Article
Automation & Control Systems
Guang Ling, Xinzhi Liu, Ming-Feng Ge, Yonghong Wu
Summary: This paper investigates cluster synchronization of complex dynamical networks with noise and time-varying delays using a delayed pinning impulsive control scheme, establishing criteria to guarantee synchronization while revealing the relationship between performance and factors like impulsive input delays. The effectiveness of the theoretical results is demonstrated through numerical examples and computer simulations.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Engineering, Multidisciplinary
Juan Chen, Xinru Li, Xiaoqun Wu, Ganbin Shen
Summary: This paper focuses on the prescribed-time synchronization of complex dynamical networks (CDNs) with and without time-varying delays. Suitable controllers are designed to obtain CDNs with directed spanning trees. Two sufficient conditions are derived using Lyapunov Stability Theory to ensure that CDNs reach synchronization within a prescribed finite time. Unlike existing works, where the settling time is determined by initial conditions or control parameters, the proposed method allows for arbitrary assignment of the settling time as needed. Two examples are provided to validate the theoretical results.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Automation & Control Systems
Rathinasamy Sakthivel, Ramalingam Sakthivel, Oh-Min Kwon, Boomipalagan Kaviarasan
Summary: This article proposes a new method to simultaneously solve the problems of fault estimation and synchronization for a class of delayed coupling complex dynamical networks. The method involves establishing an intermediate estimator for each node and designing a distributed memory state feedback controller. It does not require observer matching conditions or upper bounds of fault signals, reducing conservatism significantly.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Ilya Nachevsky, Olga Andrianova, Isaac Chairez, Alexander Poznyak
Summary: This study presents a state nonparametric identifier based on neural networks with continuous dynamics and using control barrier Lyapunov functions. The developed learning laws consider the preliminary information of the system states and the state restrictions, improving the identification results without violating the state limits.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Mathematics
Alexander N. Pchelintsev
Summary: This article discusses the search procedure for Poincare recurrences to classify solutions on an attractor of a fourth-order nonlinear dynamical system, using a previously developed high-precision numerical method. The Lyapunov exponents are calculated for the resulting limiting solution, using the modified Benettin's algorithm to study the stability of the found regime and confirm the type of attractor.
Article
Automation & Control Systems
Wenying Yuan, Yuechao Ma
Summary: This paper investigates the problem of finite-time Hoo synchronization for complex dynamical networks with time-varying delays and unknown internal coupling matrices. It presents an adaptive control method to solve the synchronization problem by utilizing appropriate adaptive controllers and devising a special Lyapunov-Krasovskii function.
Article
Mathematics, Interdisciplinary Applications
V. P. Vera-avila, J. R. Sevilla-Escoboza, R. R. Rivera Duron, J. M. Buldu
Summary: The phenomenon of dynamical consistency in a network of nonlinear oscillators was investigated both numerically and experimentally. Dynamical consistency refers to the ability of a dynamical system to respond in the same way when perturbed by the same external signal. The study focused on certain dynamical systems connected through a non-regular network, exploring synchronization and consistency arising from varying coupling strengths within the network.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Mathematics
Yifan Zhang, Tianzeng Li, Zhiming Zhang, Yu Wang
Summary: This paper investigates the global synchronization of complex networks with fractional-order chaotic nodes. A simple Lyapunov function and a feedback controller are used. The authors propose the GMMP method to obtain numerical solutions and new synchronous criteria based on feedback control. Numerical simulations demonstrate the effectiveness and universality of the proposed method.
Article
Computer Science, Artificial Intelligence
Sanbo Ding, Zhanshan Wang, Xiangpeng Xie
Summary: This article investigates the periodic event-triggered synchronization of discrete-time complex dynamical networks (CDNs), proposing a method that avoids point-to-point monitoring of measurements and enlarges the lower bound of inter-event intervals. "Discontinuous" Lyapunov functionals are constructed to handle the sawtooth constraint of sampling signals, and sufficient conditions for ultimately bounded synchronization are derived for the networks with or without considering communication delays. The method is shown effective and improvements are demonstrated through simulation examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Aili Fan, Junmin Li
Summary: This paper proposes a prescribed performance adaptive learning control scheme for complex dynamical networks, which ensures that the states of all nodes synchronize to the specified target trajectory while satisfying performance constraints. Based on Lyapunov stability theory, it is proven that all signals in the closed-loop systems are bounded and the synchronization errors converge to a prescribed residual set. Simulation results validate the proposed approach.
Article
Automation & Control Systems
Haibo Gu, Xiong Wang, Kexin Liu, Jinhu Lu
Summary: This paper focuses on the novel synchronization protocol design problem of nonlinear stochastic dynamical networked systems, with sufficient conditions given for selecting protocol parameters to achieve global synchronization.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Dan Yang, Xiaodi Li, Shiji Song
Summary: This paper investigates the globally exponential synchronization of complex dynamical networks with switching topology involving impulse action. A novel state-dependent switching approach is designed to tune the switching topology based on the information of the node dynamics with impulses. Sufficient conditions for network synchronization based on the proposed approach are proposed in the framework of impulse action. The relationship between impulse action, network structure, and switching topology is established, and two different impulse actions on the network dynamics are considered.
