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2,312 publications from this institution

DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs

With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This paper investigates three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax. It is found that different attack methods have their specific attack preferences on changing the target network structures. Such attack pattern are further verified by experimental results on some real-world networks, revealing that generally the top four most important network attributes on detecting adversarial samples suffice to explain the preference of an attack method. Based on these findings, the network attributes are utilized to design machine learning models for adversarial sample detection and attack method recognition with outstanding performance.

Junhao Zhu, Yalu Shan, Jinhuan Wang et al. 2021Preprint

Control of period-doubling bifurcation and chaos in a discrete nonlinear system by the feedback of states and parameter adjustment

In this paper, the control of delaying period-doubling bifurcations and unstable periodic orbits embedded in a chaotic attractor of a discrete nonlinear dynamical system is effectively realized by using the state variables feedback and parameter variation. Moreover, the 2n periodic orbits of the system can be controlled into the 2m (m<n) periodic orbits by the methods proposed.

Luo Xiao-Shu, Guanrong Chen, Bing-Hong Wang et al. 2003Article

Adaptive Neural Network-Based Security Asynchronous Control for Uncertain Markov Jump Power Systems with Dead Zone under DoS Attack

The paper deals with the security control stabilization problem of uncertain Markov jump power systems with input dead zone under stochastic denial-of-service (DoS) attack. DoS attack is modeled as a discrete-time Markov process. Dual hidden Markov models are respectively used to detect the modes of the original power systems and the one under DoS attack. Based on the detected modes and neural networks (NNs), adaptive NN-based security asynchronous control strategies are proposed, where both state feedback and output feedback are studied simultaneously. With the developed control laws, all trajectories of the closed-loop systems are bounded stable in the stochastic setting. Simulation results demonstrate the correctness and usefulness of the proposed techniques.

Enjun Liu, Shanling Dong, Bo Wang et al. 2024Preprint

SECURE COMMUNICATION BASED ON CHAOTIC SYNCHRONIZATION

No abstract is provided for this article.

Ping Li, Zhong Li, Wolfgang A. Halang et al. 2004Article

Anticontrol of Chaos for Continuous-Time Systems

Guanrong Chen, Ling Yang, Zengrong Liu 2002Article

On some controllability conditions for chaotic dynamics control

In this paper, we present some conventional feedback controller design principles for chaos control, with mathematical controllability conditions derived via the Lyapunov function methods. The chaotic Chua's circuit and Duffing oscillator are used as examples to illustrate the fundamental concepts and basic methodology employed by this unified Lyapunov approach, in both linear and non-linear controllers design, for the control of chaotic dynamics.

Guanrong Chen 1997Article

Data-Driven Discovery of Block-Oriented Nonlinear Models Using Sparse Null-Subspace Methods

This article develops an identification algorithm for nonlinear systems. Specifically, the nonlinear system identification problem is formulated as a sparse recovery problem of a homogeneous variant searching for the sparsest vector in the null subspace. An augmented Lagrangian function is utilized to relax the nonconvex optimization. Thereafter, an algorithm based on the alternating direction method and a regularization technique is proposed to solve the sparse recovery problem. The convergence of the proposed algorithm can be guaranteed through theoretical analysis. Moreover, by the proposed sparse identification method, redundant terms in nonlinear functional forms are removed and the computational efficiency is thus substantially enhanced. Numerical simulations are presented to verify the effectiveness and superiority of the present algorithm.

Junlin Li, Xiuting Li, Hai‐Tao Zhang et al. 2020Article

Notes and References

No abstract is provided for this article.

Charles K. Chui, Guanrong Chen 1989Chapter in a book

Realization of Biquadratic Impedance as Five-Element Bridge Networks

This report includes the original manuscript and the supplementary material of "Realization of Biquadratic Impedance as Five-Element Bridge Networks".

