Impact of systematic and amplitude model correlations within and between systems of combined input: a case study with ϕ2 (α)
Sign Up to like & getrecommendations! Published in 2021 at "Journal of High Energy Physics"
DOI: 10.1007/jhep10(2021)110
Abstract: Abstract The pursuit of experimental precision in the CP-violating weak phase ϕ2 (α) is not without its challenges, in part due to the need to combine multiple physical observables from various related decay channels, and… read more here.
Keywords: case; systematic amplitude; model; model uncertainty ... See more keywords
Counterfactual explanation of Bayesian model uncertainty
Sign Up to like & getrecommendations! Published in 2021 at "Neural Computing and Applications"
DOI: 10.1007/s00521-021-06528-z
Abstract: Artificial intelligence systems are becoming ubiquitous in everyday life as well as in high-risk environments, such as autonomous driving, medical treatment, and medicine. The opaque nature of the deep neural network raises concerns about its… read more here.
Keywords: counterfactual explanation; model; bayesian model; model uncertainty ... See more keywords
Bond of recycled coarse aggregate concrete: Model uncertainty and reliability-based calibration of design equations
Sign Up to like & getrecommendations! Published in 2021 at "Engineering Structures"
DOI: 10.1016/j.engstruct.2021.112290
Abstract: Abstract This paper concerns the design of lap splice lengths for ribbed steel reinforcement bars embedded in concrete produced with coarse recycled concrete aggregates. Recycled aggregates are weaker and typically lead to concrete with lower… read more here.
Keywords: aggregate concrete; model; model uncertainty; bond ... See more keywords
On the model uncertainty of wave induced platform motions and mooring loads of a semisubmersible based wind turbine
Sign Up to like & getrecommendations! Published in 2018 at "Ocean Engineering"
DOI: 10.1016/j.oceaneng.2017.11.001
Abstract: Abstract This work quantifies the model uncertainty in the wave-induced motion calculations of the codes in the Offshore Code Comparison Study Phase II, Semisubmersible Platform Based Floating Wind Turbine. Root mean square values are used… read more here.
Keywords: uncertainty; uncertainty wave; wave induced; wind turbine ... See more keywords
Quantification of model uncertainty in RANS simulations: A review
Sign Up to like & getrecommendations! Published in 2019 at "Progress in Aerospace Sciences"
DOI: 10.1016/j.paerosci.2018.10.001
Abstract: In computational fluid dynamics simulations of industrial flows, models based on the Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important role in decades to come. However, model uncertainties are still a major obstacle… read more here.
Keywords: simulations review; quantification model; uncertainty; model uncertainty ... See more keywords
Identifying elastoplastic parameters with Bayes’ theorem considering output error, input error and model uncertainty
Sign Up to like & getrecommendations! Published in 2019 at "Probabilistic Engineering Mechanics"
DOI: 10.1016/j.probengmech.2018.08.004
Abstract: Abstract We discuss Bayesian inference for the identification of elastoplastic material parameters. In addition to errors in the stress measurements, which are commonly considered, we furthermore consider errors in the strain measurements. Since a difference… read more here.
Keywords: input; model; model uncertainty; error model ... See more keywords
BlindNet: an untrained learning approach toward computational imaging with model uncertainty
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Physics D: Applied Physics"
DOI: 10.1088/1361-6463/ac2ad4
Abstract: The solution of an inverse problem in computational imaging (CI) often requires the knowledge of the physical model and/or the object. However, in many practical applications, the physical model may not be accurately characterized, leading… read more here.
Keywords: learning approach; model; untrained learning; computational imaging ... See more keywords
Comparing Kalman Filters and Observers for Power System Dynamic State Estimation With Model Uncertainty and Malicious Cyber Attacks
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2876883
Abstract: Kalman filters (KFs) and dynamic observers are two main classes of the dynamic state estimation (DSE) routines. The Power system DSE has been implemented by various KFs, such as the extended KF (EKF) and the… read more here.
Keywords: power system; cyber attacks; model uncertainty;
Robust Safety-Critical Control for Dynamic Robotics
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Automatic Control"
DOI: 10.1109/tac.2021.3059156
Abstract: We present a novel method of optimal robust control through quadratic programs that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems in the… read more here.
Keywords: safety critical; safety; robotics; model uncertainty ... See more keywords
Low-Complexity Prescribed Performance Control for Unmanned Aerial Manipulator Robot System Under Model Uncertainty and Unknown Disturbances
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2021.3117262
Abstract: This article presents a trajectory tracking control method for the unmanned aerial manipulator robot system (UAMRS) under model uncertainty and unknown disturbances. More specifically, a low-complexity prescribed performance controller is proposed to effectively reduce the… read more here.
Keywords: prescribed performance; low complexity; performance; model uncertainty ... See more keywords
A Novel Smooth Variable Structure Filter for Target Tracking Under Model Uncertainty
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2021.3058806
Abstract: Model uncertainty is a serious challenge for robustness of tracking algorithms in radar systems. The smooth variable structure filter (SVSF) achieves error-bounded estimations for target state by scaling the magnitude of kinematic modeling error and… read more here.
Keywords: structure filter; smooth variable; model uncertainty; variable structure ... See more keywords