38,525 research outputs found
Local ensemble transform Kalman filter, a fast non-stationary control law for adaptive optics on ELTs: theoretical aspects and first simulation results
We propose a new algorithm for an adaptive optics system control law, based
on the Linear Quadratic Gaussian approach and a Kalman Filter adaptation with
localizations. It allows to handle non-stationary behaviors, to obtain
performance close to the optimality defined with the residual phase variance
minimization criterion, and to reduce the computational burden with an
intrinsically parallel implementation on the Extremely Large Telescopes (ELTs).Comment: This paper was published in Optics Express and is made available as
an electronic reprint with the permission of OSA. The paper can be found at
the following URL on the OSA website: http://www.opticsinfobase.org/oe/ .
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How do you make a time series sing like a choir? Using the Hilbert-Huang transform to extract embedded frequencies from economic or financial time series
The Hilbert-Huang transform (HHT) was developed late last century but has still to be introduced to the vast majority of economists. The HHT transform is a way of extracting the frequency mode features of cycles embedded in any time series using an adaptive data method that can be applied without making any assumptions about stationarity or linear data-generating properties. This paper introduces economists to the two constituent parts of the HHT transform, namely empirical mode decomposition (EMD) and Hilbert spectral analysis. Illustrative applications using HHT are also made to two financial and three economic time series.business cycles; growth cycles; Hilbert-Huang transform (HHT); empirical mode decomposition (EMD); economic time series; non-stationarity; spectral analysis
A low-order decomposition of turbulent channel flow via resolvent analysis and convex optimization
We combine resolvent-mode decomposition with techniques from convex
optimization to optimally approximate velocity spectra in a turbulent channel.
The velocity is expressed as a weighted sum of resolvent modes that are
dynamically significant, non-empirical, and scalable with Reynolds number. To
optimally represent DNS data at friction Reynolds number , we determine
the weights of resolvent modes as the solution of a convex optimization
problem. Using only modes per wall-parallel wavenumber pair and temporal
frequency, we obtain close agreement with DNS-spectra, reducing the wall-normal
and temporal resolutions used in the simulation by three orders of magnitude
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