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Identifying influential spreaders in complex networks based on density entropy and community structure

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Chinese Physics B 2024年第5期33卷 765-774页

作者：苏湛陈磊艾均郑雨语别娜 school of Optical-electrical and computer engineering University of Shanghai for Science and Technology

In recent years,exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers,given its fundamental theoretical significance and practical *** address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities,this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure(DECS).The proposed method initially integrates a community detection algorithm to obtain the community partition results of the *** then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its *** validation is conducted on eight networks of varying sizes through susceptible–infected–recovered(SIR)propagation experiments and network static attack *** experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions,particularly in terms of information spreading capability,thereby enhancing the accurate identification of critical nodes in networks.

关键词： corrigendum electron impact ionization cross sections carbon monoxide

Improved Subsynchronous Oscillation Parameter Identification with Synchrophasor Based on Matrix Pencil Method in Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 22-33页

作者： Xiaoxue Zhang Fang Zhang Wenzhong Gao Jinghan He the school of electrical engineering Beijing Jiaotong UniversityBeijing 100044China the Department of electrical and computer engineering University of DenverDenverUSA

The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power *** realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method(MPM),this paper propos es an improved MPM-based parameter identification with syn *** MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coeffi cients of the matrix pencil to ensure the accuracy of the identi fied parameters,since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation ***,the identification and recovery of bad data are proposed by uti lizing the difference in temporal continuity of the synchropha sors before and after noise *** proposed parameter identification is verified with synthetic,simulated,and actual measured phase measurement unit(PMU)*** with the existing MPM,the improved MPM achieves better accuracy for parameter identification of each component in SSOs,better real-time performance,and significantly reduces the effect of bad data.

关键词： Subsynchronous oscillations(SSOs) synchrophasor parameter identification matrix pencil method bad data

SwiftTheft: A Time-Efficient Model Extraction Attack Framework Against Cloud-Based Deep Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 90-100页

作者： Wenbin YANG Xueluan GONG Yanjiao CHEN Qian WANG Jianshuo DONG school of Cyber Science and engineering Wuhan University school of computer Science Wuhan University College of electrical engineering Zhejiang University

With the rise of artificial intelligence and cloud computing, machine-learning-as-a-service platforms,such as Google, Amazon, and IBM, have emerged to provide sophisticated tasks for cloud applications. These proprietary models are vulnerable to model extraction attacks due to their commercial value. In this paper, we propose a time-efficient model extraction attack framework called Swift Theft that aims to steal the functionality of cloud-based deep neural network models. We distinguish Swift Theft from the existing works with a novel distribution estimation algorithm and reference model settings, finding the most informative query samples without querying the victim model. The selected query samples can be applied to various cloud models with a one-time selection. We evaluate our proposed method through extensive experiments on three victim models and six datasets, with up to 16 models for each dataset. Compared to the existing attacks, Swift Theft increases agreement(i.e., similarity) by 8% while consuming 98% less selecting time.

关键词： Performance evaluation Cloud computing Analytical models Computational modeling Estimation Artificial neural networks Artificial intelligence

Winding Function Model-based Performance Evaluation of a PM Transverse Flux Generator for Applications in Direct-drive Systems

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CES Transactions on electrical Machines and Systems 2024年第2期8卷 216-226页

作者： Mehrage Ghods Jawad Faiz Ali A Pourmoosa IEEE school of electrical and computer engineering College of Engineeringthe University of Tehran

The magnetic flux in a permanent magnet transverse flux generator(PMTFG) is three-dimensional(3D), therefore, its efficacy is evaluated using 3D magnetic field analysis. Although the 3D finite-element method(FEM) is highly accurate and reliable for machine simulation, it requires a long computation time, which is crucial when it is to be used in an iterative optimization process. Therefore, an alternative to 3DFEM is required as a rapid and accurate analytical technique. This paper presents an analytical model for PMTFG analysis using winding function method. To obtain the air gap MMF distribution, the excitation magneto-motive force(MMF) and the turn function are determined based on certain assumptions. The magnetizing inductance, flux density, and back-electro-magnetomotive force of the winding are then determined. To assess the accuracy of the proposed method, the analytically calculated parameters of the generator are compared to those obtained by a 3D-FEM. The presented method requires significantly shorter computation time than the 3D-FEM with comparable accuracy.

关键词： Index Terms—Permanent magnet transverse flux generator Winding function 3D-FEM Cogging torque Prototyping

Stochastic Computing Convolutional Neural Network Architecture Reinvented for Highly Efficient Artificial Intelligence Workload on Field-Programmable Gate Array

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Research 2024年第2期2024卷 55-79页

作者： Yang Yang Lee Zaini Abdul Halim Mohd Nadhir Ab Wahab Tarik Adnan Almohamad school of electrical and Electronic engineering Universiti Sains MalaysiaNibong Tebal 14300 Penang Malaysia school of computer sciences Universiti Sains MalaysiaGelugor11800 PenangMalaysia electrical-Electronics engineering Department Faculty of EngineeringKarabuk University78050 KarabukTurkiye

