Article
Automation & Control Systems
Nitin K. Singh, Abhisek K. Behera
Summary: In this paper, a twisting observer is proposed for robustly estimating the states of a second-order uncertain system. The observer approximates the unknown sign term for the non-measurable state with a delayed output-based switching function, and achieves the desired steady-state accuracy by controlling the delay parameter. The application of the observer to output feedback stabilization is also discussed.
Article
Computer Science, Information Systems
Xudong Zhang, Lei Wang
Summary: This paper focuses on robust sparse M-estimation over decentralized networks in the presence of Byzantine attacks. It proposes algorithms that are provably robust against Byzantine attacks and achieve linear convergence rates by combining robust aggregation rules, gradient tracking, and proximal algorithm.
INFORMATION SCIENCES
(2024)
Article
Physics, Multidisciplinary
Markus Rambach, Mahdi Qaryan, Michael Kewming, Christopher Ferrie, Andrew G. White, Jacquiline Romero
Summary: Self-guided tomography is demonstrated to be a practical, efficient, and robust technique for measuring higher-dimensional quantum states with high fidelities. The technique shows excellent performance for both pure and mixed states, achieving record high fidelities. It also exhibits robustness against various sources of experimental noise.
PHYSICAL REVIEW LETTERS
(2021)
Article
Chemistry, Multidisciplinary
Byung Wook Kim, Pankaj Singh, Sung-Yoon Jung
Summary: Recently, display-to-camera (D2C) communication, including display field communication (DFC), has gained attention due to advancements in display technology and the widespread availability of cameras in handheld devices. In this study, we proposed an iterative pilot-based reference-frame estimation scheme to increase the data rate of a 2D-DFC system. Simulation results show that the proposed scheme significantly boosts the achievable data rate of the 2D-DFC communication system by almost twofold, while maintaining the unobtrusiveness of the display.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Kai Wan
Summary: This paper investigates the robust tracking problem of a high-order iterative learning control algorithm for two classes of two-dimensional linear discrete time-varying systems, proposing an extended high-order linear discrete inequality to guarantee convergence of the tracking error. Simulation results validate the effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Engineering, Electrical & Electronic
Manyun Huang, Junbo Zhao, Zhinong Wei, Marco Pau, Guoqiang Sun
Summary: This study proposes a decentralized robust state estimation method for hybrid AC/DC distribution systems, which improves estimation accuracy through deep neural networks and smart meter data.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Tengpeng Chen, He Ren, Po Li, Gehan A. J. Amaratunga
Summary: This article proposes a robust power system DSE method under non-Gaussian noise by combining a robust exponential-absolute-value-based estimator and the unscented Kalman filter. The method effectively mitigates the effects of bad data or outliers.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Chemistry, Physical
Junyan Li, Ming Lu, Weijia Zheng, Wei Zhang
Summary: MXenes are two-dimensional materials with unique structures and properties, which have attracted significant scientific interest. Ion intercalation, as an important mechanism, plays a crucial role in regulating the electronic and chemical properties of MXene materials. This review provides an overview of the interaction events between ions and MXenes, including advanced characterization techniques, influencing factors, mechanisms, and functionalization roles of ion intercalation.
ENERGY STORAGE MATERIALS
(2024)
Article
Physics, Multidisciplinary
Yotam Shapira, Sapir Cohen, Nitzan Akerman, Ady Stern, Roee Ozeri
Summary: In this study, we enhance the fidelity and robustness of entangling gates in quantum computers by introducing spin-dependent squeezing.
PHYSICAL REVIEW LETTERS
(2023)
Article
Materials Science, Multidisciplinary
Suyash Rijal, Changsong Xu, L. Bellaiche
Summary: First principles combined with Monte Carlo simulations predict that compressive uniaxial strains in two-dimensional CrGeTe3 can tune magnetic parameters, leading to a phase transition from ferromagnetic to antiferromagnetic phases. The region between these phases, characterized by high frustration, can be utilized for fine-tuning the system to exhibit exotic states. Additionally, these strains also dramatically tune the critical temperatures, Neel temperatures for the AFM phase and Curie temperatures for the FM phase, with significant technological and fundamental implications.
Article
Engineering, Electrical & Electronic
Geon Choi, Jeonghun Park, Nir Shlezinger, Yonina C. Eldar, Namyoon Lee
Summary: This paper introduces a robust EKF algorithm called Split-KalmanNet that utilizes deep learning for state estimation. Split-KalmanNet calculates the Kalman gain using a split structure and is able to compensate for state and measurement model mismatch effects, outperforming traditional EKF and the state-of-the-art KalmanNet algorithm in various scenarios of model mismatch.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Automation & Control Systems
Linxiang Cheng, Lin Tie
Summary: This paper investigates the small-controllability of discrete-time state-affine nonlinear systems and improves the previous controllability criterion. For systems with dimension two and single-input, a sufficient algebraic criterion that is easier to apply is derived. For bilinear state-affine nonlinear systems, a necessary and sufficient algebraic criterion for small-controllability can be obtained using invariant sets. The derived controllability criteria are also extended to continuous-time systems and multi-input systems.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
Article
Materials Science, Multidisciplinary
Havard H. Haugen, Asle Sudbo
Summary: Motivated by the observation of an intermediate bosonic metallic state in the twodimensional superconductor-insulator transition at T = 0, an extended Bose-Hubbard model reveals the existence of a 2D superfluid phase, an edge metal state, and a phase closely related to a supersolid phase. These results are connected to STM experiments on MoS2 showing brims of finite density of states around the entire edge of 2D MoS2 samples.
