Página 1 dos resultados de 135 itens digitais encontrados em 0.010 segundos

A simple ESR identification methodology for electrolytic capacitors condition monitoring

Leite, V.; Teixeira, H.; Cardoso, A.J. Marques; Araújo, R.
Fonte: Instituto Politécnico de Bragança Publicador: Instituto Politécnico de Bragança
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
56%
Electrolytic capacitors are usually used in power electronic systems for smoothing, energy storage or filtering. They have the best overall performance for these objectives being a critical element in the design of these systems, in several applications, with different requirements. But, unfortunately, in most cases, they are the most life-limiting device. The expected life of electrolytic capacitors depends on their internal temperature and is determined by the ratio of the electrolyte solution evaporation used in their fabrication. The deterioration caused by this evaporation is reflected in electrical parameters, mainly the equivalent series resistance (ESR). As the volume of the electrolyte decreases, ESR increases and capacitance decreases. Additionally, the increase in ESR has a positive feedback effect since it leads the temperature to increase and this in turn leads to further evaporation of the electrolyte leading to the ESR increase and so forth. This paper presents a simple ESR identification methodology for electrolytic capacitors condition monitoring in view of preventive maintenance to signal the need of maintenance and or replacement. The identification methodology is based on a simple continuous-time model and some recursive prediction error methods...

A boot-strap estimator for joint flux and parameters online identification for vector controlled induction motor drives

Leite, V.; Araújo, R.; Freitas, D.
Fonte: Instituto Politécnico de Bragança Publicador: Instituto Politécnico de Bragança
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
35.92%
This paper presents a new approach for joint rotor flux and electrical parameters on-line identification in vector controlled high-performance induction motor drives based on a boot-strap estimator that uses a reduced order extended Kalman filter for rotor flux components and rotor parameters estimation and a recursive prediction error method for stator parameters estimation. Within the prediction error method some approaches are used and compared that affect both the adaptation gain and the direction in which the updates of stator parameters are made. The induction motor model structures are described in the rotor reference frame in order to reduce the computational effort by using a higher sampling time interval.

Classes of model structures for state and parameter identification of vector controlled induction machines

Leite, V.; Araújo, R.; Freitas, D.
Fonte: Instituto Politécnico de Bragança Publicador: Instituto Politécnico de Bragança
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
45.78%
The purpose of this paper is to present a synthesis of classes of model structures for joint state and parameter identification of vector controlled induction motors for real time and normal operating conditions. Based on its classical model a set of new classes of model structures is discussed and proposed for simultaneous estimation of rotor flux components and electrical parameters.

100m and 200m front crawl performance prediction based on anthropometric and physiological measurements

Reis, V.M.; Silva, A.J.; Carneiro, André Luiz; Marinho, D.A.; Novaes, G.; Barbosa, Tiago M.
Fonte: International Federation of Sports Medicine Publicador: International Federation of Sports Medicine
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
35.89%
Background: The identification of the variables that are able to predict swimming performance is one of the main purposes of the “swimming science” community. Research question: The aims of the study were: (i) to compare the anthropometric and physiological profiles of 100m and 200m front crawl swimmers and; (ii) to identify anthropometric and physiological variables that account for the prediction of the swimming performance at the 100m and 200m front crawl events. Methods: Twenty-six male swimmers were divided in two groups (12 for 100m group and 14 to 200m group). The swimmers’ personal best performance for the 100m and the 200m front crawl was converted to FINA points. The subjects performed a graded swimming test and an all-out test (100 or 200m maximal swims) in different days, in which physiological measures were evaluated. Forward step-by-step linear regression models were computed to predict swimming performance. The subjects’ performances (season best and all-out test) were taken as dependent variables. The age, physiological and anthropometric measures were selected as independent variables. Results: Anthropometric and physiological profiles of 100 and 200m swimmers are different and the mean oxygen uptake during exercise combined with training experience may explain 200m front crawl best season performance with a high precision (≈2% error). The models computed were able to predict from 44 % (i.e. 200m all-out bout) to 61 % (i.e. 200m season best) swimming performance. Predictive power of the models was less accurate in the 100m event (error > 10%). Conclusions: The authors conclude that the extent to which the physiological and anthropometric variables combine to predict performance probable is group-specific.

Identification and the information matrix : how to get just sufficiently rich?

