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SSRN 2024年

作者： Gui, Yufei Lang, Zi-Qiang Liu, Zepeng Laalej, Hatim Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom School of Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Advanced Manufacturing Research Centre University of Sheffield SheffieldS10 2TN United Kingdom

Process monitoring aims to timely detect faults or abnormal conditions, ensuring safety and achieving satisfactory product quality. Time-varying processes are common in various industries, including chemical, semiconductor, and advanced manufacturing. A key challenge in time-varying process monitoring is how to accommodate deterministic but unknown time-varying trends in process responses. Existing multivariate statistical process monitoring (MSPM) approaches designed for time-varying processes face challenges including intensive computation and worse adaption to unknown process changes. In this study, a new process monitoring approach is proposed based on a novel idea referred to as recursive time-sensor decomposition (RTSD). The multi-sensor data are compressed into dominant time and sensor domain components. In the time domain, the feature vectors are extracted to represent process states. In the sensor domain, the process information distributed across different sensors is used to accommodate the normal time-varying trend and determine the control limit. The proposed approach is validated first by analysing a simulated time-varying process and then by an application of monitoring tool wear states in a real milling experiment. The results demonstrate that the proposed approach is highly effective, has higher fault detection rates and lower false alarm rates when compared to existing methods, and is applicable in a real time-varying industrial process. © 2024, The Authors. All rights reserved.

关键词： Process monitoring

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On the Gaussian Filtering for Nonlinear Dynamic systems Using Variational Inference

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Ji Li Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements.

关键词： Target tracking Smoothing methods Filtering Estimation Filtering algorithms Prediction algorithms Minimization Nonlinear dynamical systems Bayes methods Iterative methods

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Recursive Time-Sensor Domain Data Decomposition: A Novel Method for Time-Varying Process Monitoring

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SSRN 2024年

关键词： Process monitoring

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Time-Sensor Domain Data Decomposition and Analysis for Fault Diagnosis of Cutting Tools

Time-Sensor Domain Data Decomposition and Analysis for Fault...

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International Conference on control, Automation, robotics and Vision (ICARCV)

作者： Yufei Gui Zi-Qiang Lang Zepeng Liu Yunpeng Zhu Hatim Laalej Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK School of Engineering and Materials Science Queen Mary University of London London UK Advanced Manufacturing Research Centre The University of Sheffield Sheffield UK

ISBN: (纸本)9781665476881

In the present study, a novel time and sensor domain data decomposition and analysis framework is proposed to perform fault diagnosis of cutting tools. The problem of excessive computation burden existing in multi-sensor data-driven tool condition monitoring (TCM) system is resolved at the data level by compressing raw signals into a significantly smaller set of time and sensor domain data. The utilisation of the time domain components eliminates the influence of environmental noise on raw signals. Meanwhile, the introduction of the sensor domain components reveals the correlation relationship within multiple sensors. Experimental studies are conducted to verify the effectiveness of the proposed approach and illustrate the advantages of the time and sensor domain features compared with raw signal features.

关键词： Fault diagnosis Cutting tools Correlation Working environment noise Milling Robot sensing systems Robustness

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Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm

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Construction robotics 2021年第2期5卷 131-146页

作者： Meng, Yiping Sun, Yiming Chang, Wen-shao People Environments and Performance Group School of Architecture The University of Sheffield Sheffield UK Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK

In this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.

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Biomarkers and neuromodulation techniques in substance use disorders

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Bioelectronic Medicine 2020年第1期6卷 1-17页

作者： Habelt, Bettina Arvaneh, Mahnaz Bernhardt, Nadine Minev, Ivan Department of Psychiatry and Psychotherapy Medical Faculty Carl Gustav Carus Technische Universität Dresden Dresden Germany Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders. © 2020, The Author(s).

关键词： Closed loop systems

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A Coalitional Game for Demand-Side Management in a Low-Voltage Resistive Micro-Grid with Multiple Electricity Retailers

arXiv

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arXiv 2024年

作者： Mendoza, Fernando Genis Baldivieso-Monasterios, Pablo R. Bauso, Dario Konstantopoulos, George The Department of Automatic Control and Systems Engineering The University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom The Department of Electrical and Computer Engineering University of Patras Rion26500 Greece The Jan C. Willems Center for Systems and Control ENTEG Fac. Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands The Netherlands and Dipartimento di Ingegneria Università di Palermo viale delle Scienze Palermo Italy

An existing challenge in power systems is the implementation of optimal demand management through dynamic pricing. This paper encompasses the design, analysis and implementation of a novel on-line pricing scheme based on coalitional game theory. The setting consists of a network with multiple energy retailers competing to attract consumers by announcing a price in a hierarchical leader-follower structure. The process of coalition formation under such a pricing scheme can be viewed as a game for which we show that a Stackelberg equilibrium exists, i.e., given a price, consumers will respond by conforming to a reciprocal power consumption quantity. We propose a coalition formation algorithm and perform a game-theoretic stability analysis on the resulting coalitions. We integrate the pricing setting with a resistive micro-grid dynamic model. In this context we analyse the behaviour of the integrated system, bridging the gap between market and physical layers of the problem. Simulations provide a comparison of profits generated by the proposed scheme against a more traditional single retailer scheme, while simultaneously showing convergence towards a steady-state equilibrium. Additionally, we shed light into the system's physical response when subject to our proposed pricing scheme. © 2024, CC BY.

