Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method

By Zhaohua Wu, Norden E. Huang. Advances in Data Science and Adaptive Analysis, 2009

8,064 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a97ee1d4a15c04160c323bd650e9cb9dff9dfced

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Naveed Urr Rehman
2. Hojjat Adeli
3. Danilo P. Mandic
4. Norden E. Huang
5. Juan Pablo Amezquita-Sanchez
6. Zhaohua Wu
7. Mirko van der Baan
8. Jiajun Han

In Control and Systems Engineering

1. Naveed Urr Rehman
2. Mirko van der Baan
3. Jiajun Han
4. Danilo P. Mandic
5. Norden E. Huang
6. Zhaohua Wu
7. Wei Mo
8. Zhi Li

In Signal Processing

1. Mirko van der Baan
2. Jiajun Han
3. Naveed Urr Rehman
4. Christian L. E. Franzke
5. Danilo P. Mandic
6. Hojjat Adeli
7. Wei Mo
8. Zhi Li

Citations in the corpus

• Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis (2023, methodology): “As the classic improvement, ensemble EMD (EEMD) was proposed by Wu and Huang [10] assisted with background noise to eliminate”
• Antarctica, Greenland and Gulf of Alaska land-ice evolution from an iterated GRACE global mascon solution (2013, methodology): “The EEMD is a noise-assisted data analysis improvement of the EMD method, which provides an adaptive decomposition of the time series into components known as intrinsic mode functions (IMFs) (Wu and Huang, 2009).”
• Forward Prediction of Runoff Data in Data-Scarce Basins with an Improved Ensemble Empirical Mode Decomposition (EEMD) Model (2018, methodology): “In order to promote the development of multiple time-scale analysis approaches, Huang proposed a novel signal analysis method in 1998 called empirical mode decomposition (EMD) [26].”
• Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding (2015, methodology): “Two variants were recently introduced to overcome some of the negative features associated with EMD, namely ensemble EMD (EEMD) (Wu and Huang, 2009) and complete ensemble EMD (CEEMD) (Torres et al.”
• Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, background): “Wu and Huang (2009) proposed EEMD based on a simple principle: white, finite amplitude noise would be added in order to uniformly populate the entire time–frequency space with the constituent components of various scales.”