Coronal hole tracking using Big Data and Computer Vision
Space Weather forecasting poses several difficult problems. Given the complexity of the Sun’s immediate environment and behavior, automatically performing accurate predictions on high speed streams coming from the Sun remains an unresolved problem. This work aims to solve a classic problem in this c...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/65282 |
| Acceso en línea: | http://hdl.handle.net/10017/65282 |
| Access Level: | acceso abierto |
| Palabra clave: | Space Weather Big Data Machine Learning Coronal Holes Computer Vision Física Physics |
| Sumario: | Space Weather forecasting poses several difficult problems. Given the complexity of the Sun’s immediate environment and behavior, automatically performing accurate predictions on high speed streams coming from the Sun remains an unresolved problem. This work aims to solve a classic problem in this context, that of coronal hole identification. Additionally, in order to help deal with the amounts of data involved in the study of the Sun, it also attempts to provide the foundation for a Big Data architecture that can run our newly developed algorithms. This document provides an overview of the currently taken steps, flaws and advantages found during validation of the resulting system, and plans for future developments in this area. |
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