Article
Mathematics, Applied
Xifen Wu, Haibo Bao
Summary: This paper investigates the Hc,, state estimation problem of multiplex networks with sensor saturations. It designs a set of Hc,, state estimators to estimate the state of the networks through available output measurements. The relationship between the inter-layer couplings of the networks and the estimation time is analyzed. The proposed approach is verified to be effective through numerical simulations.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Bogang Qu, Zidong Wang, Bo Shen
Summary: This paper investigates the fusion estimation problem for a class of multi-rate power systems with randomly occurring delays in SCADA measurements. A new approach is developed to transform the multi-rate power system into a single-rate one, and the effectiveness of the proposed fusion estimation scheme is illustrated through simulation experiments on the IEEE 14-bus system.
Article
Automation & Control Systems
Wei Qian, Simeng Guo, Yunji Zhao, Xiaozhuo Xu, Shumin Fei
Summary: This article addresses the distributed H-infinity resilient state estimation problem for a class of nonlinear systems with randomly occurring communication delays and missing measurements in sensor networks. A novel sensor model and Lyapunov-Krasovskii functional are proposed to ensure system stability and performance constraints. The effectiveness of the algorithm is illustrated through a numerical example.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2022)
Article
Engineering, Electrical & Electronic
Weihao Song, Zidong Wang, Jianan Wang, Fuad Alsaadi, Jiayuan Shan
Summary: This paper investigates the particle filtering problem for a class of nonlinear/non-Gaussian systems with energy harvesting sensors subject to randomly occurring sensor saturations. The study considers the characteristics of random occurrences of sensor saturations, the effects of energy levels, and measurement losses induced by insufficient energies, and derives an explicit expression of the likelihood function. Numerical simulation examples are provided to demonstrate the feasibility and effectiveness of the proposed particle filtering algorithm.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2021)
Article
Computer Science, Information Systems
Wen Yang, Xinting Zhang, Weijie Luo, Zongyu Zuo
Summary: With the development of technology, cyber attacks have become more complex and intelligent, posing threats to system operation. Our study on secure estimation problem involves a distributed estimator and detector, exploring their relationship and performance impact, and demonstrating effectiveness through numerical examples.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Liankun Sun, Yanyu Wang, Wanru Wang
Summary: This paper proposes an improved algorithm for delayed neural networks with randomly occurring uncertainties, optimizing stability in a receding horizon. By transforming the ROU problem into a linear matrix inequality and generating new matrix variables with more information, the method reduces conservatism and increases delay upper limit. Illustration of method superiority is provided through simulation numerical examples.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jun Hu, Yan Gao, Cai Chen, Junhua Du, Chaoqing Jia
Summary: This paper discusses the issue of partial-neurons-based H-8 state estimation for time-varying recurrent neural networks subject to randomly occurring time delays under variance constraint index. The aim is to propose the non-augmented partial-neurons based state estimation strategy. Finally, a simulation example is used to demonstrate the feasibility of presented partial-neurons-based H-8 state estimation algorithm.
NEURAL PROCESSING LETTERS
(2023)
Article
Biochemical Research Methods
Jing Wang, Haitao Wang, Hao Shen, Bing Wang, Ju H. Park
Summary: This article addresses the problem of finite-time H-infinity state estimation for switched genetic regulatory networks with randomly occurring uncertainties. It proposes a versatile switching rule and utilizes random variables obeying the Bernoulli distribution to represent the uncertainties. The article aims to design an estimator that ensures the boundedness of estimation error system and satisfies the H-infinity performance.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Nan Li, Qi Li, Jinghui Suo
Summary: This paper addresses the dynamic event-triggered state estimation issue for a class of discrete time-delay complex networks with randomly occurring nonlinearities. By utilizing a dynamic ET scheme and matrix inequality technology, state estimators are designed to improve energy utilization efficiency and system stability. A numerical simulation example demonstrates the usefulness of the proposed estimator design algorithm.
