5 research outputs found
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
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
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
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
Real-Time Multimodal Feedback with the CPR Tutor
We developed the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects mistakes using recurrent neural networks for real-time time-series classification. From a multimodal data stream consisting of kinematic and electromyographic data, the CPR Tutor system automatically detects the chest compressions, which are then classified and assessed according to five performance indicators. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. To test the validity of the CPR Tutor, we first collected the data corpus from 10 experts used for model training. Hence, to test the impact of the feedback functionality, we ran a user study involving 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: 1) an architecture design, 2) a methodological approach to design multimodal feedback and 3) a field study on real-time feedback for CPR training