Search results for " estimation"

showing 10 items of 562 documents

Non-crossing parametric quantile functions: an application to extreme temperatures

2019

Quantile regression can be used to obtain a non-parametric estimate of a conditional quantile function. The presence of quantile crossing, however, leads to an invalid distribution of the response and makes it difficult to use the fitted model for prediction. In this work, we show that crossing can be alleviated by modelling the quantile function parametrically. We then describe an algorithm for constrained optimisation that can be used to estimate parametric quantile functions with the noncrossing property. We investigate climate change by modelling the long-term trends of extreme temperatures in the Arctic Circle.

Parametric quantile functions quantile regression coefficients modelling (QRCM) R package qrcm estimation of extremes climate change.Settore SECS-S/01 - Statistica
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Probabilistic Selection Approaches in Decomposition-based Evolutionary Algorithms for Offline Data-Driven Multiobjective Optimization

2022

In offline data-driven multiobjective optimization, no new data is available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogates. The accuracy of the approximated solutions depends on the surrogates and approximations typically involve uncertainties. In this paper, we propose probabilistic selection approaches that utilize the uncertainty information of the Kriging models (as surrogates) to improve the solution process in offline data-driven multiobjective optimization. These approaches are designed for decomposition-base…

Pareto optimalitypareto-tehokkuusgaussiset prosessitGaussian processesevoluutiolaskentamonitavoiteoptimointiTheoretical Computer ScienceKrigingComputational Theory and Mathematicsmetamodellingsurrogatekernel density estimationkriging-menetelmäSoftware
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Precise measurement of the neutrino mixing parameter θ23 from muon neutrino disappearance in an off-axis beam

2014

New data from the T2K neutrino oscillation experiment produce the most precise measurement of the neutrino mixing parameter theta_{23}. Using an off-axis neutrino beam with a peak energy of 0.6 GeV and a data set corresponding to 6.57 x 10^{20} protons on target, T2K has fit the energy-dependent nu_mu oscillation probability to determine oscillation parameters. Marginalizing over the values of other oscillation parameters yields sin^2 (theta_{23}) = 0.514 +0.055/-0.056 (0.511 +- 0.055), assuming normal (inverted) mass hierarchy. The best-fit mass-squared splitting for normal hierarchy is Delta m^2_{32} = (2.51 +- 0.10) x 10^{-3} eV^2/c^4 (inverted hierarchy: Delta m^2_{13} = (2.48 +- 0.10) …

Particle physicsGeneral PhysicsPhysics MultidisciplinaryMODELSGeneral Physics and AstronomyFOS: Physical sciencesMASS01 natural sciences09 EngineeringHigh Energy Physics - ExperimentNuclear physicsHigh Energy Physics - Experiment (hep-ex)Physics and Astronomy (all)0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]SCATTERINGMuon neutrino010306 general physicsNeutrino oscillationDETECTORMixing (physics)01 Mathematical SciencesPhysicsNeutronsScience & Technology02 Physical Sciences010308 nuclear & particles physicsScatteringOscillationhep-exPhysicsFísicaT2K CollaborationPhysical SciencesSYMMETRIESHigh Energy Physics::ExperimentNeutrinoHigh energy physics Mixing Parameter estimation Parameter extractionConfidence limit Energy dependent Neutrino oscillations Off-axis neutrino beam Oscillation parameters Oscillation probabilities Precise measurements Statistical uncertaintyBeam (structure)Energy (signal processing)
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Estimators and confidence intervals of f2 using bootstrap methodology for the comparison of dissolution profiles

2021

Abstract Background and objectives: The most widely used method to compare dissolution profiles is the similarity factor f 2 . When this method is not applicable, the confidence interval of f 2 using bootstrap methodology has been recommended instead. As neither details of the estimator nor the types of confidence intervals are described in the guidelines, the suitability of five estimators and fourteen types of confidence intervals were investigated in this study by simulation. Methods: One million individual dissolution profiles were simulated for the reference and test populations with predefined target population f 2 values, where random samples of different sizes were drawn without rep…

PercentileSimilarity (network science)Sample size determinationStatisticsStatistical inferenceEstimatorHealth InformaticsPoint estimationExpected valueSoftwareConfidence intervalComputer Science ApplicationsMathematicsComputer Methods and Programs in Biomedicine
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Enhanced Current Loop PI Controllers with Adaptive Feed-Forward Neural Network via Estimation of Grid Impedance: Application to Three-Phase Grid-Tied…

2022

This paper describes a single-stage grid-connected three-phase photovoltaic inverter feeding power to the grid. Using the Recursive Least Squares (RLS) Estimator, an online grid impedance technique is proposed in the stationary reference frame. The method iteratively estimates the grid resistance and inductance values and is effective in detecting inverter islanding according to IEEE standard 929-2000. An Adaptive Feedforward Neural (AFN) Controller has also been developed using the inverse of the system to improve the performance of the inner-loop Proportional-Integral controllers under dynamical conditions and provide better DC link voltage stability. The neural network weights are comput…