Gevers, Michel; Bazanella, Alexandre Sanfelice; Bombois, Xavier; Miskovic, Ljubisa
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Artigo de Revista Científica Formato: application/pdf
ENG
Relevância na Pesquisa
45.74%
In prediction error identification, the information matrix plays a central role. Specifically, when the system is in the model set, the covariance matrix of the parameter estimates converges asymptotically, up to a scaling factor, to the inverse of the information matrix. The existence of a finite covariance matrix thus depends on the positive definiteness of the information matrix, and the rate of convergence of the parameter estimate depends on its “size”. The information matrix is also the key tool in the solution of optimal experiment design procedures, which have become a focus of recent attention. Introducing a geometric framework, we provide a complete analysis, for arbitrary model structures, of the minimum degree of richness required to guarantee the nonsingularity of the information matrix. We then particularize these results to all commonly used model structures, both in open loop and in closed loop. In a closed-loop setup, our results provide an unexpected and precisely quantifiable trade-off between controller degree and required degree of external excitation.

Structural real time control using fluid magnetorheologic damper

Neto, Camilo Mesquita; Santos, Rodrigo Borges; Bueno, Douglas Domingues; Marqui, Clayton Rodrigo; Lopes Jr., Vicente
Fonte: Universidade Estadual Paulista Publicador: Universidade Estadual Paulista
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
46.05%
In the last decades there was a great development in the study of control systems to attenuate the harmful effect of natural events in great structures, as buildings and bridges. Magnetorheological fluid (MR), that is an intelligent material, has been considered in many proposals of project for these controllers. This work presents the controller design using feedback of states through LMI (Linear Matrix Inequalities) approach. The experimental test were carried out in a structure with two degrees of freedom with a connected shock absorber MR. Experimental tests were realized in order to specify the features of this semi-active controller. In this case, there exist states that are not measurable, so the feedback of the states involves the project of an estimator. The coupling of the MR damper causes a variation in dynamics properties, so an identification methods, based on experimental input/output signal was used to compare with the numerical application. The identification method of Prediction Error Methods - (PEM) was used to find the physical characteristics of the system through realization in modal space of states. This proposal allows the project of a semi-active control, where the main characteristic is the possibility of the variation of the damping coefficient.

Closed-Loop Identification: Application to the Estimation of Limb Impedance in a Compliant Environment

Westwick, David. T.; Perreault, Eric J.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
35.96%
The force and position data used to construct models of limb impedance are often obtained from closed-loop experiments. If the system is tested in a stiff environment, it is possible to treat the data as if they were obtained in open loop. However, when limb impedance is studied in a compliant environment, the presence of feedback cannot be ignored. While unbiased estimates of a system can be obtained directly using the prediction error method, the same cannot be said when linear regression or correlation analysis are used to fit nonparametric time- or frequency domain models. We develop a prediction error minimization based identification method for a nonparametric time-domain model augmented with a parametric noise model. The identification algorithm is tested on a dynamic mass-spring-damper system, and returns consistent estimates of the system’s properties under both stiff and compliant feedback control. The algorithm is then used to estimate the impedance of a human elbow joint in both stiff and compliant environments.

Internet transport layer system identification

White, L.
Fonte: IEEE - Institute of Electrical and Electronics Engineers Publicador: IEEE - Institute of Electrical and Electronics Engineers
Tipo: Conference paper
Publicado em //2001 EN
Relevância na Pesquisa
45.74%
This paper addresses the problem of building appropriate statistical models of the way the Internet appears from the point of view of congestion, to a transmission control protocol (TCP) sender. TCP is a mechanism for implementing full duplex, acknowledged, end-to-end transmission over an Internet protocol (IP) network. This work has been motivated by the TCP variant, the so-called Vegas implementation. TCP Vegas is really the first implementation to be based loosely on system theoretic ideas in the sense that it measures the segment round-trip times across the network to adjust its transmission rate. This paper develops a new linear system framework for TCP, and applies recursive prediction error identification techniques to specify statistical models which may be used to develop alternative control strategies.; © Copyright 2001 IEEE

Forecasting Hospital Emergency Department Visits for Respiratory Illness Using Ontario's Telehealth System: An Application of Real-Time Syndromic Surveillance to Forecasting Health Services Demand

PERRY, ALEXANDER
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 8189225 bytes; application/pdf
EN; EN
Relevância na Pesquisa
36.12%
Background: Respiratory illnesses can have a substantial impact on population health and burden hospitals in terms of patient load. Advance warnings of the spread of such illness could inform public health interventions and help hospitals manage patient services. Previous research showed that calls for respiratory complaints to Telehealth Ontario are correlated up to two weeks in advance with emergency department visits for respiratory illness at the provincial level. Objectives: This thesis examined whether Telehealth Ontario calls for respiratory complaints could be used to accurately forecast the daily and weekly number of emergency department visits for respiratory illness at the health unit level for each of the 36 health units in Ontario up to 14 days in advance in the context of a real-time syndromic surveillance system. The forecasting abilities of three different time series modeling techniques were compared. Methods: The thesis used hospital emergency department visit data from the National Ambulatory Care Reporting System database and Telehealth Ontario call data and from June 1, 2004 to March 31, 2006. Parallel Cascade Identification (PCI), Fast Orthogonal Search (FOS), and Numerical Methods for Subspace State Space System Identification (N4SID) algorithms were used to create prediction models for the daily number of emergency department visits using Telehealth call counts and holiday/weekends as predictors. Prediction models were constructed using the first year of the study data and their accuracy was measured over the second year of data. Factors associated with prediction accuracy were examined. Results: Forecast error varied widely across health units. Prediction error increased with lead time and lower call-to-visits ratio. Compared with N4SID...

