In order to improve the intelligent energy efficiency management of ships, evaluate the fuel utilization efficiency of marine diesel engine. In this paper, a fuel consumption model of marine diesel ...

Using neural networks with multiple layers, it is possible to realize the deeper learning of features contained in the data in a stepwise manner. Deep learning-based approaches, such as the deep ...

Deep Belief Network (DBN) Deep Autoencoder (DAE) Stacked Autoencoder (sAE) Stacked Sparse Autoencoder (sSAE) Stacked Denoising Autoencoder (sDAE) Convolutional Neural Network (CNN) Visual Geometry ...

In this letter, we demonstrate a novel strategy for designing a low complexity deep neural network (DNN) receiver. The compact-stacked Autoencoder (CSAE) receiver is designed based on the proposed ...

Autoencoder An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). The encoding is validated and refined by attempting to ...

An overview of our proposed model and how the sparse autoencoder is used as feature selection to the deep neural network. The limitation of the feature lead to better generalizability of the model ...

In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural ...

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases.