The D-CNN-LSTM Autoencoder method optimizes the anomaly detection rate for all of the anomalies, specifically in the case of low magnitude anomalies, enhancing F1-score up to 18.12% in single types of ...

This article presents the selection of an appropriate deep learning Long Short-Term Memory (LSTM) based probabilistic hour-ahead forecasting model for a grid connected industrial solar PV power plant ...

We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical inductive bias of material stability. By learning from the data distribution of stable materials, the decoder ...