Article
Computer Science, Information Systems
Weiwei Sun, Zidong Wang, Xinyu Lv, Fuad E. Alsaadi, Hongjian Liu
Summary: This paper investigates the design problem of H-infinity observer for discrete-time Hamiltonian systems subject to missing measurement and sensor saturations governed by Bernoulli distributed random variables. The aim is to design an observer that ensures the exponentially mean-square stability of the error dynamics in state estimation with prescribed H-infinity performance. By utilizing Lyapunov function and the properties of Hamiltonian system, sufficient conditions are derived for the existence of the desired observer. Moreover, observer gains are designed based on certain matrix inequalities. An illustrative example is provided to demonstrate the effectiveness of the proposed observer design scheme.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Qiang Li, Jinling Liang, Hong Qu
Summary: This article addresses the H-infinity estimation problem for stochastic semi-Markovian switching complex-valued neural networks with incomplete measurement outputs. By introducing a sequence of random variables with known statistical property and utilizing complex analysis methods, mode-dependent sufficient conditions are presented to ensure the stability of the estimation error system. The effectiveness of the theoretical results is demonstrated through a numerical example.
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
Computer Science, Artificial Intelligence
Jinghui Suo, Nan Li, Qi Li
Summary: This paper investigates the H-infinity state estimation problem for a class of discrete-time delayed switched stochastic neural networks with sensor saturations. An event-triggered state estimator is designed to improve resource utilization efficiency, and a persistent dwell-time switching strategy is adopted. Criteria are presented to ensure the stability of the estimation error system and disturbance attenuation.
Article
Automation & Control Systems
Ming Lin, Yan-Ni Zeng, Hui Chen, Chang Liu, Hongxia Rao
Summary: This paper investigates reliable mixed H2/H infinity distributed state estimation for periodic nonlinear systems using a sensor network with time-varying topology described by a period index dependent Markov chain. A distributed state estimator is designed based on local and neighbors' innovation information, while considering non-fragile estimator to improve robustness. Sufficient conditions for stochastic stability and mixed H2/H infinity performance are derived, and the expected estimator gains are solved based on these conditions. The proposed state estimation method is validated through numerical example and comparative experiments.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Yaping Tang, Weiwei Sun, Dongqing Liu
Summary: This paper focuses on the simultaneous exponential stabilization of a set of stochastic port-controlled Hamiltonian (PCH) systems. The paper considers the limited bandwidth of channels, fading channels, transmission delays, and actuator saturation constraint. Sufficient criterions are given for controller design based on dissipative Hamiltonian structural and saturating actuator properties.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Weiwei Sun, Zidong Wang, Xinyu Lv, Fuad E. Alsaadi, Hongjian Liu
Summary: This paper focuses on the event-triggered H-infinity fusion estimation problem for uncertain discrete-time Hamiltonian systems with time-varying delays and sensor saturations. By introducing a component-based event-triggered mechanism and utilizing Lyapunov-Krasovskii stability, sufficient conditions are established to guarantee local exponential stability of the augmented system. The estimator gain is obtained using a certain matrix inequality. Two examples are provided to demonstrate the effectiveness of the proposed scheme.
INFORMATION FUSION
(2022)
Article
Engineering, Electrical & Electronic
Yongze Jin, Nan Feng, Yankai Li, Xinhong Hei, Ning Han, Fan Gao, Guo Xie
Summary: In this paper, a state estimation method is proposed for high-speed train based on multi-rate asynchronous sensors fusion with missing measurements. A state space model for multi-rate asynchronous speed measurement is established considering the actual operating environment. The proposed method achieves accurate fusion and estimation of train state while ensuring real-time performance, whether the monitoring data is intermittent missing or continuous missing.
IEEE SENSORS JOURNAL
(2023)
Article
Automation & Control Systems
Bogang Qu, Zidong Wang, Bo Shen, Hongli Dong
Summary: This paper explores the distributed state estimation problem for renewable energy microgrids with sensor saturations, proposing a system model and a distributed recursive estimation scheme to minimize estimation error covariance. The performance evaluation shows the effectiveness of the developed state estimation scheme through simulation experiments.
