Article
Automation & Control Systems
Shuaibing Zhu, Jin Zhou, Xinghuo Yu, Jun-an Lu
Summary: This article addresses the bounded synchronization of heterogeneous complex dynamical networks by establishing a general theorem for analyzing both local and global synchronization. Utilizing a joint diagonalization-like technique, several easy-to-use bounded synchronization criteria with low-dimensional linear matrix inequalities are derived based on the general theorem. Estimates of synchronization error and admissible initial values are provided, with examples included to verify the theoretical results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
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
Automation & Control Systems
Ruimei Zhang, Deqiang Zeng, Ju H. Park, Kaibo Shi, Yajuan Liu
Summary: This paper investigates the stability and stabilizability of complex-valued memristive neural networks with random time-varying delays via non-fragile sampled-data control. A non-fragile sampled-data controller is designed for CVMNNs, taking into account the influence of gain fluctuations. The new stability and stabilizability criteria are derived based on the average values of the maximum and minimum of the memristive connection weights, different from existing results.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaohong Wang, Zhen Wang, Jianwei Xia, Qian Ma
Summary: This paper addresses the problem of quantized sampled-data control for CVNNs with time-varying delay under the assumption that only quantized measurements are transmitted to the controller. By utilizing stability theory and estimation techniques, a conservative stability criterion is obtained and a corresponding controller is designed, with simulation results demonstrating the effectiveness of the criteria.
NEURAL PROCESSING LETTERS
(2021)
Article
Mathematics, Applied
Ramasamy Saravanakumar, Yang Cao, Ali Kazemy, Quanxin Zhu
Summary: This paper investigates the extended dissipative synchronization problem for stochastic complex dynamical networks with variable coupling delay using sampled-data control. The main contribution of this work is the establishment of unified criteria for extended dissipative synchronization and the design of corresponding control gain matrices.
DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES S
(2022)
Article
Automation & Control Systems
Taotao Hu, Ju H. Park, Xinzhi Liu, Zheng He, Shouming Zhong
Summary: This article investigates the sampled-data-based event-triggered synchronization control for fractional and impulsive complex networks with time-varying delay, proposing novel synchronization criteria and control methods, which are validated through numerical simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Renan L. Pereira, Matheus S. de Oliveira
Summary: This technical article proposes new robust stabilization conditions for discrete-time linear parameter-varying (LPV) systems with linear fractional representation (LFR). The proposed conditions rely on the use of slack variables and decision matrices associated with the LFR approach to provide new controller designs. The article also addresses parameter-dependent Lyapunov functions and full-block multipliers to obtain less conservative synthesis conditions for discrete-time LPV/LFR systems. Design conditions are formulated as linear matrix inequalities to generate robust state-feedback and output-feedback controllers. Numerical examples demonstrate the effectiveness of the proposed method.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Oscar Jaramillo, Bernardino Castillo-Toledo, Stefano Di Gennaro
Summary: In this study, an impulsive observer-based control design for a class of nonlinear systems with time-varying uncertainties is proposed based on the LMI framework, with the use of local Lipschitz conditions. By utilizing sampled measurements of the system output and a time-varying Lyapunov function, sufficient conditions for the existence of the control are provided. Feasible solutions of the LMIs proposed are used to determine the observer and controller gain, showing that the approach effectively estimates and stabilizes all states both mathematically and through simulation.
IEEE CONTROL SYSTEMS LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Hong Wang, Yongjing Ni, Jiawei Wang, Jiaping Tian, Chao Ge
Summary: This paper proposes a new approach to address the master-slave synchronization problem of Markovian jumping neural networks with control packet dropout and sampled-data control. The proposed method guarantees synchronization by establishing stability criteria and employing convex combination and free-matrix-based inequality techniques.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Imran Ghous, Jian Lu, Zhaoxia Duan
Summary: This work investigates the stability and stabilization problems of memristive neural networks (MNNs) considering time-varying delay and external disturbance. The MNNs are transformed into a tractable model by defining logical switched functions. A new Lyapunov-Krasovskii functional is proposed to study the exponential stability (ES) problem of the transformed MNNs model. The design scheme of a state feedback controller is devised to ensure the stability of the overall closed-loop system. The efficacy of the proposed results is demonstrated through suitable examples.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Matheus Senna de Oliveira, Renan Lima Pereira
Summary: The article presents novel filter design conditions for discrete-time linear parameter-varying systems, capable of handling system matrix variations and systems with bounded rates of parameter variation. Filter stability and H-infinity filtering problems are addressed using poly-quadratic Lyapunov functions in terms of linear matrix inequalities, with numerical examples demonstrating the effectiveness of the proposed designs.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Mathematics, Interdisciplinary Applications
M. Hymavathi, G. Muhiuddin, M. Syed Ali, Jehad F. Al-Amri, Nallappan Gunasekaran, R. Vadivel
Summary: This paper investigates the global exponential stability of fractional order complex-valued neural networks with leakage delay and mixed time varying delays. Sufficient conditions for global exponential stability are established by constructing a proper Lyapunov-functional. The stability conditions are expressed in terms of linear matrix inequalities and the effectiveness of the obtained results is illustrated through two numerical examples.
FRACTAL AND FRACTIONAL
(2022)
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
R. Kiruthika, R. Krishnasamy, S. Lakshmanan, M. Prakash, A. Manivannan
Summary: This study proposes a non-fragile sampled-data control scheme to solve the master and slave synchronization problem of chaotic fractional-order delayed neural networks. By incorporating uncertainty information in the control gain matrix and using an appropriate Lyapunov functional, the delay-dependent stability criteria are derived in the form of linear matrix inequality. The proposed scheme successfully synchronizes the fractional-order master and slave systems, as demonstrated by numerical simulations. Overall, the method and control scheme are effective.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Automation & Control Systems
Daniel Cunico, Angelo Cenedese, Luca Zaccarian, Mauro Borgo
Summary: This paper addresses the modeling and control of a gate access automation system. A mechatronic system model is derived and identified, and an approximate explicit feedback linearization scheme is proposed to achieve almost linear response between the electronic driver duty cycle input and the delivered torque. Through offline solving of a nonlinear optimization problem, a feasible trajectory is generated and a low-level feedback controller is designed to track it. The proposed control strategy is tested on an industrial device and meets the requirements in terms of robustness, load disturbance rejection, and tracking performance.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(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.