Research studies on data-driven approaches to rotating components and rolling element
bearing (REB) prognostics have recently witnessed a rapid increase. These data-driven …

As the development of smart grids and electricity markets around the world, short-term load
forecasting (STLF) plays an increasingly important role in safe and economical operations of …

This paper proposes an electricity demand and price forecast model of the smart city large
datasets using a single comprehensive Long Short-Term Memory (LSTM) based on a …

The emergence of prognostics and health management as a condition-based maintenance
approach has greatly improved productivity, maintainability, and most essentially, reliability …

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI)
provides a novel method for analyzing biomolecule concentrations in tissues without …

Long short term memory (LSTM) is a powerful model in building of an ASR system whereas
standard recurrent networks are generally inefficient to obtain better performance. Although …

The accurate forecast of integrated energy loads, which has important practical significance,
is the premise of the design, operation, scheduling and management of integrated energy …

The use of deep learning approaches for prognostics and remaining useful life predictions
have become obviously prevalent. Artificial recurrent neural networks like the long short …

This paper analyzes the potential influence in Chinese electricity market due to the reform
and access of the electricity spot market. On this occasion, a deep learning based model for …

This paper presents language models based on Long Short-Term Memory (LSTM) neural
networks for very large vocabulary continuous Russian speech recognition. We created …