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Modelling COVID-19 pandemic dynamics using transparent, interpretable, parsimonious and simulatable (TIPS) Machine learning models: A case study from systems thinking and system identification perspectives

arXiv

引用

arXiv 2021年

作者： Wei, Hua-Liang Billings, Stephen A. Department of Automatic Control and Systems Engineering The University of Sheffield INSIGNEOInstitute for in Silico Medicine The University of Sheffield SheffieldS1 3JD United Kingdom

Since the outbreak of COVID-19, an astronomical number of publications on the pandemic dynamics appeared in the literature, of which many use the susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR) models, or their variants, to simulate and study the spread of the coronavirus. SIR and SEIR are continuous-time models which are a class of initial value problems (IVPs) of ordinary differential equations (ODEs). Discrete-time models such as regression and machine learning have also been applied to analyze COVID-19 pandemic data (e.g. predicting infection cases), but most of these methods use simplified models involving a small number of input variables preselected based on a priori knowledge, or use very complicated models (e.g. deep learning), purely focusing on certain prediction purposes and paying little attention to the model interpretability. There have been relatively fewer studies focusing on the investigations of the inherent time-lagged or time-delayed relationships e.g. between the reproduction number (R number), infection cases, and deaths, analyzing the pandemic spread from a systems thinking and dynamic perspective. The present study, for the first time, proposes using systems engineering and system identification approach to build transparent, interpretable, parsimonious and simulatable (TIPS) dynamic machine learning models, establishing links between the R number, the infection cases and deaths caused by COVID-19. The TIPS models are developed based on the well-known NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) model, which can help better understand the COVID-19 pandemic dynamics. A case study on the UK COVID-19 data is carried out, and new findings are detailed. The proposed method and the associated new findings are useful for better understanding the spread dynamics of the COVID-19 pandemic. © 2021, CC BY-NC-ND.

关键词： Dynamics

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Human factor security: evaluating the cybersecurity capacity of the industrial workforce

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Journal of systems and Information Technology 2019年第1期21卷 2-35页

作者： Ani, Uchenna Daniel He, Hongmei Tiwari, Ashutosh Cranfield University Cranfield United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

Purpose: As cyber-attacks continue to grow, organisations adopting the internet-of-things (IoT) have continued to react to security concerns that threaten their businesses within the current highly competitive environment. Many recorded industrial cyber-attacks have successfully beaten technical security solutions by exploiting human-factor vulnerabilities related to security knowledge and skills and manipulating human elements into inadvertently conveying access to critical industrial assets. Knowledge and skill capabilities contribute to human analytical proficiencies for enhanced cybersecurity readiness. Thus, a human-factored security endeavour is required to investigate the capabilities of the human constituents (workforce) to appropriately recognise and respond to cyber intrusion events within the industrial control system (ICS) environment. Design/methodology/approach: A quantitative approach (statistical analysis) is adopted to provide an approach to quantify the potential cybersecurity capability aptitudes of industrial human actors, identify the least security-capable workforce in the operational domain with the greatest susceptibility likelihood to cyber-attacks (i.e. weakest link) and guide the enhancement of security assurance. To support these objectives, a Human-factored Cyber Security Capability Evaluation approach is presented using conceptual analysis techniques. Findings: Using a test scenario, the approach demonstrates the capacity to proffer an efficient evaluation of workforce security knowledge and skills capabilities and the identification of weakest link in the workforce. Practical implications: The approach can enable organisations to gain better workforce security perspectives like security-consciousness, alertness and response aptitudes, thus guiding organisations into adopting strategic means of appropriating security remediation outlines, scopes and resources without undue wastes or redundancies. Originality/value: This paper demonstrat

关键词： Network security

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Phase mixing of propagating Alfvén waves in a single-fluid partially ionized solar plasma

arXiv

引用

arXiv 2023年

作者： McMurdo, Max Ballai, Istvan Verth, Gary Alharbi, Abdulaziz Fedun, Viktor Plasma Dynamics Group School of Mathematics and Statistics The University of Sheffield Hicks Building Hounsfield Road SheffieldS3 7RH United Kingdom Department of Mathematics Jamoum University College Umm Al-Qura University Makkah Jamoum25375 Saudi Arabia Plasma Dynamics Group Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom

