Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition

2012 ◽  
Vol 271-272 ◽  
pp. 932-935
Author(s):  
Hong Ying Hu ◽  
Wen Long Li ◽  
Feng Qiang Zhao
Keyword(s):  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Estimation Method ◽  
Regression Function ◽  
Regression Estimation ◽  
Function Estimation ◽  
Wavelet Regression ◽  
Nonparametric Regression Estimation ◽  
Mode Decomposition ◽  
Non Stationary Signal

Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. It has been applied in many engineering fields. EMD has many similarities with wavelet decomposition. But EMD Decomposition has its own characteristics, especially in accurate trend extracting. Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD. Then, according to wavelet regression estimation method, a new regression function estimation method based on EMD is presented. The simulation proves the advantages of the approach with easy computation and more accurate result.

Fully Nonparametric Probability Density Function Estimation Based on Empirical Mode Decomposition

2012 ◽  
Vol 460 ◽  
pp. 189-192
Author(s):  
Hong Ying Hu ◽  
Chun Ming Kan
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Density Estimation ◽  
Empirical Mode Decomposition ◽  
Estimation Method ◽  
Function Estimation ◽  
Mode Decomposition ◽  
Non Stationary Signal ◽  
Density Function Estimation

Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. It has been applied in many engineering fields. EMD has many similarities with wavelet decomposition. But EMD Decomposition has its own characteristics, especially in accurate rend extracting. Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD. Then, according to wavelet probability density function estimation method, a new density estimation method based on EMD is presented. The simulations of Gaussian single and mixture model density estimation prove the advantages of the approach with easy computation and more accurate result

Wavelet regression estimation over Lp risk based on negatively associated sample

2020 ◽  
Vol 18 (04) ◽  
pp. 2050024
Author(s):  
Huijun Guo ◽  
Junke Kou
Keyword(s):  
Convergence Rates ◽  
Regression Function ◽  
Regression Estimation ◽  
Negatively Associated ◽  
Wavelet Regression ◽  
Nonparametric Regression Estimation ◽  
Independent Case ◽  
Optimal Convergence Rates ◽  
Negatively Associated Sample ◽  
Wavelet Estimators

This paper considers wavelet estimations of a regression function based on negatively associated sample. We provide upper bound estimations over [Formula: see text] risk of linear and nonlinear wavelet estimators in Besov space, respectively. When the random sample reduces to the independent case, our convergence rates coincide with the optimal convergence rates of classical nonparametric regression estimation.

scholarly journals Research on Detection and Location of Fluid-Filled Pipeline Leakage Based on Acoustic Emission Technology

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3628 ◽  
Author(s):  
Shengshan Pan ◽  
Zhengdan Xu ◽  
Dongsheng Li ◽  
Dang Lu
Keyword(s):  
Acoustic Emission ◽  
Wavelet Decomposition ◽  
Estimation Method ◽  
Reference Value ◽  
Time Delay Estimation ◽  
Operating Conditions ◽  
Support Vector ◽  
Acoustic Emission Sensor ◽  
Mode Decomposition ◽  
Acoustic Emission Sensors

Because of the inconvenience of installing sensors in a buried pipeline, an acoustic emission sensor is initially proposed for collecting and analyzing leakage signals inside the pipeline. Four operating conditions of a fluid-filled pipeline are established and a support vector machine (SVM) method is used to accurately classify the leakage condition of the pipeline. Wavelet decomposition and empirical mode decomposition (EMD) methods are initially used in denoising these signals to address the problem in which original leakage acoustic emission signals contain too much noise. Signals with more information and energy are then reconstructed. The time-delay estimation method is finally used to accurately locate the leakage source in the pipeline. The results show that by using SVM, wavelet decomposition and EMD methods, leakage detection in a liquid-filled pipe with built-in acoustic emission sensors is effective and accurate and provides a reference value for real-time online monitoring of pipeline operational status with broad application prospects.

scholarly journals Application of variational mode decomposition to seismic random noise reduction

