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arXiv 2019年

作者： Loskot, Pavel Atitey, Komlan Mihaylova, Lyudmila College of Engineering Swansea University Swansea United Kingdom Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

Key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. Model dynamics are crucially dependent on parameter values which are often estimated from observations. Over past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. Statistical inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability and checking as well as optimum experiment design, sensitivity analysis, bifurcation analysis and other. The aim of this review paper is to explore developments of past decade to understand what BRN models are commonly used in literature, and for what inference tasks and inference methods. Initial collection of about 700 publications excluding books in computational biology and chemistry were screened to select over 260 research papers and 20 graduate theses concerning estimation problems in BRNs. The paper selection was performed as text mining using scripts to automate search for relevant keywords and terms. The outcome are tables revealing the level of interest in different inference tasks and methods for given models in literature as well as recent trends. In addition, a brief survey of general estimation strategies is provided to facilitate understanding of estimation methods which are used for BRNs. Our findings indicate that many combinations of models, tasks and methods are still relatively sparse representing new research opportunities to explore those that have not been considered - perhaps for a good reason. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics and state space representations whereas the most common tasks in cited papers are parameter inference and model ident

关键词： Surveys

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Universal Privacy Guarantees for Smart Meters

Universal Privacy Guarantees for Smart Meters

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IEEE International Symposium on Information Theory

作者： Miguel Arrieta Iñaki Esnaola Michelle Effros Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Dept. of Electrical Engineering California Institute of Technology Pasadena CA USA

Smart meters enable improvements in electricity distribution system efficiency at some cost in customer privacy. Users with home batteries can mitigate this privacy loss by applying charging policies that mask their underlying energy use. A battery charging policy is proposed and shown to provide universal privacy guarantees subject to a constraint on energy cost. The guarantee bounds our strategy's maximal information leakage from the user to the utility provider under general stochastic models of user energy consumption. The policy construction adapts coding strategies for non-probabilistic permuting channels to this privacy problem.

关键词： Batteries Privacy Energy consumption Measurement Mutual information Silicon Random processes

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Text Mining of Medical Records

Text Mining of Medical Records

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E-Health and Bioengineering Conference (EHB)

作者： Irina Andra Tache Monica Dragoicea Elena-Simona Apostol Ciprian-Octavian Truica Dept. of Automatic Control and Systems Engineering University Politehnica of Bucharest Bucharest Romania Computer Science and Engineering Department University Politehnica of Bucharest Bucharest Romania

The patients with cardiovascular diseases undergo complex medical procedures, including medical imaging, blood biochemistry analysis, physical examination, etc. The digitization of information is an intensive process in e-health therefore, in this paper there are presented some methods to extract important data from medical records of patients suffering from coronary artery diseases organized in PDF files.

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From self-tuning regulators to reinforcement learning and back again

From self-tuning regulators to reinforcement learning and ba...

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IEEE Annual Conference on Decision and control

作者： Nikolai Matni Alexandre Proutiere Anders Rantzer Stephen Tu Dept. of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Automatic Control Lund University Lund Sweden Google Brain New York City NY

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings, the algorithms had better come with the same type of reliability, robustness, and safety bounds that are hallmarks of control theory, or failures could be catastrophic. Thus, as learning algorithms are increasingly and more aggressively deployed in safety critical settings, it is imperative that control theorists join the conversation. The goal of this tutorial paper is to provide a starting point for control theorists wishing to work on learning related problems, by covering recent advances bridging learning and control theory, and by placing these results within an appropriate historical context of system identification and adaptive control.

关键词： Adaptive control Tools Tuning control theory Adaptation models Robustness Learning (artificial intelligence)

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Erratum to “Ideal benefits of exceeding fixed voltage limits on lithium-ion batteries with increasing cycle age” [J. Power Sources 441 (2019) 227179]

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Journal of Power Sources 2020年 447卷

作者： Lukas Wikander Björn Fridholm Sébastien Gros Torsten Wik Automatic Control Div. of Systems and Control Dept. of Electrical Engineering Chalmers University of Technology Göteborg Sweden Volvo Car Corporation Göteborg Sweden

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Using MATLAB GUIs to Improve Student Engagement and Understanding

Using MATLAB GUIs to Improve Student Engagement and Understa...

