Machine learning tracks methane emissions from orbit

Methane is the second most important anthropogenic greenhouse gas after carbon dioxide, with a global warming potential ...

Predicting Airline Delays at Scale with Spark ML

A UC Berkeley team used Apache Spark ML to predict airline delays at scale, training models on millions of flight records and ...
IEEE

Physically Jacobian-Informed Encoder-Decoder ANNs for Nonlinear Power Flow Regression

Abstract: Power flow (PF) is the basis of steady-state analysis and control of power systems. The conventional model-driven PF formulated by a set of implicated nonlinear equations is solved ...
Scientific Research Publishing

KAN-Transformer Fusion with Mixture of Experts for Temporal Imputation of Spatiotemporal Air Pollution Data ()

The Kolmogorov-Arnold Network (abbr. KAN) is a novel neural network architecture inspired by the Kolmogorov-Arnold ...
IEEE

Nonlinear Flux Observer for Sensorless Control of IPMSM Based on a Novel Regression Model

Abstract: The nonlinear flux observer is an effective position sensorless method that has been proven in practice. For the interior permanent magnet synchronous motor (IPMSM), this article establishes ...
Visual Studio Magazine

Random Forest Regression Using C#

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...