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检索条件"任意字段=IFAC Symposium on Modelling and Control in Biomedical Systems"

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Learning physics based models of Lithium-ion Batteries 16

Learning physics based models of Lithium-ion Batteries

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16th ifac symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Mckay, Maricela Best Wetton, Brian Gopaluni, R. Bhushan Univ British Columbia Dept Math Vancouver BC Canada Univ British Columbia Dept Chem & Biol Engn Vancouver BC Canada

Lithium-ion (Li-ion) batteries are increasingly pervasive and important in daily life. We present a surrogate modeling approach that uses synthetic data generated by an electrochemical model to approximate Li-ion battery dynamics using a Deep Neural Network. Elechtrochemical models are needed to describe high current operation but are computationally costly. As an initial study, we prototype our approach for the Single Particle Model. We use a battery use-cycle and observations of concentrations and voltages to predict future battery behavior. Given only the use cycle and knowledge that the battery is fully charged, the surrogate model can accurately forecast observations of Li-ion concentrations and voltages for the entire use cycle, as well as give a window of time for battery depletion. Copyright (C) 2021 The Authors.

关键词： Time series modelling Nonlinear model reduction Application of power electronics Optimal operation and control of power systems Real time simulation and dispatching Modeling and simulation of power systems

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Detection of Glucose Sensor Faults in an Artificial Pancreas via Whiteness Test on Kalman Filter Residuals 19

Detection of Glucose Sensor Faults in an Artificial Pancreas...

引用

19th ifac symposium on System Identification (SYSID)

作者： Manzoni, Eleonora Rampazzo, Mirco Del Favero, Simone Univ Padua Dept Informat Engn Via G Gradenigo 6-B I-35131 Padua Italy

Continuous Glucose Monitoring (CGM) sensors are key components in an artificial pancreas, an emerging tool for type 1 diabetes treatment. Malfunctioning of this component might reduce the efficacy of glucose control achieved by the system and even pose the safety of the patient at risk. Therefore, accurate and prompt detection of these anomalies is an important problem. This paper investigates a model-based method to detect CGM failures. Based on an individualized linear model of the subject, identified on hystorical data, the method predicts future glucose concentration through a one-step ahead Kalman predictor. The correct functioning of the system is then monitored using two different criteria: the first checks the magnitude of prediction residuals. The second checks the whiteness of the residuals through a correlogram test. The effectiveness of the two criteria is investigated and compared by performing tests on an in-silico dataset obtained by means of UVA/Padova Type 1 Diabetes simulator, accepted by the US Food and Drug Administration as a substitute of animal testing prior to artificial pancreas clinical trials on humans. Copyright (C) 2021 The Authors.

关键词： Fault detection and diagnosis biomedical system modeling simulation and visualization Artificial pancreas or organs Statistical methods/signal analysis for FDI Real-time algorithms scheduling programming

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Identification of Fast Time -varying Communication Channels Using the Preestimation Technique 19

Identification of Fast Time -varying Communication Channels ...

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19th ifac symposium on System Identification (SYSID)

作者： Niedzwiecki, Maciej Gancza, Artur Kaczmarek, Piotr Gdansk Univ Technol Dept Automat Control Fac Elect Telecommun & Comp Sci Ul Narutowicza 11-12 Gdansk Poland

Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately chosen basis functions. The paper shows that tracking performance of the resulting local basis function estimation algorithms can be further improved by means of regularization. The method is illustrated by an important recent application identification of fast time-varying acoustic channels used in underwater communication. Copyright (C) 2021 The Authors.

关键词： identification of time-varying systems underwater acoustic communication channels local basis function approach regularization leave-one-out cross-validation

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Individualized control of the depth of anesthesia based on online identification and retuning 11th

Individualized control of the depth of anesthesia based on o...

