Abstract: Reliable and accurate wind power prediction is fundamental for efficient operation of wind energy system. However, the stochasticity and volatility cause a lot of colored noises, making wind ...

A dual-model battery health assessment framework analyzes real-world voltage data from retired EV batteries in grid storage. Using incremental capacity and probability density methods, it improves ...

The stochastic nature of renewable energy sources (RESs) necessitates treating power system frequency response as a random process with a nonstationary probability density function (PDF). Based upon ...

This research provides density functions and descriptive statistics for the distance between points for basic shapes in Cartesian space. Both Euclidean and Rectilinear Distances are determined for ...

In the realm of relativistic quantum mechanics, we address a fundamental question: Which one, between the Dirac or the Foldy-Wouthuysen density, accurately provide a probability density for finding a ...

In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning method, ...

A package to describe amortized (conditional) normalizing-flow PDFs defined jointly on tensor products of manifolds with coverage control. The connection between different manifolds is fixed via an ...

Abstract: An analytical derivation of the EMG signal’s amplitude probability density function (EMG PDF) is presented and used to study how an EMG signal builds-up, or fills, as the degree of muscle ...