Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
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...read more
Citations
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An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach
TL;DR: The results show that the AEEMD-ANN model can improve forecast accuracy in flood seasons, but it is not as good as ANN, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and seasonal first-order autoregressive (SAR) models in dry seasons.
Journal ArticleDOI
Comparison of spectral analysis methods for characterizing brain oscillations
TL;DR: Simulation methods are used to characterize the similarities and differences between three spectral analysis methods: wavelets, multitapers and P(episode), a novel method that quantifies the fraction of time that oscillations exceed amplitude and duration thresholds.
Journal ArticleDOI
EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs
TL;DR: A new signal-filtering, which combines the empirical mode decomposition (EMD) and a similarity measure, to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode.
Journal ArticleDOI
A review of machine learning for the optimization of production processes
TL;DR: This study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry and shows that there is hardly any correlation between the used data, the amount ofData, the machine learning algorithms, the used optimizers, and the respective problem from the production.
Journal ArticleDOI
Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition
R. Balocchi,Danilo Menicucci,Enrica L. Santarcangelo,Laura Sebastiani,Angelo Gemignani,Brunello Ghelarducci,Maurizio Varanini +6 more
TL;DR: In this article, the authors applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing.
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
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.
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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Ensemble empirical mode decomposition: a noise-assisted data analysis method
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