Search results for "least squares"

showing 10 items of 268 documents

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…

Regularized least squaresSeries (mathematics)business.industryComputer scienceShadowKernel regressionCloud computingbusinessFocus (optics)Nonlinear regressionRemote sensingDomain (software engineering)MultiTemp 2013: 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images
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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…

ReproducibilityMean squared errorChemistryFlavourAnalytical chemistryBiochemistryFourier transform spectroscopyStandard deviationAnalytical ChemistryAttenuated total reflectionPartial least squares regressionCalibrationEnvironmental ChemistrySpectroscopyAnalytica Chimica Acta
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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…

Resolution (mass spectrometry)Elemental analysisChemistryAttenuated total reflectionPartial least squares regressionAnalytical chemistryTrace elementchemistry.chemical_elementSedimentFourier transform infrared spectroscopyNitrogenSpectroscopyVibrational Spectroscopy
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Comparative modelling study on enantioresolution of structurally unrelated compounds with amylose-based chiral stationary phases in reversed phase li…

2020

[EN] Polysaccharide-based chiral stationary phases (CSPs) are the most used chiral selectors in HPLC. These CSPs can be used in normal, polar organic and aqueous-organic mobile phases. However, normal and polar organic mobile phases are not adequate for chiral separation of polar compounds, for the analysis of aqueous samples and for MS detection. In these situations, reversed phase conditions, without the usual non-volatile additives incompatible with MS detection, are preferable. Moreover, in most of the reported chiral chromatographic methods, retention is too large for routine work. In this paper, the chiral separation of 53 structurally unrelated compounds is studied using three commer…

Resolution (mass spectrometry)Reversed phase liquid hromatography010402 general chemistryMass spectrometry01 natural sciencesBiochemistryHigh-performance liquid chromatographyAmylose-based chiral stationary phasesMass SpectrometryAnalytical Chemistrychemistry.chemical_compoundAmylosePhase (matter)Least-Squares AnalysisAcetonitrileEnantioresolution modelling and descriptionChromatography High Pressure LiquidChromatography Reverse-PhaseAqueous solutionChromatography010401 analytical chemistryOrganic ChemistryDiscriminant partial least squaresStereoisomerismGeneral MedicineReversed-phase chromatography0104 chemical sciencesModels ChemicalchemistryFeature selectionRegression AnalysisAmyloseJournal of Chromatography A
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Value, supplier dependence and long‐term orientation

2011

PurposeIn organizational markets, many companies tend to reduce the number of providers to focus on establishing relationships with few of them. The purpose of this paper is to analyze the influence of relationship value and dependence of supplier on long‐term orientation and customer loyalty in the setting of relationships between travel agencies and their main providers.Design/methodology/approachA partial least square regression is performed to test a proposed model that links several relational variables with outcomes in terms of customer loyalty.FindingsResults provide support for the positive indirect influence of relationship value on long‐term orientation, while customer dependence …

Service (business)Customer retentionStrategy and ManagementIndustrial and Manufacturing EngineeringComputer Science ApplicationsManagement Information SystemsTerm (time)Loyalty business modelMicroeconomicsSupplier relationship managementOrientation (mental)Industrial relationsPartial least squares regressionBusinessMarketingValue (mathematics)Industrial Management & Data Systems
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Unlocking behaviors of long-term service consumers : the role of action inertia

2017

Purpose The purpose of this paper is to examine the antecedents of word-of-mouth (WOM) in long-term service settings. Specifically, the authors examine the moderating role of action inertia in the relationships between satisfaction and repatronage intention, satisfaction and WOM, and repatronage intention and WOM. Design/methodology/approach The proposed model was empirically tested using survey data from 1,385 telecommunications service subscribers. The data were analyzed using partial least squares path modeling. Findings Results suggest that a positive link between repatronage intention and WOM, hereto a neglected relationship in the marketing literature, in contrast to previous literat…

Service (business)Service qualityStrategy and Management05 social sciencessatisfactionWord of mouthTelecommunications servicerepatronage intentionsAdvertisingservice qualityLoyalty business modelAction (philosophy)word-of-mouth0502 economics and businessPartial least squares path modelingSurvey data collection050211 marketingaction inertiaMarketingPsychology050203 business & managementperceived value
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Iteratively reweighted least squares in crystal structure refinements

2011

The use of robust techniques in crystal structure multipole refinements of small molecules as an alternative to the commonly adopted weighted least squares is presented and discussed. As is well known, the main disadvantage of least-squares fitting is its sensitivity to outliers. The elimination from the data set of the most aberrant reflections (due to both experimental errors and incompleteness of the model) is an effective practice that could yield satisfactory results, but it is often complicated in the presence of a great number of bad data points, whose one-by-one elimination could become unattainable. This problem can be circumvented by means of a robust least-squares regression that…

Settore GEO/06 - MineralogiaLeast trimmed squarescomputer.software_genreRegressionRobust regressionIteratively reweighted least squaresData setRobust regression outlier refinementData pointStructural BiologyOutlierSensitivity (control systems)Data miningcomputerAlgorithmMathematicsActa Crystallographica Section A Foundations of Crystallography
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Adaptive Feed-Forward Neural Network for Wind Power Delivery

2022

This paper describes a grid connected wind energy conversion system. The interconnecting filter is a simple inductor with a series resistor to minimize three-phase current Total Harmonic Distortion (THD). Using the Recursive Least Squares (RLS) Estimator, an online grid impedance technique is proposed in the stationary reference frame using the Recursive Least Squares (RLS) Estimator. An Adaptive Feedforward Neural (AFN) Controller has also been developed using the inverse of the system to improve the performance of the current Proportional-Integral controller under dynamical conditions and provide better DC link voltage stability. The neural network weights are computed in real-time using …

Settore ING-INF/04 - AutomaticaWind energy conversion systemNeural NetworkRecursive Least Squares EstimationAdaptiveGrid Connected InverterGrid ImpedanceFeedforward
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On invariant manifolds of saddle points for 3D multistable models

2017

In dynamical systems a particular solution is completely determined by the parameters considered and the initial conditions. Indeed, when the model shows a multistability, starting from different initial state, the trajectories can evolve towards different attractors. The invariant manifolds of the saddle points separate the vector field into the basins of attraction of different stable equilibria. The aim of this work is the reconstruction of these separation surfaces in order to know in advance the geometry of the basins. In this paper three-dimensional models with three or more stable fixed points is investigated. To this purpose a procedure for the detection of the scattered data lying …

Settore MAT/08 - Analisi NumericaDynamical systems Invariant manifolds Separatrix Meshfree method Moving Least Squares.
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Nonlinear Distribution Regression for Remote Sensing Applications

2020

In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kern…

Signal Processing (eess.SP)FOS: Computer and information sciencesComputer Science - Machine LearningArtificial neural networkRemote sensing applicationComputer science0211 other engineering and technologies02 engineering and technologyLeast squaresRandom forestMachine Learning (cs.LG)Kernel (linear algebra)symbols.namesakeKernel (statistics)symbolsFOS: Electrical engineering electronic engineering information engineeringGeneral Earth and Planetary SciencesElectrical Engineering and Systems Science - Signal ProcessingElectrical and Electronic EngineeringGaussian processAlgorithm021101 geological & geomatics engineeringCurse of dimensionalityIEEE Transactions on Geoscience and Remote Sensing
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