Article
Mathematics
Xiufeng Miao, Yaoqun Xu, Fengge Yao
Summary: This paper addresses the problem of ultimately exponentially bounded estimate for nonlinear stochastic discrete-time systems under generalized Lipschitz conditions. A new sufficient condition is proposed to ensure the uniform exponential boundedness of the estimation error system in the mean square sense. The gain matrix can be obtained by solving a matrix inequality. Numerical examples are provided in the last section to validate the effectiveness of the proposed conclusions.
Article
Automation & Control Systems
Jie Wang, Xin Chen
Summary: This paper focuses on the H-infinity consensus control problem of Markov jump multi-agent systems with partly unknown transition probabilities and multiplicative noise. The transition probabilities of the Markov jumps are considered to be partly unknown, which is more applicable in practical scenarios. Sufficient conditions for the H-infinity consensus control problem are derived using less linear matrix inequalities. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed approach.
ASIAN JOURNAL OF CONTROL
(2023)
Article
Automation & Control Systems
Wenhai Qi, Guangdeng Zong, Yakun Hou, Mohammed Chadli
Summary: This article focuses on the discrete-time sliding mode control (DSMC) for nonlinear semi-Markovian switching systems (S-MSSs). Due to the difficulty in obtaining complete information of the semi-Markov Kernel in practical applications, it is commonly considered to be partly unknown. By utilizing the prior information of the sojourn-time upper bound for each switching mode, this article proposes sufficient conditions under the equivalent DSMC law for mean square stability. Moreover, the designed DSMC law achieves finite-time reachability of the sliding region and finite-time convergence of the sliding dynamics to the predesignated sliding region. A numerical example and an electronic throttle model are provided to validate the proposed control strategy.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Weizhong Chen, Ming-Zhe Dai, Chaoxu Guan, Zhongyang Fei
Summary: This paper addresses the synchronization problem for semi-Markov jump master-slave neural networks with extended dissipativity performance and partly unknown transition rates. Sufficient stability and dissipativity criteria are established, and a state feedback controller is designed to ensure synchronization. A numerical example is provided to verify the results.
Article
Computer Science, Artificial Intelligence
Guangtao Ran, Jian Liu, Chuanjiang Li, Hak-Keung Lam, Dongyu Li, Hongtian Chen
Summary: This article addresses the event-triggered asynchronous fault detection problem of fuzzy-model-based nonlinear Markov jump systems with partially unknown transition probabilities. An adaptive event-triggered scheme and a hidden Markov model are introduced to propose a new fault detection method, which is verified through numerical simulation.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Weiqiang Song, Aijuan Jin
Summary: This paper tackles the issue of model reference tracking control for linear systems based on the observer in the context of Markov jump systems with unknown transition rates. The main contributions include the design of a descriptor observer using matrix transformation and the formulation of a tracking control law utilizing a feedforward compensator and feedback control. The stability of the system is ensured by the feedback component, while the feedforward component serves as a complete parametric tracking compensator. Both components are solved separately, and a controller is proposed under the condition of partial unknown transition rates using Lyapunov stability theory. The feedforward parametric solution is provided through the generalized Sylvester equation. The effectiveness of the algorithm and criteria is demonstrated through various examples and compared with existing findings.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Ioannis Tzortzis, Charalambos D. Charalambous, Christoforos N. Hadjicostis
Summary: This article presents a robust LQR approach for nonhomogeneous Markov jump linear systems, deriving a robust optimal controller via dynamic programming and limiting the influence of uncertainty. Numerical results demonstrate the applicability and effectiveness of the proposed method.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Yusheng Wei, Zongli Lin
