Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Guignard, Fabian"'
Autor:
Amato, Federico, Guignard, Fabian, Walch, Alina, Mohajeri, Nahid, Scartezzini, Jean-Louis, Kanevski, Mikhail
The growth of wind generation capacities in the past decades has shown that wind energy can contribute to the energy transition in many parts of the world. Being highly variable and complex to model, the quantification of the spatio-temporal variatio
Externí odkaz:
http://arxiv.org/abs/2108.00859
Uncertainty quantification is crucial to assess prediction quality of a machine learning model. In the case of Extreme Learning Machines (ELM), most methods proposed in the literature make strong assumptions on the data, ignore the randomness of inpu
Externí odkaz:
http://arxiv.org/abs/2011.01704
Publikováno v:
ESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Online event, 2-4 October 2020, i6doc.com publ., ISBN 978-2-87587-074-2. Available from http://www.i6doc.com/en/
The presence of irrelevant features in the input dataset tends to reduce the interpretability and predictive quality of machine learning models. Therefore, the development of feature selection methods to recognize irrelevant features is a crucial top
Externí odkaz:
http://arxiv.org/abs/2010.05744
Publikováno v:
Sci Rep 10, 22243 (2020)
As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate
Externí odkaz:
http://arxiv.org/abs/2007.11836
Publikováno v:
Proceedings of the 10th International Conference on Climate Informatics CI2020. Association for Computing Machinery, New York, NY, USA, 37 43
Climate is known for being characterised by strong non-linearity and chaotic behaviour. Nevertheless, few studies in climate science adopt statistical methods specifically designed for non-stationary or non-linear systems. Here we show how the use of
Externí odkaz:
http://arxiv.org/abs/2006.12386
Publikováno v:
Front. Earth Sci. 8:255 (2020)
Complex non-linear time series are ubiquitous in geosciences. Quantifying complexity and non-stationarity of these data is a challenging task, and advanced complexity-based exploratory tool are required for understanding and visualizing such data. Th
Externí odkaz:
http://arxiv.org/abs/1912.02452
Air pollution is known to be a major threat for human and ecosystem health. A proper understanding of the factors generating pollution and of the behavior of air pollution in time is crucial to support the development of effective policies aiming at
Externí odkaz:
http://arxiv.org/abs/1909.11484
Publikováno v:
Entropy 2019, 21(1), 47
1Hz wind time series recorded at different levels (from 1.5 to 25.5 meters) in an urban area are investigated by using the Fisher-Shannon (FS) analysis. FS analysis is a well known method to get insight of the complex behavior of nonlinear systems, b
Externí odkaz:
http://arxiv.org/abs/1812.00822
High frequency wind time series measured at different heights from the ground (from 1.5 to 25.5 meters) in an urban area were investigated by using the variance of the coefficients of their wavelet transform. Two ranges of scales were identified, sen
Externí odkaz:
http://arxiv.org/abs/1811.12723