Journal ArticleDOI
Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network
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TLDR
In this paper , a novel hybrid neural network scheme based on convolutional neural network (CNN) and long short-term memory (LSTM) is proposed for multi-step wind speed prediction.About:
This article is published in Ocean Engineering.The article was published on 2022-06-01. It has received 36 citations till now. The article focuses on the topics: Benchmark (surveying) & Mean squared error.read more
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A hybrid forecasting system with complexity identification and improved optimization for short-term wind speed prediction
TL;DR: In this article , the authors proposed a hybrid wind power forecasting system, where the energy entropy theory (EVMD) is used to determine the number of VMD decompositions to solve the problem of over-decomposition, and the sample entropy (SE) is utilized to identify the complexity of the intrinsic mode functions (IMFs) of EVMD, and applied different methods to forecast.
Journal ArticleDOI
Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction
Sujan Ghimire,Thong Nguyen-Huy,Ramendra Prasad,Ravinesh C. Deo,D. Casillas-Pérez,Sancho Salcedo-Sanz,Binayak Bhandari +6 more
Journal ArticleDOI
Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm
TL;DR: In this paper , a hybrid wind speed prediction model based on Time Varying Filtering based Empirical Mode Decomposition (TVFEMD), Fuzzy Entropy (FE), Partial Autocorrelation Function (PACF), improved Chimp Optimization Algorithm (IChOA) and Bi-directional Gated Recurrent Unit (BiGRU) was proposed.
Journal ArticleDOI
A novel time-frequency recurrent network and its advanced version for short-term wind speed predictions
TL;DR: In this article , a novel recurrent neural network known as the time-frequency recurrent neural networks, or TFR for short, is developed to improve the accuracy of short-term wind speed prediction, and the CNN-TFR model with strong and robust prediction ability can be utilized to anticipate real wind speed.
Journal ArticleDOI
Automatic defect depth estimation for ultrasonic testing in carbon fiber reinforced composites using deep learning
TL;DR: In this paper , an automatic signal classification method based on deep learning is proposed for depth estimation of the detects introduced by low-velocity impact (LVI) in carbon fiber reinforced plastics (CFRPs).
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.