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Modeling the effects of a lightbridge on oscillations in a solar pore

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arXiv 2024年

作者： Schiavo, Luiz A.C.A. Gordovskyy, Mykola Browning, Philippa Silva, Suzana S.A. Verth, Gary Ballai, Istvan Shelyag, Sergiy Ruzheinikov, Sergey N. McLaughlin, James A. Fedun, Viktor Department of Mathematics Physics and Electrical Engineering Northumbria University NewcastleNE1 8ST United Kingdom Centre for Astrophysics Research Department of Physics Astronomy & Mathematics University of Hertfordshire HatfieldAL10 9AB United Kingdom Jodrell Bank Centre for Astrophysics University of Manchester ManchesterM13 9PL United Kingdom Plasma Dynamics Group Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom Plasma Dynamics Group School of Mathematics and Statistics University of Sheffield Hounsfield Road SheffieldS3 7RH United Kingdom College of Science and Engineering Flinders University TonsleySA5042 Australia

Solar pores are ideal magnetic structures for wave propagation and transport of energy radially-outwards across the upper layers of the solar atmosphere. We aim to model the excitation and propagation of magnetohydrodynamic waves in a pore with a lightbridge modelled as two interacting magnetic flux tubes separated by a thin, weaker field, layer. We solve the three-dimensional MHD equations numerically and calculate the circulation as a measure of net torsional motion. We find that the interaction between flux tubes results in the natural excitation of propagating torsional Alfvén waves, but find no torsional waves in the model with a single flux tube. The torsional Alfvén waves propagate with wave speeds matching the local Alfvén speed where wave amplitude peaks. Copyright © 2024, The Authors. All rights reserved.

关键词： Plasma magnetohydrodynamic waves

Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure

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arXiv 2023年

作者： Zou, Xinying Perlaza, Samir M. Esnaola, Iñaki Altman, Eitan INRIA Centre Inria d'Université Côte d'Azur Sophia Antipolis06902 France Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States GAATI Laboratory Université de la Polynésie Française Faaa98702 French Polynesia Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Laboratoire d'Informatique d'Avignon Université d'Avignon France

In this paper, the worst-case probability measure over the data is introduced as a tool for characterizing the generalization capabilities of machine learning algorithms. More specifically, the worst-case probability measure is a Gibbs probability measure and the unique solution to the maximization of the expected loss under a relative entropy constraint with respect to a reference probability measure. Fundamental generalization metrics, such as the sensitivity of the expected loss, the sensitivity of the empirical risk, and the generalization gap are shown to have closed-form expressions involving the worst-case data-generating probability measure. Existing results for the Gibbs algorithm, such as characterizing the generalization gap as a sum of mutual information and lautum information, up to a constant factor, are recovered. A novel parallel is established between the worst-case data-generating probability measure and the Gibbs algorithm. Specifically, the Gibbs probability measure is identified as a fundamental commonality of the model space and the data space for machine learning algorithms. © 2023, CC BY.

关键词： Learning algorithms

Predicting decision propagation in complex sociotechnical system

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TechRxiv

TechRxiv 2021年

作者： Hassannezhad, Mohammad Cassidy, Steve Clarkson, P. John Department of Automatic Control and Systems Engineering University of Sheffield United Kingdom Engineering Design Centre Department of Engineering University of Cambridge United Kingdom Future Organizations Lab British Telecommunications Ipswich United Kingdom

Making informed decisions in today’s ever more connected organizations requires a proactive approach to quantify the desirability of its possible consequences. A prominent problem within this realm is that the consequence of an individual’s decision might propagate far beyond its local impact and globally affect multiple decisions and the entire system, often with amplifying and overlapping sphere of influences. Despite numerous efforts, the algorithmic complexity of overlapping propagation paths in a dense network is not resolved yet. This paper presents the development of a new computational model to tackles this problem from the window of change prediction. The proposed method, called Decision Propagation Method, aims to direct organizational decisions towards the most efficient set of sociotechnical interventions through predicting the risk of concurrent decision propagations. Designed on a case study in the Operations engineering Group in British Telecom, it is illustrated that the proposed method, supported by an interactive prototype tool, offers a dynamic change prediction at a far less computational complexity and a better reproducibility which also requires less domain Knowledge from the experts. © 2021, CC BY-NC-SA.