引用

11th ifac symposium on Biological and Medical systems (BMS)

作者： Silva, Jorge Mendonca, Teresa Rocha, Paula Univ Porto FEUP Fac Engn Porto Portugal Res Ctr Syst & Technol SYSTEC Porto Portugal

In this paper a control scheme based on a simplified model for the joint action of propofol and remifentanil on the depth of anesthesia, measured by the BIS level, is proposed. The simplified model contains four patient dependent parameters, two of which can be easily estimated from the patient's BIS response to an initial bolus of propofol. Instead of estimating the remaining necessary parameters, initial guesses are assumed for these parameters in order to compute the drug dosages corresponding to a desired reference BIS level by steady-state model inversion. The dosages are updated by means of a successive retuning procedure that ensures that the patient's BIS response achieves and maintains the desired reference level. This successive retuning scheme yields betters results than a single retuning procedure by decreasing the settling time at the target BIS value. Copyright (C) 2021 The Authors.

关键词： Depth of anesthesia automatic control retuning scheme online parameter identification drug delivery

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Kernel-based Regularized Iterative Learning control of Repetitive Linear Time-varying systems 19

Kernel-based Regularized Iterative Learning Control of Repet...

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19th ifac symposium on System Identification (SYSID)

作者： Yu, Xian Chen, Tianshi Mu, Biqiang Ljung, Lennart Chinese Univ Hong Kong Sch Data Sci Shenzhen 518172 Guangdong Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Linkoping Univ Dept Elect Engn Div Automat Control SE-58183 Linkoping Sweden

The selections for the model orders and the number of controller parameters have not been discussed for many data-driven iterative learning control (ILC) methods. If they are not chosen carefully, the estimated model and designed controller will lead to either large variance or large bias. In this paper we try to use the kernel-based regularization method (KRM) to handle the model estimation problem and the controller design problem for unknown repetitive linear time-varying systems. In particular, we have used the diagonal correlated kernel and the marginal likelihood maximization method for the two problems. Numerical simulation results show that smaller mean square errors for each time instant are obtained by using the proposed ILC method in comparison with an existing data-driven ILC approach. Copyright (C) 2021 The Authors.

关键词： Kernel-based regularization Iterative learning control Repetitive systems Linear time-varying systems

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Electro-Mechanical Coupling in Impedance-Based Tissue Differentiation Under Compression 10

Electro-Mechanical Coupling in Impedance-Based Tissue Differ...

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10th Vienna International Conference on Mathematical modelling (MATHMOD)

作者： Veil, Carina Schoene, Sandra Harland, Niklas Schuele, Johannes Somers, Peter Stenzl, Arnulf Tarin, Cristina Sawodny, Oliver Univ Stuttgart Inst Syst Dynam Stuttgart Germany Univ Hosp Tubingen Dept Urol Tubingen Germany

Impedance spectroscopy is a useful diagnostic tool in oncology to determine malignant and healthy tissue areas. However, since the electrical measurements are sensitive enough to detect structural changes in tissue, they are also prone to exterior influences such as the contact force of the sensor and mechanical stress in the tissue. These disturbances might predominate electrical properties and make tissue differentiation difficult. In this work, a multiphysical model for the electro-mechanical coupling during impedance spectroscopy is established. Increasing resistivity at rising compression levels is an often observed phenomena that can be explained by the extrusion of fluids from the compressed tissue. With fluids being important ion carriers, tissue conductivity heavily depends on the fluid content within the sample. Separate electrical and mechanical models for tissue are identified and linked through the fluid volume and its transfer to neighboring structures under compression. Measurements on both healthy urinary bladder wall and bladder tumors are carried out to identify the model parameters and to investigate if the changes induced by mechanical stress outweigh the pathological changes. The recorded data fits the electro-mechanical model and confirms that the impedance variations under compression originate from the fluid exodus. Furthermore, the electrical parameters for extracellular matrix resistivity and membrane capacity are found to differ significantly between healthy and malignant tissue, yielding to the conclusion that the apprehension of electromechanical factors prevailing over pathological changes is ungrounded for these parameters. Copyright (C) 2022 The Authors.

关键词： Electrical impedance spectroscopy tissue differentiation physical models biomedical systems

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Closed-loop control of anesthesia and hemodynamic system: a Model Predictive control approach 11th

Closed-loop control of anesthesia and hemodynamic system: a ...