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UKACC International Conference on control (control)

作者： J A Rossiter Dept. Automatic Control and Systems Engineering University of Sheffield UK

Laboratories are a core part of any engineering degree, but access to laboratories is typically limited due to a combination of timetable, space and equipment restrictions [1]. In more recent years that has been a significant growth in so-called virtual laboratories (VL), that is laboratory like activities that can be accessed via software or even a web interface, e.g. [2], [3], [5]. The advantage of VL is that the accessibility can potentially be improved to 24/7 and often these may be available on a student's own computing device thus also giving no space restrictions. Improvements in accessibility mean that staff can integrate VL far more easily into the curriculum and student independent study schedule with the consequence that, in principle, students can learn more effectively.

关键词： Matlab Uncertainty Education Level control Remote laboratories Tools

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Linear System Identification of Longitudinal Vehicle Dynamics Versus Nonlinear Physical Modelling

Linear System Identification of Longitudinal Vehicle Dynamic...

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UKACC International Conference on control (control)

作者： Sebastian James Sean R. Anderson Dept of Automatic Control and Systems Engineering University of Sheffield Sheffield UK

Mathematical modelling of vehicle dynamics is essential for the development of autonomous cars. Many of the vehicle models that are used for control design in cars are based on nonlinear physical models. However, it is not clear, especially for the case of longitudinal dynamics, whether such nonlinear models are necessary or simpler models can be used. In this paper, we identify a linear data-driven model of longitudinal vehicle dynamics and compare it to a nonlinear physically derived model. The linear model was identified in continuous-time state-space form using a prediction error method. The identification data were obtained from a Lancia Delta car, over 53 km of normal driving on public roads. The selected linear model was first order with requested torque, brake and road gradient as inputs and car velocity as output. The key results were that 1. the linear model was accurate, with a variance accounted for (VAF) metric of VAF=96.5%, and 2. the identified linear model was also superior in accuracy to the nonlinear physical model, VAF=77.4%. The implication of these results, therefore, is that for longitudinal dynamics, in normal driving conditions, a first order linear model is sufficient to describe the vehicle dynamics. This is advantageous for control design, state estimation and real-time implementation, e.g. in predictive control.

关键词： Torque Vehicle dynamics Roads Engines Predictive models Data models

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Short Term Traffic Flow Prediction with Particle Methods in the Presence of Sparse Data

Short Term Traffic Flow Prediction with Particle Methods in ...

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International Conference on Information Fusion

作者： Kennedy J. Offor Matthew Hawes Lyudmila Mihaylova Dept of Automatic Control and Systems Engineering The University of Sheffield Sheffield United Kingdom

Traffic prediction approaches face challenges when presented with sparse or missing data. This can be caused by numerous factors such as: i) sensors not being operational; ii) communication issues; iii) cost prohibiting full monitoring of a road network. This present work adds to existing body of knowledge by proposing a particle based framework for dealing with these challenges. An expression of the likelihood function is derived for the case when the missing value is calculated based on Kriging interpolation. With the Kriging interpolation, the missing values of the measurements are predicted, which are subsequently used in the computation of likelihood terms in the particle filter algorithm. The results show 23% to 36.34% improvement in RMSE values for the synthetic data used.

关键词： Mathematical model Computational modeling Interpolation Roads Atmospheric measurements Particle measurements Estimation

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Universal mutual information privacy guarantees for smart meters

arXiv

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arXiv 2019年

作者： Arrieta, Miguel Esnaola, Iñaki Effros, Michelle Dept. of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Dept. of Electrical Engineering Princeton University PrincetonNJ08544 United States Dept. of Electrical Engineering California Institute of Technology PasadenaCA91125 United States

关键词： Smart meters

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Variational bayesian inference of hidden stochastic processes with unknown parameters

arXiv

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arXiv 2019年

作者： Atitey, Komlan Loskot, Pavel Mihaylova, Lyudmila Swansea University College of Engineering Fabian Way Skewen SwanseaSA1 8EN United Kingdom University of Sheffield Dept. of Automatic Control and Systems Engineering Mappin Street SheffieldS1 3JD United Kingdom

Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning approach to devise variational Bayesian inference for such scenarios. In particular, a random process generated by the autoregressive moving average (ARMA) linear model is inferred from non-linearity noisy observations. The posterior distributions of hidden states are approximated by a set of weighted particles generated by the sequential Monte Carlo (SMC) algorithm involving sampling with importance samplingresampling (SISR). Numerical efficiency and estimation accuracy of the proposed inference method are evaluated by computer simulations. Furthermore, the proposed inference method is demonstrated on a practical problem of estimating the missing values in the gene expression time series assuming vector autoregressive (VAR) data model. Copyright © 2019, The Authors. All rights reserved.

关键词： Random processes

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