Article
Automation & Control Systems
Manyu Zhao, Zhengxin Wang, Jun Ye
Summary: This paper investigates the quasi-synchronization problem for a class of heterogeneous dynamical networks using a non-fragile memory sampled-data controller. A control scheme with norm-bounded uncertainty and constant signal transmission delay is designed to consider controller gain fluctuation and communication delay. Theoretical results are illustrated through numerical examples to show the effectiveness of the proposed approach.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2021)
Article
Automation & Control Systems
Zhengxin Wang, Haibo He, Guo-Ping Jiang, Jinde Cao
Summary: This paper investigates quasi-synchronization in networked heterogeneous harmonic oscillators, proposing two distributed synchronization protocols and establishing sufficient conditions for quasi-synchronization. The theoretical results are illustrated through an electrical network and the effectiveness of the sufficient criteria is demonstrated with two examples.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Guoliang Chen, Jianwei Xia, Ju H. Park, Hao Shen, Guangming Zhuang
Summary: This article investigates an aperiodic sampled-data control problem for polytopic uncertain switched complex dynamical networks subject to actuator saturation. By constructing parameters-dependent loop-based Lyapunov functionals, mean-square exponential stability criteria and an asynchronous aperiodic sampled-data controller design method are proposed. The effectiveness of the method is verified through an example using switched Chua's circuit.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Shuiming Cai, Meiyuan Hou
Summary: This paper focuses on the quasi-synchronization problem for fractional-order heterogeneous dynamical networks via aperiodic intermittent pinning control. A general sufficient condition is derived to ensure global quasi-synchronization, and criteria for quasi-synchronization are provided. Additionally, the exponential convergence rate and error bound of the quasi-synchronization are estimated, and an algorithm for designing suitable aperiodic intermittent pinning controllers is presented.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Computer Science, Artificial Intelligence
Guang Ling, Ming-Feng Ge, Xinghua Liu, Gaoxi Xiao, Qingju Fan
Summary: This paper investigates the quasi-synchronization problem of stochastic heterogeneous complex dynamical networks with impulsive couplings and multiple time-varying delays. Through the Lyapunov stability theory, sufficient criteria for quasi-synchronization are established, revealing the relationship between quasi-synchronization performance and stochastic perturbations, as well as the frequency and strength of impulsive coupling. Numerical examples are provided to illustrate the effectiveness of the main results.
Article
Engineering, Multidisciplinary
Xiaona Song, Renzhi Zhang, Choon Ki Ahn, Shuai Song
Summary: This paper focuses on the synchronization problem in Complex Dynamic Networks (CDNs) with semi-Markovian jumping topologies. Two different models are proposed and investigated separately, and some optimizations are introduced to reduce conservatism. Examples and comparative studies are provided to validate the effectiveness and superiority of the theoretical results.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Mathematics
Can Zhao, Jinde Cao, Kaibo Shi, Yiqian Tang, Shouming Zhong, Fawaz E. Alsaadi
Summary: This study investigates the exponential synchronization of complex dynamical networks under improved nonfragile sampled-data event-triggered control. By constructing an improved event-triggered controller, the triggering mode and frequency can be adjusted to adapt to different situations, leading to more intelligent control and ensuring exponential synchronization for the networks.
Article
Automation & Control Systems
Bohui Wang
Summary: This article proposes a distributed error estimation approach for investigating the quasi-synchronization problem of heterogeneous dynamical networks with static and adaptive coupling laws. By introducing a pining control strategy and exploring local information, the proposed approach achieves quasi-synchronization with a bounded synchronization error level, as proven by mathematical analysis.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Mathematics, Interdisciplinary Applications
Haixia Liu, Tianbo Wang
Summary: This paper investigates the exponential synchronization of complex dynamical networks using the sampled-data control method. The paper proposes a novel sampling controller and estimates the bound of the sampling interval based on the stability theory of dynamical systems. A numerical example is provided to demonstrate the effectiveness of the proposed design technique.
