The best autoencoder architectures for dimensionality reduction vary based on data characteristics and goals. Start with a basic autoencoder and progress to more complex architectures if needed ...

This repository implements five types of autoencoders: Fully Connected Autoencoder (FC-AE), Convolutional Autoencoder (CNN-AE), Sparse Autoencoder (Sparse-AE), Recurrent Autoencoder (RNN-AE), and ...

Then, the autoencoder utilizes the sparse representation technique to improve its ability to recognize abnormal data by learning a set of basis vectors that can sparsely represent normal data. Finally ...

For the safe and reliable operation of battery-driven machines, accurate state-of-charge (SOC) estimations are necessary. Unfortunately, existing methods often fail to identify patterns relevant to ...

For the performance comparison with SRL-SOA, the provided main.py supports the following band selection methods: Principal Component Analysis (PCA), Sparse Representation based Band Selection (SpaBS) ...