Article
Chemistry, Analytical
Rosa M. Fernandez-Alcala, Jesus Navarro-Moreno, Juan C. Ruiz-Molina
Summary: This study analyzes the centralized fusion estimation problem for discrete-time vectorial tessarine signals in multiple sensor stochastic systems with random one-step delays and correlated noises. New centralized fusion filtering, prediction, and fixed-point smoothing algorithms are devised, offering optimal estimators with reduced computational cost compared to traditional methods. Simulation examples demonstrate the effectiveness and superiority of the proposed T-k linear estimators over their counterparts in the quaternion domain.
Article
Computer Science, Information Systems
Xinghua Liu, Dandan Bai, Yunling Lv, Rui Jiang, Shuzhi Sam Ge
Summary: This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks, modeling attacks as signals added to measurements and utilizing an unscented Kalman filter to generate stable pose state estimates. Experimental results demonstrate that this method outperforms the traditional Kalman filter by providing more accurate estimates and guaranteeing an upper bound on error covariance.
SECURITY AND COMMUNICATION NETWORKS
(2021)
Article
Computer Science, Artificial Intelligence
Yan Gao, Jun Hu, Hui Yu, Junhua Du, Chaoqing Jia
Summary: This paper investigates the outlier-resistant variance-constrained H-infinity state estimation problem for a class of discrete time varying recurrent neural networks with randomly occurring deception attacks. The randomly occurring deception attacks are modeled by a series of random variables satisfying the Bernoulli distribution with known probability. In addition, the saturation function is introduced to reduce the negative impact from the measurement outliers onto the estimation performance. The objective of this paper is to propose an outlier-resistant finite-horizon state estimation scheme without utilizing the augmentation method such that, in the presence of measurement outliers and randomly occurring deception attacks, some sufficient criteria are obtained ensuring both the desired H-infinity performance index and the error variance boundedness. Finally, a numerical example is used to illustrate the feasibility of the presented outlier-resistant variance constrained H-infinity, state estimation algorithm.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yan Gao, Jun Hu, Cai Chen, Hui Yu, Chaoqing Jia
Summary: In this paper, the H-infinity state estimation issue for recurrent neural networks with time-varying parameters is considered. The phenomena of time-delay and randomly occurring sensor nonlinearity are addressed, and the communication is scheduled using the round-robin protocol. The objective is to develop a time-varying state estimation method that achieves both error variance boundedness and the pre-set H-infinity performance index. A simulation example with comparison tests is provided to demonstrate the feasibility of the presented method.
NEURAL PROCESSING LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Yamin Liu, Fang Fang, Jianping Zhou, Yajuan Liu
Summary: This paper addresses the H-infinity state estimation problem for Takagi-Sugeno fuzzy reaction-diffusion delayed neural networks (RDNNs) with randomly occurring gain uncertainties and semi-Markov jump parameters (sMJP). The proposed fuzzy resilient estimator design scheme considers the random gain perturbations and introduces a free weight matrix and decoupling techniques to improve the tolerance to gain variations. Two numerical simulations verify the effectiveness and superiority of the proposed scheme.
Article
Mathematics, Interdisciplinary Applications
N. Boonsatit, R. Sugumar, D. Ajay, G. Rajchakit, C. P. Lim, P. Hammachukiattikul, M. Usha, P. Agarwal
Summary: This article examines the mixed Script capital H-infinity and passivity synchronization of Markovian jumping neutral-type complex dynamical network (MJNTCDN) models with randomly occurring coupling delays and actuator faults. Novel Lyapunov-Krasovskii functional (LKF) is constructed to verify the stability of the error model and performance level, using Jensen's inequality and a new integral inequality. Sufficient conditions for the synchronization error system (SES) are given in terms of linear matrix inequalities (LMIs), with numerical illustrations provided to exhibit the usefulness of the obtained results.