2017 ◽  
Vol 14 (4) ◽  
pp. 888-898 ◽  
Author(s):  
Wei Liu ◽  
Siyuan Cao ◽  
Zhiming Wang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Random Noise ◽  
Computational Cost ◽  
Superior Performance ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Reconstruction Performance ◽  
Non Stationary Signal

Abstract We have proposed a new denoising method for the simultaneous noise reduction and preservation of seismic signals based on variational mode decomposition (VMD). VMD is a recently developed adaptive signal decomposition method and an advance in non-stationary signal analysis. It solves the mode-mixing and non-optimal reconstruction performance problems of empirical mode decomposition that have existed for a long time. By using VMD, a multi-component signal can be non-recursively decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs), each of which has a relatively local frequency range. Meanwhile, the signal will focus on a smaller number of obtained IMFs after decomposition, and thus the denoised result is able to be obtained by reconstructing these signal-dominant IMFs. Synthetic examples are given to demonstrate the effectiveness of the proposed approach and comparison is made with the complete ensemble empirical mode decomposition, which demonstrates that the VMD algorithm has lower computational cost and better random noise elimination performance. The application of on field seismic data further illustrates the superior performance of our method in both random noise attenuation and the recovery of seismic events.

scholarly journals An Improvement in Representation of Audio Signal in Time-Frequency Plane using EMD-2TEMD Based Approach

2016 ◽  
Vol 44 ◽  
pp. 141-150
Author(s):  
Kazi Mahmudul Hassan ◽  
Md. Ekramul Hamid ◽  
Takayoshi Nakai
Keyword(s):  
Empirical Mode Decomposition ◽  
Source Separation ◽  
Audio Signal ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Separation Result ◽  
Mode Decomposition ◽  
Recent Approach ◽  
Non Stationary Signal

This study proposed an enhanced time-frequency representation of audio signal using EMD-2TEMD based approach. To analyze non-stationary signal like audio, timefrequency representation is an important aspect. In case of representing or analyzing such kind of signal in time-frequency-energy distribution, hilbert spectrum is a recent approach and popular way which has several advantages over other methods like STFT, WT etc. Hilbert-Huang Transform (HHT) is a prominent method consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA). An enhanced method called Turning Tangent empirical mode decomposition (2T-EMD) has recently developed to overcome some limitations of classical EMD like cubic spline problems, sifting stopping condition etc. 2T-EMD based hilbert spectrum of audio signal encountered some issues due to the generation of too many IMFs in the process where EMD produces less. To mitigate those problems, a mutual implementation of 2T-EMD & classical EMD is proposed in this paper which enhances the representation of hilbert spectrum along with significant improvements in source separation result using Independent Subspace Analysis (ISA) based clustering in case of audio signals. This refinement of hilbert spectrum not only contributes to the future work of source separation problem but also many other applications in audio signal processing.

ON THE FILTERING PROPERTIES OF THE EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (04) ◽  
pp. 397-414 ◽  
Author(s):  
ZHAOHUA WU ◽  
NORDEN E. HUANG
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Mathematical Expression ◽  
Delta Function ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Energy Domain ◽  
The Fourier Transform ◽  
Special Case

The empirical mode decomposition (EMD) based time-frequency analysis has been used in many scientific and engineering fields. The mathematical expression of EMD in the time-frequency-energy domain appears to be a generalization of the Fourier transform (FT), which leads to the speculation that the latter may be a special case of the former. On the other hand, the EMD is also known to behave like a dyadic filter bank when used to decompose white noise. These two observations seem to contradict each other. In this paper, we study the filtering properties of EMD, as its sifting number changes. Based on numerical results of the decompositions using EMD of a delta function and white noise, we conjecture that, as the (pre-assigned and fixed) sifting number is changed from a small number to infinity, the EMD corresponds to filter banks with a filtering ratio that changes accordingly from 2 (dyadic) to 1; the filter window does not narrow accordingly, as the sifting number increases. It is also demonstrated that the components of a delta function resulted from EMD with any prescribed sifting number can be rescaled to a single shape, a result similar to that from wavelet decomposition, although the shape changes, as the sifting number changes. These results will lead to further understandings of the relations of EMD to wavelet decomposition and FT.