Phase mixing of Alfvén waves is one of the most promising mechanisms for heating of the solar atmosphere. The damping of waves in this case requires small transversal scales, relative to the magnetic field direction. Here this requirement is achieved by considering a transversal inhomogeneity in the equilibrium plasma density profile. Using a single fluid approximation of a partially ionized chromospheric plasma we study the effectiveness of the damping of phase mixed shear Alfvén waves and investigate the effect of varying the ionization degree on the dissipation of waves. Our results show that the dissipation length of shear Alfvén waves strongly depends on the ionization degree of the plasma, but more importantly, in a partially ionized plasma, the damping length of shear Alfvén waves is several orders of magnitude shorter than in the case of a fully ionized plasma, providing evidence that phase mixing could be a large contributor to heating the solar chromosphere. The effectiveness of phase mixing is investigated for various ionization degrees, ranging from very weakly to very strongly ionized plasmas. Our results show that phase mixed propagating Alfvén waves in a partially ionized plasma with ionization degrees in the range µ = 0.518−0.657, corresponding to heights of 1916−2150 km above the solar surface, can provide sufficient heating to balance chromospheric radiative losses in the quiet Sun. © 2023, CC BY.

关键词： Magnetohydrodynamics

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Robust learning via ensemble density propagation in deep neural networks

arXiv

引用

arXiv 2021年

作者： Carannante, Giuseppina Dera, Dimah Rasool, Ghulam Bouaynaya, Nidhal C. Mihaylova, Lyudmila Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States University of Sheffield Department of Automatic Control and Systems Engineering United Kingdom

Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for robust learning that builds upon Bayesian estimation and Variational Inference. We formulate the problem of density propagation through layers of a DNN and solve it using an Ensemble Density Propagation (EnDP) scheme. The EnDP approach allows us to propagate moments of the variational probability distribution across the layers of a Bayesian DNN, enabling the estimation of the mean and covariance of the predictive distribution at the output of the model. Our experiments using MNIST and CIFAR-10 datasets show a significant improvement in the robustness of the trained models to random noise and adversarial attacks. © 2021, CC BY.

关键词： Deep neural networks

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Risk of Bias in Machine Learning and Statistical Models to Predict Height or Weight: A Systematic Review in Fetal and Paediatric Medicine

SSRN

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SSRN 2024年

作者： Lawrence, Neil Richard Bacila, Irina Tonge, Joseph Tucker, Anthea Dawson, Jeremy Lang, Zi-Qiang Krone, Nils P. Dhiman, Paula Collins, Gary S. Division of Clinical Medicine University of Sheffield SheffieldS10 2RX United Kingdom Division of Medicine Sheffield Children’s Hospital SheffieldS10 2TH United Kingdom Health Sciences Library University of Sheffield SheffieldS10 2RX United Kingdom Division of Population Health University of Sheffield SheffieldS10 2RX United Kingdom Management School University of Sheffield SheffieldS10 2RX United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom University of Oxford OxfordOX3 7LD United Kingdom

BackgroundPrediction of suboptimal growth allows early intervention that can improve outcomes for developing fetus’ as well as infants and children. We assessed the risk of bias of published prediction models that predict height and weight in fetal and paediatric medicine to understand their *** systematically searched MEDLINE and EMBASE for peer reviewed original research studies published in 2022. We included studies if they developed or validated a multivariable model to predict height or weight of an individual, and excluded genetic studies. Risk of bias was assessed for all prediction models and analyses using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).ResultsSixty-four studies were included, in which we assessed the development of 180 models and validation of 61 models. Models performed best in the outcomes domain, with 61% of models developed and 85% of models rated at low risk of bias. However, 100% of the models were rated as high risk of bias in the analysis domain, resulting in an overall high risk of bias in all 241 models assessed. Only 29% (53/180) of the developed models were reported in their respective studies hindering further research and external *** models developed and validated in 2022 to predict height and weight in fetuses and children are at high risk of bias and are unsuitable for application in clinical practice. Research into prediction of growth should focus on model updating using appropriate data sources with transparent reporting to allow for external validation. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Atmospheric waves disturbances from the solar terminator according to the VLF radio stations data

arXiv

引用

arXiv 2023年

作者： Cheremnykh, Oleg Fedorenko, Alla Voitsekhovska, Anna Selivanov, Yuriy Ballai, Istvan Verth, Gary Fedun, Viktor Space Research Institute 40 Acad. Glushkov Ave. Kyiv03187 Ukraine Plasma Dynamics Group School of Mathematics and Statistics The University of Sheffield Hicks Building Hounsfield Road SheffieldS3 7RH United Kingdom Plasma Dynamics Group Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom

