Results 31 to 40 of about 106 (92)
Solving Matrix Equations via Empirical Gramians
, 2019 C. Himpe. Solving Matrix Equations via Empirical Gramians. 8th Workshop on Matrix Equations and Tensor Techniques (METT), 2019.
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Mathematical and Computer Modelling of Dynamical Systems, 2018 Correct selection of spatial basis functions is crucial for model reduction for nonlinear distributed parameter systems in engineering applications.
Mian Jiang +3 more
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IET Electric Power Applications, Volume 19, Issue 1, January/December 2025.This study proposes a multi‐modal feature fusion (MFF) method for diagnosing loosening fasteners in converter valve saturable reactors. By deeply fusing multi‐source features, it achieves 96% accuracy for noncontact loosening fault detection, offering a novel voiceprint application approach for complex valve halls.
Yimiao Wang +5 more
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CoRRObservability quantification is a key problem in dynamic network sciences. While it has been thoroughly studied for linear systems, observability quantification for nonlinear networks is less intuitive and more cumbersome. One common approach to quantify observability for nonlinear systems is via the Empirical Gramian (Empr-Gram) -- a generalized form ...
Mohamad H. Kazma, Ahmad F. Taha
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emgr - EMpirical GRamian Framework (Version 5.4)
, 2019 presented at MATLAB Expo 2018.
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emgr - EMpirical GRamian Framework (Version 5.7)
, 2019 presented at ModRed, 2019.
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Optimal Control with Limited Sensing via Empirical Gramians and Piecewise Linear Feedback
, 2016 This paper is concerned with the design of optimal control for finite-dimensional control-affine nonlinear dynamical systems. We introduce an optimal control problem that specifically optimizes nonlinear observability in addition to ensuring stability of the closed loop system.
Alaeddini, Atiye +2 more
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Acta HorticulturaeEstimating crop states accurately through combining measurements and a dynamical model in a data assimilation algorithm is a promising alternative for high tech crop sensing investments. In order to successfully do this, the employed model and chosen sensor configuration need to be observable. This paper demonstrates, in a lettuce greenhouse model, the
Boersma, S., van Mourik, S.
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Balanced truncation for model reduction of biological oscillators. [PDF]
Biol Cybern, 2021 Padoan A, Forni F, Sepulchre R.
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Prevalence and scalable control of localized networks. [PDF]
Proc Natl Acad Sci U S A, 2022 Duan C, Nishikawa T, Motter AE.
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