Search results for "kernel"
showing 10 items of 357 documents
Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine
2021
For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …
Probabilistic Forecast for Northern New Zealand Seismic Process Based on a Forward Predictive Kernel Estimator
2011
In seismology predictive properties of the estimated intensity function are often pursued. For this purpose, we propose an estimation procedure in time, longitude, latitude and depth domains, based on the subsequent increments of likelihood obtained adding an observation one at a time. On the basis of this estimation approach a forecast of earthquakes of a given area of Northern New Zealand is provided, assuming that future earthquakes activity may be based on the smoothing of past earthquakes.
Decomposing changes in the conditional variance of GDP over time
2017
A well established fact in the growth empirics literature is the increasing (unconditional) variation in output per capita across countries. We propose a nonparametric decomposition of the conditional variation of output per capita across countries to capture different channels over which the variation might be increasing. We find that OECD countries have experienced diminishing conditional variation while other regions have experienced increasing conditional variation. Our decomposition suggests that most of these changes in the conditional variance of output are due to unobserved factors not accounted for by the traditional growth determinants. In addition to this we show that these facto…
Un modelo no hidrostático global con coordenada vertical basada en altura
2015
Memoria presentada en la Universitat de València para optar grado de de Doctor Esta tesis documenta la investigación que he realizado en modelización atmosférica: se parte de las ecuaciones físicas de la atmósfera y se aplican métodos numéricos eficientes para encontrar una solución a dichas ecuaciones a partir de unas condiciones iniciales dadas. Para este fin, se ha desarrollado un modelo atmosférico cuyas características principales son: espectral en la representación horizontal de los campos, discretización vertical de alto orden de exactitud, y semi-implícito en la integración temporal. Además, el modelo es no hidrostático y tiene una coordenada vertical basada en altura, en vez de la …
Ultra-nonlocality in density functional theory for photo-emission spectroscopy.
2014
We derive an exact expression for the photo-current of photo-emission spectroscopy using time-dependent current density functional theory (TDCDFT). This expression is given as an integral over the Kohn-Sham spectral function renormalized by effective potentials that depend on the exchange-correlation kernel of current density functional theory. We analyze in detail the physical content of this expression by making a connection between the density-functional expression and the diagrammatic expansion of the photo-current within many-body perturbation theory. We further demonstrate that the density functional expression does not provide us with information on the kinetic energy distribution of…
Going standalone and platform-independent, an example from recent work on the ATLAS Detector Description and interactive data visualization
2019
Until recently, the direct visualization of the complete ATLAS experiment geometry and final analysis data was confined within the software framework of the experiment. To provide a detailed interactive data visualization capability to users, as well as easy access to geometry data, and to ensure platform independence and portability, great effort has been recently put into the modernization of both the core kernel of the detector description and the visualization tools. In this proceedings we will present the new tools, as well as the lessons learned while modernizing the experiment’s code for an efficient use of the detector description and for user-friendly data visualization. Until rece…
Sensorless Control of PMSM Fractional Horsepower Drives by Signal Injection and Neural Adaptive-Band Filtering
2012
This paper presents a sensorless technique for permanent-magnet synchronous motors (PMSMs) based on high-frequency pulsating voltage injection. Starting from a speed estimation scheme well known in the literature, this paper proposes the adoption of a neural network (NN) based adaptive variable-band filter instead of a fixed-bandwidth filter, needed for catching the speed information from the sidebands of the stator current. The proposed NN filter is based on a linear NN adaptive linear neuron (ADALINE), trained with a classic least mean squares (LMS) algorithm, and is twice adaptive. From one side, it is adaptive in the sense that its weights are adapted online recursively. From another si…
Descriptor-type Robust Kalman Filter and Neural Adaptive Speed Estimation Scheme for Sensorless Control of Induction Motor Drive Systems
2012
Abstract This paper deals with robust estimation of speed and rotor flux for sensorless control of motion control systems which use induction motors as actuators. Due to the observability lack of five and six order Extended Kalman Filters, speed is here estimated by means of a Total Least Square algorithm with Neural Adaptive mechanism. This allows the use of a fourth-order Kalman Filter for estimating rotor flux and to filter stator currents. To cope with motor-load parameter variations, a descriptor-type robust Kalman Filter is designed taking explicitly into account these variations. The descriptor-type structure allows direct translation of parameter variations into variations of the co…
Spatial pattern analysis using hybrid models: an application to the Hellenic seismicity
2016
Earthquakes are one of the most destructive natural disasters and the spatial distribution of their epi- centres generally shows diverse interaction structures at different spatial scales. In this paper, we use a multi-scale point pattern model to describe the main seismicity in the Hellenic area over the last 10 years. We analyze the interaction between events and the relationship with geo- logical information of the study area, using hybrid models as proposed by Baddeley et al. ( 2013 ). In our analysis, we find two competing suitable hybrid models, one with a full parametric structure and the other one based on nonpara- metric kernel estimators for the spatial inhomogeneity.
Fair Kernel Learning
2017
New social and economic activities massively exploit big data and machine learning algorithms to do inference on people’s lives. Applications include automatic curricula evaluation, wage determination, and risk assessment for credits and loans. Recently, many governments and institutions have raised concerns about the lack of fairness, equity and ethics in machine learning to treat these problems. It has been shown that not including sensitive features that bias fairness, such as gender or race, is not enough to mitigate the discrimination when other related features are included. Instead, including fairness in the objective function has been shown to be more efficient.