Wavelet filtering to reduce conservatism in aeroservoelastic robust stability margins

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National Aeronautics and Space Administration, Dryden Flight Research Center, National Technical Information Service, distributor , Edwards, Calif, [Springfield, Va
Wavelet analysis., Aeroservoelasticity., Transfer funct
StatementMarty Brenner, Rick Lind.
SeriesNASA/TM -- 1998-206545., NASA technical memorandum -- 206545.
ContributionsLind, Rick., Dryden Flight Research Facility.
The Physical Object
FormatMicroform
Pagination1 v.
ID Numbers
Open LibraryOL15509391M

This paper augments wavelet filtering with wavelet-based modal parameter extraction to produce robust stability margins with reduced-norm uncertainty sets of both complex-nonparametric and real-parametric petur-bations.

Details Wavelet filtering to reduce conservatism in aeroservoelastic robust stability margins EPUB

The decrease in conservatism results in a more practical and valuable robust stability margin. Get this from a library. Wavelet filtering to reduce conservatism in aeroservoelastic robust stability margins. [Marty Brenner; Rick Lind; Dryden Flight Research Facility.].

Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stab ility Margins Author: Marty Brenner and Rick Lind Subject: 08 Keywords: Aeroservoelasticity, Modal estimation, Robust stability, Uncertainty mod eling Wavelet analysis Created Date: 11/7/ PM.

Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process.

Nonparametric wavelet processing of data was used to reduce the effects of external desirableness Author: Rick Lind and Marty Brenner.

Brenner and R. Lind, "Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins," AIAA Structures, Structural Dynamics, and Materials Conference, Long Beach, CA, AIAA, April An Introduction to Wavelets and Other Filtering Methods in Finance and Economics presents a unified view of filtering techniques with a special focus on wavelet analysis in finance and economics.

It emphasizes the methods and explanations of the theory that underlies them. An on-line method based on robust mu analysis is presented to evaluate the stability margin of multi-loop aeroservoelastic system from flight testing data. The flight data is preprocessed to remove noise by wavelet filter in time-frequency domain.

Robust Flutter Margins of an F/A Aircraft from Aeroelastic Flight Data. Analyzing aeroservoelastic stability margins using the mu method. Rick Lind and Marty Brenner; 22 August Wavelet filtering to reduce conservatism in aeroservoelastic robust stability by: Development of an integrated aeroservoelastic analysis program and correlation with test data [microform Wavelet filtering to reduce conservatism in aeroservoelastic robust stability margins [microform] / Mart Aeroservoelastic modeling and validation of a thrust-vectoring F/A aircraft [microform] / Martin J.

Here are the approximations and details produced using the Haar wavelet at level 4 (Fig. The approximations and details reveal the vibration signal and the noise. In wavelet analysis, the approximations are the output of the low-pass filters, and the details are the output of the high-pass filters Cited by: The wavelet transform or wavelet analysis is probably the most recent solution to overcome the shortcomings of the Fourier transform.

In wavelet analysis the use of a fully scalable modulated window solves the signal-cuttingFile Size: KB. 39th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference and Exhibit. The book code is designed to work with the freeware R package WaveThresh4, but the book can be read independently of R.

The book introduces the wavelet transform by starting with the simple Haar wavelet transform, and then builds to consider more general wavelets, complex-valued wavelets, non-decimated transforms, multidimensional wavelets.

An Introduction to Wavelets and Other Filtering Methods in Finance and Economics presents a unified view of filtering techniques with a special focus on wavelet analysis in finance and economics. It emphasizes the methods and explanations of the theory that underlies them.4/5(1).

The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Downloadable (with restrictions).

An Introduction to Wavelets and Other Filtering Methods in Finance and Economics presents a unified view of filtering techniques with a special focus on wavelet analysis in finance and economics.

It emphasizes the methods and explanations of the theory that underlies them. It also concentrates on exactly what wavelet analysis (and filtering methods in general Cited by:   Buy An Introduction to Wavelets and Other Filtering Methods in Finance and Economics by Gencay, Ramazan, Selcuk, Faruk, Whitcher, Brandon (ISBN: ) from Amazon's Book Store.

Everyday low prices and free delivery on eligible orders.4/5(1). Regular Article. WAVELET ANALYSIS FOR HOPF BIFURCATIONS WITH AEROELASTIC APPLICATIONS. Abstract. A wavelet analysis on the output signal of a non-linear system in the neighbourhood of a Hopf bifurcation (i.e., a limit-cycle oscillation) has been performed to point out the linear and non-linear signatures of the by: 5.

