Article
Computer Science, Artificial Intelligence
Lingzhong Zhang, Jie Zhong, Jianquan Lu
Summary: This paper investigates the finite-time synchronization problem for fractional-order complex dynamical networks with intermittent control. A general fractional-order intermittent differential inequality is obtained and criteria for finite-time convergence are established. The theoretical results are illustrated by numerical examples.
Article
Automation & Control Systems
Yan Dong, Junwei Chen, Jinde Cao
Summary: The present study investigates the issue of fixed-time synchronization for delayed complex networks under intermittent pinning control. By developing a new differential inequality lemma and utilizing Lyapunov theory and pinning control approach, sufficient conditions are proposed to guarantee fixed-time pinning synchronization for intermittently controlled delayed complex networks. Additionally, the settling time is explicitly estimated, and the scenario of periodic complete intermittent control is also discussed.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Engineering, Electrical & Electronic
Changjiang Song, Jin Zhou, Jinshui Wang
Summary: This paper focuses on the finite time inter-layer synchronization of duplex networks using intermittent control. Unlike previous works, the intermittent control scheme proposed in this paper is event-dependent, allowing for more flexibility in control time. By using the proposed intermittent control mechanism, a sufficient condition is provided to guarantee the finite time inter-layer synchronization of the duplex networks. Numerical examples are presented to demonstrate the validity of the theoretical results.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Engineering, Electrical & Electronic
Xingting Geng, Jianwen Feng, Na Li, Yi Zhao, Chengbo Yi, Jingyi Wang
Summary: This paper addresses the finite-time synchronization (FTS) of directed multiweighted complex networks (MWCNs) with stochastic perturbations using intermittent control. Unlike previous FTS approaches for single-weight networks, FTS of directed MWCNs is achieved by introducing an allowable deviation synchronization bound. A novel Lyapunov function is constructed by selecting a vector that satisfies the allowable deviation synchronization bound. Discontinuous intermittent control is used to achieve FTS due to limited resources. Theoretical results are validated through examples.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Mathematics, Applied
Huawei Liu, Xiangyong Chen, Jianlong Qiu, Feng Zhao
Summary: This paper addresses the finite-time synchronization problem of complex networks with hybrid-coupled time-varying delay by combining aperiodically intermittent pinning control method and event-triggered control method. The controller design ensures synchronization of all nodes in finite time, with reduced communication frequency through the event-triggered mechanism and periodic reselection of pinning nodes. Theoretical results are verified through a simulation example.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2021)
Article
Automation & Control Systems
MingYu Wang, Feng Zhao, JianLong Qiu, XiangYong Chen
Summary: This paper addresses the finite-time synchronization problem of stochastic complex networks with mixed delays. A controller based on adaptive aperiodic intermittent control strategy is proposed to achieve synchronization in the finite-time, even for network systems with unknown characteristics or a wide range of disturbance. The Lyapunov function and various inequality techniques are utilized to establish the finite-time synchronization criterion. The theoretical results are validated through an example of Chua's circuit system.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Mathematics
Yi Liang, Yunyun Deng, Chuan Zhang
Summary: This paper investigates the outer synchronization problem of multi-layer dynamical networks with additive couplings using aperiodically intermittent pinning control. The study proposes a state-feedback intermittent pinning controller and derives sufficient conditions for achieving outer synchronization based on Lyapunov stability theory and matrix inequalities. Furthermore, an adaptive intermittent pinning controller is introduced to discuss the outer synchronization problem of the multi-layer networks, and an appropriate Lyapunov function is chosen to prove the synchronization criteria. The effectiveness of the control schemes is demonstrated through three simulation examples.
Article
Automation & Control Systems
Jianwen Feng, Jingxin Chen, Jingyi Wang, Yi Zhao
Summary: This paper investigates the exponential synchronization problem for complex networks with hybrid delays using the event intermittent control (EIC) strategy. A modified lemma related to delays is derived without pre-designing intermittent instants, and synchronization criteria with less conservatism are established using linear matrix inequalities (LMIs). The effectiveness of the proposed EIC strategy is verified through simulations of a numerical example.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Zhihong Liang, Sanbo Ding, Yanhui Jing, Xiangpeng Xie
Summary: This paper studies the synchronization control of discrete-time complex dynamical networks under intermittent networked communication. A novel aperiodic intermittent event-triggered mechanism is proposed to save communication and computing resources. The concepts of average working time ratio and average working period are introduced to describe intermittent aperiodic performance. A sufficient criterion for the synchronization of discrete-time complex dynamical networks is obtained.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Tianrui Chen, Wenhua Wang, Yongbao Wu
Summary: This article focuses on quasi-synchronization of fuzzy heterogeneous complex networks and proposes an aperiodically intermittent discrete-time state observations control strategy. A valid approach combining Lyapunov method with graph theory is proposed. The relationship between control gain and convergence domain size is established. The simulation results of an illustrative example validate the feasibility and validity of the obtained results.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jin-Liang Wang, Lin-Hao Zhao, Huai-Ning Wu, Tingwen Huang
Summary: This article focuses on finite-time passivity (FTP) and finite-time synchronization (FTS) for complex dynamical networks with multiple state/derivative couplings. The PD control method is utilized to formulate criteria for FTP, and further investigate FTS in the networks. The validity of the proposed PD controllers is demonstrated through numerical examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Wenjia Zhou, Yuanfa Hu, Xiaoyang Liu, Jinde Cao
Summary: This paper investigates the finite-time synchronization problem of coupled neural networks with parameter uncertainties. By utilizing the adaptive periodically intermittent control method and finite-time stability theory, sufficient conditions are established to achieve synchronization within a finite time. Both delayed and non-delayed neural network models are considered, and the upper bounds of synchronization time are estimated. Two illustrative examples are provided to demonstrate the effectiveness and applicability of the proposed theoretical results.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Liyan Cheng, Fangcheng Tang, Xinli Shi, Xiangyong Chen, Jianlong Qiu
Summary: This article investigates the finite-time and fixed-time synchronization for memristive neural networks (MNNs) with mixed time-varying delays under the adaptive aperiodically intermittent adjustment strategy. The article employs the aperiodically intermittent adjustment feedback control and adaptive control to drive the MNNs to achieve synchronization in finite time and fixed time. The sufficient conditions for finite-time and fixed-time synchronization of the drive-response MNNs are obtained by designing an effective aperiodically intermittent adjustment with adaptive updating law.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Tianhu Yu, Jinde Cao, Leszek Rutkowski, Yi-Ping Luo
Summary: The article explores the finite-time synchronization problem for master-slave complex-valued memristive neural networks. A novel Lyapunov-function based stability criterion with impulsive effects is proposed for designing the decentralized finite-time synchronization controller. The sufficient synchronization condition provides not only the settling time but also the attractive domain with respect to impulsive gain, average impulsive interval, and initial values. Two examples demonstrate the effectiveness of the hybrid control strategy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Mathematics, Applied
Beibei Guo, Yu Xiao
Summary: This paper studies exponential synchronization for hybrid multi-weighted complex networks by considering both Markov jump and reaction-diffusion effects. Several synchronization criteria are derived using network split technique, graph theory, and Lyapunov method, showing the effects of multiple weights, Markov jump, and reaction-diffusion. An application to Cohen-Grossberg neural networks is also conducted with corresponding synchronization criterion. Numerical simulations are presented to demonstrate the effectiveness of the obtained theoretical results.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(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.