关键词： Decision making

The effect of linear background rotational flows on magnetoacoustic modes of a photospheric magnetic flux tube

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arXiv 2022年

作者： Skirvin, Samuel J. Fedun, Viktor Silva, Suzana S.A. Van Doorsselaere, Tom Claes, Niels Goossens, Marcel Verth, Gary Centre for mathematical Plasma Astrophysics Mathematics Department KU Leuven Celestijnenlaan 200B bus 2400 LeuvenB-3001 Belgium Plasma Dynamics Group Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS3 7RH United Kingdom Plasma Dynamics Group Department of Mathematics and Statistics The University of Sheffield SheffieldS3 7RH United Kingdom

Magnetoacoustic waves in solar magnetic flux tubes may be affected by the presence of background rotational flows. Here, we investigate the behaviour of m = 0 and m = ±1 modes of a magnetic flux tube in the presence of linear background rotational flows embedded in a photospheric environment. We show that the inclusion of a background rotational flow is found to have little effect on the obtained eigensolutions for the axisymmetric m = 0 sausage mode. However, solutions for the kink mode are dependent on the location of the flow resonance modified by the slow frequency. A background rotational flow causes the modified flow resonances to possess faster phase speeds in the thin-tube (TT) limit for the case m = 1. This results in solutions for the slow body and slow surface kink modes to follow this trajectory, changing their dispersive behaviour. For a photospheric flux tube in the TT limit, we show that it becomes difficult to distinguish between the slow surface and fast surface kink (m = 1) modes upon comparison of their eigenfunctions. 2D velocity field plots demonstrate how these waves, in the presence of background rotational flows, may appear in observational data. For slow body kink modes, a swirling pattern can be seen in the total pressure perturbation. Furthermore, the tube boundary undergoes a helical motion from the breaking of azimuthal symmetry, where the m = 1 and m = −1 modes become out of phase, suggesting the resulting kink wave is circularly polarised. These results may have implications for seismology of magnetohydrodynamic waves in solar magnetic vortices. © 2022, CC BY.

关键词： Magnetohydrodynamics

A modified dynamic time warping (MDTW) approach and innovative average non-self Match distance (ANSD) method for anomaly detection in ECG recordings

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arXiv 2021年

作者： Yao, Xinxin Wei, Hua-Liang Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom INSIGNEO Institute for in Silico Medicine University of Sheffield SheffieldS1 3JD United Kingdom

ECGs objectively reflects the working conditions of the hearts as these signals contain vast physiological and pathological information. In this work, in order to improve the efficiency and accuracy of "best so far" time series analysis-based ECG anomaly detection methods, a novel method, comprising a modified dynamic time warping (MDTW) and an innovative average non-self match distance (ANSD) measure, is proposed for ECG anomaly detection. To evaluate the performance of the proposed method, the proposed method is applied to real ECG data selected from the MIT-BIH heartbeat database. To provide a reference for comparison, two existing anomaly detection methods, namely, brute force discord discovery (BFDD) and adaptive window discord discovery (AWDD), are also applied to the same data. The experimental results show that our proposed method outperforms BFDD and AWD. © 2021, CC BY-NC-ND.

关键词： Electrocardiography

Actuation attacks on constrained linear systems: a set-theoretic analysis

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IFAC-PapersOnLine 2020年第2期53卷 6963-6968页

作者： P.A. Trodden J.M. Maestre H. Ishii Department of Automatic Control & Systems Engineering University of Sheffield Sheffield UK Department of Systems and Automation Engineering University of Seville Seville Spain Department of Computer Science Tokyo Institute of TechnologyYokohama Japan

This paper considers a constrained discrete-time linear system subject to actuation attacks. The attacks are modelled as false data injections to the system, such that the total input (control input plus injection) satisfies hard input constraints. We establish a sufficient condition under which it is not possible to maintain the states of the system within a compact state constraint set for all possible realizations of the actuation attack. The developed condition is a simple function of the spectral radius of the system, the relative sizes of the input and state constraint sets, and the proportion of the input constraint set allowed to the attacker.