Article
Mathematics, Interdisciplinary Applications
Jiaju An, Wei Yang, Xiaohui Xu, Tianxiang Chen, Bin Du, Yi Tang, Quan Xu
Summary: This paper proposes novel decentralized adaptive control protocols for addressing the quasi-consensus problem in multiagent systems with heterogeneous nonlinear dynamics. The protocols adapt control gains and coupling weights, steering consensus errors to bounded regions, without adding complex nonlinear control terms. Numerical tests demonstrate the feasibility of the theoretical derivation, highlighting the achievement of quasi-consensus in heterogeneous multiagent systems.
DISCRETE DYNAMICS IN NATURE AND SOCIETY
(2021)
Article
Mathematics, Interdisciplinary Applications
N. Sakthivel, Yong-Ki Ma, M. Mounika Devi, G. Manopriya, V. Vijayakumar, Mooyul Huh
Summary: This paper discusses the problem of exponential synchronization of semi-Markov jump stochastic complex dynamical networks using nonuniform sampled-data control with random delayed information exchanges among dynamical nodes. The random delayed information exchanges are modeled by a Bernoulli distribution, and stochastic variables are used to capture the randomness. To achieve exponential synchronization, a nonuniform sampled-data control approach is designed. Sufficient criteria in terms of linear matrix inequalities are obtained by constructing an appropriate Lyapunov-Krasovskii functional and using the Wirtinger inequality. Numerical examples are implemented to demonstrate the effectiveness and superiority of the proposed design techniques.
Article
Computer Science, Artificial Intelligence
Seonghyeon Jo, Wookyong Kwon, Sang Jun Lee, Sangmoon Lee, Yongsik Jin
Summary: This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach and enhances the stabilization criterion using linear matrix inequalities (LMIs) and weighting function information. Comparison results show that the presented method outperforms previous methods, verifying the enhancement of the proposed parameterized control.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hong-Bing Zeng, Zheng-Liang Zhai, Huaicheng Yan, Wei Wang
Summary: This article investigates the problem of sampled-data-based synchronization of neural networks with and without considering time delay. By introducing a novel looped functional and employing a generalized free-matrix-based integral inequality, several sufficient conditions are derived to ensure the synchronization between the slave system and the master system. Moreover, the sampled-data controller can be obtained using the linear matrix inequality technique.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hongfei Li, Chuandong Li, Deqiang Ouyang, Sing Kiong Nguang
Summary: This article considers impulsive synchronization for inertial neural networks with unbounded delay and actuator saturation via sampled-data control. Theoretical analysis and numerical simulations demonstrate the effectiveness of the proposed approach. Additionally, a new image encryption algorithm based on hybrid control synchronization theory is presented and validated through experiments.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Information Systems
Li Shu, Runan Guo, Xingyue Liang
Summary: This article focuses on the issue of exponential synchronization in complex dynamical networks. The nonfragile memory sampled-data control is used to mitigate the impact of controller uncertainties. An augmented Lyapunov-Krasovskii functional (LKF) is built using a modified two-sided looped-functional that requires the positive definite only at sampling instants instead of throughout the sampling period. Some exponential synchronization criteria are obtained by combining the Wirtinger-based integral inequality with the optimal reciprocally convex technology, resulting in a larger sampling upper bound. Extensively used numerical simulation examples confirm the validity and advantage of the proposed method.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Mechanical
Lixin Zhao, Chengdai Huang, Xinyu Song
Summary: This paper investigates the Hopf bifurcation of a four-dimensional fractional-order delay competitive website system based on the Lotka-Volterra competition model. A nonlinear feedback controller is introduced to control the start time of the Hopf bifurcation.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Lina Rong, Peng Su, Guo-Ping Jiang, Shengyuan Xu
Summary: In this paper, an edge-based augmented system approach is proposed for second-order nodal consensus, utilizing the edge loop transfer matrix of the closed-loop multi-agent system. This approach introduces additional poles and zeros into edges to construct a channel filter with previous data stored in the memories. The effectiveness of the proposed method is verified through illustrative simulations.
