. The system has system basic configuration management, real-time data monitoring, data query, data analysis, early warning reminder management and other functions. While running, by establishing a connection, the distribution network operation data collected by the lower computer terminal can be obtained in real time and displayed on the monitoring main page. Users only need to control the relatively simple form controls to realize the basic information configuration and online monitoring of the system. The commissioning operation results show that the system can realize remote monitoring and timely warning reminders, so that the distribution network operation data can be used in real time, and the work efficiency can be effectively improved.

Research on Shared Cache Partition Algorithm Based on Multi-Core Processors

LÜ Haiyu, LUO Guang, ZHU Jiawei, ZHANG Fengdeng

Electronic Science and Technology. 2024, 37(9):  27-33.  doi:10.16180/j.cnki.issn1007-7820.2024.09.005

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In order to optimize the performance of multi-core processors, this study deeply investigates the management strategy of shared Cache on multi-core processors, and proposes a shared Cache partitioning algorithm MT-FTP(Memory Time based Fair and Throughput Partitioning) based on the fairness of cache time and throughput rate. A mathematical model based on the fairness and throughput index is established, and the partitioning flow of the algorithm is analyzed in the proposed study. The simulation results show that the MT-FTP algorithm has excellent performance in system throughput, and its average IPC (Instructions Per Cycles) value is 1.3% higher than that of UCP(Use Case Point) algorithm and 11.6% higher than that of LRU (Least Recently Used) algorithm. The average fairness of MT-FTP algorithm is 17% higher than that of LRU algorithm, and 16.5% higher than that of UCP algorithm. This algorithm realizes the fairness of shared Cache partition and takes into account the throughput of the system.

Hybrid Image Super-Resolution Reconstruction with Multiple and Multi-Scale Attention

KUAI Xinchen, LI Ye

Electronic Science and Technology. 2024, 37(9):  34-42.  doi:10.16180/j.cnki.issn1007-7820.2024.09.006

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Image itself information is naturally robust to image reconstruction, yet most current super-resolution methods do not fully utilize global feature information. This study proposes a new image super-resolution model mixing multiple and multi-scale attentions, including two new modules: Multi-scale hybrid non-local attention upsampling module and residual dense attention block. Different from previous nonlocal methods, multi-scale hybrid non-local attention upsampling module mixes pixel-based and patch-based nonlocal attention and establishes patch-level upsampling mapping relationships at multiple scales, which enables a wider global search space. The residual dense attention block establishes attention associations in channel and spatial dimensions, which enhances the transfer and fusion of front-to-back attention information through dense connections. In this study, quantitative and qualitative evaluations are conducted on several benchmark datasets, and the experimental results show that the model outperforms similar super-resolution models in terms of performance and reconstruction quality.

Comprehensive Evaluation of Power Quality Based on Graph Convolutional Network

HUANG Hongqing, NI Daohong, LIU Xuesong

Electronic Science and Technology. 2024, 37(9):  43-47.  doi:10.16180/j.cnki.issn1007-7820.2024.09.007

Abstract ( 37 )   HTML ( 1 )   PDF (1094KB) ( 18 )  

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The increasingly widespread use of new power equipment has brought new disturbances to the power system and has placed increasing demands on power quality. In order to make full use of the power quality indicators in the national standards and to make a more comprehensive and integrated evaluation of power quality, this study proposes a power quality evaluation method based on graph convolutional network. A power quality assessment system with graded indicators is proposed according to the current national standards. The correlation between the various power quality assessment indicators is initially determined, and on this basis the indicator relationship diagram is determined, a graph neural network model is built and trained, and the error rate of the test set is 9.02%. A comparison and analysis with other assessment methods using actual measurement data of a power system proves that the proposed method is more effective in assessing power quality over a long time span.

Swin-Transformer-Based Carotid Ultrasound Image Plaque Segmentation

HE Zhiqiang, SUN Zhanquan

Electronic Science and Technology. 2024, 37(9):  48-56.  doi:10.16180/j.cnki.issn1007-7820.2024.09.008

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The evaluation of carotid ultrasound image plaque requires a large number of experienced clinicians, and the ultrasound image has the characteristics of blurred boundary and strong noise interference, making the evaluation of plaques time-consuming and laborious. Therefore, a fully automated carotid plaque segmentation method is urgently needed to solve the problem of manpower scarcity. This study proposes a deep neural network model based on Swin-Transformer (Shifted-Windows Transformer) block for the automatic segmentation of carotid plaques. Based on the U-Net(U-Convolutional Network) architecture, the encoding part uses three convolutional blocks for image down-sampling to obtain feature images of different resolution sizes, and then adds six pairs of two consecutive Swin-Transformer blocks for more refined feature extraction. The decoding part up-samples the refined features output by the Swin-Transformer module step by step, and jump-joints them with the feature maps of each resolution level in the encoding part, respectively. The comparison experiments based on the data set of Tong Ren Hospital show that the Dice index of the proposed deep neural network model reaches 0.814 2, which is higher than that of other comparison networks. The results demonstrate that the proposed model can effectively extract the features of carotid ultrasound image plaques and achieve automated and high-precision plaque segmentation.

