When training data are scarce, it is challenging to train a deep neural network without causing the overfitting problem. For overcoming this challenge, this article proposes a new data augmentation ...

In the cause of working out the challenge of remaining life prediction (RUL) of proton exchange membrane fuel cell (PEMFC) under dynamic operating conditions, this article proposes a PEMFC RUL ...

Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

Get Code Download Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from the majority of the source items. There are many ...

The algorithm of autoencoder is composed by two parts: encoder and decoder. The encoder consists in compressing the inputs into a lower-dimensions space (so-called latent-space) and then the decoder ...

Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog ...

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of ...