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Journal articles on the topic 'Empirical mode decomposition (EMD)'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

TSUI, PO-HSIANG, CHIEN-CHENG CHANG, and NORDEN E. HUANG. "NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 01 (2010): 25–37. http://dx.doi.org/10.1142/s1793536910000410.

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The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as t

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2

NIAZY, R. K., C. F. BECKMANN, J. M. BRADY, and S. M. SMITH. "PERFORMANCE EVALUATION OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 01, no. 02 (2009): 231–42. http://dx.doi.org/10.1142/s1793536909000102.

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Empirical mode decomposition (EMD) is an adaptive, data-driven algorithm that decomposes any time series into its intrinsic modes of oscillation, which can then be used in the calculation of the instantaneous phase and frequency. Ensemble EMD (EEMD), where the final EMD is estimated by averaging numerous EMD runs with the addition of noise, was an advancement introduced by Wu and Huang (2008) to try increasing the robustness of EMD and alleviate some of the common problems of EMD such as mode mixing. In this work, we test the performance of EEMD as opposed to normal EMD, with emphasis on the e

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Chen, Zhongzhe, Baqiao Liu, Xiaogang Yan, and Hongquan Yang. "An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition." Energies 12, no. 16 (2019): 3077. http://dx.doi.org/10.3390/en12163077.

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Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is develop

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4

Zhou, Xiaohang, Deshan Shan, and Qiao Li. "Morphological Filter-Assisted Ensemble Empirical Mode Decomposition." Mathematical Problems in Engineering 2018 (September 17, 2018): 1–12. http://dx.doi.org/10.1155/2018/5976589.

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In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to impr

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5

Zhu, Licheng, and Abdollah Malekjafarian. "On the Use of Ensemble Empirical Mode Decomposition for the Identification of Bridge Frequency from the Responses Measured in a Passing Vehicle." Infrastructures 4, no. 2 (2019): 32. http://dx.doi.org/10.3390/infrastructures4020032.

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In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is created using the finite element (FE) method in Matlab. The EMD is employed to decompose the signals measured on the vehicle to their main components. It is shown that the bridge component of the response measured on the vehicle can be extracted using the EMD method. The influence of some factors, such as the road roughness profile and measurement

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MHAMDI, FAROUK, JEAN-MICHEL POGGI, and MÉRIEM JAÏDANE. "TREND EXTRACTION FOR SEASONAL TIME SERIES USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 03, no. 03 (2011): 363–83. http://dx.doi.org/10.1142/s1793536911000696.

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In this paper, we investigate eligibility of trend extraction through the empirical mode decomposition (EMD) and performance improvement of applying the ensemble EMD (EEMD) instead of the EMD for trend extraction from seasonal time series. The proposed method is an approach that can be applied on any time series with any time scales fluctuations. In order to evaluate our algorithm, experimental comparisons with three other trend extraction methods: EMD-energy-ratio approach, EEMD-energy-ratio approach, and the Hodrick–Prescott filter are conducted.

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BEKKA, RAÏS EL'HADI, and YAAKOUB BERROUCHE. "IMPROVEMENT OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION BY OVER-SAMPLING." Advances in Adaptive Data Analysis 05, no. 03 (2013): 1350012. http://dx.doi.org/10.1142/s179353691350012x.

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The empirical mode decomposition (EMD) is a useful method for the analysis of nonlinear and nonstationary signals and found immediate applications in diverse areas of signal processing. However, the major inconvenience of EMD is the mode mixing. The ensemble EMD (EEMD) was proposed to solve the problem of mode-mixing with the assistance of added noises producing the residue noise in the signal reconstructed. The residue noise in the IMFs can be reduced with a large number of ensemble trials at the expense of the increase of computational time. Improving the computing time of the EEMD by reduci

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8

HUANG, JIANFENG, and LIHUA YANG. "A PIECEWISE MONOTONOUS MODEL FOR EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 05, no. 04 (2013): 1350019. http://dx.doi.org/10.1142/s1793536913500192.