Article
Engineering, Electrical & Electronic
Lina Rong, Yuhan Kan, Xiangpeng Xie, Guo-Ping Jiang, Shengyuan Xu
Summary: This study focuses on the problem of edge-preserving consensus in discrete-time multi-agent systems. A method for designing channel filters, consisting of nominal elements and masking elements, is proposed using an edge-based parallel system design approach. Design criteria for edge-preserving protocols are provided based on the edge sensitivity matrix of the multi-agent system being studied. It is demonstrated that the design of masking elements is based on a single parameter of the nominal element and the H-infinity norm of a collapsed system. An algorithm for designing heterogeneous channel filters is also presented, using a cascade realization of several subsystems.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Engineering, Electrical & Electronic
Huifen Hong, Wenwu Yu, Guo-Ping Jiang, He Wang
Summary: In this paper, a novel type of fixed-time algorithms is investigated to solve time-varying convex optimization problems with time-dependent cost function, constraints or both. First, a general framework algorithm is developed for the unconstrained time-varying optimization problem to track its optimal trajectory within fixed time, which includes the gradient flow-based scheme and Newton-type method as special cases. Then, another algorithm with fixed-time convergence is designed for time-varying optimization problems involving equality constraint, including Newton-type scheme as a special case. The simulation result using first-order Euler discretization is provided to verify the fixed-time convergence achieved by the proposed approach.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Automation & Control Systems
Yayong Wu, Xinwei Wang, Guo-Ping Jiang, Mengqi Gu
Summary: In this paper, a completely data-driven method based on compressive sensing is proposed to detect the structure of faulty edges in complex dynamical networks. The method can directly obtain the structure of faulty edges from limited measurements and is more efficient in data processing.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Min Xiao, Gong Chen, Feilong Wang, Zunshui Cheng, Yi Yao
Summary: Population ecosystems can exhibit tipping points for species extinction. Predicting and understanding the evolution mechanism of tipping points are crucial for ecological balance. Utilizing bifurcation theory, we can predict the emergence of tipping points in a spatiotemporal predator-prey system with fear effect. Both Turing instability and Hopf bifurcation are found to induce tipping points, and explicit formulae are derived to determine their stability and direction. Multiple tipping points are observed in ecological competition systems, occurring more frequently with increasing fear delay. Simulation examples are provided to support the analytical findings.
Article
Automation & Control Systems
Binbin Tao, Min Xiao, Wei Xing Zheng, Ying Zhou, Jie Ding, Guoping Jiang, Xiaoqun Wu
Summary: This article proposes a time-delay feedback control strategy with distributed characteristics to address the flaws in existing control strategies. The designed controller ensures the invariance of equilibriums and continuity of stability intervals, effectively improving the bifurcation threshold of the controlled object.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Mathematics, Interdisciplinary Applications
Y. Wang, H. Yang, X. Y. Yan, G. P. Jiang
Summary: In this paper, a novel coupled code shifted M-ary differential chaos shift keying modulation scheme is proposed for reference removal. The scheme transmits only two information-bearing signals, modulated by selected Walsh code sequences and coupled by the same chaotic message bearer. Theoretical BER expressions are derived and simulations show better performance with lower complexity.
