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pro vyhledávání: '"Voelz"'
This paper presents the open-source stochastic model predictive control framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It provides several uncertainty propagation methods to predict stochastic moments of the system state
Externí odkaz:
http://arxiv.org/abs/2407.09261
Publikováno v:
2023 62nd IEEE IEEE Conference on Decision and Control (CDC), Singapore, Singapore, December 13 - 15, 2023, pp. 322--327
Trajectory planners of autonomous vehicles usually rely on physical models to predict the vehicle behavior. However, despite their suitability, physical models have some shortcomings. On the one hand, simple models suffer from larger model errors and
Externí odkaz:
http://arxiv.org/abs/2407.06605
This paper presents a distributed model predictive control (DMPC) scheme for nonlinear continuous-time systems. The underlying distributed optimal control problem is cooperatively solved in parallel via a sensitivity-based algorithm. The algorithm is
Externí odkaz:
http://arxiv.org/abs/2406.03134
To quantify how well theoretical predictions of structural ensembles agree with experimental measurements, we depend on the accuracy of forward models. These models are computational frameworks that generate observable quantities from molecular confi
Externí odkaz:
http://arxiv.org/abs/2405.18532
Connected automated driving promises a significant improvement of traffic efficiency and safety on highways and in urban areas. Cooperative maneuver planning at unsignalized intersections may facilitate active guidance of connected automated vehicles
Externí odkaz:
http://arxiv.org/abs/2403.16478
Autor:
Raddi, Robert M., Voelz, Vincent A.
Accurate force fields are essential for reliable molecular simulations. These models are refined against quantum mechanical calculations and experimental measurements, which are subject to random and systematic errors. Bayesian Inference of Conformat
Externí odkaz:
http://arxiv.org/abs/2402.11169
The non-uniform blur of atmospheric turbulence can be modeled as a superposition of linear motion blur kernels at a patch level. We propose a regression convolutional neural network (CNN) to predict angle and length of a linear motion blur kernel for
Externí odkaz:
http://arxiv.org/abs/2402.07796
Autor:
Schmider, François-Xavier, Gaulme, Patrick, Morales-Juberías, Raúl, Jackiewicz, Jason, Gonçalves, Ivan, Guillot, Tristan, Simon, Amy A., Wong, Michael H., Underwood, Thomas, Voelz, David, Sanchez, Cristo, DeColibus, Riley, Kovac, Sarah A., Sellers, Sean, Gilliam, Doug, Boumier, Patrick, Appourchaux, Thierry, Dejonghe, Julien, Rivet, Jean Pierre, Markham, Steve, Howard, Saburo, Abe, Lyu, Mekarnia, Djamel, Ikoma, Masahiro, Hanayama, Hidekazu, Sato, Bun'ei, Kunitomo, Masanobu, Izumiura, Hideyuki
Publikováno v:
Planet. Sci. J. 5 100 (2024)
We present three-dimensional (3D) maps of Jupiter's atmospheric circulation at cloud-top level from Doppler-imaging data obtained in the visible domain with JIVE, the second node of the JOVIAL network, which is mounted on the Dunn Solar Telescope at
Externí odkaz:
http://arxiv.org/abs/2312.16888