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Empirical mode decomposition (EMD) lacks theoretical support. We propose a piecewise monotonous model for EMD, and prove that the trend-subtracting iteration converges and IMF-separating procedure ends up in finite steps under mild conditions. Experiments are implemented and compared with the classical EMD.

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9

Jiang, Xiu Shan, Rui Feng Zhang, and Liang Pan. "Short-Time Fluctuation Characteristic and Combined Forecasting of High-Speed Railway Passenger Flow Based on EEMD." Applied Mechanics and Materials 409-410 (September 2013): 1071–74. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1071.

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Take Wuhan-Guangzhou high-speed railway for example. By adopting the empirical mode decomposition (EMD) attempt to analyze mode from the perspective of volatility of high speed railway passenger flow fluctuation signal. Constructed the ensemble empirical mode decomposition-gray support vector machine (EEMD-GSVM) short-term forecasting model which fuse the gray generation and support vector machine with the ensemble empirical mode decomposition (EEMD). Finally, by the accuracy of predicted results, explains the EEMD-GSVM model has the better adaptability.

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10

Divya, Sathya Sree.I, and Pangedaiah.B. "A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 3 (2020): 461–66. https://doi.org/10.35940/ijrte.C4556.099320.

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Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events.

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11

Niu, Xiao-dong, Li-rong Lu, Jian Wang, Xing-cheng Han, Xuan Li, and Li-ming Wang. "An Improved Empirical Mode Decomposition Based on Local Integral Mean and Its Application in Signal Processing." Mathematical Problems in Engineering 2021 (February 1, 2021): 1–30. http://dx.doi.org/10.1155/2021/8891217.

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Empirical mode decomposition (EMD) is an effective method to deal with nonlinear nonstationary data, but the lack of orthogonal decomposition theory and mode-mixing are the main problems that limit the application of EMD. In order to solve these two problems, we propose an improved method of EMD. The most important part of this improved method is to change the mean value by envelopes of signal in EMD to the mean value by the definite integral, which enables the mean value to be mathematically expressed strictly. Firstly, we prove that the signal is orthogonally decomposed by the improved metho

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12

Yu, Jian Ming, and Ze Zhang. "Research on Feature Extraction for Ultrasonic Echo Signal Based on EEMD Approach." Applied Mechanics and Materials 321-324 (June 2013): 1311–16. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1311.

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The bonding quality of composite materials have a critical influence on the quality of the product in modern industry, while the current technology can only make judgments on bonding and de-bonding instead of quantitative evaluation of different de-bonding degrees. We present HHT method to extract features of echo signals used for quantitative recognition of bonding quality of thin plates. For the non-stationary characteristic of the ultrasonic echo signal, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) are put forward to decompose the signal and calculate it

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13

WU, ZHAOHUA, and NORDEN E. HUANG. "ON THE FILTERING PROPERTIES OF THE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 04 (2010): 397–414. http://dx.doi.org/10.1142/s1793536910000604.

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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. Ba

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14

FALTERMEIER, R., A. ZEILER, A. M. TOMÉ, A. BRAWANSKI, and E. W. LANG. "WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 03, no. 04 (2011): 509–26. http://dx.doi.org/10.1142/s1793536911000891.

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The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, cal

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15

XIE, QIWEI, BO XUAN, SILONG PENG, JIANPING LI, WEIXUAN XU, and HUA HAN. "BANDWIDTH EMPIRICAL MODE DECOMPOSITION AND ITS APPLICATION." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 06 (2008): 777–98. http://dx.doi.org/10.1142/s0219691308002689.

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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 Bandwid

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16

Du, Wei, and Quan Liu. "A Novel Empirical Mode Decomposition Denoising Scheme." Advanced Materials Research 143-144 (October 2010): 527–32. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.527.