Summary: In this article, a design is proposed to stabilize general, possibly exponentially unstable, discrete-time linear systems by constructing an observer-based output feedback law, as long as the delay does not exceed a certain amount. For systems with all the open-loop poles on or inside the unit circle, a low gain feedback design is presented, which allows stabilization to be achieved for an arbitrarily large bounded delay when the low gain parameter is chosen small enough.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Computer Science, Interdisciplinary Applications
DanHua ShangGuan
Summary: The Monte Carlo method is a powerful tool in many research fields, but the increasing complexity of physical models and mathematical models requires efficient algorithms to overcome the computational cost.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Min Xue, Huaicheng Yan, Hao Zhang, Zhichen Li, Shiming Chen, Chaoyang Chen
Summary: This article focuses on event-triggered guaranteed cost control for a class of Markovian jump systems with time-varying delays and partly unknown transition probabilities described by the Takagi-Sugeno fuzzy model. The stability criterion for the system with a guaranteed cost index is derived using the Lyapunov-Krasovskii functional, and sufficient conditions for the existence of admissible fuzzy controllers are provided. The proposed approach allows for the co-design of fuzzy control gains and event-triggered parameters.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Engineering, Chemical
Huiying Chen, Renwei Liu, Weifeng Xia, Zuxin Li
Summary: This paper focuses on the problem of event-triggered H-infinity asynchronous filtering for Markov jump nonlinear systems with varying delay and unknown probabilities. An event-triggered scheduling scheme is adopted to decrease the transmission rate of measured outputs. The designed filter is mode dependent and asynchronous with the original system, which is represented by a hidden Markov model (HMM). Under this framework, a sufficient condition is given and the filter is further devised to ensure the resulting filtering error dynamic system is stochastically stable with a desired H-infinity disturbance attenuation performance.
Article
Automation & Control Systems
Jiawei Lu, Yinhe Wang, Shengping Li
Summary: This article investigates the outer synchronization for two discrete-time complex dynamic networks. The more universal mathematical models of the networks with unknown coupling and interaction are proposed, and an adaptive state feedback controller is synthesized based on the available state of networks and the estimation of summarized unknown parameters. The simulation results demonstrate the validity and advantages of the controller.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Automation & Control Systems
Pengcheng Ding, Ziwei Li, Feng Li, Jing Wang, Hao Shen
Summary: This paper addresses the consensus problem of Markov jump multi-agent systems under dynamic event-triggered communication. A dynamic event-triggered method is adopted to make efficient use of limited network resources and improve data transmission efficiency. By introducing a hidden Markov model, considering the challenge of obtaining system mode information, the case with partially unknown probabilities in the transition probability matrix and the observation probability matrix is discussed, making the conclusion more realistic. Moreover, a sampled-data consensus protocol is proposed, and several sufficient conditions based on the Lyapunov stability theory are derived to ensure system consensus under specified H-infinity performance. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed protocol.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2023)
Article
Computer Science, Information Systems
Yuan Li, Zhongxin Yu, Yang Liu, Junchao Ren
Summary: This paper investigates state feedback controller design for discrete-time Markovian jump systems with time delay and two Markov chains, proposing an improved Lyapunov-Krasovskii functional and a time-delay-dependent state feedback controller design method to reduce complexity in calculations. Two simulation examples are presented to demonstrate the effectiveness of the proposed method, which reduces conservatism and the total number of matrix inequalities compared to existing literature.
