scholarly journals Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique

2020 ◽  
Vol 424 ◽  
pp. 109017 ◽  
Author(s):  
Sheen Mclean Cabaneros ◽  
John Kaiser Calautit ◽  
Ben Hughes
Keyword(s):  
Neural Networks ◽  
Wavelet Decomposition ◽  
Deep Neural Networks ◽  
Decomposition Technique ◽  
Spatial Estimation

scholarly journals Automatic spatial estimation of white matter hyperintensities evolution in brain MRI using disease evolution predictor deep neural networks

2020 ◽  
Vol 63 ◽  
pp. 101712
Author(s):  
Muhammad Febrian Rachmadi ◽  
Maria del C. Valdés-Hernández ◽  
Stephen Makin ◽  
Joanna Wardlaw ◽  
Taku Komura
Keyword(s):  
Neural Networks ◽  
White Matter ◽  
Deep Neural Networks ◽  
White Matter Hyperintensities ◽  
Brain Mri ◽  
Disease Evolution ◽  
Spatial Estimation

A New Hybrid Method for Wind Power Forecasting Based on Wavelet Decomposition and Artificial Neural Networks

2011 ◽  
Author(s):  
Maria Grazia De Giorgi ◽  
Marco Tarantino ◽  
Antonio Ficarella
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Power ◽  
Hybrid Method ◽  
Wavelet Decomposition ◽  
Decomposition Technique ◽  
Numerical Weather ◽  
Time Horizons ◽  
Statistical Approaches ◽  
Artificial Neural

Depending on their input, wind power forecasting models are classified as physical or statistical approaches or a combination of both. Physical models use physical considerations, as meteorological information (Numerical Weather Prediction) and technical characteristics of the wind turbines (hub height, power curve, thrust coefficient). Statistical models use explanatory variables and online measurements, usually employing recursive techniques, like recursive least squares or artificial neural networks (ANNs) which perform a non-linear mapping and provide a robust approach for wind prediction. In this paper a new hybrid method (mixing physical and statistical approaches) is proposed, based on the wavelet decomposition technique and on artificial neural networks, in order to predict power production of a wind farm in different time horizons: 1, 3, 6, 12 and 24 hours. In particular, two approaches are compared, both based on the time series of on-line measured wind power and on the Numerical Weather Predictions; in the first approach, the forecast is carried out only through the training of a neural network which, in the second approach is, instead, used in combination with the wavelet decomposition technique, improving the performance especially over the short time horizons. The error of the different forecast systems is investigated for various forecasting horizons and statistical distributions of the error are calculated and presented.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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