Photovoltaic System Adaptive Feedforward Grid Connected Inverter Grid Impedance Neural Network and Recursive Least Squares Estimation.
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Measurement of damping and temperature: Precision bounds in Gaussian dissipative channels

2011

We present a comprehensive analysis of the performance of different classes of Gaussian states in the estimation of Gaussian phase-insensitive dissipative channels. In particular, we investigate the optimal estimation of the damping constant and reservoir temperature. We show that, for two-mode squeezed vacuum probe states, the quantum-limited accuracy of both parameters can be achieved simultaneously. Moreover, we show that for both parameters two-mode squeezed vacuum states are more efficient than either coherent, thermal or single-mode squeezed states. This suggests that at high energy regimes two-mode squeezed vacuum states are optimal within the Gaussian setup. This optimality result i…

PhysicsQuantum PhysicsOptimal estimationGaussianFOS: Physical sciencesQuantum entanglement01 natural sciencesLinear subspaceAtomic and Molecular Physics and Optics010305 fluids & plasmasCondensed Matter - Other Condensed Mattersymbols.namesakeMinimum-variance unbiased estimatorQuantum mechanics0103 physical sciencessymbolsDissipative systemCutoffStatistical physicsQuantum Physics (quant-ph)010306 general physicsQuantum information scienceOther Condensed Matter (cond-mat.other)
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Hilbert Space Average Method and adiabatic quantum search

2009

6 pages, 1 figure.-- ISI article identifier:000262979000049.-- ArXiv pre-print avaible at:http://arxiv.org/abs/0810.1456

PhysicsQuantum PhysicsQuantum decoherenceHilbert spaceFOS: Physical sciencesAtomic and Molecular Physics and Opticssymbols.namesakeQuantum error correctionQuantum mechanicssymbolsQuantum operationQuantum phase estimation algorithmQuantum algorithmAdiabatic processQuantum Physics (quant-ph)Quantum computer
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Stable control of pulse speed in parametric three-wave solitons.

2006

International audience; We analyze the control of the propagation speed of three wave packets interacting in a medium with quadratic nonlinearity and dispersion. We find analytical expressions for mutually trapped pulses with a common velocity in the form of a three-parameter family of solutions of the three-wave resonant interaction. The stability of these novel parametric solitons is simply related to the value of their common group velocity.

Physics[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]Analytical expressionsWave packetMathematical analysisDispersion (waves); parameter estimation; quadratic programmingFOS: Physical sciencesGeneral Physics and Astronomy01 natural sciencesStability (probability)Physics - Plasma Physics010305 fluids & plasmasPulse (physics)Plasma Physics (physics.plasm-ph)Classical mechanics[ PHYS.PHYS.PHYS-AO-PH ] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]Dispersion relation0103 physical sciencesDispersion (optics)Group velocity010306 general physicsOptics (physics.optics)Parametric statisticsPhysics - OpticsPhysical review letters
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Sex differences in estimation of time intervals and in reaction time are removed by moderate but not high doses of caffeine in coffee

2002

Estimation of the passage of time in the seconds-to-minutes range and reaction time are strongly dependent on a hypothetical internal clock. Dopamine is the neurotransmitter most closely related to the rate of this clock. Caffeine, probably the most consumed drug in the world, leads to an augmentation of dopamine neurotransmission. In this study coffee, which reproduces the conditions under which caffeine is normally ingested, containing 3, 75, 150 or 300 mg of caffeine, was given to healthy male and female volunteers. A computerized time estimation and reaction time test was carried out 50 min after ingestion. Sex differences in placebo control subjects (who took decaffeinated coffee with …

PhysiologyCoffee consumptionPlaceboControl subjectsToxicologyPsychiatry and Mental healthchemistry.chemical_compoundNeurologyPharmacokineticschemistryTime estimationHigh dosesIngestionPharmacology (medical)Neurology (clinical)PsychologyCaffeineHuman Psychopharmacology: Clinical and Experimental
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Speeding-Up Differential Motion Detection Algorithms Using a Change-Driven Data Flow Processing Strategy

2007

A constraint of real-time implementation of differential motion detection algorithms is the large amount of data to be processed. Full image processing is usually the classical approach for these algorithms: spatial and temporal derivatives are calculated for all pixels in the image despite the fact that the majority of image pixels may not have changed from one frame to the next. By contrast, the data flow model works in a totally different way as instructions are only fired when the data needed for these instructions are available. Here we present a method to speed-up low level motion detection algorithms. This method is based on pixel change instead of full image processing and good spee…

PixelComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingMotion detectionData flow diagramMotion fieldComputer Science::Computer Vision and Pattern RecognitionMotion estimationDigital image processingComputer visionArtificial intelligencebusinessAlgorithmFeature detection (computer vision)
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