Zobrazeno 1 - 10
of 5 507
pro vyhledávání: '"Gutiérrez, José A"'
Deep Learning (DL) has shown promise for downscaling global climate change projections under different approaches, including Perfect Prognosis (PP) and Regional Climate Model (RCM) emulation. Unlike emulators, PP downscaling models are trained on obs
Externí odkaz:
http://arxiv.org/abs/2411.05850
Super-resolution (SR) is a promising cost-effective downscaling methodology for producing high-resolution climate information from coarser counterparts. A particular application is downscaling regional reanalysis outputs (predictand) from the driving
Externí odkaz:
http://arxiv.org/abs/2410.12728
Autor:
Gutierrez-Martin, Laura, Ongil, Celia Lopez, Lanza-Gutierrez, Jose M., Calero, Jose A. Miranda
Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (Affective Computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense,
Externí odkaz:
http://arxiv.org/abs/2410.03696
Autor:
Gutierrez, Jose A. Garcia
The analysis of social media information has undergone significant evolution in the last decade due to advancements in artificial intelligence (AI) and machine learning (ML). This paper provides an overview of the state-of-the-art techniques in socia
Externí odkaz:
http://arxiv.org/abs/2408.01911
Regional climate models (RCMs) are essential tools for simulating and studying regional climate variability and change. However, their high computational cost limits the production of comprehensive ensembles of regional climate projections covering m
Externí odkaz:
http://arxiv.org/abs/2311.03378
Autor:
González-Abad, Jose, Hernández-García, Álex, Harder, Paula, Rolnick, David, Gutiérrez, José Manuel
Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they often operate at a coarse spatial resolution that limits their accuracy in reproducing local-scale phenomena. Stat
Externí odkaz:
http://arxiv.org/abs/2308.01868
Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional-to-local scales from large-scale atmospheric fields following the perfect-prognosis (PP) approach. Given their complexity, it is crucial to properly evalu
Externí odkaz:
http://arxiv.org/abs/2302.01771
Autor:
CARRO OLVERA, ADRIANA1 acarroo1@yahoo.com.m, LIMA GUTIÉRREZ, JOSÉ ALFONSO2 alimagu_1@yahoo.com.mx
Publikováno v:
Revista Mexicana de Investigación Educativa. oct-dic2024, Vol. 29 Issue 103, p987-1000. 14p.
Autor:
Churkin, Andrey, Kong, Wangwei, Gutierrez, Jose N. Melchor, Ceseña, Eduardo A. Martínez, Mancarella, Pierluigi
The integration of distributed energy resources (DER) makes active distribution networks (ADNs) natural providers of flexibility services. However, the optimal operation of flexible units in ADNs is highly complex, which poses challenges for distribu
Externí odkaz:
http://arxiv.org/abs/2210.03589
Autor:
Rivera-Sylva, Héctor E., Longrich, Nicholas R., Padilla-Gutierrez, José M., Guzmán-Gutiérrez, José Rubén, Escalante-Hernández, Víctor M., González-Ávila, José G.
Publikováno v:
In Journal of South American Earth Sciences January 2024 133