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Wyszukujesz frazę "Rauh, Andreas" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
A computationally inexpensive algorithm for determining outer and inner enclosures of nonlinear mappings of ellipsoidal domains
Autorzy:
Rauh, Andreas
Jaulin, Luc
Powiązania:
https://bibliotekanauki.pl/articles/2055165.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
bounded uncertainty
guaranteed enclosures
ellipsoidal enclosures
inner approximations
outer approximations
nonlinear system
confidence interval
niepewność ograniczona
układ nieliniowy
przedział ufności
Opis:
A wide variety of approaches for set-valued simulation, parameter identification, state estimation as well as reachability, observability and stability analysis for nonlinear discrete-time systems involve the propagation of ellipsoids via nonlinear functions. It is well known that the corresponding image sets usually possess a complex shape and may even be nonconvex despite the convexity of the input data. For that reason, domain splitting procedures are often employed which help to reduce the phenomenon of overestimation that can be traced back to the well-known dependency and wrapping effects of interval analysis. In this paper, we propose a simple, yet efficient scheme for simultaneously determining outer and inner ellipsoidal range enclosures of the solution for the evaluation of multi-dimensional functions if the input domains are themselves described by ellipsoids. The Hausdorff distance between the computed enclosure and the exact solution set reduces at least linearly when decreasing the size of the input domains. In addition to algebraic function evaluations, the proposed technique is-for the first time, to our knowledge-employed for quantifying worst-case errors when extended Kalman filter-like, linearization-based techniques are used for forecasting confidence ellipsoids in a stochastic setting.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 399--415
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An unscented transformation approach to stochastic analysis of measurement uncertainty in magnet resonance imaging with applications in engineering
Autorzy:
Rauh, Andreas
John, Kristine
Wüstenhagen, Carolin
Bruschewski, Martin
Grundmann, Sven
Powiązania:
https://bibliotekanauki.pl/articles/1838185.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
magnet resonance imaging
compressed sensing
stochastic uncertainty
unscented transformation
rezonans magnetyczny
próbkowanie oszczędne
niepewność stochastyczna
Opis:
In the frame of stochastic filtering for nonlinear (discrete-time) dynamic systems, the unscented transformation plays a vital role in predicting state information from one time step to another and correcting a priori knowledge of uncertain state estimates by available measured data corrupted by random noise. In contrast to linearization-based techniques, such as the extended Kalman filter, the use of an unscented transformation not only allows an approximation of a nonlinear process or measurement model in terms of a first-order Taylor series expansion at a single operating point, but it also leads to an enhanced quantification of the first two moments of a stochastic probability distribution by a large signal-like sampling of the state space at the so-called sigma points which are chosen in a deterministic manner. In this paper, a novel application of the unscented transformation technique is presented for the stochastic analysis of measurement uncertainty in magnet resonance imaging (MRI). A representative benchmark scenario from the field of velocimetry for engineering applications which is based on measured data gathered at an MRI scanner concludes this contribution.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 73-83
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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