关键词： Costs

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Vision-based Runway Detection and Landing for Unmanned Aerial Vehicle Enhanced Autonomy

Vision-based Runway Detection and Landing for Unmanned Aeria...

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IEEE International Conference on Mechatronics and Automation

作者： Kyriacos Tsapparellas Nickolay Jelev Jonathon Waters Sabine Brunswicker Lyudmila S Mihaylova Department of Automatic Control and Systems Engineering University of Sheffield UK Windracers Distributed Avionics Healthaid House Malborough Hill London UK Research Center for Digital Innovation Purdue University USA

Introducing autonomy is a task of paramount importance and is currently investigated in many areas, especially for autonomous cars and Unmanned Aerial Vehicles (UAVs). Most UAVs are still remotely human-controlled. A necessity is to implement on-board solutions, able to work in all weather conditions and at any time. Hence, on this topic, we give an overview of recent advances for vision-based landing of UAVs. A thorough classification of the main recently developed methods is introduced with a discussion of their advantages and disadvantages. The paper presents a new solution for autonomous UAV vision-based landing, focusing on runway detection using a hybrid approach combining multi-image matching, SIFT and object tracking. The results are evaluated and validated using simulated images sampled with the X-Plane 11 flight simulator and real-world videos collected during automated flights performed by the ULTRA vehicle, one of the biggest UAVs in the UK [1]. The statistical analysis from the validation of the proposed approach shows a high level of accuracy around 94.89% in clear weather conditions and real-time computational performance.

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Towards Data-Driven Modelling of Sumoylation following Heat Shock 7

Towards Data-Driven Modelling of Sumoylation following Heat ...

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7th International Conference on Biomedical and Bioinformatics engineering, ICBBE 2020

作者： Zhang, Manyu Zhang, Yifei Zhao, Alice Guo, Chun Guo, Lingzhong University of Sheffield United Kingdom The University of Sheffield Department of Automatic Control and Systems Engineering United Kingdom The University of Sheffield Department of Biomedical Science United Kingdom The University of Sheffield Department of Automatic Control and Systems Engineering INSIGNEO Institute for in Silico Medicine United Kingdom

ISBN: (纸本)9781450388221

Understanding how cell fate is determined when exposed to extreme stresses such as heat shock is critical in biomedical systems. It has long been understood that exposure of cells to high temperature typically protect themselves with a heat shock response (HSR), where accumulation of denatured or unfolded proteins triggers the synthesis of heat shock proteins (HSPs) through the heat shock transcription factor, e.g., heat shock factor 1 (HSF1). Recent experimental work has also shown that protein posttranslational modifications (PTMs) such as SUMOylation play crucial roles in cellular responses to heat shock. As a complementary approach to the current experimental methodologies, in this study we aim to develop a mathematical model of SUMOylation-development synergism of HSR for the purpose of studying the dynamical behaviour of HSR quantitatively. The structure of our dynamical model is derived mostly from mass action kinetics while the model parameters are optimized by using a genetic algorithm (GA) based data-driven approach. The preliminary results show GA based data-driven approach has potentials for our modelling purpose. © 2020 ACM.

关键词： Genetic algorithms

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On-The-Fly CNC interpolation using frequency-domain FFT-based filtering

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Procedia CIRP 2022年 107卷 1571-1576页

作者： Rob Ward Burak Sencer George Panoutsos Erdem Ozturk Department of Automatic Control and Systems Engineering University of Sheffield Sheffield S1 3JD UK Advanced Manufacturing Research Centre University of Sheffield Rotherham S60 5TZ UK Mechanical Engineering Department Oregon State University Corvallis OR USA

Finite Impulse Response filtering is increasingly becoming the interpolation method of choice in modern computer numerically controlled (CNC) machining centres. Recently, On-The-Fly (OTF) interpolation was introduced using direct convolution to adaptively change the FIR flter time constant throughout the toolpath. In this paper, it is shown that computational advantages can be gained by using frequency domain methods instead. This paper introduces a novel on-the-fly CNC interpolation method using Fast Fourier Transforms (FFTs). The presented OTF FFT method demonstrates up to 10 times increase in computational speed than the direct convolution OTF method. The effectiveness of the proposed method is validated in simulation based case studies.

关键词： CNC Interpolation FFT Convolution Motion control

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