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This paper presents a novel and fast scheme for signal denoising by using Empirical mode decomposition (EMD). The EMD involves the adaptive decomposition of signal into a series of oscillating components, Intrinsic mode functions(IMFs), by means of a decomposition process called sifting algorithm. The basic principle of the method is to reconstruct the signal with IMFs previously selected and thresholded. The denoising method is applied to four simulated signals with different noise levels and the results compared to Wavelets, EMD-Hard and EMD-Soft methods.

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17

MOHGUEN, WAHIBA, and RAÏS EL’HADI BEKKA. "Fast Ensemble Empirical Mode Decomposition Using the Savitzky Golay filter." Algerian Journal of Signals and Systems 1, no. 1 (2021): 79–86. http://dx.doi.org/10.51485/ajss.v1i1.21.

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Empirical mode decomposition (EMD) is a powerful algorithm proposed to analysis of nonlinear and non-stationary signals. The phenomenon of mode mixing is one of the major disadvantages of the EMD. The Ensemble EMD (EEMD) was introduced to eliminate the mode-mixing effect. The principle of EEMD is to add additional white noise into the signal with many trials. The noise in each trial is different; and the added noise can be completely cancelled out on average, if the number of trials is very high. The number of trials is a high computational load. The improvement on computational efficiency of

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18

Ge, Hengqing, Guibin Chen, Haichun Yu, Huabao Chen, and Fengping An. "Theoretical Analysis of Empirical Mode Decomposition." Symmetry 10, no. 11 (2018): 623. http://dx.doi.org/10.3390/sym10110623.

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This work suggests a theoretical principle about the oscillation signal decomposition, which is based on the requirement of a pure oscillation component, in which the mean zero is extracted from the signal. Using this principle, the validity and robustness of the empirical mode decomposition (EMD) method are first proved mathematically. This work also presents a modified version of EMD by the interpolation solution, which is able to improve the frequency decomposition of the signal. The result shows that it can provide a primary theoretical basis for the development of EMD. The simulation sign

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19

Zhao, Yanqing, Kondo H. Adjallah, Alexandre Sava, and Zhouhang Wang. "Influence of Sampling Frequency Ratio on Mode Mixing Alleviation Performance: A Comparative Study of Four Noise-Assisted Empirical Mode Decomposition Algorithms." Machines 9, no. 12 (2021): 315. http://dx.doi.org/10.3390/machines9120315.

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Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode

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20

Rehman, N., and D. P. Mandic. "Multivariate empirical mode decomposition." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 466, no. 2117 (2009): 1291–302. http://dx.doi.org/10.1098/rspa.2009.0502.

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Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they are a prerequisite for direct multichannel data analysis. An important step in this direction is the computation of the local mean, as the concept of local extrema is not well defined for multivariate signals. To this end, we propose to use real-valued projections along multiple directions on hyperspheres ( n -spheres) in order to calculate the envelopes and the local mean of multivariate signals, l

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21

Hu, Hong Ying, Wen Long Li, and Feng Qiang Zhao. "Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition." Applied Mechanics and Materials 271-272 (December 2012): 932–35. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.932.

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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 mo

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22

Zhang, Xin, and Xiu Li Du. "Frequency Modulated Empirical Mode Decomposition Method." Advanced Materials Research 433-440 (January 2012): 4776–81. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4776.

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Frequency modulation procedure is proposed to overcome the mode-mixing problem associated with the EMD method when processing signals with closely spaced frequencies. This procedure also provides the flexibility to start the realization of IMFs either from the high frequency end as does the original EMD or from the low frequency end when the signal contains unwanted high frequency components. The EMD procedure, under the circumstances, may behave as high pass, low pass or band pass/stop filters. The proposed method, assisted by the Hilbert-Huang transform on the governing equations, identifies

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23

Sadhu, Ayan. "An integrated multivariate empirical mode decomposition method towards modal identification of structures." Journal of Vibration and Control 23, no. 17 (2015): 2727–41. http://dx.doi.org/10.1177/1077546315621207.