Michael Z. Q. Chen, Kai Wang, Chanying Li et al. 2017Preprint

A Channel-Fused Dense Convolutional Network for EEG-Based Emotion Recognition

Human emotion recognition could greatly contribute to human–computer interaction with promising applications in artificial intelligence. One of the challenges in recognition tasks is learning effective representations with stable performances from electroencephalogram (EEG) signals. In this article, we propose a novel deep-learning framework, named channel-fused dense convolutional network, for EEG-based emotion recognition. First, we use a 1-D convolution layer to receive weighted combinations of contextual features along the temporal dimension from EEG signals. Next, inspired by state-of-the-art object classification techniques, we employ 1-D dense structures to capture electrode correlations along the spatial dimension. The developed algorithm is capable of handling temporal dependencies and electrode correlations with the effective feature extraction from noisy EEG signals. Finally, we perform extensive experiments based on two popular EEG emotion datasets. Results indicate that our framework achieves prominent average accuracies of 90.63% and 92.58% on the SEED and DEAP datasets, respectively, which both receive better performances than most of the compared studies. The novel model provides an interpretable solution with excellent generalization capacity for broader EEG-based classification tasks.

Zhongke Gao, Xinmin Wang, Yuxuan Yang et al. 2020Article

Neural sliding-mode pinning control for output synchronization for uncertain general complex networks

A novel approach for output synchronization for uncertain general complex networks with non-identical nodes is proposed. This goal can be solved applying to a small fraction of network nodes (pinned ones) a neural controller; this controller is composed of an on-line identifier based on a recurrent high-order neural network, and a sliding-mode controller. An illustrative example is included, which is composed of a network of ten nodes, with different self-dynamics, illustrating the effectiveness and good performance of the proposed control scheme.

Oscar J. Suárez, Carlos J. Vega, Edgar N. Sánchez et al. 2019Article

Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence

This article surveys the interdisciplinary research of neuroscience, network science, and dynamic systems, with emphasis on the emergence of brain-inspired intelligence. To replicate brain intelligence, a practical way is to reconstruct cortical networks with dynamic activities that nourish the brain functions, instead of using only artificial computing networks. The survey provides a complex network and spatiotemporal dynamics (abbr. network dynamics) perspective for understanding the brain and cortical networks and, furthermore, develops integrated approaches of neuroscience and network dynamics toward building brain-inspired intelligence with learning and resilience functions. Presented are fundamental concepts and principles of complex networks, neuroscience, and hybrid dynamic systems, as well as relevant studies about the brain and intelligence. Other promising research directions, such as brain science, data science, quantum information science, and machine behavior are also briefly discussed toward future applications.

Bin Hu, Zhi‐Hong Guan, Guanrong Chen et al. 2021Article

Linked PDF of Table of Contents

Mechanisms of the Nickel-Catalysed Hydrogenolysis and Cross-Coupling of Aryl Ethers Ni OMe Nu Nucleophile L n Ni(II) Ar O Me LA L n Ni(0) Ar OMe Nu Ni(I) L n Nu Oxidative addition Lewis acid assisted Anionic nickelates Ni(I) intermediates

Andryj M. Borys, Eva Hevia, Regina Westphal et al. 2022Article

Density evolution method and threshold decision for irregular LDPC codes

Density evolution is a new method for analyzing the asymptotic performance of network capability approaching error-correcting codes. For irregular LDPC codes with message-passing decoding, the density evolution method can track the messages to find out the threshold, enabling optimization of the degree distribution. In this paper, the principle of density evolution combined with the decoding process is firstly explored. Then, two algorithms for programming the evolution proceeding are discussed: the discretized density evolution and the Gaussian approximation, as well as their application conditions. Finally, simulation results are presented.

Lin Wang, Juan Xiao, Guanrong Chen 2004Article

Dynamics of Double Locally Active Memristors-Based Neuron and its Circuit Implementation