Article
Automation & Control Systems
Linchuang Zhang, Yonghui Sun, Yingnan Pan, Hak-Keung Lam
Summary: This article addresses the fault detection problem for a class of T-S fuzzy semi-Markov jump systems with partly unknown transition rates subject to output quantization. A more general PUTRs model is constructed to describe the situation where some elements' information is completely unknown. The reduced-order filter is utilized to solve the fault detection problem, with the stochastic failure phenomenon injected into it. The logarithmic quantizer is employed to handle the limited bandwidth problem in the communication channel. New sufficient conditions based on the Lyapunov theory are developed to obtain the desired reduced-order filter.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Automation & Control Systems
Xi Li, Qiankun Song, Yurong Liu, Fuad E. Alsaadi
Summary: This article presents the Hurwicz model of the zero-sum uncertain differential game with jump based on uncertainty theory. It formulates a dynamic system using an uncertain differential equation that satisfies both the canonical Liu process and V-jump uncertain process. An equilibrium equation for solving the saddle-point of the game is proposed. Furthermore, the article analyzes the game with a linear dynamic system and quadratic objective function. Finally, it describes a resource extraction problem using the theoretical results.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Guijun Ma, Zidong Wang, Weibo Liu, Jingzhong Fang, Yong Zhang, Han Ding, Ye Yuan
Summary: This article proposes a two-stage integrated method for predicting the remaining useful life (RUL) of lithium-ion batteries. In the first stage, a convolutional neural network (CNN) is used to estimate the cycle life of each battery, and a similar degradation mode is chosen for capacity identification. In the second stage, a personalized prediction is made using the identified parameters. Experimental results demonstrate the superiority of this method over standard CNN-based and GPR-based prediction methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Anqi Pan, Chuang Wang, Bo Shen, Lei Wang
Summary: This paper proposes a novel robust performance evaluation approach for evolutionary multiobjective optimization algorithm, which selects and preserves solutions based on their historical performance to maintain exploration strength in convergence potential areas.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Automation & Control Systems
Qinyuan Liu, Zidong Wang, Hongli Dong, Changjun Jiang
Summary: In this article, the state estimation problem for networked systems with energy harvesting technologies is investigated. A binary encoding scheme is utilized to transmit the measurement results, which are quantized into a bit string and transmitted via memoryless binary symmetric channels. A minmax robust estimator is designed to minimize the worst-case covariance of the estimation error. The influence of the length of the bit stream on the transmission rate and estimation performance is discussed, and conditions for the boundedness of the proposed estimator are provided.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Chunyu Li, Zidong Wang, Weihao Song, Shixin Zhao, Jianan Wang, Jiayuan Shan
Summary: This article investigates the resilient unscented Kalman filtering fusion issue for a class of nonlinear systems under the dynamic event-triggered mechanism. The dynamic event-triggered scheme is capable of scheduling data transmission frequency more efficiently, reducing communication burden and energy consumption. Furthermore, the sequential covariance intersection fusion strategy is introduced to solve the problem of computing cross covariance between local filters.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Shengkun Jiang, Xianfeng Tang, Silong Huang, Zhifang Lyu, Zhanliang Wang, Tao Tang, Huarong Gong, Yubin Gong, Zhaoyun Duan
Summary: To develop a high power and compact terahertz (THz) sheet beam traveling-wave tube (TWT), an all metal metamaterial (MTM)-inspired slow wave structure (SWS) is proposed. The MTM-inspired SWS exhibits advantages such as high interaction impedance, double beam tunnels, and compactness. Through simulation, it is predicted that the maximum output power of the 0.22 THz TWT with double sheet beams can reach 400 W with a 3-dB bandwidth of 5.4 GHz, while having a total length of only 29.2 mm.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
Article
Engineering, Electrical & Electronic
Junwan Zhu, Zhigang Lu, Jingrui Duan, Zhanliang Wang, Huarong Gong, Yubin Gong
Summary: This paper proposes a modified staggered double grating traveling wave tube (SDG-TWT) slow wave structure (SWS) for wide-band and high-power TWTs operating in the W-band or higher terahertz band, which shows improved performance in terms of saturated power, electron efficiency, and gain.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
Article
Medicine, General & Internal
Shuang Liu, Limei Yuan, Jinzhu Li, Yurong Liu, Haibo Wang, Xingye Ren
Summary: The aim of this research was to explore the diagnostic value of circDENND4C in EOC and the corresponding mechanism. The expression of circDENND4C and miR-200b/c in tissues, serum, and cell lines of EOC were analyzed. It was found that circDENND4C was lowest while miR-200b/c was highest in EOC tissues and serums. Furthermore, circDENND4C was involved in the malignant progression of EOC by suppressing cell proliferation and stimulating apoptosis through downregulating miR-200b/c. Serum circDENND4C showed a higher specificity and accuracy than serum CA125 or HE4 in EOC diagnosis.