Open-loop vs. closed-loop identification of Box-Jenkins systems in a least costly identification context

Anderson, Brian
Fonte: Conference Organising Committee Publicador: Conference Organising Committee
Tipo: Conference paper
Relevância na Pesquisa
45.82%
In this paper, we compare open-loop and closed-loop prediction error identification. In particular, we determine whether open-loop or closed-loop identification is optimal in the least costly identification experiment design framework. The least costly ex

Robustness Analysis Tools for an Uncertainty Set Obtained by Prediction Error Identification

Bombois, Xavier; Gevers, Michel; Scorletti, Gérard; Anderson, Brian
Fonte: Pergamon-Elsevier Ltd Publicador: Pergamon-Elsevier Ltd
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
76%
This paper presents a robust stability and performance analysis for an uncertainty set delivered by classical prediction error identification. This nonstandard uncertainty set, which is a set of parametrized transfer functions with a parameter vector in a

Chaotic time series Part II: System identification and prediction

Lillekjendlie, Bjoern; Kugiumtzis, Dimitris; Christophersen, Nils
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
35.93%
This paper is the second in a series of two, and describes the current state of the art in modelling and prediction of chaotic time series. Sampled data from deterministic non-linear systems may look stochastic when analysed with linear methods. However, the deterministic structure may be uncovered and non-linear models constructed that allow improved prediction. We give the background for such methods from a geometrical point of view, and briefly describe the following types of methods: global polynomials, local polynomials, multi layer perceptrons and semi-local methods including radial basis functions. Some illustrative examples from known chaotic systems are presented, emphasising the increase in prediction error with time. We compare some of the algorithms with respect to prediction accuracy and storage requirements, and list applications of these methods to real data from widely different areas.; Comment: 17 pages and 3 pages with figures all in uuencoded tar-compressed postscript format. Sent to Modeling, Identification and Control (MIC), Norway

Identification of stable models via nonparametric prediction error methods

Romeres, Diego; Pillonetto, Gianluigi; Chiuso, Alessandro
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/07/2015
Relevância na Pesquisa
45.98%
A new Bayesian approach to linear system identification has been proposed in a series of recent papers. The main idea is to frame linear system identification as predictor estimation in an infinite dimensional space, with the aid of regularization/Bayesian techniques. This approach guarantees the identification of stable predictors based on the prediction error minimization. Unluckily, the stability of the predictors does not guarantee the stability of the impulse response of the system. In this paper we propose and compare various techniques to address this issue. Simulations results comparing these techniques will be provided.; Comment: number of pages = 6, number of figures = 3

Simulation and analytical approach to the identification of significant factors

Bulinski, Alexander V.; Rakitko, Alexander S.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 04/06/2014
Relevância na Pesquisa
36.06%
We develop our previous works concerning the identification of the collection of significant factors determining some, in general, non-binary random response variable. Such identification is important, e.g., in biological and medical studies. Our approach is to examine the quality of response variable prediction by functions in (certain part of) the factors. The prediction error estimation requires some cross-validation procedure, certain prediction algorithm and estimation of the penalty function. Using simulated data we demonstrate the efficiency of our method. We prove a new central limit theorem for introduced regularized estimates under some natural conditions for arrays of exchangeable random variables. Keywords: nonbinary random response; identification of significant factors; regularized estimates of prediction error; exchangeable random variables; central limit theorem.; Comment: 25 pages, 6 tables, 3 figures

Evidence-Based Identification of Weighting Factors in Bayesian Model Updating Using Modal Data

Goller, B.; Beck, J. L.; Schuëller, G. I.
Fonte: American Society of Civil Engineers Publicador: American Society of Civil Engineers
Tipo: Article; PeerReviewed Formato: application/pdf
Publicado em /05/2012
Relevância na Pesquisa
35.92%
In Bayesian model updating, parameter identification of structural systems using modal data can be based on the formulation of the likelihood function as a product of two probability density functions, one relating to modal frequencies and one to mode-shape components. The selection of the prior distribution of the prediction-error variances relating to these two types of data has to be performed carefully so that the relative contributions are weighted to give balanced results. A methodology is proposed in this paper to select these weights by performing Bayesian updating at the model class level, where the model classes differ by having different ratios of the two prediction-error variances. The most probable model class on the basis of the modal data then gives the best choice for this variance ratio. Two illustrative examples, one using simulated data and one using experimental data, point out the effect of the different relative contributions of the modal frequencies and mode-shape components to the total amount of information extracted from the modal data.

