Search results for " Least squares"
showing 10 items of 223 documents
Prediction of ionic liquid's heat capacity by means of their in silico principal properties
2016
The in silico principal properties (PPs) of ionic liquids (ILs), derived by means of the VolSurf+ approach, were used to develop a Partial Least Squares (PLS) model able to find a quantitative correlation among IL descriptors (accounting for both cationic and anionic structural features) and heat capacity values, providing affordable predictions validated by experimental Cp measurements for an external set of ILs. In silico predictions allowed the selection of a limited number of structurally different ILs with similar Cp values, providing the possibility to select an optimal IL according to efficiency, as well as to environmental and economic sustainability. The present general procedure, …
Modeling the chiral resolution ability of highly sulfated β-cyclodextrin for basic compounds in electrokinetic chromatography
2013
Abstract Despite the fact that extensive research in the field of enantioseparations by capillary electrophoresis has been carried out, it is difficult to predict whether a concrete chiral selector would be useful for the separation of a racemic compound. Hence, several experimental effort is necessary to test the abilities of individual chiral selectors, usually by trial and error procedures. Thus, the enantioseparation of a new racemate becomes a time- and money-consuming task. In this work, the ability of highly sulfated β-cyclodextrin (HS-β-CD) as chiral selector in electrokinetic chromatography (EKC) is modeled for the first time, using exclusively directly-available structural data of…
Online topology estimation for vector autoregressive processes in data networks
2017
An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorit…
A fully-automated procedure for measuring the electrical parameters of an induction motor drive with rotor at standstill
2003
The paper presents an automatic procedure to measure at standstill the electrical parameters of an induction motor fed by a PWM voltage source inverter. The proposed procedure executes automatically three tests using only the available PWM inverter control technique to obtain the required motor supply voltages. It allows the measurement of all the T-form circuit electrical parameters starting from the nameplate data as data-entry. It uses only a current sensor and no voltage sensor and process on line the collected data samples with a fast and easy to implement recursive least squares algorithm. Effectiveness of the automated procedure has been proved both by simulation and experimental tes…
Sensorless Control of Induction-Motor Drive Based on Robust Kalman Filter and Adaptive Speed Estimation
2014
This paper deals with robust estimation of rotor flux and speed for sensorless control of motion control systems with an induction motor. Instead of using sixth-order extended Kalman filters (EKFs), rotor flux is estimated by means of a fourth-order descriptor-type robust KF, which explicitly takes into account motor parameter uncertainties, whereas the speed is estimated using a recursive least squares algorithm starting from the knowledge of the rotor flux itself. It is shown that the descriptor-type structure allows for a direct translation of parameter uncertainties into variations of the coefficients appearing in the model, and this improves the degree of robustness of the estimates. E…
Non-linear RLS-based algorithm for pattern classification
2006
A new non-linear recursive least squares (RLS) algorithm is presented in the context of pattern classification problems. The algorithm incorporates the non-linearity of the filter's output in the updating rules of the classical RLS algorithm. The proposed method yields lower stationary error levels when compared to the standard LMS and RLS algorithms in a classical application of pattern classification, such as the channel equalization problem.
Graph recursive least squares filter for topology inference in causal data processes
2017
In this paper, we introduce the concept of recursive least squares graph filters for online topology inference in data networks that are modelled as Causal Graph Processes (CGP). A Causal Graph Process (CGP) is an auto regressive process in the time series associated to different variables, and whose coefficients are the so-called graph filters, which are matrix polynomials with different orders of the graph adjacency matrix. Given the time series of data at different variables, the goal is to estimate these graph filters, hence the associated underlying adjacency matrix. Previously proposed algorithms have focused on a batch approach, assuming implicitly stationarity of the CGP. We propose…
A kernel regression approach to cloud and shadow detection in multitemporal images
2013
Earth observation satellites will provide in the next years time series with enough revisit time allowing a better surface monitoring. In this work, we propose a cloud screening and a cloud shadow detection method based on detecting abrupt changes in the temporal domain. It is considered that the time series follows smooth variations and abrupt changes in certain spectral features will be mainly due to the presence of clouds or cloud shadows. The method is based on linear and nonlinear regression analysis; in particular we focus on the regularized least squares and kernel regression methods. Experiments are carried out using Landsat 5 TM time series acquired over Albacete (Spain), and compa…
Determination of the energetic value of fruit and milk-based beverages through partial-least-squares attenuated total reflectance-Fourier transform i…
2005
Abstract The estimation of important nutritional parameters, such as carbohydrates content and energetic value (calories) in commercially available fruit juice and flavour milk shakes has been made by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) using a partial-least-square (PLS) calibration approach. A highly heterogeneous population of 65 samples obtained from the Spanish market, covering fruit juices, flavour milk shakes and milk-added fruit juices was used. The spectral range and the size of the calibration set for building the PLS model have been evaluated. Considering a calibration set comprised of 27 samples, selected via hierarchical cluster analys…
Estuarine sediment quality assessment by Fourier-transform infrared spectroscopy
2010
Partial least squares Fourier-transform infrared (PLS-FTIR) models were developed for the quality assessment of estuarine sediments through the evaluation of several physico-chemical parameters. Models were based on the chemometric treatment of attenuated total reflection (ATR) spectra directly obtained from samples previously lyophilized and sieved through a lower than 63 μm grid. Spectra were scanned from 3997 to 523 cm-1, averaging 36 scans per spectrum with a nominal resolution of 8 cm-1. Models were built using reference data obtained for sediment samples collected from Ria de Arousa estuary. Hierarchical cluster classification of sediment ATR spectra was employed for the establishment…