Stochastic computing(SC)has a substantial amount of study on application-specific integrated circuit(ASIC)design for artificial intelligence(AI)edge computing,especially the convolutional neural network(CNN)***,SC has little to no optimization on field-programmable gate array(FPGA).Scaling up the ASIC logic without FPGA-oriented designs is inefficient,while aggregating thousands of bitstreams is still challenging in the conventional *** research has reinvented several FPGA-efficient 8-bit SC CNN computing architectures,i.e.,SC multiplexer multiply-accumulate,multiply-accumulate function generator,and binary rectified linear unit,and successfully scaled and implemented a fully parallel CNN model on Kintex7 *** proposed SC hardware only compromises 0.14%accuracy compared to binary computing on the handwriting Modified National Institute of Standards and Technology classification task and achieved at least 99.72%energy saving per image feedforward and 31?more data throughput than modern *** to SC,early decision termination pushed the performance baseline exponentially with minimum accuracy loss,making SC CNN extremely lucrative for AI edge computing but limited to classification *** SC's inherent noise heavily penalizes CNN regression performance,rendering SC unsuitable for regression tasks.

关键词： hardware Highly rendering

Effects of electrothermal ageing on dielectric performance of metallised biaxial orientation polypropylene film capacitors in strong magnetic fields

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High Voltage 2024年第4期9卷 911-919页

作者： Haoliang Liu Boxue Du Meng Xiao Ke Chen Yanwei Ma school of electrical and Information engineering Tianjin UniversityTianjinChina Institute of electrical engineering Chinese Academy of SciencesBeijingChina

The dielectric performance of the pristine and the electrothermal-aged metallised polypropylene capacitors are investigated under different magnetic *** energy storage capacity decreases under the magnetic *** 12 T,the capacitance value of the pristine capacitors decreases by 8.4%-17%and that of the aged capacitors declines by 15%-20%.The dielectric loss is reduced by 2.6%-6.3%,but the equivalent series resistance shows an 8.3%-23.1%*** leakage conductivity increases to 202%-354%,which leads to a weakened voltage holding *** addition to the effects of the magnetic fields,the impacts of ageing have also been *** effects of the magnetic field and ageing on the electrical parameters of the capacitors are discussed *** molecular chains of the dielectric are oxidised and decomposed during the ageing process,which reduces the insulating properties of the *** magnetic field inhibits polarisation by changing the polarisation behaviour through Lorentz force and excites the electrons to enhance charge transmission.

关键词： magnetic performance fields

Numerical simulation for the initial state of avalanche in polydisperse particle systems

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Chinese Physics B 2024年第2期33卷 405-412页

作者：韩韧李亭迟志鹏杨晖李然 school of Optical-electrical and computer engineering University of Shanghai for Science and TechnologyShanghai 200093China

Numerical simulation is employed to investigate the initial state of avalanche in polydisperse particle *** and propagation processes are illustrated for pentadisperse and triadisperse particle systems,*** these processes,particles involved in the avalanche grow slowly in the early stage and explosively in the later stage,which is clearly different from the continuous and steady growth trend in the monodisperse *** examining the avalanche propagation,the number growth of particles involved in the avalanche and the slope of the number growth,the initial state can be divided into three stages:T1(nucleation stage),T2(propagation stage),T3(overall avalanche stage).We focus on the characteristics of the avalanche in the T2 stage,and find that propagation distances increase almost linearly in both axial and radial directions in polydisperse *** also consider the distribution characteristics of the average coordination number and average velocity for the moving *** results support that the polydisperse particle systems are more stable in the T2 stage.

关键词： avalanche initial state polydisperse particle systems propagation

Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 427-431页

作者： Chao Ren Han Yu Yan Xu Zhao Yang Dong school of computer Science and engineering Nanyang Technological UniversitySingapore school of electrical and Electronic engineering Nanyang Technological UniversitySingapore

This letter proposes a reliable transfer learning(RTL)method for pre-fault dynamic security assessment(DSA)in power systems to improve DSA performance in the presence of potentially related unknown *** takes individual discrepancies into consideration and can handle unknown faults with incomplete *** experiment results demonstrate high DSA accuracy and computational efficiency of the proposed RTL *** analysis shows RTL can guarantee system performance.

关键词： Adversarial training dynamic security assessment maximum classifier discrepancy missing data transfer learning

Analytical Verification of Performance of Deep Neural Network Based Time-synchronized Distribution System State Estimation

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1126-1134页

作者： Behrouz Azimian Shiva Moshtagh Anamitra Pal Shanshan Ma school of electrical Computer and Energy EngineeringArizona State UniversityTempeAZ 85281USA the school of electrical Computer and Energy EngineeringArizona State UniversityTempeAZ 85281USA Quanta Technology RaleighNC 27607USA

Recently,we demonstrated the success of a time-synchronized state estimator using deep neural networks(DNNs)for real-time unobservable distribution *** this paper,we provide analytical bounds on the performance of the state estimator as a function of perturbations in the input *** has already been shown that evaluating perfor-mance based only on the test dataset might not effectively indi-cate the ability of a trained DNN to handle input *** such,we analytically verify the robustness and trustworthi-ness of DNNs to input perturbations by treating them as mixed-integer linear programming(MILP)*** ability of batch normalization in addressing the scalability limitations of the MILP formulation is also *** framework is val-idated by performing time-synchronized distribution system state estimation for a modified IEEE 34-node system and a real-world large distribution system,both of which are incompletely observed by micro-phasor measurement units.

关键词： Deep neural network(DNN) distribution system state estimation(DSSE) mixed-integer linear programming(MILP) robustness trustworthiness