The perturbations from the solar terminator in the range of acoustic-gravity waves (AGWs) periods from 5 minutes to 1 hour were analysed with the use of measurements of VLF radio signals amplitudes on the European radio path GQD-A118 (Great Britain-France). These observations provide information on the propagation of waves at altitudes near the mesopause (∼ 90 km), where VLF radio waves are reflected. On the considered radio path a systematic increase in fluctuations in the amplitudes of radio waves was observed within a few hours after the passage of the evening terminator. For April, June, October 2020 and February 2021 events, the distribution of the number of wave perturbations with large amplitudes over AGWs time periods has been studied. Our results show that the evening terminator for different seasons is dominated by waves in the range of periods of 15-20 minutes. The amplitudes of the AGWs from the terminator at the heights of the mesosphere (fluctuations in the concentration of neutral particles, velocity components and vertical displacement of the volume element) are approximately determined by the fluctuations of the amplitudes of the VLF radio signals. The amplitudes of the AGWs on the terminator are 12-14% in relative concentration fluctuations, which correspond to the vertical displacement of the atmospheric gas volume of 1.1-1.3 km. Based on the analysis of the AGW energy balance equation, it was concluded that the waves predominantly propagate in a quasi-horizontal direction at the terminator. The possibility of studying the long-term changes in the mesosphere parameters using fluctuations in the amplitudes of VLF radio waves at the terminator is shown. © 2023, CC BY.

关键词： Radio waves

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

引用

arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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A Novel Mechanical Design of a Wearable Fingertip Haptic Device for Remote Meniscus Palpation

引用

Journal of Medical robotics Research 2023年第1-2期8卷

作者： Morad, Samir Jaffer, Zainab Dogramadzi, Sanja Department of Engineering The University of East London University Way London E16 2RD United Kingdom Department of Life and Health Science Aston University Birmingham B4 7ET United Kingdom Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield S1 3JD United Kingdom

A pneumatic model of a fingertip haptic device (FHD) had been previously tested in virtual reality allowing the perception of different materials with a promising result. However, numerous drawbacks were noted in this design, including bulky size, less portability, and discomfort. In this paper, FHD is redesigned to provide haptic feedback for human meniscus palpation. A user study was performed to evaluate the effectiveness of the redesigned FHD. The study showed that the redesigned model could successfully provide a more evident perception of different stiffness levels but it compromised the comfort of the user when mounted on the finger for long periods. © 2023 World Scientific Publishing Company.

关键词： bolus materials haptics mechanical system Surgical robotics tele-operation

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High throughput event filtering: The interpolation-based DIF algorithm hardware architecture

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Microprocessors and Microsystems 2025年

作者： Marcin Kowalczyk Tomasz Kryjak Embedded Vision Systems Group Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Krakow Poland

In recent years, there has been rapid development in the field of event vision. It manifests itself both on the technical side, as better and better event sensors are available, and on the algorithmic side, as more and more applications of this technology are proposed and scientific papers are published. However, the data stream from these sensors typically contains a significant amount of noise, which varies depending on factors such as the degree of illumination in the observed scene or the temperature of the sensor. We propose a hardware architecture of the Distance-based Interpolation with Frequency Weights(DIF) filter and implement it on an FPGA chip. To evaluate the algorithm and compare it with other solutions, we have prepared a new high-resolution event dataset, which we are also releasing to the community. Our architecture achieved a throughput of 403.39 million events per second (MEPS) for a sensor resolution of 1280 × 720 and 428.45 MEPS for a resolution of 640 × 480. The averagevalues of the Area Under the Receiver Operating Characteristic (AUROC) index ranged from 0.844 to 0.999, depending on the dataset, which is comparable to the state-of-the-art filtering solutions, but with much higher throughput and better operation over a wide range of noise levels.

关键词： Dataset Denoising DVS FPGA High-throughput

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