A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
Monitoring and identifying wind turbine generator bearing faults using deep belief network and EWMA control charts
H Li, J Deng, S Yuan, P Feng… - Frontiers in Energy …, 2021 - frontiersin.org
Wind turbines are widely installed as the new source of cleaner energy production. Dynamic
and random stress imposed on the generator bearing of a wind turbine may lead to …
and random stress imposed on the generator bearing of a wind turbine may lead to …
A deep learning approach for liver and tumor segmentation in CT images using ResUNet
According to the most recent estimates from global cancer statistics for 2020, liver cancer is
the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting …
the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting …
Survey on deep learning methods in human action recognition
M Koohzadi, NM Charkari - IET Computer Vision, 2017 - Wiley Online Library
A study on one of the most important issues in a human action recognition task, ie how to
create proper data representations with a high‐level abstraction from large dimensional …
create proper data representations with a high‐level abstraction from large dimensional …
Transfer learning for short-term wind speed prediction with deep neural networks
Q Hu, R Zhang, Y Zhou - Renewable Energy, 2016 - Elsevier
As a type of clean and renewable energy source, wind power is widely used. However,
owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model …
owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model …
EEG-based prediction of driver's cognitive performance by deep convolutional neural network
We considered the prediction of driver's cognitive states related to driving performance using
EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) …
EEG signals. We proposed a novel channel-wise convolutional neural network (CCNN) …
[图书][B] Machine learning and deep learning in real-time applications
Artificial intelligence and its various components are rapidly engulfing almost every
professional industry. Specific features of AI that have proven to be vital solutions to …
professional industry. Specific features of AI that have proven to be vital solutions to …
Noniterative deep learning: Incorporating restricted Boltzmann machine into multilayer random weight neural networks
A general deep learning (DL) mechanism for a multiple hidden layer feed-forward neural
network contains two parts, ie, 1) an unsupervised greedy layer-wise training and 2) a …
network contains two parts, ie, 1) an unsupervised greedy layer-wise training and 2) a …
Prediction of driver's drowsy and alert states from EEG signals with deep learning
M Hajinoroozi, Z Mao, Y Huang - 2015 IEEE 6th international …, 2015 - ieeexplore.ieee.org
We investigate in this paper deep learning (DL) solutions for prediction of driver's cognitive
states (drowsy or alert) using EEG data. We discussed the novel channel-wise convolutional …
states (drowsy or alert) using EEG data. We discussed the novel channel-wise convolutional …
A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power
In the contemporary data-driven era, the fields of machine learning, deep learning, big data,
statistics, and data science are essential for forecasting outcomes and getting insights from …
statistics, and data science are essential for forecasting outcomes and getting insights from …