185 research outputs found
Generalized time-frequency coherency for assessing neural interactions in electrophysiological recordings
Time-frequency coherence has been widely used to quantify statistical dependencies in bivariate data and has proven to be vital for the study of neural interactions in electrophysiological recordings. Conventional methods establish time-frequency coherence by smoothing the cross and power spectra using identical smoothing procedures. Smoothing entails a trade-off between time-frequency resolution and statistical consistency and is critical for detecting instantaneous coherence in single-trial data. Here, we propose a generalized method to estimate time-frequency coherency by using different smoothing procedures for the cross spectra versus power spectra. This novel method has an improved trade-off between time resolution and statistical consistency compared to conventional methods, as verified by two simulated data sets. The methods are then applied to single-trial surface encephalography recorded from human subjects for comparative purposes. Our approach extracted robust alpha- and gamma-band synchronization over the visual cortex that was not detected by conventional methods, demonstrating the efficacy of this method
Wind speed prediction using multidimensional convolutional neural networks
Accurate wind speed forecasting is of great importance for many economic,
business and management sectors. This paper introduces a new model based on
convolutional neural networks (CNNs) for wind speed prediction tasks. In
particular, we show that compared to classical CNN-based models, the proposed
model is able to better characterise the spatio-temporal evolution of the wind
data by learning the underlying complex input-output relationships from
multiple dimensions (views) of the input data. The proposed model exploits the
spatio-temporal multivariate multidimensional historical weather data for
learning new representations used for wind forecasting. We conduct experiments
on two real-life weather datasets. The datasets are measurements from cities in
Denmark and in the Netherlands. The proposed model is compared with traditional
2- and 3-dimensional CNN models, a 2D-CNN model with an attention layer and a
2D-CNN model equipped with upscaling and depthwise separable convolutions.Comment: 8 pages, 6 figure
Block method for numerical solution of fuzzy differential equations
In this paper the 2 point 2 step method for solving fuzzy initial value problem is proposed. This method at each step will estimate the solutions of the fuzzy initial value problem at two points simultaneously using variable step size. The stability of the proposed method is discussed. Examples are presented to illustrate the computational aspect
of the method
SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture
Weather forecasting is dominated by numerical weather prediction that tries
to model accurately the physical properties of the atmosphere. A downside of
numerical weather prediction is that it is lacking the ability for short-term
forecasts using the latest available information. By using a data-driven neural
network approach we show that it is possible to produce an accurate
precipitation nowcast. To this end, we propose SmaAt-UNet, an efficient
convolutional neural networks-based on the well known UNet architecture
equipped with attention modules and depthwise-separable convolutions. We
evaluate our approaches on a real-life datasets using precipitation maps from
the region of the Netherlands and binary images of cloud coverage of France.
The experimental results show that in terms of prediction performance, the
proposed model is comparable to other examined models while only using a
quarter of the trainable parameters.Comment: 9 pages, 4 figure
Deep coastal sea elements forecasting using U-Net based models
The supply and demand of energy is influenced by meteorological conditions.
The relevance of accurate weather forecasts increases as the demand for
renewable energy sources increases. The energy providers and policy makers
require weather information to make informed choices and establish optimal
plans according to the operational objectives. Due to the recent development of
deep learning techniques applied to satellite imagery, weather forecasting that
uses remote sensing data has also been the subject of major progress. The
present paper investigates multiple steps ahead frame prediction for coastal
sea elements in the Netherlands using U-Net based architectures. Hourly data
from the Copernicus observation programme spanned over a period of 2 years has
been used to train the models and make the forecasting, including seasonal
predictions. We propose a variation of the U-Net architecture and further
extend this novel model using residual connections, parallel convolutions and
asymmetric convolutions in order to introduce three additional architectures.
In particular, we show that the architecture equipped with parallel and
asymmetric convolutions as well as skip connections outperforms the other three
discussed models.Comment: 12 pages, 11 figure
TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start
This paper proposes an interpretable two-stream transformer CORAL networks
(TransCORALNet) for supply chain credit assessment under the segment industry
and cold start problem. The model aims to provide accurate credit assessment
prediction for new supply chain borrowers with limited historical data. Here,
the two-stream domain adaptation architecture with correlation alignment
(CORAL) loss is used as a core model and is equipped with transformer, which
provides insights about the learned features and allow efficient
parallelization during training. Thanks to the domain adaptation capability of
the proposed model, the domain shift between the source and target domain is
minimized. Therefore, the model exhibits good generalization where the source
and target do not follow the same distribution, and a limited amount of target
labeled instances exist. Furthermore, we employ Local Interpretable
Model-agnostic Explanations (LIME) to provide more insight into the model
prediction and identify the key features contributing to supply chain credit
assessment decisions. The proposed model addresses four significant supply
chain credit assessment challenges: domain shift, cold start, imbalanced-class
and interpretability. Experimental results on a real-world data set demonstrate
the superiority of TransCORALNet over a number of state-of-the-art baselines in
terms of accuracy. The code is available on GitHub
https://github.com/JieJieNiu/TransCORALN .Comment: 13 pages, 7 figure
Deep Graph Convolutional Networks for Wind Speed Prediction
In this paper, we introduce a new model for wind speed prediction based on spatio-temporal graph convolutional networks. Here, weather stations are treated as nodes of a graph with a learnable adjacency matrix, which determines the strength of relations between the stations based on the historical weather data. The self-loop connection is added to the learnt adjacency matrix and its strength is controlled by additional learnable parameter. Experiments performed on real datasets collected from weather stations located in Denmark and the Netherlands show that our proposed model outperforms previously developed baseline models on the referenced datasets
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