Article
Automation & Control Systems
Kaiqun Zhu, Zidong Wang, Yun Chen, Guoliang Wei
Summary: In this article, the event-triggered cost-guaranteed control problem for shift-varying linear repetitive processes (LRPs) with multiplicative noises under probabilistic constraints is investigated. The event-triggered mechanism is exploited over the limited bandwidth communication network to improve efficiency. A novel event generator function is constructed to determine the order of the event triggering sequence. Probabilistic constraints are enforced onto the shift-varying LRPs with the aid of the event-triggered mechanism. The proposed controller design algorithm ensures the satisfaction of probabilistic constraints and quadratic cost index.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Lei Zou, Zidong Wang, Jun Hu, Hongli Dong
Summary: This article focuses on the state estimation problem for delayed complex networks subject to intermittent measurement outliers. A novel multiple-order-holder approach is proposed to resist the effects of the outliers, and sufficient conditions for bounded estimation error are provided.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Fan Wang, Zidong Wang, Jinling Liang, Carlos Silvestre
Summary: This article addresses the recursive filtering problem of a two-dimensional system array with random sensor failures and dynamic quantizations. The occurrence of sensor failures is governed by a random variable with known statistical properties. To deal with data transmission over networks with limited bandwidth, a dynamic quantizer is utilized to compress raw measurements into quantized ones. The main objective of this article is to design a recursive filter that guarantees a locally minimal upper bound on the filtering error variance. To support the filter design, the states of the dynamic quantizer and the target plant are integrated into an augmented system, which enables the derivation of an upper bound on the filtering error variance and its subsequent minimization at each step. The expected filter gain is parameterized by solving coupled difference equations. Furthermore, the article discusses the monotonicity of the resulting minimum upper bound with respect to the quantization level and investigates its boundedness. Finally, the effectiveness of the developed filtering strategy is demonstrated through a simulation example.
Article
Automation & Control Systems
Shaoying Wang, Zidong Wang, Hongli Dong, Yun Chen
Summary: This paper investigates the recursive quadratic state estimation problem for a class of stochastic nonlinear systems subject to non-Gaussian noises using energy-harvesting sensors. The original system is transformed into a new nonlinear system that exploits more information about the non-Gaussian noises. A quadratic estimator is designed using a recursive variance-minimization algorithm. The effectiveness of the proposed quadratic estimation algorithm is demonstrated through a simulation example.
Article
Automation & Control Systems
Qinyuan Liu, Zidong Wang, Xiao He, Hongli Dong, Changjun Jiang
Summary: This paper focuses on the remote state estimation problem of a class of linear discrete time-varying stochastic systems under communication constraints. A Try-Once-Discard (TOD) protocol is used to regulate signal transmissions over the sensor-to-estimator communication channel in order to mitigate data collisions. The paper investigates the approximate minimum mean-square error (MMSE) state estimation problem under the TOD protocol and develops a recursive algorithm for MMSE estimator design with comparable computational complexity to the conventional Kalman filter. The effectiveness of the proposed MMSE estimator is illustrated through a numerical example.
Article
Engineering, Electrical & Electronic
Xin Wang, Xuanming Zhang, Jianjun Zou, Shaozhe Wang, Junjie Huang, Shifeng Li, Yongming Li, Yurong Liu, Min Hu, Yubin Gong, Edl Schamiloglu, B. N. Basu, Zhaoyun Duan
Summary: In this experiment, an S-band MW-level metamaterial-inspired klystron using all-metal complementary electric split ring resonators (CeSRRs) was successfully realized. The miniaturized structure of this klystron has a volume of only 0.44 of conventional counterparts. In the hot-test, the klystron delivered a maximum output power of 5.51 MW, with a gain of 55.6 dB and electronic efficiency of 57.4% at 2.852 GHz. This compact metamaterial-inspired klystron has potential applications in proton therapy facilities, tokamaks for the low-hybrid wave heating, and accelerators.
IEEE ELECTRON DEVICE LETTERS
(2023)
Article
Computer Science, Artificial Intelligence
Zhigang Liu, Xin Luo, Zidong Wang, Xiaohui Liu
Summary: This study proposes a Constraintinduced Symmetric Nonnegative Matrix Factorization (C-SNMF) model for community detection. Experimental results demonstrate that the proposed model significantly outperforms benchmarks and state-of-the-art models in achieving highly-accurate community detection results.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Zhongyi Zhao, Zidong Wang, Lei Zou, Yun Chen, Weiguo Sheng
Summary: This paper studies the distributed fusion estimation problem for a class of nonlinear networked systems with unknown-but-bounded (UBB) noises. It proposes a zonotopes-based distributed fusion estimator by designing local estimators and fusion methods. The effectiveness of the proposed method is illustrated through a numerical example.
