Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

This paper is to provide practical options for prognostics so that beginners can select
appropriate methods for their fields of application. To achieve this goal, several popular …

Electrical power systems are evolving from today's centralized bulk systems to more
decentralized systems. Penetrations of renewable energies, such as wind and solar power …

Over recent years a significant amount of research has been undertaken to develop
prognostic models that can be used to predict the remaining useful life of engineering …

Prediction intervals (PIs) have been proposed in the literature to provide more information by
quantifying the level of uncertainty associated to the point forecasts. Traditional methods for …

Wind power interval prediction (WPIP) plays an increasingly important role in evaluations of
the uncertainty of wind power and becomes necessary for managing and planning power …

A novel method for estimating prediction uncertainty using machine learning techniques is
presented. Uncertainty is expressed in the form of the two quantiles (constituting the …

Improving the quality of Wind Speed Interval prediction is important to maximize the usage of
integrated wind energy as well as to reduce the adverse effects of the uncertainties …

The fragility curve is defined as the conditional probability of failure of a structure, or its
critical components, at given values of seismic intensity measures (IMs). The conditional …