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A threshold effect of coupling delays on intra-layer synchronization in duplex networks

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Science China(Technological Sciences) 2018年第12期61卷 1907-1914页

作者： TANG longKun lU JunAn lü jinhu Fujian Province University Key laboratory of Computation Science School of Mathematical Sciences Huaqiao University Department of Mathematics & Statistics Georgia State University School of Mathematics and Statistics Wuhan University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates the impact of inter-layer coupling functions and intra-layer coupling delays on intra-layer synchronization regions and sychronizability. It is found that the inter-layer coupling functions have great influence on intra-layer synchronization regions, as well as on the intra-layer synchronizability. In particular, there exists an inter-layer coupling function such that the inter-layer coupling strength neither improves nor weakens the intra-layer synchronizability. Furthermore, no matter which one of three inter-layer coupling functions is chosen, a small intra-layer delay always keeps the intra-layer synchronized regions almost unchanged, implying that the small delay neither enhances nor suppresses the intra-layer synchronizability. At the same time the delay greatly frustrates the synchronizability in each layer when it is greater than some threshold. Our results may have potential applications for interconnected technological networks where communication delays are inevitably present.

关键词： multiplex network master stability function intra-layer synchronization synchronized region time delay

Substrate concentration effect on gene expression in genetic circuits with additional positive feedback

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Science China(Technological Sciences) 2018年第8期61卷 1175-1183页

作者： WU lulu WANG Pei lü jinhu Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Mathematics and Statistics Henan University School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University

In gene regulatory networks, gene regulation loops often occur with multiple positive feedback, multiple negative feedback and coupled positive and negative feedback forms. In above gene regulation loops, auto-activation loops are ubiquitous regulatory motifs. This paper aims to investigate a two-component dual-positive feedback genetic circuit, which consists of a double negative feedback circuit and an additional positive feedback loop(APFl). We study effect of substrate concentration on gene expression in the single and the networked systems with APFls, respectively. We find that substrate concentration can tune stochastic switch behavior in the signal system and then we explore relationship of substrate concentration with positive feedback strength in aspect of stochastic switch behavior. Furthermore, we also discuss gene expression and stochastic switch behavior in the networked systems with APFls. Based on analysis in the networked systems, we discover that genes express in some specific cells and do not express in the other cells when the expression achieves its steady state. These results can be used to well explain the character of regionalization in the expression of genes and the phenomenon of gene differentiation.

关键词： substrate concentration additional positive feedback loop stochastic switch behavior gene expression gene differ-entiation

Finite-time adaptive consensus of a class of multi-agent systems

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Science China(Technological Sciences) 2016年第1期59卷 22-32页

作者： lIU KeXin WU lulu lü jinhu ZHU HengHui Academy of Mathematics and Systems Science Chinese Academy of Sciences

Multi-agent systems(MASs) are ubiquitous in natural and artificial systems. This paper aims to establish the finite-time adaptive consensus criterion for a class of MASs with nonlinear dynamics. Traditionally, the finite-time consensus criterion is often established based on the prior information on lipschitz constants and the eigenvalues of laplacian matrix. However, it is difficult to acquire the above prior information for most real-world engineering systems. To overcome the above difficulty, this paper develops the finite-time consensus criteria for a class of MASs with nonlinear dynamics via adaptive technique. In detail, we design the finite-time distributed node-based and edge-based adaptive consensus protocols for a class of MASs with fixed and switching topologies. Numerical simulations are also given to validate the proposed finite-time adaptive consensus criterion.

关键词： Multi-agent systems adaptive control finite-time consensus switching topology

Complex Cyber-Physical Networks： From Cybersecurity to Security Control

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Journal of Systems Science & Complexity 2017年第1期30卷 46-67页

作者： WEN Guanghui YU Wenwu YU Xinghuo lü jinhu Department of Mathematics Southeast University Nanjing 210096 China. School of Engineering RMIT University Melbourne VIC 3001 Australia Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190 China

Complex cyber-physical network refers to a new generatio~ of complex networks whose normal functioning significantly relies on tight interactions between its physical and cyber compo- nents. Many modern critical infrastructures can be appropriately modelled as complex cyber-physical networks. Typical examples of such infrastructures are electrical power grids, WWW, public trans- portation systems, state financial networks, and the Interact. These critical facilities play important roles in ensuring the stability of society as well as the development of economy. Advances in informa- tion and communication technology open opportunities for malicious attackers to launch coordinated attacks on cyber-physical critical facilities in networked infrastructures from any Interact-accessible place. Cybersecurity of complex cyber-physical networks has emerged as a hot topic within this con- text. In practice, it is also very crucial to understand the interplay between the evolution of underlying network structures and the collective dynamics on these complex networks and consequently to design efficient security control strategies to protect the evolution of these networks. In this paper, cybersecu- rity of complex cyber-physical networks is first outlined and then some security enhancing techniques, with particular emphasis on safety communications, attack detection and fault-tolerant control, are suggested. Furthermore, a new class of efficient secure the achievement of desirable pinning synchronization control strategies are proposed for guaranteeing behaviors in complex cyber-physical networks against malicious attacks on nodes. The authors hope that this paper motivates to design enhanced security strategies for complex cyber-physical network systems, to realize resilient and secure critical infrastructures.

