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The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

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TLDR
In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Abstract
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...

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EEG-Based Prediction of Epileptic Seizures Using Phase Synchronization Elicited from Noise-Assisted Multivariate Empirical Mode Decomposition

TL;DR: It was found that PLVs calculated with the NA-MEMD algorithm could be used as a potential biological marker for seizure prediction, and the gamma frequency band was useful for discriminating between interictal and preictal stages.
Journal ArticleDOI

Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm

TL;DR: A new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting, and the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-stepWind speed forecasting.
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Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

TL;DR: The developed diagnostic system for detecting the onset of degradation, isolating the degrading bearing, classifying the type of defect is based on an hierarchical structure of K-Nearest Neighbours classifiers.
Journal ArticleDOI

Identification of Natural Frequencies and Dampings of In Situ Tall Buildings Using Ambient Wind Vibration Data

TL;DR: In this paper, the Hilbert transform is applied to each free vibration modal response to identify natural frequencies and damping ratios of in situ tall buildings using ambient wind vibration data, which is based on the empirical mode decomposition (EMD) method, the random decrement technique (RDT), and the Hilbert-Huang transform.
Journal ArticleDOI

Deep-Learning-Based Fault Classification Using Hilbert–Huang Transform and Convolutional Neural Network in Power Distribution Systems

TL;DR: A deep-learning-based fault classification method in small current grounding power distribution systems is presented and has the characteristics of high accuracy and adaptability in fault classification of power Distribution systems.
References
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Journal ArticleDOI

Deterministic nonperiodic flow

TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
Book

Linear and Nonlinear Waves

G. B. Whitham
TL;DR: In this paper, a general overview of the nonlinear theory of water wave dynamics is presented, including the Wave Equation, the Wave Hierarchies, and the Variational Method of Wave Dispersion.
Book

RANDOM DATA Analysis and Measurement Procedures

TL;DR: A revised and expanded edition of this classic reference/text, covering the latest techniques for the analysis and measurement of stationary and nonstationary random data passing through physical systems, is presented in this article.

Theory of communication

Dennis Gabor
Journal ArticleDOI

Mathematical analysis of random noise

TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.
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