Article
Automation & Control Systems
Yutao Wu, Zehui Mao, Yueyang Li, Shuai Liu
Summary: The problem of robust fault estimation for linear discrete time-varying systems subject to multiplicative noise is investigated using finite impulse response (FIR) filter. A novel analytical redundancy is established to construct the fault estimator, and a new performance index based on matrix trace function is proposed to ensure accurate estimation. An easy-to-check condition is presented to obtain the optimal filter gain. The study demonstrates the computational advantages of the proposed algorithm in updating the filter gain, especially for time-varying systems.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Automation & Control Systems
Lei Ma, Zidong Wang, Chenxiao Cai, Fuad E. Alsaadi
Summary: This article investigates the H-infinity control problem for a class of discrete-time singularly perturbed systems with time-delays and sensor saturations. It proposes a dynamic event-triggered mechanism to regulate data-packet transmissions and develops an output-feedback controller design scheme to ensure system stability and H-infinity performance. The effectiveness of the algorithm is demonstrated through simulation examples.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Chaoqing Jia, Jun Hu, Xiaojian Yi, Hongjian Liu, Jinpeng Huang, Zhipeng Cao
Summary: This paper investigates the algorithm design problem of recursive state estimation (RSE) for a class of complex networks subject to quantized coupled parameter, missing measurements and amplify-and-forward relay. A recursive state estimator is constructed and the estimator gain is parameterized by optimizing the trace of state estimation error covariance (SEECUB). Theoretical analysis establishes the monotonicity relationship between the minimized SEECUB trace and the probabilities of missing measurements. Simulation study demonstrates the feasibility and validity of the proposed RSE approach.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
P. Selvaraj, O. M. Kwon, S. H. Lee, R. Sakthivel
Summary: This paper addresses the stabilization issue of linear time delay system with input saturation and distinct input delays via predictor feedback boundary control algorithm by employing transport partial differential equations (PDEs). By employing transport PDEs and feedback boundary control algorithm, this paper solves the stabilization issue of linear time delay system with input saturation and distinct input delays, and establishes novel exponential stability conditions.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Mathematics, Applied
Baoyan Sun, Jun Hu, Yan Gao
Summary: This paper focuses on the robust H-infinity state estimation problem for a class of discrete time-varying uncertain neural networks with uniform quantization and time-delay under variance constraints. A time-varying finite-horizon state estimator is designed to satisfy the error variance boundedness and the H-infinity performance constraint. By using stochastic analysis technique, a new H-infinity SE algorithm without resorting to the augmentation method is proposed for DTVUNNs with uniform quantization.
Article
Engineering, Mechanical
Teng-Fei Li, Xiao-Heng Chang, Ju H. Park
Summary: The paper investigates the finite-time H-infinity control problem of nonlinear parabolic partial differential equation systems with parametric uncertainties. Controllers with quantization, including static state feedback controller and dynamic state feedback controller, are introduced based on the definition of the quantizer. Finite-time H-infinity control design strategies are proposed to analyze the effect of quantization on the nonlinear parabolic PDE systems. By constructing appropriate Lyapunov functionals, sufficient conditions for the existence of feedback control gains and quantizer's adjusting parameters are expressed as nonlinear matrix inequalities, which are transformed to standard linear matrix inequalities using inequalities and decomposition techniques. Optimization problems subject to the LMIs are solved to pursue optimal H-infinity control performances. An application to the catalytic rod in a reactor is explored, and a numerical example is provided to illustrate the feasibility and effectiveness of the finite-time H-infinity control design strategies.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
Saijie Fan, Wei Liang, Derui Ding, Hui Yu
Summary: This paper proposes a Lightweight Attention-guided ConvNeXt Network (LACN) for low-light image enhancement. By introducing a parameter-free attention module and stacking and fusing features, the LACN is able to effectively enhance low-light images. The experimental results demonstrate significant improvements in the visual quality of low-light images.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(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
Engineering, Electrical & Electronic
Jingyu Guo, Yang Dong, Ningjie Shi, Shaomeng Wang, Zhanliang Wang, Xianzhu Liu, Tao Tang, Huarong Gong, Zhigang Lu, Zhaoyun Duan, Yubin Gong
Summary: A 0.34-THz standing wave enhanced sheet electron beam traveling-wave tube (SWE-SEB-TWT) is proposed, consisting of staggered double-vane slow wave structures (SDV-SWSs) and multigap resonant cavities. Particle-in-cell (PIC) simulations show an output power of 25 W with a voltage of 19.5 kV and a current of 60 mA, corresponding to a gain exceeding 40 dB. Test results of the high-frequency structure fabricated by CNC milling validate its expected characteristics.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
Article
Engineering, Electrical & Electronic
Jingrui Duan, Zhigang Lu, Junwan Zhu, Zhanliang Wang, Shaomeng Wang, Huarong Gong, Yubin Gong
