Results 201 to 210 of about 138,063 (253)
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Although the empirical mode decomposition is a powerful tool for analyzing complicated datasets, many irrelevant intrinsic mode functions may appear in the decomposition. In this paper, we develop an energy-based method to detect relevant intrinsic mode functions.
Douglas Baptista De Souza +2 more
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Intrinsic Fourier Mode Functions
Advances in Data Science and Adaptive Analysis, 2017 In this paper, we study a class of functions that exhibit properties expected from intrinsic mode functions. A type of an empirical instantaneous frequency, depending on the extrema scale, is introduced and its proximity to the classical analytic instantaneous frequency is discussed.
Vesselin Vatchev
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CONSTRUCTING CROSSOVER-FRACTALS USING INTRINSIC MODE FUNCTIONS
Advances in Adaptive Data Analysis, 2010 Real nonstationary time sequences are in general not monofractals. That is, they cannot be characterized by a single value of fractal dimension. It has been shown that many real-time sequences are crossover-fractals: sequences with two fractal dimensions — one for the short and the other for long ranges.
Sy-Sang Liaw, Feng-Yuan Chiu
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A CRITERION FOR SELECTING RELEVANT INTRINSIC MODE FUNCTIONS IN EMPIRICAL MODE DECOMPOSITION
Advances in Adaptive Data Analysis, 2010Information extraction from time series has traditionally been done with Fourier analysis, which use stationary sines and cosines as basis functions. However, data that come from most natural phenomena are mostly nonstationary. A totally adaptive alternative method has been developed called the Hilbert–Huang transform (HHT), which involves generating ...
Nii Attoh-Okine
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Decomposition of functions into pairs of intrinsic mode functions
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2008The intrinsic mode functions (IMFs) arise as basic modes from the application of the empirical mode decomposition (EMD) to functions or signals. In this procedure, instantaneous frequencies are subsequently extracted from the IMFs by the simple application of the Hilbert transform, thereby providing a multiscale analysis of the signal's ...
Robert Sharpley
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Advances in Adaptive Data Analysis, 2010 Empirical Mode Decomposition (EMD) has been widely used to analyze non-stationary and nonlinear signal by decomposing data into a series of intrinsic mode functions (IMFs) and a trend function through sifting processes. For lack of a firm mathematical foundation, the implementation of EMD is still empirical and ad hoc.
Gang Wang +4 more
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Correspondence Between Intrinsic Mode Functions and Slow Flows
Volume 1: 22nd Biennial Conference on Mechanical Vibration and Noise, Parts A and B, 2009 We study the correspondence between analytical and empirical slow-flow analyses, which will form a basis for a time-domain nonparametric nonlinear system identification method. Given a sufficiently dense set of sensors, measured time series recorded throughout a mechanical or structural system contains all information regarding the dynamics of that ...
Young S. Lee +4 more
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Bearing Fault Detection Using Intrinsic Mode Functions Statistical Information
2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), 2018 This paper investigates the bearing fault detection using vibration signals. For these mechanical type of fault, the energy in the signal is distributed in the frequency domain which is relatively wide. These particular frequencies linked to the fault occurrence is difficult to isolate and characterise.
Zahra Mezni +3 more
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A De-noising Scheme Based on the Null Hypothesis of Intrinsic Mode Functions
IEEE Signal Processing Letters, 2016Empirical mode decomposition is a nonparametric adaptive tool that decomposes signals into a set of zero-mean modes called intrinsic mode functions (IMFs) that can be used to denoise a signal by selecting the relevant (noise-free) modes. In this paper, the statistical properties of IMFs, produced by a range of signal distributions, are examined ...
Mahdi H Al-Badrawi, Nicholas J Kirsch
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