ANNALS OF MEDICINE
(2023)
Article
Mathematics, Applied
Dan Liu, Zidong Wang, Yurong Liu, Changfeng Xue, Fuad E. Alsaadi
Summary: In this paper, a distributed filter is proposed for time-varying systems corrupted by dynamic bias and packet disorders over sensor networks. The system, which includes stochastic bias governed by a dynamical equation, takes into account transmission delays described by random variables with known probability distributions. The paper focuses on the construction of a distributed and recursive filter under the corruption of dynamic bias and packet disorders. Upper bounds on attained error covariances are obtained and minimized by parameterizing filter gains. Additionally, a sufficient condition is presented to ensure mean-square boundedness of filtering errors. An example is provided for verification of the proposed method. (c) 2022 Elsevier Inc. All rights reserved.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Bogang Qu, Zidong Wang, Bo Shen, Hongli Dong
Summary: This article studies the problem of state estimation in a class of renewable-electricity-generation-based microgrids with measurement outliers. A state-space system model is proposed for microgrids using the physical laws of power systems, without considering prior knowledge of the measurement outliers. To enhance insensitivity against measurement outliers, an outlier-resistant SE algorithm is developed with two distinct features: adopting a saturation function to constrain the innovation term in the state estimator and minimizing the estimation error covariance by selecting proper gain parameters. Simulation studies on a benchmark islanded microgrid with two renewable-electricity-generation units are conducted to illustrate the validity of the developed algorithm.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Hengli Cheng, Bo Shen, Jie Sun
Summary: In this paper, the distributed fusion filtering issue is investigated for multi-sensor systems with the constraints from both time-correlated fading channels and energy harvesters. A dynamic energy-allocated rule is proposed to properly deal with the energy supply relationship between a battery and multiple sensors. The local filter is designed to minimize the upper bound of the local filtering error covariance under the effects of the time-correlated fading channels and energy harvesters, and the fusion estimates are obtained using the covariance intersection approach.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Chuang Wang, Zidong Wang, Qing-Long Han, Fei Han, Hongli Dong
Summary: In this article, a novel leader-follower-based particle swarm optimization (LFPSO) algorithm is proposed, which maintains the diversity of the particle population while improving the possibility of escaping from the locally optimal solution. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy and convergence rate of conventional particle swarm optimization algorithms, and its superiority is verified in denoising real-time signals in an oilfield pipeline network.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Kaiqun Zhu, Zidong Wang, Guoliang Wei, Xiaohui Liu
Summary: This article investigates the adaptive neural network-based set-membership state estimation problem for a class of nonlinear systems subject to bit rate constraints and unknown-but-bounded noises. A bit rate allocation mechanism is proposed to relieve the communication burden and improve state estimation accuracy. An NN-based set-membership estimator is designed using the NN learning method, relying upon a prediction-correction structure. The existence of adaptive tuning parameters and set-membership estimators is ensured, and the convergence of NN weights is analyzed.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Luo, Yurong Zhong, Zidong Wang, Maozhen Li
Summary: This study proposes an ASNL model for handling large-scale undirected networks, which can efficiently represent incomplete and imbalanced data of SHDI matrices, and has fast model convergence and high computational efficiency. Empirical studies on four SHDI matrices demonstrate that ASNL significantly outperforms other models in prediction accuracy and computational efficiency.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Hailong Tan, Bo Shen, Qi Lid, Tingwen Huang
Summary: This paper studies the problem of zonotopic set-membership estimation (SME) for time-varying systems subject to dynamical biases and uniform quantization. A mathematical method is proposed to estimate the state of the system by analyzing the dynamics of biases and states. An auxiliary zonotope is constructed to minimize the estimation error, and an external approximation is used to reduce the computational burden. The effectiveness of the proposed method is demonstrated through simulations.
INFORMATION SCIENCES
(2024)
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.
