Search results for "Estimation theory"
showing 10 items of 84 documents
Cavity-aided quantum parameter estimation in a bosonic double-well Josephson junction
2014
We describe an apparatus designed to make non-demolition measurements on a Bose-Einstein condensate (BEC) trapped in a double-well optical cavity. This apparatus contains, as well as the bosonic gas and the trap, an optical cavity. We show how the interaction between the light and the atoms, under appropriate conditions, can allow for a weakly disturbing yet highly precise measurement of the population imbalance between the two wells and its variance. We show that the setting is well suited for the implementation of quantum-limited estimation strategies for the inference of the key parameters defining the evolution of the atomic system and based on measurements performed on the cavity field…
Estimation of the prevalence and incidence of motor neuron diseases in two Spanish regions: Catalonia and Valencia
2021
AbstractAccording to the degree of upper and lower motor neuron degeneration, motor neuron diseases (MND) can be categorized into amyotrophic lateral sclerosis (ALS), primary lateral sclerosis (PLS) or progressive muscular atrophy (PMA). Although several studies have addressed the prevalence and incidence of ALS, there is a high heterogeneity in their results. Besides this, neither concept has been previously studied in PLS or PMA. Thus, the objective of this study was to estimate the prevalence and incidence of MND, (distinguishing ALS, PLS and PMA), in the Spanish regions of Catalonia and Valencia in the period 2011–2019. Two population-based Spanish cohorts were used, one from Catalonia …
Physics-Aware Gaussian Processes for Earth Observation
2017
Earth observation from satellite sensory data pose challenging problems, where machine learning is currently a key player. In recent years, Gaussian Process (GP) regression and other kernel methods have excelled in biophysical parameter estimation tasks from space. GP regression is based on solid Bayesian statistics, and generally yield efficient and accurate parameter estimates. However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only. Very often a forward model encoding the well-understood physical relations is available though. In this work, we review three GP models that respect and learn the physics of the underlying processes …
Series resistance determination and further characterization of c-Si PV modules
2012
Abstract This paper presents a new algorithm for determination of the series resistance of crystalline-Si PV modules from individual illuminated I–V curves. The ideality factor and the reverse saturation current are then extracted in the classic way. The approach is applied to in-situ measured data from modules based on two types of mc-Si feedstock. The results indicate that the method yields physically meaningful parameters. An improved definition of local ideality factor is suggested, resulting in m-V plots unaffected by the series resistance. In addition, m-I plots are introduced for the first time. The novel differential techniques reveal an unexpected rise of the ideality factor at ope…
Modeling and parameter identification of crystalline silicon photovoltaic devices
2011
In this paper the physical correctness of the standard single-exponential (one-diode) model of crystalline-Si photovoltaic devices is examined. In particular, we focus on the shunt current. I-V curves of in situ illuminated polycrystalline-Si photovoltaic modules are measured, and based on these measurements, we extract the shunt current. There is a certain voltage range in which the shunt current shows an Ohmic-like behavior, but the value of the resistance varies with irradiance and the quality of illumination. In addition, the Ohmic behavior takes place at voltages well below the maximum-power point (MPP). At higher voltages, the shunt current drops to negligible values. We conclude that…
Approximation of the Feasible Parameter Set in worst-case identification of Hammerstein models
2005
The estimation of the Feasible Parameter Set (FPS) for Hammerstein models in a worst-case setting is considered. A bounding procedure is determined both for polytopic and ellipsoidic uncertainties. It consists in the projection of the FPS of the extended parameter vector onto suitable subspaces and in the solution of convex optimization problems which provide Uncertainties Intervals of the model parameters. The bounds obtained are tighter than in the previous approaches. hes.
Robust estimation of partial directed coherence by the vector optimal parameter search algorithm
2009
We propose a method for the accurate estimation of Partial Directed Coherence (PDC) from multichannel time series. The method is based on multivariate vector autoregressive (MVAR) model identification performed through the recently proposed Vector Optimal Parameter Search (VOPS) algorithm. Using Monte Carlo simulations generated by different MVAR models, the proposed VOPS algorithm is compared with the traditional Vector Least Squares (VLS) identification method. We show that the VOPS provides more accurate PDC estimates than the VLS (either overall and single-arc errors) in presence of interactions with long delays and missing terms, and for noisy multichannel time series. ©2009 IEEE.
Adaptive estimation of Laguerre models with time-varying delay
2000
Abstract An Orthonormal Basis Functions (OBF) approach is effectively used in adaptive parameter estimation of linear(ized) open-loop stable, possibly nonminimum phase plants with time-varying delay. In particular, discrete Laguerre models are considered in detail. A special attention is paid to the numerical conditioning issue in case of ’poor’ excitation of a plant under control, where OBF models are of particular value. Closed-loop predictive control simulations confirm the usefulness of adaptive OBF modelling, especially for systems with time-varying delays.
Linear parameter estimation and predictive constrained control of wiener/hammerstein systems
2003
Abstract A new, analytical, orthonormal basis functions (OBF)-based design methodology for adaptive predictive constrained control of open-loop stable, possibly nonminimum phase, time-varying Wiener and Hammerstein systems is presented. A linear adaptive least-squares parameter estimation algorithm is applied both to a nonlinear static part and a linear dynamic, OBF-modeled factor of the Wiener/Hammerstein system. A notion of inverse systems is crucial for linear estimation of both Wiener and Hammerstein systems, with in verses of the nonlinear or linear parts respectively involved. The adaptive estimator is coupled with a simple but robust, predictive control strategy called Extended Horiz…
The Hu-Washizu variational principle for the identification of imperfections in beams
2008
This paper presents a procedure for the identification of imperfections of structural parameters based on displacement measurements by static tests. The proposed procedure is based on the well-known Hu–Washizu variational principle, suitably modified to account for the response measurements, which is able to provide closed-form solutions to some inverse problems for the identification of structural parameter imperfections in beams. Copyright © 2008 John Wiley & Sons, Ltd.