Dynamic Estimation of Rigid Motion from Perspective Views via Recursive Identification of Exterior Differential Systems with Parameters on a Topological Manifold

Soatto, Stefano; Frezza, Ruggero; Perona, Pietro
Fonte: California Institute of Technology Publicador: California Institute of Technology
Tipo: Report or Paper; PeerReviewed Formato: application/pdf; application/postscript
Publicado em 15/02/1994
Relevância na Pesquisa
36%
We formulate the problem of estimating the motion of a rigid object viewed under perspective projection as the identification of a dynamic model in Exterior Differential form with parameters on a topological manifold. We first describe a general method for recursive identification of nonlinear implicit systems using prediction error criteria. The parameters are allowed to move slowly on some topological (not necessarily smooth) manifold. The basic recursion is solved in two different ways: one is based on a simple extension of the traditional Kalman Filter to nonlinear and implicit measurement constraints, the other may be regarded as a generalized "Gauss-Newton" iteration, akin to traditional Recursive Prediction Error Method techniques in linear identification. A derivation of the "Implicit Extended Kalman Filter" (IEKF) is reported in the appendix. The ID framework is then applied to solving the visual motion problem: it indeed is possible to characterize it in terms of identification of an Exterior Differential System with parameters living on a C0 topological manifold, called the "essential manifold". We consider two alternative estimation paradigms. The first is in the local coordinates of the essential manifold: we estimate the state of a nonlinear implicit model on a linear space. The second is obtained by a linear update on the (linear) embedding space followed by a projection onto the essential manifold. These schemes proved successful in performing the motion estimation task...

Model Validation for Control and Controller Validation in a Prediction error Identification Framework - Part II: illustrations

Covers, Michel; Bombois, Xavier; Codrons, Bonoit; Scorletti, Gérard; Anderson, Brian
Fonte: Pergamon-Elsevier Ltd Publicador: Pergamon-Elsevier Ltd
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
96.14%
The results on model validation for control and controller validation in a prediction error identification framework are illustrated. The results are illustrated with two realistic identification and control design applications. The first is the control of a flexible mechanical system with a tracking objective and the second is the control of a ferrosilicon production process with a disturbance rejection objective.

Quantification of frequency domain error bounds with guaranteed confidence level in prediction error identification

Bombois, Xavier; Anderson, Brian; Gevers, Michel
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
96.2%
This paper considers prediction error identification of linearly parametrized models in the situation where the system is in the model set. For such situation it is easy to construct a confidence ellipsoid in parameter space in which the true parameter lies with an a priori fixed probability level, α. Surprisingly perhaps, the construction of a corresponding uncertainty set in the frequency domain, to which the true system belongs with probability α, is still an open problem. We show in this paper how to construct such frequency domain uncertainty set with a probability level of at least α.

Model Validation for Control and Controller Validation in a Prediction error Identification Framework - Part I

Gevers, Michel; Bombois, Xavier; Codrons, Bonoit; Scorletti, Gérard; Anderson, Brian
Fonte: Pergamon-Elsevier Ltd Publicador: Pergamon-Elsevier Ltd
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
86.15%
We propose a model validation procedure that consists of a prediction error identification experiment with a full order model. It delivers a parametric uncertainty ellipsoid and a corresponding set of parameterized transfer functions, which we call prediction error (PE) uncertainty set. Such uncertainty set differs from the classical uncertainty descriptions used in robust control analysis and design. We develop a robust control analysis theory for such uncertainty sets, which covers two distinct aspects: (1) Controller validation. We present necessary and sufficient conditions for a specific controller to stabilize - or to achieve a given level of performance with - all systems in such PE uncertainty set. (2) Model validation for robust control. We present a measure for the size of such PE uncertainty set that is directly connected to the size of a set controllers that stabilize all systems in the model uncertainty set. This allows us to establish that one uncertainty set is better tuned for robust control design than another, leading to control-oriented validation objectives.

Closed-loop Identification with an Unstable or Nonminimum Phase Controller

Codrons, Bonoit; Anderson, Brian; Gevers, Michel
Fonte: Pergamon-Elsevier Ltd Publicador: Pergamon-Elsevier Ltd
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
55.9%
In many practical cases, the identification of a system is done in closed loop with some controller. In this paper, we show that the internal stability of the resulting model, in closed loop with the same controller, is not always guaranteed if this controller is unstable and/or nonminimum phase, and that the classical closed-loop prediction-error identification methods present different properties regarding this stability issue. With some of these methods, closed-loop instability of the identified model is actually guaranteed. This is a serious drawback if this model is to be used for the design of a new controller. We give guidelines to avoid the emergence of this instability problem; these guidelines concern both the experiment design and the choice of the identification method.