关键词： Communication topology complex cyber-physical network cybersecurity Internet ofThings secure control.

Stability and performance analysis of the compressed Kalman filter algorithm for sparse stochastic systems

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Science China(Technological Sciences) 2024年第2期67卷 380-394页

作者： lI RongJiang GAN Die XIE SiYu lüjinhu Key laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Zhongguancun laboratory Beijing 100049China School of Aeronautics and Astronautics University of Electronic Science and Technology of ChinaChengdu 611731China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic *** deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed Kalman filter(KF)*** algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional ***,the original high-dimensional sparse signals can be well recovered by a reconstruction *** ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback *** further present the performance of the compressed KF ***,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and ***,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.

关键词： sparse signal compressed sensing Kalman filter algorithm compressed excitation condition stochastic stability tracking performance

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Identifying topologies and system parameters of uncertaintime-varying delayed complex networks

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Science China(Technological Sciences) 2019年第1期62卷 94-105页

作者： WANG Xiong GU HaiBo WANG QianYao lü jinhu lSC Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Node dynamics and network topologies play vital roles in determining the network features and network dynamical *** it is of great theoretical significance and practical value to recover the topology structures and system parameters of uncertain complex networks with available information. This paper presents an adaptive anticipatory synchronization-based approach to identify the unknown system parameters and network topological structures of uncertain time-varying delayed complex networks in the presence of noise. Moreover, during the identification process, our proposed scheme guarantees anticipatory synchronization between the uncertain drive and constructed auxiliary response network simultaneously. Particularly, our method can be extended to several special cases. Furthermore, numerical simulations are provided to verify the effectiveness and applicability of our method for reconstructing network topologies and node parameters. We hope our method can provide basic insight into future research on addressing reconstruction issues of uncertain realistic and large-scale complex networks.

关键词： system parameters and network topologies identification anticipatory synchronization uncertain time-varying delayed complex networks noise-perturbed complex networks

What makes for uniformity for non-contrastive self-supervised learning?

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Science China(Technological Sciences) 2022年第10期65卷 2399-2408页

作者： WANG YinQuan ZHANG XiaoPeng TIAN Qi lü jinhu Key laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Huawei Inc. Shenzhen 518128China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijing 100191China

Recent advances in self-supervised learning(SSl) have made remarkable progress, especially for contrastive methods that target pulling two augmented views of one image together and pushing the views of all other images away. In this setting, negative pairs play a key role in avoiding collapsed representation. Recent studies, such as those on bootstrap your own latent(BYOl)and SimSiam, have surprisingly achieved a comparable performance even without contrasting negative samples. However, a basic theoretical issue for SSl arises: how can different SSl methods avoid collapsed representation, and is there a common design principle? In this study, we look deep into current non-contrastive SSl methods and analyze the key factors that avoid *** achieve this goal, we present a new indicator of uniformity metric and study the local dynamics of the indicator to diagnose collapses in different scenarios. Moreover, we present some principles for choosing a good predictor, such that we can explicitly control the optimization process. Our theoretical analysis result is validated on some widely used benchmarks spanning differentscale datasets. We also compare recent SSl methods and analyze their commonalities in avoiding collapses and some ideas for future algorithm designs.

关键词： contrastive learning self-supervised learning representation uniformity dynamics

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Neural-network-based fully distributed formation control for nonlinear multi-agent systems with event-triggered communication

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Science China(Technological Sciences) 2024年第1期67卷 209-220页

作者： ZHU Guoliang lIU KeXin GU HaiBo lÜjinhu School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Zhongguancun laboratory Beijing 100094China

This paper investigates the consensus-based formation control problem for multi-agent systems with unknown nonlinear *** achieve the desired formation,we propose two formation controllers to achieve the desired formation,one based on system states and the other on system *** proposed controllers utilize adaptive gains to avoid global information and neural networks to estimate and compensate for *** proposed event-triggered schemes avoid continuous communication among agents and exclude the Zeno *** analysis reveals that formation errors are bounded,and numerical simulations are used to validate the effectiveness of the proposed approaches.

关键词： formation control neural network adaptive control event-triggered communication multi-agent systems

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Networked Knowledge and Complex Networks:An Engineering View

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1366-1383页

作者： jinhu lü Guanghui Wen Ruqian lu Yong Wang Songmao Zhang School of Automation Science and Electrical Engineering State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijing 100083 Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China Department of Systems Science School of MathematicsSoutheast UniversityNanjing 211189China

Along with the development of information technologies such as mobile Internet,information acquisition technology,cloud computing and big data technology,the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important *** this context,it is required to develop new methodologies as well as technical tools for network-based knowledge representation,knowledge services and knowledge ***,the term“network”has different meanings in different ***,some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation,knowledge services and knowledge *** paper first reviews some recent advances on complex networks,and then,in conjunction with knowledge graph,proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex *** the unique advantages of deep learning in acquiring and processing knowledge,this paper reviews its development and emphasizes the role that it played in the development of knowledge ***,some challenges and further trends are discussed.

关键词： Complex network knowledge graph networked knowledge neural network