While deep convolutional neural networks (DCNNs) have demonstrated superiority in their ability to classify image data, one of the primary downsides of DCNNs is that their training normally requires ...

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 ...

Subsequently, following the noise_factor we set in the configuration step, we add the noise to the pure data, creating the noisy_input which we'll feed to the autoencoder.

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 ...