Locally active memristor (LAM), which has an ability to amplify fluctuations, is a natural component for constructing artificial neuron circuits. This paper proposes a novel third-order neuron circuit by paralleling two LAMs and a capacitor. Firstly, two parallel LAMs are equivalently modeled as a second-order LAM to facilitate theoretical analysis. Regarding the third-order neuron, the parameter design and operating condition are obtained by calculating its small signal impedance functions poles or Jacobin matrixs eigenvalues. It is demonstrated that the neuron exhibits various neuromorphic behaviors, including periodic spiking, chaos and burst-number adaptation. Due to the two different LAMs, the proposed thirdorder system has multiple equilibrium points, leading to the generation of coexisting attractors. Interestingly, the generated chaotic attractor does not revolve around a single unstable equilibrium point, but is located between two unstable equilibrium points. Furthermore, the emergence of oscillating behaviors is dependent on the distance between the two unstable equilibrium points. Detailed theoretical and simulation analysis are presented to investigate the neuron dynamics and provide an explanation for the observed neuromorphic behaviors. Finally, physical circuit implementation of the neuron is constructed based on the memristor emulator, which also demonstrates the practicability of the proposed neuron model and the correctness of the theoretical analysis.

Yan Liang, Qingdian Geng, Qingqing Cai et al. 2025Article

Fixed-time consensus tracking of multi-agent systems under a directed communication topology

This paper considers the fixed-time consensus tracking problem for a class of first-order multi-agent systems with directed topology, where the agents may be subject to unknown disturbances. First, some consensus tracking protocols for the nominal multi-agent systems are designed to make the states of followers track those of the leader within fixed time. Then, a new class of consensus tracking protocols with augmented term is further designed to ensure fixed-time robust consensus tracking of multi-agent systems in the presence of disturbance. Finally, some simulations are performed to verify the theoretical results proposed in this paper.

He Wang, Wenwu Yu, Guanghui Wen et al. 2016Article

A new electrochemiluminescent sensing system for glucose based on the electrochemiluminescent reaction of bis-[3,4,6-trichloro-2-(pentyloxycarbonyl)-phenyl] oxalate

In this paper, the electrochemiluminescence (ECL) behavior of bis-[3,4,6-trichloro-2-(pentyloxycarbonyl)-phenyl] oxalate (BTPPO) at glassy carbon electrode (GCE) in phosphate buffer solution in the presence of hydrogen peroxide has been investigated when linear sweep voltammetry was applied. The optimum chemical conditions and electrochemical parameters for this ECL system have been investigated in detail. Under the optimum conditions, it was found that the concentration of BTPPO was linear with the ECL intensity in the range of 3.0×l0−6 to 3.0×10−4 mol/L, and the detection limit (S/N=3) for BTPPO was 1.0×10−7 mol/L. The possible mechanism for ECL of BTPPO at the GCE in the presence of hydrogen peroxide was also discussed. Furthermore, based on the fact that glucose oxidase can react with glucose to produce hydrogen peroxide, a new ECL sensing system of BTPPO has been developed for detection of glucose. The enhanced ECL intensity has a linear relationship with the concentration of glucose in the range of 1.0×l0−4 to 1.0×10−3 mol/L, and the detection limit for glucose is found to be 5.0×10−5 mol/L (S/N=3).

Zuliang Chen, Jun Wang, Zhibin Lin et al. 2007Article

Research Frontier in Chaos Theory and Complex Networks

In recent years, as natural and social sciences are rapidly evolving, classical chaos theoryand modern complex networks studies are gradually interacting each other with a great joineddevelopment [...]

Guanrong Chen, Marius‐F. Danca, Xiao‐Song Yang et al. 2018Editorial Material

Realizations of biquadratic impedances as five-element bridge networks containing one inductor and one capacitor

This paper is concerned with the realization problem of biquadratic impedances as a five-element bridge network with one inductor and one capacitor. First, a group of networks that can handle all the cases are listed, which are classified as three quartets. Then, together with the principle of duality, the realizability conditions of two quartets among them are derived. In the process of the derivation, the realizability conditions based on the existence of positive roots that satisfy certain conditions are converted into the conditions only in terms of the coefficients of the function. Finally, utilizing a canonical form of biquadratic functions, combining the conditions yields a necessary and sufficient condition for any biquadratic impedance to be realizable as a five-element bridge network with one inductor and one capacitor.

Michael Z. Q. Chen, Kai Wang, Chanying Li et al. 2014Article

Suspension Design of Formula Racing Vehicle with Roll Independent Control Function

No abstract is provided for this article.

Zhe Liu, Zhonghao Li, Guanrong Chen et al. 2022Chapter in a book

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