Persistent Clean-Label Backdoor Attack for Semi-Supervised Graph Node Classification

YANG Xiao, LI Gaolei

Electronic Science and Technology. 2024, 37(9):  57-63.  doi:10.16180/j.cnki.issn1007-7820.2024.09.009

Abstract ( 59 )   HTML ( 2 )   PDF (1479KB) ( 19 )  

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Semi-supervised graph learning aims to infer the class of unlabeled nodes or graphs by using various prior knowledge in a given graph. By improving the automation of data labeling, semi-supervised graph learning has high efficiency in node classification, but as a deep learning architecture, it also faces the threat of backdoor attacks, but no effective backdoor attack method has been developed for semi-supervised graph node classification tasks. This study propose a persistent clean-label backdoor attack method for semi-supervised graph node classification models, which generates poisoned samples by adaptively adding triggers and perturbations on unlabeled training data, and then trains to obtain poisoned semi-supervised graph node classification models without modifying the labels. The attacker can poison the model more stealthily with a poisoning rate no higher than 4%. To ensure the persistence of the backdoor in the model, a hyperparameter tuning strategy is also proposed to select the optimal value of the perturbation. Extensive experiments on several semi-supervised graph node classification models and open-source datasets show that the proposed approach achieves an attack success rate of up to 96.25% with little loss of classification accuracy of the model on normal samples.

Smart Contract Vulnerability Detection Method Based on Meta-Operation

WANG Shun, XU Xianghua, WANG Ran

Electronic Science and Technology. 2024, 37(9):  64-71.  doi:10.16180/j.cnki.issn1007-7820.2024.09.010

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In view of the problem that the static analysis tool has a high cost of expanding the vulnerability due to the strong coupling of the vulnerability scanning process code in the tool code, a smart contract vulnerability detection method based on meta-operations is proposed. This method can convert the most basic detection process in the tool into an abstract form of meta-operation and customize the vulnerability scanning process through the combination of different meta-operations and logical operators, so as to realize the detection of different vulnerabilities by writing a small amount of vulnerability scanning process syntax. The detection can improve the ability of the tool to expand the new vulnerabilities of smart contracts. The experiment results prove that the number of characters of the vulnerability description required to be written according to this method is only 8.9%~12.7% of the logic characters of the Slither tool vulnerability detection, and the false positive rate is 2% lower than that of the Slither tool. It is proved that this method can provide stronger scalability and flexibility based on ensuring the reliability of tool detection.

Design and Implementation of EMC Test Antenna Based on Active Matching Technology

CHEN Jiaxing, ZHANG Xinlin, JIANG Bo, ZHANG Jiahai, HUANG Xing, QIN Yangzhen, LU Hongmin

Electronic Science and Technology. 2024, 37(9):  72-78.  doi:10.16180/j.cnki.issn1007-7820.2024.09.011

Abstract ( 45 )   HTML ( 2 )   PDF (1218KB) ( 19 )  

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In low frequency band, miniaturized EMC(Electromagnetic Compatibility) test antennas are mostly electrically small antennas, which have the problems of high antenna factor and low test sensitivity. To solve the problem, based on active matching technology, the electrically small antennas loaded with negative impedance converter circuit and field effect transistor high-impedance input circuit are simulated and analyzed respectively. The simulation results show that the field effect transistor high-impedance input circuit has advantages over the negative impedance converter circuit in the ability of reducing antenna factor and the stability of circuit, and is helpful to the design and implementation of miniaturized EMC test antenna. The test results of the miniaturized EMC test antenna loaded with field effect transistor high-impedance input matching circuit show that, in 2~30 MHz, antenna factor can be improved by 22~46 dB compared with the EMC test antenna without matching circuit.

Design of High Precision Over-Temperature Protection Circuit for Power Management Chip

DU Wenhe, XU Zheng, KANG Jiahao, YANG Ke, PAN Jingxue

Electronic Science and Technology. 2024, 37(9):  79-86.  doi:10.16180/j.cnki.issn1007-7820.2024.09.012

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Power management chips is damaged to varying degrees when they work at the ambient temperature beyond their acceptable range, and the over-temperature protection circuit plays an important role in improving the reliability and robustness of this kind of chip. This study designs a high-precision over-temperature protection circuit with the dual functions of turning off when the temperature is too high and reminding when the temperature is too low. The positive and negative temperature coefficient voltages are used to detect the chip temperature in real time, and then four logic turnover points are obtained by comparing them with different reference voltages at the output end of the band-gap reference circuit. After processing by the high-precision comparator circuit and hysteresis logic circuit, the hysteresis logic signals are output to control the working state of the chip or to remind the low temperature. The design and relevant simulation have been carried out based on 0.18 μm BCD(Bipolar-Complementary Metal Oxied Semiconductor-Double diffused Metal Oxide Semiconductor) process, and the simulation results show that when the power supply voltage ranges from 3.0~ 5.5 V, the maximum offset of the temperature threshold corresponding to the hysteresis logic turnover signal at the output end of the circuit is within 0.3 ℃. The circuit has high precision and can be widely integrated in various power management chips requiring over-temperature protection.

Researchon Short Text News Title Generation Method

ZHAO Ming

Electronic Science and Technology. 2024, 37(9):  87-94.  doi:10.16180/j.cnki.issn1007-7820.2024.09.013

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Today's news has the characteristics of short text, frequent release, timeliness, etc. A media account releases dozens of news in a day. Developing suitable and attractive headlines for large volumes of news has become a major part of the work of media workers. Media workers need a system that automatically generates short text headlines to relieve their stress. To solve this problem, this study proposes a short text news title generation model. The model adopts sequence-to-sequence structure, using pre-trained language model and layered self-attention decoder in encoder and decoder respectively. In order to make the generated headlines contain the key information of the original news, a staged training method based on LCSTS data set and Weibo4 data set is proposed, and the model learns to extract the key news information and construct a stylized expression from the two data sets respectively, so that the generated headlines can accurately express the core content of the news and attract readers.