Article
Chemistry, Analytical
Min Wang, Huabo Liu
Summary: In this paper, the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks are addressed. The extended Kalman filter (EKF) computation generates errors of linearization approximations, which can lead to increased state estimation errors and subsequently amplifies the linearization errors. DoS attacks overload the communication networks, interfering with the transmission of measurements to the remote robust state estimator, while the communication rate is constrained. To defend against DoS attacks and linearization errors, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is proposed. Additionally, a novel state augmentation method is devised to overcome the presence of measurement delays. Numerical simulations demonstrate that the proposed robust state estimator significantly improves the accuracy of state estimation.
Article
Automation & Control Systems
Meiyu Li, Jinling Liang, Fan Wang
Summary: This paper investigates the set-membership filtering problem for a class of polytopic uncertain two-dimensional shift-varying systems with delays and sensor saturation. It aims to design a robust set-membership filter to ensure that the real system states are always contained in the state estimate ellipsoid under various uncertainties and perturbations. The paper provides sufficient conditions for the existence of the desired filter and proposes a convex optimization method to determine the optimal state estimation ellipsoid, with a numerical example to illustrate the effectiveness of the proposed strategy.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Automation & Control Systems
Ridong Zhang, Sheng Wu, Zhixing Cao, Jingyi Lu, Furong Gao
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2018)
Article
Computer Science, Interdisciplinary Applications
Zhixing Cao, Jingyi Lu, Ridong Zhang, Furong Gao
COMPUTERS & CHEMICAL ENGINEERING
(2018)
Article
Engineering, Biomedical
Zhixing Cao, Ravi Gondhalekar, Eyal Dassau, Francis J. Doyle
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2018)
Article
Multidisciplinary Sciences
Zhixing Cao, Ramon Grima
JOURNAL OF THE ROYAL SOCIETY INTERFACE
(2019)
Article
Automation & Control Systems
Jingyi Lu, Zhixing Cao, Furong Gao
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2019)
Review
Engineering, Chemical
Jingyi Lu, Zhixing Cao, Chunhui Zhao, Furong Gao
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2019)
Review
Biophysics
James Holehouse, Zhixing Cao, Ramon Grima
BIOPHYSICAL JOURNAL
(2020)
Article
Multidisciplinary Sciences
Zhixing Cao, Ramon Grima
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2020)
Article
Biophysics
Zhixing Cao, Tatiana Filatova, Diego A. Oyarzun, Ramon Grima
BIOPHYSICAL JOURNAL
(2020)
Article
Multidisciplinary Sciences
Qingchao Jiang, Xiaoming Fu, Shifu Yan, Runlai Li, Wenli Du, Zhixing Cao, Feng Qian, Ramon Grima
Summary: Using artificial neural networks to approximate solutions of non-Markovian models can effectively address the challenges in analyzing, simulating, and inferring parameters from these models. The neural network accurately captures the stochastic dynamics across parameter space.
NATURE COMMUNICATIONS
(2021)
Article
Automation & Control Systems
Jingyi Lu, Zhixing Cao, Qinran Hu, Zuhua Xu, Wenli Du, Furong Gao
Summary: This article proposes a new OILC method for addressing the robustness issue of OILC against model mismatch. The method minimizes a dynamic upper bound on tracking error and formulates the problem in the framework of convex-concave game, which can be efficiently solved by a subgradient method. Experimental results show that the proposed method is effective in handling nonlinearity.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Libin Xu, Weimin Zhong, Jingyi Lu, Furong Gao, Feng Qian, Zhixing Cao
Summary: This article presents a solution called learning of iterative learning control (ILC) based on neural networks to address the need for fast controller adaptation in flexible manufacturing. The method recommends control parameters for ILC controllers, enabling fast tracking and smaller steady-state errors for different set-point profiles. It outperforms benchmark ILC on various systems and cases, demonstrating its potential for deployment in the industrial Internet of Things.
Article
Biochemical Research Methods
Xiaoming Fu, Xinyi Zhou, Dongyang Gu, Zhixing Cao, Ramon Grima
Summary: DelaySSAToolkit.jl is a Julia package that models reaction systems with non-Markovian dynamics, particularly those with time delays. It serves as an effective model reduction technique for complex systems in biology, chemistry, ecology, and genetics by capturing multiple intermediate reaction steps implicitly. The package implements various exact formulations of the delay stochastic simulation algorithm.
Article
Biology
Xiaoming Fu, Heta P. Patel, Stefano Coppola, Libin Xu, Zhixing Cao, Tineke L. Lenstra, Ramon Grima, Anna Akhmanova
Summary: This study compares transcriptional parameters with and without correction for cell cycle phases and post-transcriptional noise in yeast cells using smFISH, finding that corrections can significantly reduce errors in parameter estimation. The study also outlines how to adjust for measurement noise in smFISH effectively.
Article
Automation & Control Systems
Zhongyi Zhang, Qingchao Jiang, Guan Wang, Chunjian Pan, Zhixing Cao, Xuefeng Yan, Yingping Zhuang
Summary: Variable selection is crucial for soft sensor development, as both redundant variables and missing important variables can negatively impact modeling performance and practical industrial applications. This paper introduces a novel neural networks-based hybrid beneficial variable selection (HBVS) and modeling method for effective soft sensing. The proposed method involves the evaluation of mutual information (MI) to remove irrelevant variables, the introduction of proxy variables for temporarily sorting variables based on their significance using a hidden gain-based evaluation method, and the use of false discovery rate for identifying the model consistency and determining beneficial variables. Experimental results on a penicillin simulation process and two actual industrial processes demonstrate the effectiveness and superiority of the proposed method over state-of-the-art existing methods.
CONTROL ENGINEERING PRACTICE
(2023)
