检索结果-南通市图书馆

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

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

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

维普期刊数据库

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

在线全文

学校读者我要写书评

暂无评论

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

维普期刊数据库

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

在线全文

学校读者我要写书评

暂无评论

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

Metarelation2vec:A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Tsinghua science and Technology 2024年第2期29卷 553-575页

作者： Lei Chen Yuan Li Yong Lei Xingye Deng school of Information and electrical engineering Hunan University of Science and TechnologyXiangtan 411201China school of computer science and engineering Hunan University of Science and TechnologyXiangtan 411201China

Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

关键词： metarelation random walk heterogeneous network metapath representation learning

Suitability of SDN and MEC to facilitate digital twin communication over LTE-A

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Digital Communications and Networks 2024年第2期10卷 347-354页

作者： Hikmat Adhami Mohammad Alja’afreh Mohamed Hoda Jiaqi Zhao Yong Zhou Abdulmotaleb El Saddik Mohamed bin Zayed university of Artificial Intelligence Masdar CityAbu DhabiUnited Arab Emirates school of computer science and Technology China University of Mining and TechnologyXuzhou221116China school of electrical engineering and computer science University of OttawaOttawaONK1N 6N5Canada

Haptic is the modality that complements traditional multimedia,i.e.,audiovisual,to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control *** will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile *** the 5th generation of mobile networks is not yet widely deployed,Long-Term Evolution(LTE-A)latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital *** work investigates an interesting solution based on the incorporation of Software-defined networking(SDN)and Multi-access Mobile Edge Computing(MEC)technologies in an LTE-A network,to deliver future multimedia applications over the Tactile Internet while overcoming the QoS *** network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators(KPIs)such as throughput,End-2-End(E2E)delay,and *** best scenario possible is clearly the one integrating MEC and SDN approaches,where the overall delay,jitter,and throughput for haptics-attained 2 ms,0.01 ms,and 1000 packets per *** results obtained give clear evidence that the integration of,both SDN and MEC,in LTE-A indicates performance improvement,and fulfills the standard requirements in terms of the above KPIs,for realizing a Digital Twin/Tactile Internet-based system.

关键词： LTE-A 5G Digital twin Tactile Internet KPIs SDN MEC NFV Haptic

BAD-FM:Backdoor Attacks Against Factorization-Machine Based Neural Network for Tabular Data Prediction

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第4期33卷 1077-1092页

作者： Lingshuo MENG Xueluan GONG Yanjiao CHEN College of electrical engineering Zhejiang University school of computer science Wuhan University

Backdoor attacks pose great threats to deep neural network models. All existing backdoor attacks are designed for unstructured data(image, voice, and text), but not structured tabular data, which has wide real-world applications, e.g., recommendation systems, fraud detection, and click-through rate prediction. To bridge this research gap, we make the first attempt to design a backdoor attack framework, named BAD-FM, for tabular data prediction models. Unlike images or voice samples composed of homogeneous pixels or signals with continuous values, tabular data samples contain well-defined heterogeneous fields that are usually sparse and discrete. Tabular data prediction models do not solely rely on deep networks but combine shallow components(e.g., factorization machine, FM) with deep components to capture sophisticated feature interactions among fields. To tailor the backdoor attack framework to tabular data models, we carefully design field selection and trigger formation algorithms to intensify the influence of the trigger on the backdoored model. We evaluate BAD-FM with extensive experiments on four datasets, i.e.,HUAWEI, Criteo, Avazu, and KDD. The results show that BAD-FM can achieve an attack success rate as high as 100%at a poisoning ratio of 0.001%, outperforming baselines adapted from existing backdoor attacks against unstructured data models. As tabular data prediction models are widely adopted in finance and commerce, our work may raise alarms on the potential risks of these models and spur future research on defenses.

关键词： Adaptation models Systematics Frequency modulation Finance Predictive models Prediction algorithms Data models

A Task Scheduling Algorithm Based on Clustering Pre-processing in Space-Based Information Network

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第1期33卷 217-230页

作者： Yufei WANG Jun LIU Shengnan ZHANG Sai XU Jingyi WANG school of computer science and engineering Northeastern University

With the diversification of space-based information network task requirements and the dramatic increase in demand, the efficient scheduling of various tasks in space-based information network becomes a new challenge. To address the problems of a limited number of resources and resource heterogeneity in the space-based information network, we propose a bilateral pre-processing model for tasks and resources in the scheduling pre-processing stage. We use an improved fuzzy clustering method to cluster tasks and resources and design coding rules and matching methods to match similar categories to improve the clustering effect. We propose a space-based information network task scheduling strategy based on an ant colony simulated annealing algorithm for the problems of high latency of space-based information network communication and high resource dynamics. The strategy can efficiently complete the task and resource matching and improve the task scheduling performance. The experimental results show that our proposed task scheduling strategy has less task execution time and higher resource utilization than other algorithms under the same experimental conditions. It has significantly improved scheduling performance.

关键词： Scheduling algorithms Heuristic algorithms Clustering methods Clustering algorithms Simulated annealing Dynamic scheduling Search problems

Data augmentation method for insulators based on Cycle-GAN

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Electronic science and Technology 2024年第2期22卷 36-47页

作者： Run Ye Azzedine Boukerche Xiao-Song Yu Cheng Zhang Bin Yan Xiao-Jia Zhou school of Automation engineering University of Electronic Science and Technology of ChinaChengdu611731China school of electrical engineering and computer science University of OttawaOttawaK1N6N5Canada Yangtze River Delta Research Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313001China

Data augmentation is an important task of using existing data to expand data *** generative countermeasure network technology to realize data augmentation has the advantages of high-quality generated samples,simple training,and fewer restrictions on the number of generated ***,in the field of transmission line insulator images,the freely synthesized samples are prone to produce fuzzy backgrounds and disordered samples of the main insulator *** solve the above problems,this paper uses the cycle generative adversarial network(Cycle-GAN)used for domain conversion in the generation countermeasure network as the initial framework and uses the self-attention mechanism and channel attention mechanism to assist the conversion to realize the mutual conversion of different insulator *** attention module with prior knowledge is used to build the generation countermeasure network,and the generative adversarial network(GAN)model with local controllable generation is built to realize the directional generation of insulator belt defect *** experimental results show that the samples obtained by this method are improved in a number of quality indicators,and the quality effect of the samples obtained is excellent,which has a reference value for the data expansion of insulator images.

关键词： Data expansion Deep learning Generate confrontation network Insulator

Post-layout simulation driven analog circuit sizing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第4期67卷 289-300页

作者： Xiaohan GAO Haoyi ZHANG Siyuan YE Mingjie LIU David Z.PAN Linxiao SHEN Runsheng WANG Yibo LIN Ru HUANG school of Integrated Circuits Peking University school of computer science Peking University Department of electrical and computer engineering University of Texas at Austin Institute of Electronic Design Automation Peking University

Post-layout simulation provides accurate guidance for analog circuit design, but post-layout performance is hard to be directly optimized at early design stages. Prior work on analog circuit sizing often utilizes pre-layout simulation results as the optimization objective. In this work, we propose a post-layoutsimulation-driven(post-simulation-driven for short) analog circuit sizing framework that directly optimizes the post-layout simulation performance. The framework integrates automated layout generation into the optimization loop of transistor sizing and leverages a coupled Bayesian optimization algorithm to search for the best post-simulation performance. Experimental results demonstrate that our framework can achieve over 20% better post-layout performance in competitive time than manual design and the method that only considers pre-layout optimization.

关键词： analog EDA transistor sizing Bayesian optimization post-layout simulation