关键词： control problems under conflict or uncertainties Constrained control Robustness

A Nonparametric Bayesian Compressive Sensing Classification

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Journal of Advances in Information Fusion 2020年第1期15卷 57-70页

作者： Chen, Ruilong Hawes, Matthew Mihaylova, Lyudmila Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom

This paper presents a novel nonparametric backpropagation Bayesian compressive sensing (BBCS) classification approach. While the state-of-the-art parametric classifiers such as logistic regression require model training and can result in inadequate models, the developed approach does not require model training. It is combined with a column-based subspace sampling process and can deal efficiently with uncertainties and highly computational tasks. Validation on a publicly available vehicle logo dataset shows that the proposed classifier can achieve up to 98% recognition accuracy as compared with the state-ofthe- art nonparametric *** with the generic Bayesian compressive sensing classification, the proposed approach decreases the mean number of misclassifications by 87% along with 68% reduction of the computational time. The robustness of the BBCS approach is demonstrated over scene recognition tasks, and its outperformance over the AlexNet convolutional neural network algorithm is demonstrated in noisy conditions. The proposed BBCS approach is generic and can be used in different areas;for example, it has shown robustness over the CIFAR-10 dataset. © 2020 JAIF.

关键词： Compressed sensing

A Transfer Learning Approach for UAV Path Design with Connectivity Outage Constraint

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arXiv 2022年

作者： Fontanesi, Gianluca Zhu, Anding Arvaneh, Mahnaz Ahmadi, Hamed The School of Electrical and Electronic Engineering University College Dublin Dublin 4D04 V1W8 Ireland The Department of Electronic Engineering University of York Heslington YorkYO10 5DD United Kingdom The Automatic Control and Systems Engineering University of Sheffield United Kingdom

The connectivity-aware path design is crucial in the effective deployment of autonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning (RL) algorithms have become the popular approach to solving this type of complex problem, but RL algorithms suffer slow convergence. In this paper, we propose a Transfer Learning (TL) approach, where we use a teacher policy previously trained in an old domain to boost the path learning of the agent in the new domain. As the exploration processes and the training continue, the agent refines the path design in the new domain based on the subsequent interactions with the environment. We evaluate our approach considering an old domain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacher path policy, previously trained at sub-6 GHz path, is the solution to a connectivity-aware path problem that we formulate as a constrained Markov Decision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network (DQN) to solve the path design at sub-6 GHz that guarantees connectivity constraint satisfaction. We empirically demonstrate the effectiveness of our approach for different urban environment scenarios. The results demonstrate that our proposed approach is capable of reducing the training time considerably at mmWave. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

XAI Sustainable Human in the Loop Maintenance

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IFAC-PapersOnLine 2022年第19期55卷 67-72页

作者： Chris Turner Okechukwu Okorie John Oyekan University of Surrey Rik Medlik Building Surrey Business School Guildford Surrey GU2 7XH United Kingdom Exeter Centre for the Circular Economy Business School The University of Exeter Rennes Drive Exeter EX4 4PU University of Sheffield Department of Automatic Control and Systems Engineering Portobello Street Sheffield S1 3JD UK

The field of Explainable Artificial Intelligence (XAI) is a relatively new approach to AI, with the aim to provide black box algorithms with human intelligible narrative functionality. It is most often in end-of-life considerations of the asset lifecycle that sustainability issues are encountered. Modern maintenance practice requires a holistic understanding of lifecycle and options for sustainable asset treatments. human in the loop solutions offer a way to leverage both machine and human skill sets to provide the next level of automaton solutions for industrial maintenance activities. This paper presents a framework for human in the loop Intelligent and Sustainable Maintenance. In bridging the gap between machines and humans XAI leverages the best of both worlds to provide a new level of agility to cyber assisted maintenance activities and full lifecycle consideration of assets; a notion that is necessary throughout the organization in the achievement of sustainability goals set by governments around the world in the achievement of a net zero carbon emission economy.

关键词： Human in the loop Intelligent maintenance systems Industry 5.0 Predictive maintenance Operator 5.0