Summary: Increasing the interaction current capacity and impedance is crucial for high-power TWT in $\textit{W}$-band and THz-band. The dual-tunnel FW (DTFW) with increased distance between metal walls is proposed, leading to improved bandwidth and interaction impedance. Particle-in-cell simulation shows the superiority of DTFW-SWS in terms of power, gain, and efficiency compared to FW-SWS. Therefore, DTFW-SWS shows promising potential for high-power and wide-bandwidth TWT in $\textit{W}$-band and THz-band.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
Article
Automation & Control Systems
Haifang Song, Derui Ding, Bo Shen, Hongli Dong
Summary: This article addresses the jointly distributed entropy filtering issues for discrete-time stochastic parameter systems with fault and non-Gaussian noise effects, using the generalized maximum correntropy criterion (GMCC). A memory-based event-triggered scheme with a time-varying threshold is proposed to govern network communication by incorporating current and historical triggered information. The constructed jointly distributed entropy filter is used to derive upper bounds of filtering error covariance matrices and obtain an ideal filter gain for maximizing GMCC, while an accessible gain is achieved through fixed-point iterative rules with disclosed convergence in theory. Finally, the effectiveness of the proposed distributed filter is demonstrated in ballistic object tracking under non-Gaussian environments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Physics, Applied
Cong Wang, Yurong Liu, Baozi Wu, Jian Sui
Summary: The study has used double-stacked gate dielectrics (DSGD) to enhance the electrical performance of zinc oxide thin-film transistors (ZnO-TFT) with single-layer NbLaO gate dielectric (SLGD). Compared to ZnO-TFT with SLGD, the ZnO-TFTs with DSGD have shown significant improvement in electrical performance, particularly for the device with NbLaO/SiO2 DSGD, with increased field-effect mobility, on/off current ratio and reduced subthreshold slope. The enhanced performance is attributed to low surface roughness and trap-state density in the bulk of the channel and at the ZnO/NbLaO interface. These findings suggest the potential application of ZnO-TFTs with DSGD in high-resolution flat panel displays.
MODERN PHYSICS LETTERS B
(2023)
Article
Computer Science, Artificial Intelligence
Yuqing Zhang, Peishu Wu, Han Li, Yurong Liu, Fuad E. Alsaadi, Nianyin Zeng
Summary: In this paper, a novel dual-pathway-fusion-based sequence-to-sequence learning model (DPF-S2S) is proposed for text recognition in the wild. It focuses on enriching spatial information and extracting high-dimensional representation features to aid decoding. The model incorporates a double alignment module to tackle text misalignment and a global fusion module to enhance recognition accuracy in complicated scenes. Benchmark evaluations on seven datasets demonstrate the superiority of DPF-S2S over other state-of-the-art text recognition methods, showcasing its competitiveness in identifying texts in regular and irregular scenes. Ablation studies further validate the effectiveness of the strategies employed in DPF-S2S.
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
Jun-Yi Li, Zidong Wang, Renquan Lu, Yong Xu
Summary: This article addresses the issue of distributed filtering for linear discrete-time systems in wireless sensor networks with bounded noises and constrained bit rate. The communication between sensor nodes is achieved through a limited bandwidth wireless digital communication network. A bit rate constraint, influenced by bandwidth allocation strategy, is used to measure the impact of network bandwidth on distributed filtering performance. An improved coding-decoding procedure enables each node to decode messages from its neighbors. Based on this procedure, a decoded-innovation-based distributed filtering scheme is proposed and a sufficient condition is established for bounded filtering error dynamics. The relationship between bit rate and specific filtering performance is then discovered.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Jinjing Luo, Jin Xu, Pengcheng Yin, Jian Zhang, Dongdong Jia, Wuyang Fan, Yue Ouyang, Lingna Yue, Jinchi Cai, Hairong Yin, Gangxiong Wu, Zhanliang Wang, Yubin Gong, Yanyu Wei
Summary: The article introduces a novel slow wave structure (SWS) called sine-shaped FWG (S-FWG) as a promising choice for a high-power amplifier. By transforming the arc-shaped segment of the traditional FWG into a sinusoidal waveguide, the proposed SWS can be optimized quickly and produce a greater interaction impedance, leading to higher output power compared to the traditional FWG SWS.
IEEE TRANSACTIONS ON ELECTRON DEVICES
(2023)
Article
Automation & Control Systems
Wei Chen, Zidong Wang, Derui Ding, Xiaojian Yi, Qing-Long Han
Summary: This article discusses the problem of distributed state estimation over wireless sensor networks, introduces a new distributed state estimator, and systematically discusses the probability distribution of energy level. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance, and the convergence of the minimized upper bound of the expected estimation error covariance is analyzed.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Engineering, Electrical & Electronic
Xinchen Guo, Guoliang Wei, Derui Ding
Summary: In this paper, a fully distributed sliding mode controller with fault compensation is proposed for the consensus problem of discrete-time first-order multi-agent systems with actuator faults. A novel fully distributed integral sliding surface (ISS) without global information is introduced to ensure consensus in the presence of faults. A fully distributed sliding mode fault-tolerant controller is constructed based on the sliding surface and identified faults, with a derived sufficient condition for the finite-time reachability of the distributed ISS. Simulation results demonstrate the effectiveness of the proposed control algorithm.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(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)