Article
Computer Science, Artificial Intelligence
Yao Xu, Wenxi Liu, Yongbao Wu, Wenxue Li
Summary: This article investigates finite-time synchronization for fractional-order fuzzy time-varying coupled neural networks subject to reaction-diffusion. It establishes a new framework under fuzzy-based feedback control and fuzzy-based adaptive control, and proposes an innovative graph-theory-based time-varying Lyapunov function. Through graph theory and the Lyapunov method, several finite-time synchronous criteria are obtained, and the estimation of the settling time is derived.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Sapna Baluni, Vijay K. Yadav, Subir Das
Summary: This article investigates the quasi-projective synchronization of time-varying delayed complex-valued Cohen Grossberg Neural Networks (CGNNs). The study aims to find a criterion for quasi-projective synchronization of two non-identical CGNNs by constructing a suitable controller and utilizing the direct method. The significant contribution is estimating the bound of the synchronization error and establishing sufficient criteria for synchronization. The proposed method's effectiveness is justified through numerical simulation in a specific example.
INFORMATION SCIENCES
(2022)
Review
Physics, Multidisciplinary
Dibakar Ghosh, Mattia Frasca, Alessandro Rizzo, Soumen Majhi, Sarbendu Rakshit, Karin Alfaro-Bittner, Stefano Boccaletti
Summary: Complex network theory has provided an ideal framework for studying the relationships between the connectivity patterns and emergent synchronized functioning in systems. This review summarizes the major results of contemporary studies on synchronization in time-varying networks, focusing on two paradigmatic frameworks. It also discusses promising directions and open problems for future research.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
(2022)
Article
Automation & Control Systems
Guoliang Wei, Wangyan Li, Derui Ding, Yurong Liu
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2020)
Article
Automation & Control Systems
Jingyang Mao, Derui Ding, Guoliang Wei, Hongjian Liu
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2019)
Article
Automation & Control Systems
Jiajia Li, Guoliang Wei, Derui Ding
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2019)
Article
Automation & Control Systems
Wangyan Li, Zidong Wang, Daniel W. C. Ho, Guoliang Wei
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2020)
Article
Computer Science, Information Systems
Xin Li, Guoliang Wei, Licheng Wang
Summary: This paper presents a distributed set-membership filter for systems with unknown but bounded noises over wireless sensor networks, considering Denial-of-Service attacks and fading effects on sensor measurements. The study aims to estimate desired sets involving actual plant states by implementing consistency tests and establishing a recursive scheme on zonotopes involving real system states. The proposed outer approximation minimization criterion and optimization issue are utilized to seek parameters of the intersection zonotope in order to demonstrate the feasibility of the filter through illustrative simulations.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Shuai Liu, Zidong Wang, Jun Hu, Guoliang Wei
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2020)
Article
Computer Science, Artificial Intelligence
Xin Tian, Guoliang Wei, Licheng Wang, Jun Zhou
Article
Computer Science, Artificial Intelligence
Shuai Liu, Zidong Wang, Yun Chen, Guoliang Wei
Article
Mathematics, Applied
Huamin Wang, Guoliang Wei, Shiping Wen, Tingwen Huang
Summary: This article focuses on constructing an impulsive disturbed neural network model with delays in quaternion space and deriving exponential stability conditions using generalized norms. By researching the stability of the IQVDNN system, several exponential stability sufficient criteria are obtained.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Yuying Dong, Yan Song, Guoliang Wei
Summary: This article investigates the efficient model-predictive control (EMPC) problem of a class of nonlinear systems in the framework of interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy. A stochastic communication protocol (SCP) governed by a Markov chain is adopted to improve data transmission reliability and reduce network communication burden. The purpose is to design desired EMPC controllers to ensure system stability and balance computation burden, feasible region, and control performance.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Automation & Control Systems
Qi Guan, Guoliang Wei, Licheng Wang, Yan Song
Summary: This article presents a novel feature points tracking algorithm based on IMU-aided information fusion, which reduces the search space by predicting the position, establishes a local search window, and attaches a feature update module, effectively improving the accuracy and efficiency of matching.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Wei Chen, Derui Ding, Hongli Dong, Guoliang Wei, Xiaohua Ge
Summary: This article focuses on the finite-horizon H8 bipartite consensus control problem for a class of discrete time-varying cooperation-competition multiagent systems with the round-robin protocol. The study aims to design a bipartite consensus controller with the RR protocol to ensure the desired H-infinity bipartite consensus over a given finite horizon. The proposed scheme is verified to be effective through a simulation example.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Automation & Control Systems
Licheng Wang, Zidong Wang, Bo Shen, Guoliang Wei
Summary: This article explores the recursive filtering problem for a class of discrete-time nonlinear stochastic systems with fading measurements, utilizing a multiple description coding scheme to minimize error variance and improve efficiency in data transmission. By introducing independent Bernoulli distributed random variables and modeling channel fading with the Mth-order Rice fading model, the study aims to design a recursive filter that can effectively handle stochastic noises, channel fading, and data coding-decoding mechanisms simultaneously. Through the use of Riccati difference equations and stochastic analysis, the explicit form of desired filter parameters is derived, and a simulation experiment is provided to demonstrate the effectiveness of the developed filtering scheme.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Lei Sun, Xin Tian, Derui Ding, Guoliang Wei
Summary: This paper investigates the set-membership consensus of cooperation-competition multi-agent systems (CCMASs) using the round-robin (RR) protocol. It proposes a novel concept of set-membership consensus for CCMASs, and provides a co-design framework of set-membership estimation and set-membership consensus. An illustrated example is given to demonstrate the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Automation & Control Systems
Wangyan Li, Fuwen Yang, David Thiel, Guoliang Wei
Summary: This paper investigates the problem of finding and identifying the minimal number of sensor nodes for a sensor network. It first proposes a minimal nodes uniform observability condition and applies it to the stability issues of the distributed Kalman filtering algorithm. Results about the relation of filtering performance before and after selecting the minimal number of sensor nodes are obtained. Finally, optimization solutions and an example are given to find the minimal number of sensor nodes for a sensor network.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
