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FES based wrist tremor suppression using multi-periodic repetitive control ⁎

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IFAC-PapersOnLine 2020年第2期53卷 10135-10140页

作者： Zan Zhang Bing Chu Yanhong Liu D.H. Owens School of Electrical Engineering Zhengzhou University Zhengzhou China Department of Electronics and Computer Science university of southampton Southampton SO17 1BJ UK Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street Sheffield S1 3JD UK

Tremor is a very common motor disorder, mainly manifested as involuntary, periodic and rhythmic movement in any part of the body, especially in hands and upper-limbs, which seriously affects the life quality of patients. Functional electrical stimulation (FES) has been shown a promising technique to suppress tremor. Most existing FES based design methods assume tremor is a single frequency signal which however is a highly idealized simplification of the real case which contains multiple-frequency or even a frequency band, therefore limiting their practical performance. To address this problem, this paper proposes a controller design method based on multi-periodic repetitive control that is capable of suppressing tremor signal with multiple frequencies. Simulation and experimental results verify the effectiveness of the proposed method.

关键词： Functional electrical stimulation (FES) tremor suppression multi-periodic repetitive control simulation experimental verification

Author Correction: H∞ observer-based control for fuzzy Hadamard fractional-order systems

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Computational and Applied Mathematics 2025年第5期44卷 1-1页

作者： Rhaima, Mohamed Gassara, Hamdi Mchiri, Lassaad Hajjaji, Ahmed El Makhlouf, Abdellatif Ben Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Laboratory of Sciences Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Panthéon-Assas University Paris II Paris France Modeling Information and Systems Laboratory University of Picardie Jules Verne UFR of Sciences Amiens France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

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Bayesian Neural Networks Uncertainty Quantification with Cub...

International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

Learning dynamical systems using local stability priors

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arXiv 2020年

作者： Mehrjou, Arash Iannelli, Andrea Schölkopf, Bernhard Empirical Inference Department of Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zürich Faculty of Electrical Engineering Zürich Switzerland Department of Electrical Engineering Automatic Control Lab ETH Zürich8092 Switzerland Empirical Inference Department of Max Planck Institute for Intelligent Systems Tübingen Germany

A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed. The nonlinear identification leverages the local stability information as a prior on the system, effectively endowing the estimate with this important structural property. In addition, the knowledge of the region of attraction plays an experiment design role by informing the selection of initial conditions from which trajectories are generated and by enabling the use of a Lyapunov function of the system as a regularization term. Numerical results show that the proposed method allows efficient sampling and provides an accurate estimate of the dynamics in an inner approximation of its region of attraction. Copyright © 2020, The Authors. All rights reserved.

关键词： Lyapunov functions

Defect engineering approaches for metal oxide semiconductor-based chemiresistive gas sensing

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Coordination Chemistry Reviews 2025年 541卷

作者： Kumar, Amit Mazumder, Julaiba Tahsina Joyen, Kenza Favier, Frédéric Mirzaei, Ali Kim, Jin-Young Kwoka, Monika Bechelany, Mikhael Jha, Ravindra Kumar Kumar, Mahesh Kim, Hyoun Woo Kim, Sang Sub Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur 342030 India Department of Electronics Assam Skill University Assam Mangaldai 784125 India European Institut of Membranes (IEM) – UMR 5635 University of Montpellier ENSCM CNRS Montpellier 34090 France Institut Charles Gerhardt Montpellier (ICGM) – UMR 5253 University of Montpellier ENSCM CNRS Montpellier 34090 France Department of Materials Science and Engineering Shiraz University of Technology Shiraz 715557-13876 Iran Customized manufacturing R&D department Korea Institute of Industrial Technology Gyeonggi-do 15014 South Korea Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland Functional Materials Group Gulf University for Science and Technology (GUST) Mubarak Al-Abdullah 32093 Kuwait Department of Electronics and Electrical Engineering & Centre for Intelligent Cyber-Physical Systems Indian Institute of Technology Guwahati Guwahati 781039 India Division of Materials Science and Engineering Hanyang University Seoul 04763 South Korea Department of Materials Science and Engineering Inha University Incheon 22212 South Korea

Defect engineering in metal oxides presents a promising approach for tailoring material properties. This strategy enhances gas sorption, catalysis, and control over key physical characteristics such as bandgap, magnetic behavior, and electrical conductivity. Despite its potential, the role of defect engineering in advancing metal oxide semiconductor (MOS) gas-sensing performance remains underexplored. This review introduces defect engineering strategies, emphasizing their applications in gas sensing. Gas sensors play a vital role in environmental monitoring, industrial safety, healthcare, and in improving energy efficiency. The demand for advanced gas sensors has never been more critical, given the need for real-time, accurate, and cost-effective detection of pollutant gases. MOS-based gas sensors are widely used in air quality monitoring, industrial safety, and health diagnostics. Vacancies and defect architectures in MOS have been widely studied for their role in sensing performance, as they fundamentally influence sensor efficiency. The effectiveness of MOS sensors largely depends on the type and concentration of defects. This review introduces vacancies and defects in MOS, followed by an in-depth discussion of defect types, factors influencing defect formation, and their role in charge transport. Additionally, it examines the correlation between elemental chemical properties and defect chemistry. Special attention is given to the defect chemistry of metal oxides, including TiO₂, ZnO, Co₃O₄, ZrO₂, WO₃, and CeO₂. It concludes with an examination of surface engineering methods for defect control to improve gas-sensing capabilities. © 2024

