Results 31 to 40 of about 197,948 (294)
Analysis of Vehicle Platform Vibration Based on Empirical Mode Decomposition
Shock and Vibration, 2021 Vehicle platform vibration (VPV) directly affects the measurement accuracy of precise measuring instrument (PMI) fixed on it. In order to reduce the influences of VPV on measurement accuracy, it is necessary to perform vibration isolation between vehicle
Chengwu Shen +5 more
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on the Influence of Sampling on the Empirical Mode Decomposition [PDF]
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006 The rationale underlying the nonlinear Empirical Mode Decomposition method is intrinsically a continuous-time approach. The method can however only be applied in practice to discrete-time signals. EMD is obtained through iterating a basic nonlinear operator for which we derive an upper bound for the effects of sampling.
Gabriel Rilling, Patrick Flandrin
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Empirical mode decomposition on surfaces
Graphical Models, 2012 Empirical Mode Decomposition (EMD) is a powerful tool for analysing non-linear and non-stationary signals, and has drawn a great deal of attentions in various areas. In this paper, we generalize the classical EMD from Euclidean space to the setting of surfaces represented as triangular meshes.
Hui Wang 0018 +4 more
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SAMPLING EFFECTS ON THE EMPIRICAL MODE DECOMPOSITION [PDF]
Advances in Adaptive Data Analysis, 2009 Standard exposition of Empirical Mode Decomposition (EMD) is usually done within a continuous-time setting whereas, in practice, the effective implementation always operates in discrete-time. The purpose of this contribution is to summarize a number of results aimed at quantifying the influence of sampling on EMD.
Gabriel Rilling, Patrick Flandrin
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Applying the Hilbert--Huang Decomposition to Horizontal Light Propagation C_n^2 data [PDF]
, 2006 The Hilbert Huang Transform is a new technique for the analysis of non--stationary signals. It comprises two distinct parts: Empirical Mode Decomposition (EMD) and the Hilbert Transform of each of the modes found from the first step to produce a Hilbert ...
Chang, Mark P. J. L. +4 more
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Graph Empirical Mode Decomposition
, 2014 Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal ...
Nicolas Tremblay +2 more
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IEEE Access, 2017 Effective signal processing methods are essential for machinery fault diagnosis. Most conventional signal processing methods lack adaptability, thus being unable to well extract the embedded meaningful information.
Zhipeng Feng, Dong Zhang, Ming J. Zuo
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Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods [PDF]
, 2007 Measurements of optical turbulence time series data using unattended instruments over long time intervals inevitably lead to data drop-outs or degraded signals.
Beran J +15 more
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Empirical mode decomposition with shape-preserving spline interpolation
Results in Applied Mathematics, 2020 Empirical mode decomposition (EMD) is a popular, novel, user-friendly algorithm to decompose a given signal into its constituting components, utilizing spline interpolation.
Maria D. van der Walt
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Spectral proper orthogonal decomposition [PDF]
, 2015 The identification of coherent structures from experimental or numerical data is an essential task when conducting research in fluid dynamics. This typically involves the construction of an empirical mode base that appropriately captures the dominant ...
Oberleithner, Kilian +2 more
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