BANDWIDTH EMPIRICAL MODE DECOMPOSITION AND ITS APPLICATION

2008 ◽  
Vol 06 (06) ◽  
pp. 777-798 ◽  
Author(s):  
QIWEI XIE ◽  
BO XUAN ◽  
SILONG PENG ◽  
JIANPING LI ◽  
WEIXUAN XU ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Wavelet Decomposition ◽  
Electricity Consumption ◽  
Decomposition Algorithm ◽  
Intrinsic Mode Functions ◽  
Fourier Decomposition ◽  
Remarkable Effect ◽  
Mode Decomposition ◽  
Consumption Data ◽  
Key Points

There are some methods to decompose a signal into different components such as: Fourier decomposition and wavelet decomposition. But they have limitations in some aspects. Recently, there is a new signal decomposition algorithm called the Empirical Mode Decomposition (EMD) Algorithm which provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Recent works have demonstrated that EMD has remarkable effect in time series decomposition, but EMD also has several problems such as scale mixture and convergence property. This paper proposes two key points to design Bandwidth EMD to improve on the empirical mode decomposition algorithm. By analyzing the simulated and actual signals, it is confirmed that the Intrinsic Mode Functions (IMFs) obtained by the bandwidth criterion can approach the real components and reflect the intrinsic information of the analyzed signal. In this paper, we use Bandwidth EMD to decompose electricity consumption data into cycles and trend which help us recognize the structure rule of the electricity consumption series.

Gearbox Fault Detection Using Empirical Mode Decomposition

2004 ◽  
Author(s):  
Xianfeng Fan ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Original Data ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Stationary Vibration ◽  
Non Stationary Signal ◽  
Envelope Spectrum

Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special IMF’s envelope spectrum will become larger when fault impulses are present. Based on this idea, we propose a new fault detection method that combines EMD with Hilbert transform. The proposed method is compared with both the Hilbert-Huang transform and the wavelet transform using simulated signal and real signal collected from a gearbox. The results obtained show that the proposed method is effective in capturing the hidden fault impulses.

scholarly journals INFORMATION QUANTIFICATION OF EMPIRICAL MODE DECOMPOSITION AND APPLICATIONS TO FIELD POTENTIALS

2011 ◽  
Vol 21 (01) ◽  
pp. 49-63 ◽  
Author(s):  
ZAREEN MEHBOOB ◽  
HUJUN YIN
Keyword(s):  
Empirical Mode Decomposition ◽  
Field Potential ◽  
Stationary Time Series ◽  
Intrinsic Mode Functions ◽  
Information Analysis ◽  
Neural Recordings ◽  
Mode Decomposition ◽  
Non Stationary Signal ◽  
Instantaneous Frequencies ◽  
Emd Method

The empirical mode decomposition (EMD) method can adaptively decompose a non-stationary time series into a number of amplitude or frequency modulated functions known as intrinsic mode functions. This paper combines the EMD method with information analysis and presents a framework of information-preserving EMD. The enhanced EMD method has been exploited in the analysis of neural recordings. It decomposes a signal and extracts only the most informative oscillations contained in the non-stationary signal. Information analysis has shown that the extracted components retain the information content of the signal. More importantly, a limited number of components reveal the main oscillations presented in the signal and their instantaneous frequencies, which are not often obvious from the original signal. This information-coupled EMD method has been tested on several field potential datasets for the analysis of stimulus coding in visual cortex, from single and multiple channels, and for finding information connectivity among channels. The results demonstrate the usefulness of the method in extracting relevant responses from the recorded signals. An investigation is also conducted on utilizing the Hilbert phase for cases where phase information can further improve information analysis and stimulus discrimination. The components of the proposed method have been integrated into a toolbox and the initial implementation is also described.

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