Wavelet analysis for filtering and system identification has been used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins is reduced with parametric and nonparametric time- frequency analysis of flight data in the model validation process.

Download Wavelet filtering to reduce conservatism in aeroservoelastic robust stability margins PDF

Nonparametric wavelet processing of data is used to reduce the effects of external disturbances Author: Martin J. Brenner and Rick Lind. The dynamical and kinematical model of the eight-rotor with high drive capability is established.

On account of the uncertainties, a robust back-stepping sliding mode control (BSMC) with self-recurrent wavelet neural network (SRWNN) method is Cited by: 1. WAVELET SPEECH ENHANCEMENT BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS Chia-Lung Wu 1, Hsiang-Ping Hsu, Syu-Siang Wang2, Jeih-Weih Hung3, Ying-Hui Lai4, Hsin-Min Wang5, and Yu Tsao2 1Investigation Bureau, Ministry of Justice, R.O.C 2Research Center for Information Technology Innovation, Academia Sinica, R.O.C 3Dept of Electrical Engineering, National.

DB4 WAVELET BUILT FROM FILTER POINTS Figure –2 point estimation of a “continuous” Daubechies 4 wavelet built from 6 equispaced filter points (the 4 original filter points and 2 end zeros) superimposed on the graph.

Some wavelets have symmetry (valuable in human vision perception) such as the Biorthogonal wavelet Size: KB. The conservatism of the robust stability margins is reduced with parametric and non parametric time-frequency analysis of flight data in the model validation process.

Nonparametric wavelet processing of data is used to reduce the effects of external disturbances and unmodeled dynamics. Orthogonal and Biorthogonal Filter Banks Daubechies’ extremal-phase wavelets, Daubechies’ least-asymmetric wavelets, Fejer-Korovkin filters, coiflets, biorthogonal spline filters Orthogonal wavelet filter banks generate a single scaling function and wavelet, whereas biorthogonal wavelet filters generate one scaling function and wavelet for biorfilt: Biorthogonal wavelet filter set.

Description Wavelet filtering to reduce conservatism in aeroservoelastic robust stability margins EPUB

is a great strength of wavelets as a tool because one can choose that class of wavelet function that is most suitable to the properties of the function to be represented. Symmetry of the function g.) is one such criterion and is the one that is seldom satisfied, except approximately; the Haar wavelet is an example of an orthogonal symmetric.

The CDF-9/7 Filter We first discuss the construction of famous Cohen-Daubechies-Feauveau (CDF) 9/7 filter [15].

CDF-9/7 filters are constructed directly based on the spectral factorization method. These wavelets have symmetric scaling and wavelet functions. Classical wavelet thresholding methods suffer from boundary problems caused by the application of the wavelet transformations to a finite signal. As a result, large bias at the edges and artificial wiggles occur when the classical boundary assumptions are not satisfied.

Although polynomial wavelet regression and local polynomial wavelet regression effectively reduce the risk of this problem Cited by: 2. aimed at economists – the excellent book by Gen¸cay, Sel¸cuk and Whitcher (), an ∗This article is an extended, corrected and revised version of Bank of Finland Discussion paperwhich possesses the title ‘An Intuitive Guide to Wavelets for Economists’.Cited by: aeroservoelastic stability margins.

The conservatism of the robust stability margins is reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process.

Nonparametric wavelet processing of data is used to reduce the effects of external disturbances and unmodeled dynamics. Parametric estimates of modal stability are also extracted using the wavelet.

Wavelet transform analysis has been widely used for the purpose of denoising, data compression, feature recognition, system nonlinearities detection and so on [47].

The wavelet transform is calculated as shifting the wavelet function in time along the input signal and calculating the convolution of. Filtering and wavelet-based algorithms may be used to reduce errors introduced by signal processing and reduce conservatism in the resulting stability margins.

Parameter uncertainty associated with the modal parameters of the linear model may be used to describe some errors in the natural frequencies and dampings observed using flight data Cited by: The wavelet function is in effect a band-pass filter and scaling it for each level halves its bandwidth.

The scaling and detail basically divide the signal into two applying a high-pass filter resulting into the detail coefficients - (which is the highest level of the transform) and a low-pass filter .Wavelet-SRNet: A Wavelet-based CNN for Multi-scale Face Super Resolution Huaibo Huang1,2,3, Ran He1,2,3, Zhenan Sun1,2,3 and Tieniu Tan1,2,3 1School of Engineering Science, University of Chinese Academy of Sciences 2Center for Research on Intelligent Perception and Computing, CASIA 3National Laboratory of Pattern Recognition, CASIA @, {rhe, znsun, tnt}@ Size: KB.