Search results for "machine"

Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes

2018

In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer. CICYT TIN2015-64210-R In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophy…

Anomaly and Change Detection in Remote Sensing Images

2021

Earth observation through satellite sensors, models and in situ measurements provides a way to monitor our planet with unprecedented spatial and temporal resolution. The amount and diversity of the data which is recorded and made available is ever-increasing. This data allows us to perform crop yield prediction, track land-use change such as deforestation, monitor and respond to natural disasters and predict and mitigate climate change. The last two decades have seen a large increase in the application of machine learning algorithms in Earth observation in order to make efficient use of the growing data-stream. Machine learning algorithms, however, are typically model agnostic and too flexi…

Análisis de técnicas de “aggregation”/“disaggregation” aplicadas a imágenes satélite para la estimación de parámetros térmicos superficiales a difere…

2023

Las aplicaciones que implican la observación de la superficie terrestre desde plataformas satélites a escala inferior a la regional, como por ejemplo, el caso del seguimiento de cultivos, requieren de una mayor disponibilidad de información térmica, en particular de la temperatura de la superficie terrestre (LST), con resoluciones espaciales apropiadas para un alcance local. Por ello, numerosos autores han propuesto y desarrollado métodos para extraer la LST a nivel “subpíxel”, mediante el empleo de productos complementarios de teledetección, con resultados adecuados para su uso en resoluciones superiores. La mayoría de estos métodos se basan en la correlación entre índices de vegetación, c…

Fundamentals of Precision Agriculture

2023

Precision agriculture or precision farming is the targeted application of crop input according to the locally determined crop needs. Therefore, it is the geo-referenced application of crop inputs, whose rates should be those required by the crop. The most essential points of information about the topic being described are: overview; brief history of precision agriculture; theoretical basics of precision agriculture; precision agriculture cycle; geo-referenced measurement of within-field parameters; analysis and interpretation of geo-referenced data for mapping within-field parameters; spatially variable rate application of crop inputs; instruments for precision agriculture; current scenario…

Improved High-Fidelity IPMSM mathematical model Including Saturation, Cross-Coupling, Torque Ripple and Iron Loss effects

2022

For enhanced control algorithm design purposes, accurate and high-fidelity electrical machine mathematical models are fundamentals. Usually, accurate evaluation of electrical machine behaviour requires finite element analysis (FEA) with consequent high-computational times. This paper presents an improved high-fidelity Interior Permanent Magnet Synchronous Machine (IPMSM) mathematical model including saturation, cross-coupling, torque ripple and iron loss effects. This model has been implemented in Matlab®/Simulink environment and its parameters are implemented by the use of lookup tables. To validate the modelling approach proposed, a comparative analysis between the proposed Matlab®/Simuli…

Impact Evaluation of Innovative Selective Harmonic Mitigation Algorithm for Cascaded H-Bridge Inverter on IPMSM Drive Application

2021

This paper presents a detailed analysis of the use of a novel Harmonic Mitigation algorithm for Cascaded H-Bridge Multilevel Inverter in electrical drives for the transportation field. For this purpose, an enhanced mathematical model of Interior Permanent Magnet Synchronous Motor (IPMSM), that takes into account simultaneously saturation, cross-coupling, spatial harmonics, and iron loss effects, has been used. In detail, this model allows estimating accurately the efficiency and the torque ripple of the IPMSM, crucial parameters for transportation applications. Moreover, two traditional pulse width modulation strategies, Sinusoidal Phase-Shifted and Switching Frequency Optimal Phase-Shifted…

Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer

2021

The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grew to maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yet full-spectrum estimation methods such as the spectral fitting method (SFM) are computationally expensive. To bypass this computational load, this work aims to approximate the SFM-based SIF retrieval by means of statistical learning, i.e., emulation. While emulators emerged as fast surrogate models of simulators, the accuracy-speedup trade-offs are still to be analyzed when the emulation concept is applied to experimental data. We evaluated the possibility of approximating the SFM-like SIF output directly based…

Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

2020

In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …

First results on the use of an innovative machine for soil tillage in terraces or steep sloped areas

2011

Ti Alloyed α-Ga2O3: Route towards Wide Band Gap Engineering

2020

The suitability of Ti as a band gap modifier for &alpha