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
(2023)
Article
Automation & Control Systems
Jiajin He, Min Xiao, Yunxiang Lu, Zhen Wang, Wei Xing Zheng
Summary: This paper introduces a fractional-order proportional-integral-derivative (PID) controller to control the dynamic evolution of network congestion. A congestion model with fractional-order PID controller is constructed and the tipping point induced by Hopf bifurcation of the uncontrolled model is studied. The tipping point can be delayed with the controller. Conditions for the occurrence of Hopf bifurcation are given and the stable and unstable ranges of control parameters for the controlled model are deduced. Simulated examples are provided to verify the theoretical results and demonstrate the superiority of the controller in tipping regulation. The bidirectional effects of the controller are also displayed by manipulating the control parameters.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Green & Sustainable Science & Technology
Chaoyue Zheng, Guoping Jiang, Zhiliang Jin
Summary: This paper studies the doping of phosphorus elements in Co3(PO4)2 during the high-temperature phosphorylation reaction. UV-Vis experiments show that the color of the phosphorylation catalyst changes from pink to black-purple, with significantly increased light absorption range and intensity. SEM images reveal the growth of many nanoparticles on the catalyst surface. XPS experiments demonstrate the growth of a stable P (6- )-Co bond on the catalyst surface. The P (6- )-Co bond can inhibit the complexation of photogenerated electron and hole pairs and enhance the transport efficiency of photogenerated electrons, thus improving the hydrogen production performance. Phosphorus-doped Co3(PO4)2 releases three times more hydrogen than CO3(PO4)2. This paper provides a novel design solution for the photocatalyst preparation method.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Computer Science, Interdisciplinary Applications
Wentong Du, Min Xiao, Jie Ding, Yi Yao, Zhengxin Wang, Xinsong Yang
Summary: This paper proposes a delayed fractional-order predator-prey system with trans-species infectious diseases and implements the corresponding control strategy using fractional-order proportional-derivative (PD) control. The stability and bifurcation of the uncontrolled system are investigated, and the impacts of the controller on stability and bifurcation are explored. The effects of fractional order and control parameters on dynamics are also examined.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Mathematics, Applied
Sijiao Sun, Zhengxin Wang, Chongfang Jin, Yuanzhen Feng, Min Xiao, Cong Zheng
Summary: This paper investigates point-to-point quasi-synchronization in a two-layer heterogeneous neural network system, which is more practical due to the presence of multiple subsystems and different types of interaction. The conditions for global exponential synchronization in the driving layer are derived, and the global point-to-point quasi-synchronization between the driver layer and response layer is studied under hybrid control and event-triggered control. The hybrid control strategy ensures that the event is triggered only when the trigger condition is violated, and dynamical adaptive control is also involved. The elimination of Zeno behavior is achieved through the application of Lyapunov method and matrix theory. A numerical example is provided to validate the theoretical results.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2023)
Article
Computer Science, Artificial Intelligence
Jiajin He, Min Xiao, Jing Zhao, Zhengxin Wang, Yi Yao, Jinde Cao
Summary: The influence of network topology on the response dynamics of neural networks is still not completely understood. Understanding the relationship between topological structures and dynamics is crucial for understanding brain function. In this study, a new diffusion neural network model with a binary tree structure and multiple delays was proposed to explore the role of topological structures in the response dynamic. A novel full-dimensional nonlinear state feedback control strategy was also introduced to optimize relevant neurodynamics. The effectiveness of the proposed control strategy was demonstrated through numerical examples and comparative experiments.
Article
Automation & Control Systems
Jie Ding, Shimeng Huang, Yuefei Hao, Min Xiao
Summary: In this paper, a Levy reptile search algorithm (LRSA) is proposed to improve the global search capability and convergence speed of reptile search algorithm. Through the introduction of circle chaotic mapping and Levy flight strategy, the LRSA achieves a more uniform and diversified initial population distribution, as well as enhanced accuracy and convergence speed in global search. Experimental results demonstrate the effectiveness of LRSA in terms of average convergence speed and its application in fractional order model identification of lithium batteries.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2023)
Article
Computer Science, Artificial Intelligence
Chengdai Huang, Heng Liu, Tingwen Huang, Jinde Cao
Summary: This article explores the bifurcations in a fractional-order neutral-type neural network with two nonidentical delays, demonstrating the stability performance with a smaller time delay and comparing it with integer-order neural networks. The effects of coefficients and time delay on bifurcation points are analyzed, and the superiority of fractional-order delayed neural network is highlighted. Neglecting the reverberations of neutral delays can lead to inaccurate stability results. Numerical experiments validate the findings.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
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.