关键词： Chemiresistive gas sensing Defect Engineering Metal-oxide semiconductor

Intellectual multi agent control of tripod 5th

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Intellectual multi agent control of tripod

5th International Conference on Robot Intelligence Technology and Applications, RiTA 2017

作者： Gaiduk, Anatoliy Kapustyan, Sergey Dyachenko, Alexander Prakapovich, Ryhor Podmazov, Ivan Department of Automatic Control Systems Southern Federal University Taganrog Russia Southern Scientific Center of the Russian Academy of Sciences Rostov-on-Don Russia Scientific Research Institute of Multiprocessor Computer Systems Southern Federal University Taganrog Russia United Institute of Informatics Problems NAS of Belarus Minsk Belarus

ISBN: (纸本)9783319784519

The analytical design method of the intellectual multi agent control for the mechanism of parallel structure of type the manipulator-tripod is considered in this paper. Tripod is intended for maintenance of the spatial technological movements of the a gripper with a working tool. A tripod’s construction is simple, but if the tripod’s control is intellectual then the big variety of technological operations can be realized. The tripod’s control system includes three agents. The algorithms of these agents are designed here. Application of the multi agent system simplifies the algorithm of the tripod’s control, in particular, with using of the intellectual approach. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Manipulators

Fundamental issues regarding uncertainties in artificial neural networks

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arXiv 2020年

作者： Thacker, N.A. Twining, C.J. Tar, P.D. Notley, S. Ramesh, V. Division of Informatics Imaging & Data Sciences University of Manchester United Kingdom Department of Computer Science University of Manchester United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom Center for Cognition and Computation Goethe-Universität Frankfurt am Main Germany

Artificial Neural Networks (ANNs) implement a specific form of multi-variate extrapolation and will generate an output for any input pattern, even when there is no similar training pattern. Extrapolations are not necessarily to be trusted, and in order to support safety critical systems, we require such systems to give an indication of the training sample related uncertainty associated with their output. Some readers may think that this is a well known issue which is already covered by the basic principles of pattern recognition. We will explain below how this is not the case and how the conventional (Likelihood estimate of) conditional probability of classification does not correctly assess this uncertainty. We provide a discussion of the standard interpretations of this problem and show how a quantitative approach based upon long standing methods can be practically applied. The methods are illustrated on the task of early diagnosis of dementing diseases using Magnetic Resonance Imaging. Copyright © 2020, The Authors. All rights reserved.

关键词： Extrapolation

Reconstruction of photospheric velocity fields from highly corrupted data

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arXiv 2022年

作者： Rempel, Erico L. Chertovskih, Roman Davletshina, Kamilla R. Silva, Suzana S.A. Welsch, Brian T. Chian, Abraham C.-L. SP São José dos Campos12228–900 Brazil P.O. Box 515 SP São José dos Campos12227-010 Brazil Electrical and Computer Engineering Department Faculty of Engineering University of Porto Rua Dr. Roberto Frias s/n Porto4200-465 Portugal Yandex Lva Tolstogo street 16 Moscow119021 Russia Plasma Dynamics Group Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom University of Wisconsin Green BayWI54311 United States University of Adelaide School of Mathematical Sciences AdelaideSA5005 Australia

The analysis of the photospheric velocity field is essential for understanding plasma turbulence in the solar surface, which may be responsible for driving processes such as magnetic reconnection, flares, wave propagation, particle acceleration, and coronal heating. Currently, the only available methods to estimate velocities at the solar photosphere transverse to an observer’s line of sight infer flows from differences in image structure in successive observations. Due to data noise, algorithms such as local correlation tracking (LCT) may lead to a vector field with wide gaps where no velocity vectors are provided. In this letter, a novel method for image inpainting of highly corrupted data is proposed and applied to the restoration of horizontal velocity fields in the solar photosphere. The restored velocity field preserves all the vector field components present in the original field. The method shows robustness when applied to both simulated and observational data. Copyright © 2022, The Authors. All rights reserved.

关键词： Velocity

Small-gain theorem for stability, cooperative control and distributed observation of infinite networks

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arXiv 2020年

作者： Noroozi, Navid Mironchenko, Andrii Kawan, Christoph Zamani, Majid Otto-von-Guericke University Magdeburg Laboratory for Systems Theory and Automatic Control Magdeburg Germany Faculty of Computer Science and Mathematics University of Passau Passau94032 Germany Institute of Informatics Ludwig Maximilian University of Munich Germany Computer Science Department University of Colorado BoulderCO80309 United States Computer Science Department Ludwig Maximilian University of Munich Germany

Motivated by a paradigm shift towards a hyper-connected world, we develop a computationally tractable small-gain theorem for a network of infinitely many systems, termed as infinite networks. The proposed small-gain theorem addresses exponential input-to-state stability with respect to closed sets, which enables us to analyze diverse stability problems in a unified manner. The small-gain condition, expressed in terms of the spectral radius of a gain operator collecting all the information about the internal Lyapunov gains, can be numerically computed for a large class of systems in an efficient way. To demonstrate broad applicability of our small-gain theorem, we apply it to the stability analysis of infinite time-varying networks, to consensus in infinite-agent systems, as well as to the design of distributed observers for infinite networks. Copyright © 2020, The Authors. All rights reserved.

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