Correlation analysis between climate indices and Korean precipitation and temperature using empirical mode decomposition : I. Data decomposition and characteristic analysis

2016 ◽  
Vol 49 (3) ◽  
pp. 197-205
Author(s):  
Si-Kweon Ahn ◽  
Wonyoung Choi ◽  
Taereem Kim ◽  
Jun-Haeng Heo
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Climate Indices ◽  
Characteristic Analysis ◽  
Data Decomposition ◽  
Mode Decomposition ◽  
Precipitation And Temperature

Misalignment Characteristic Analysis Based on Advanced Empirical Mode Decomposition

2013 ◽  
Vol 325-326 ◽  
pp. 1559-1563
Author(s):  
Hui Min Li ◽  
Wei Zhao ◽  
Yun Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Rotor System ◽  
Experimental Results ◽  
Intrinsic Mode Functions ◽  
Characteristic Analysis ◽  
Wavelet Method ◽  
Vibration Signals ◽  
Multicomponent Signal ◽  
Mode Decomposition ◽  
Misalignment Fault

A new method of misalignment characteristic analysis, which is based on advanced empirical mode decomposition (AEMD), is presented in this paper. At first the vibration signals of a rotor system with different misalignments is collected separately. Then the multicomponent signal x (t) is decomposed into a number of the so-called intrinsic mode functions (IMFs) by use of AEMD respectively. For these IMFs the wavelet method is used to extract the interesting features. It is found that the IMF2 contains the interesting misalignment character. Additionally the experimental results substantiate that the proposed method for misalignment analysis can identify the varying trend of misalignment fault clearly.

Minimizing the trend effect on detrended cross-correlation analysis with empirical mode decomposition

2012 ◽  
Vol 45 (2) ◽  
pp. 166-173 ◽  
Author(s):  
Xiaojun Zhao ◽  
Pengjian Shang ◽  
Chuang Zhao ◽  
Jing Wang ◽  
Rui Tao
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Cross Correlation ◽  
Mode Decomposition ◽  
Cross Correlation Analysis

A novel approach to the extraction of fetal electrocardiogram based on empirical mode decomposition and correlation analysis

2017 ◽  
Vol 40 (3) ◽  
pp. 565-574 ◽  
Author(s):  
Peyman Ghobadi Azbari ◽  
Mostafa Abdolghaffar ◽  
Saeed Mohaqeqi ◽  
Mohammad Pooyan ◽  
Alireza Ahmadian ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Fetal Electrocardiogram ◽  
Novel Approach ◽  
Mode Decomposition

THE TIME-DEPENDENT INTRINSIC CORRELATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (02) ◽  
pp. 233-265 ◽  
Author(s):  
XIANYAO CHEN ◽  
ZHAOHUA WU ◽  
NORDEN E. HUANG
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Sliding Window ◽  
Time Dependent ◽  
New Approach ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Time Dependent Intrinsic Correlation ◽  
Intrinsic Correlation

A Time-Dependent Intrinsic Correlation (TDIC) method is introduced. This new approach includes both auto- and cross-correlation analysis designed especially to analyze, capture and track the local correlations between nonlinear and nonstationary time series pairs. The approach is based on Empirical Mode Decomposition (EMD) to decompose the nonlinear and nonstationary data into their intrinsic mode functions (IMFs) and uses the instantaneous periods of the IMFs to determine a set of the sliding window sizes for the computation of the running correlation coefficients for multi-scale data. This new method treats the selection of the sliding window sizes as an adaptive process determined by the data itself, not a "tuning" process. Therefore, it gives an intrinsic correlation analysis of the data. Furthermore, the multi-window approach makes the new method applicable to complicated data from multi-scale phenomena. The synthetic and time series from real world are used to demonstrate conclusively that the new approach is far more superior over the traditional method in its ability to reveal detailed and subtle correlations unavailable through any other methods in existence. Thus, the TDIC represents a major advance in statistical analysis of data from nonlinear and nonstationary processes.

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