Background/foreground separation is the first step in video surveillance system to detect
moving objects. Recent research on problem formulations based on decomposition into low …

This paper presents a randomized algorithm for computing the near-optimal low-rank
dynamic mode decomposition (DMD). Randomized algorithms are emerging techniques to …

Recently, deep learning models have shown promising performances in many research
areas, including traffic states prediction, due to their ability to model complex nonlinear …

We introduce the method of compressed dynamic mode decomposition (cDMD) for
background modeling. The dynamic mode decomposition is a regression technique that …

This paper focuses on a new framework for obtaining a nonintrusive (ie, not requiring
projecting of the governing equations onto the reduced basis modes) reduced order model …

The Accurate prediction of a hot spot temperature (HST) is critical for ensuring the reliable
operation of transformers. The existing HST prediction methods are based on the black-box …

Infrastructure systems are critical for society's resilience, government operation, and overall
defense. Thereby, it is imperative to develop informative and computationally efficient …

Dynamic mode decomposition (DMD) is widely used for extracting dominant structures of
unsteady flow fields. However, the traditional mode time coefficient of DMD is assumed to …

The characterization of intermittent, multiscale, and transient dynamics using data-driven
analysis remains an open challenge. We demonstrate an application of the dynamic mode …

Computing the numerical solution of the Kadanoff–Baym equations, a set of nonlinear
integral differential equations satisfied by the two-time Green's functions derived from many …