Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
[图书][B] Bayesian filtering and smoothing
S Särkkä, L Svensson - 2023 - books.google.com
Now in its second edition, this accessible text presents a unified Bayesian treatment of state-
of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state …
of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state …
Digital twin: Values, challenges and enablers
A digital twin can be defined as an adaptive model of a complex physical system. Recent
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …
[PDF][PDF] Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression.
In this paper, we revisit batch state estimation through the lens of Gaussian process (GP)
regression. We consider continuous-discrete estimation problems wherein a trajectory is …
regression. We consider continuous-discrete estimation problems wherein a trajectory is …
Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing: A look at Gaussian process regression through Kalman filtering
Gaussian process-based machine learning is a powerful Bayesian paradigm for
nonparametric nonlinear regression and classification. In this article, we discuss …
nonparametric nonlinear regression and classification. In this article, we discuss …
Accurate continuous–discrete unscented Kalman filtering for estimation of nonlinear continuous-time stochastic models in radar tracking
GY Kulikov, MV Kulikova - Signal Processing, 2017 - Elsevier
This paper presents a new state estimation technology grounded in the unscented Kalman
filtering for nonlinear continuous-time stochastic systems. The resulting accurate continuous …
filtering for nonlinear continuous-time stochastic systems. The resulting accurate continuous …
Batch nonlinear continuous-time trajectory estimation as exactly sparse Gaussian process regression
In this paper, we revisit batch state estimation through the lens of Gaussian process (GP)
regression. We consider continuous-discrete estimation problems wherein a trajectory is …
regression. We consider continuous-discrete estimation problems wherein a trajectory is …
Spatio-temporal Gaussian process models for extended and group object tracking with irregular shapes
W Aftab, R Hostettler, A De Freitas… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Extended object tracking has become an integral part of many autonomous systems during
the last two decades. For the first time, this paper presents a generic spatio-temporal …
the last two decades. For the first time, this paper presents a generic spatio-temporal …
Deep ensemble model for COVID-19 diagnosis and classification using chest CT images
Simple Summary Coronavirus disease 2019 is a worldwide pandemic posing significant
health risks. Medical imaging tools can be considered as a supporting diagnostic testing …
health risks. Medical imaging tools can be considered as a supporting diagnostic testing …