INFORMATION FUSION
(2023)
Article
Automation & Control Systems
Yanyang Lu, Bo Shen, Yuxuan Shen, Jinghui Suo
Summary: This paper addresses the issue of measurement outlier (MO)-resistant mobile robot localization (MRL). A time-varying state estimator with a saturation function containing variable saturation level is proposed to mitigate the effect of MOs. The goal is to devise an effective solution for the MRL problem by ensuring that the estimation error dynamics meets the H-∞ performance constraint over a finite horizon. The paper derives the existing condition of the estimator by constructing an appropriate Lyapunov function, and provides the desired state estimator gain through solving a set of matrix inequalities, presenting the MO-resistant MRL algorithm. An example is conducted to demonstrate the usefulness of the proposed MRL algorithm.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2023)
Article
Automation & Control Systems
Shijuan Li, Qiankun Song, Yurong Liu
Summary: This paper investigates the stability of a class of distributed-order nonlinear systems using an event-triggered control method. It first establishes an inequality for the solution of distributed-order nonlinear inequality systems using Laplace transform. Then, by designing a state feedback controller and event-triggered strategy and using Lyapunov stability theory and matrix inequality technique, a sufficient condition for the asymptotic stability of the considered systems is obtained in the form of a linear matrix inequality. Furthermore, a criterion to exclude Zeno behavior in the event-triggered strategy is provided. Finally, the proposed method is verified through a simulation example.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Engineering, Multidisciplinary
Weihao Song, Zidong Wang, Zhongkui Li, Hongli Dong, Qing-Long Han
Summary: This paper investigates the particle filtering problem for a class of discrete-time nonlinear complex networks with stochastic perturbations under the scheduling of random access protocol. The stochastic perturbations include on-off stochastic coupling, non-Gaussian noises, and measurement censoring. A random access protocol is used to alleviate data collision over the networks, and two expressions of the modified likelihood function are established to weaken the adverse effects from measurement censoring. A protocol-based filter is designed in the auxiliary particle filtering framework to generate new particles and assign weights based on the derived likelihood function. The developed filtering scheme is demonstrated to be practicable and effective through a multi-target tracking application.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Bogang Qu, Zidong Wang, Bo Shen, Hongli Dong, Hongjian Liu
Summary: This paper investigates the joint state and unknown input estimation (SUIE) problem for multi-machine power grids within energy networks under the event-triggered mechanism. It develops easy-to-implement algorithms to estimate the field voltage and mechanical torque of the synchronous generator (SG), which are generally difficult to be measured in engineering practice. An event-based transmission strategy is used to coordinate the massive PMU-based signal transmissions, and an event-based joint SUIE algorithm is designed to guarantee and minimize the estimation error covariances of both the unknown input and the state. Simulation experiments on the IEEE 39-bus system validate the developed estimation algorithm.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(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
Computer Science, Artificial Intelligence
Jun-Yi Li, Zidong Wang, Renquan Lu, Yong Xu
Summary: This article studies the cluster synchronization control problem for discrete-time complex dynamical networks under constrained bit rate. A bit-rate model is presented to quantify the limited network bandwidth and evaluate its effects on the control performance. Sufficient conditions are proposed to ensure boundedness of the error dynamics and the fundamental relationship between bit rate and performance index is established. Two optimization problems are formulated to design synchronization controllers and co-design issues are discussed to reduce conservatism. The developed synchronization control scheme is validated through simulation examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Optics
Lyu Zhi-Fang, Zhang Chang-Qing, Wang Zhan-Liang, Jiang Sheng-Kun, Ruan Cun-Jun, Feng Jin-Jun, Gong Yu-Bin, Duan Zhao-Yun
Summary: This paper provides a brief summary of the generation, formation, and focusing methods of sheet beam, as well as the state-of-the-art of terahertz sheet beam devices. The challenges and development tendencies of stable transport are also discussed.
JOURNAL OF INFRARED AND MILLIMETER WAVES
(2023)
