C&EN

A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator.
IEEE

Linear Shrinkage Coefficient-Based Source Number Estimation Using Semi-Supervised GAN With Small Samples

Abstract: A deep learning-based source number estimation method is presented in this article, where the deep generative adversarial network (GAN) combined with semi-supervised learning is applied, ...
Frontiers

Stroke rate–stroke length dynamics in elite freestyle swimming: application of kernel density estimation

Objectives: To analyse stroke rate (SR) and stroke length (SL) combinations among elite swimmers to better understand stroke strategies across all race distances of freestyle events. Design: We ...
IEEE

Sideslip Angle Estimation Using Linear Kalman Filter with Neural Network-Based Covariance Adaptation

Abstract: A methodology for estimating the sideslip angle using a linear Kalman Filter with a model adaptation is presented. In this adaptation, neural networks are trained to estimate the model ...