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11th ifac symposium on Biological and Medical systems (BMS)

作者： Maxim, Anca Copot, Dana Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Blvd D Mangeron 27 Iasi 700050 Romania Univ Ghent Res Grp Dynam Syst & Control Tech Lane Sci Pk 125 B-9052 Ghent Belgium Flanders Make Consortium EEDT Core Lab Decis & Control Tech Lane Sci Pk 125 B-9052 Ghent Belgium

This paper proposes a Model Predictive control (MPC) approach of both anesthesia and hemodynamic systems. The designed control strategy has been validated on a novel and unique patient simulator. The aim of this paper is to evaluate the feasibility MIMO closed-loop control of anesthesia and hemodynamic variables taking into account the interaction (synergic and antagonistic) between subsystems. The proposed methodology takes into account patient variability, is robust to subsystems interaction and meets the clinical objectives. The algorithm is tested in simulation on a hypnosis-hemodynamic combined model for use during general anesthesia. The preliminary results are promising and show the effectiveness of the control procedure. Copyright (C) 2021 The Authors.

关键词： anesthesia hemodynamic regulation closed-loop control of anesthesia Model Predictive control optimal drug dosing

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Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming 19

Toolbox for Discovering Dynamic System Relations via TAG Gui...

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19th ifac symposium on System Identification (SYSID)

作者： Nechita, Stefan-Cristian Toth, Roland Khandelwal, Dhruv Schoukens, Maarten Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Inst Comp Sci & Control Syst & Control Lab Kende U 13-17 H-1111 Budapest Hungary

Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models. Copyright (C) 2021 The Authors.

关键词： Nonlinear system identification Equation discovery Tree Adjoining Grammar Genetic Programming Data-driven system modeling

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A concave approach to errors-in-variables sparse linear system identification 19

A concave approach to errors-in-variables sparse linear syst...

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19th ifac symposium on System Identification (SYSID)

作者： Fosson, S. M. Cerone, V Regruto, D. Abdalla, T. Politecn Torino Dept Control & Comp Engn Turin Italy

Sparse linear system identification can be performed through convex optimization, by the minimization of an I-1 -norm functional. If an errors-in-variables model is considered, the problem is more challenging as inherently non-convex. The I-1-norm approach for the errorsin-variables model is studied in recent literature. In this work, we propose to replace the L-1 norm functional by a concave functional. Concave functionals have been shown to improve the performance in practical experiments of sparse linear regression;nevertheless, theoretical analyses of this improvement are missing in the errors-in-variables setting. The goal of this paper is to fill this gap, by studying conditions that guarantee that the concave approach is variable selection consistent. Moreover, we illustrate how to implement it through fi reweighting techniques, and we present numerical simulations that show its effectiveness. Copyright (C) 2021 The Authors.

关键词： Errors-in-variables identification linear systems sparse optimization non-convex optimization compressed sensing

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A data-driven modelling based approach to evaluating prognostic value of Electrical Impedance Spectroscopy for cervical cancer diagnosis 11th

A data-driven modelling based approach to evaluating prognos...

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11th ifac symposium on Biological and Medical systems (BMS)

作者： Li, Ping Highfield, Peter E. Lang, Zi-Qiang Kell, Darren Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Zilico Ltd Rutherford HousePencroft Way Manchester M15 6SZ Lancs England

Electrical impedance spectroscopy (EIS) has been used as an adjunct to colposcopy for cervical cancer diagnosis for many years. The study presented in this paper was a longitudinal EIS data analysis where women with a negative colposcopy were followed up to three years and their initial EIS readings were analyzed to see if it was possible to predict the women who subsequently developed cervical cancer. A data-driven modelling approach was proposed to extract features from EIS readings and cross validation techniques were then used to choose the best classification model constructed from the selected features to separate the group of women who developed cervical cancer from those who didn't within the follow-up years. The developed method was applied to analyze a real EIS data set and the results showed that EIS does offer prognostic information on the risk of cervical cancer development over three follow-up years. The method developed is of long-term benefit for EIS-based cervical cancer diagnosis and, in conjunction with standard colposcopy, there is potential with the developed method to provide more effective and efficient patient management strategies for clinic practice. Copyright (C) 2021 The Authors.

关键词： Electrical impedance spectroscopy (EIS) spectrum curve fitting multivariate analysis of variance (MANOVA) logistic regression cross validation cervical cancer bio impedance Cole equation

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