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In this paper, a hybrid empirical mode decomposition (EMD) method is proposed to undertake ambient modal identification of civil structures. Unlike univariate EMD that uses single channel measurement independently, multivariate EMD (MEMD) is employed to estimate the joint information of multichannel vibration measurements of structural systems. The mode mixing in the resulting modal responses from MEMD is then circumvented using ensemble EMD (EEMD). The proposed hybrid MEMD method is validated using a suite of numerical models and experimental studies including the presence of low energy modes

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Zhang, Jian, Ru Qiang Yan, and Robert X. Gao. "Ensemble Empirical Mode Decomposition for Machine Health Diagnosis." Key Engineering Materials 413-414 (June 2009): 167–74. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.167.

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Ensemble Empirical Mode Decomposition (EEMD) is a new signal processing technique aimed at solving the problem of mode mixing present in the original Empirical Mode Decomposition (EMD) algorithm. This paper investigates its utility for machine health monitoring and defect diagnosis. The mechanism of EEMD is first introduced. Parameters that affect effectiveness of the EEMD are then discussed with the assistance of a simulated signal in which the mode mixing exists. Experimental study on bearing vibration signal analysis verified its effectiveness of EEMD for machine health monitoring and defec

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Li, Zhi Bin, Bao Xing Wu, and Yun Hui Xu. "The Applied Research of the Hilbert-Huang Transform and Wavelet Transform in the Fault Location of Transmission Line." Applied Mechanics and Materials 291-294 (February 2013): 2432–36. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2432.

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In the process of the Hilbert-Huang transform, empirical mode decomposition (EMD) may result in the end effect and modal aliasing when processing data, so proposing Ensemble Empirical Mode Decomposition (EEMD) instead of EMD, and assessing the accuracy of the two decomposition processes according to the total energy of the signal before and after the decomposition. Take a comparison between the Hilbert-Huang transform and the wavelet transform, the localization showed that the Hilbert-Huang transform is better than wavelet transform in the fault location of transmission line.

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Bejček, Michal, and Josef Kokeš. "Empirical Mode Decomposition and Quantization Effects." International Journal of Engineering Research in Africa 18 (October 2015): 175–83. http://dx.doi.org/10.4028/www.scientific.net/jera.18.175.

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The article deals with phenomena that arise when trying to apply EMD decomposition of signals with quantization noise. It explains the basic procedures of EMD as a part of Hilbert-Huang transform and shows how it can be affected by quantization. A simple method to suppress these phenomena is proposed and examples to illustrate the functionality of this method are shown.

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Feng, Wei, Xiaojun Zhou, Xiang Zeng, and Chenlong Yang. "Ultrasonic Flaw Echo Enhancement Based on Empirical Mode Decomposition." Sensors 19, no. 2 (2019): 236. http://dx.doi.org/10.3390/s19020236.

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The detection of flaw echoes in backscattered signals in ultrasonic nondestructive testing can be challenging due to the existence of backscattering noise and electronic noise. In this article, an empirical mode decomposition (EMD) methodology is proposed for flaw echo enhancement. The backscattered signal was first decomposed into several intrinsic mode functions (IMFs) using EMD or ensemble EMD (EEMD). The sample entropies (SampEn) of all IMFs were used to select the relevant modes. Otsu’s method was used for interval thresholding of the first relevant mode, and a window was used to separate

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Bakshi, Amit, Mamata Panigrahy, and Jitendra Das. "Optimized EEMD feature extraction using bio-inspired optimization algorithms from electrocardiogram signals." Facta universitatis - series: Electronics and Energetics 37, no. 4 (2024): 619–38. https://doi.org/10.2298/fuee2404619b.

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Electrocardiogram (ECG) signal analysis is crucial for diagnosing heart conditions. The empirical Mode Decomposition (EMD) technique is quite effective in analyzing non-stationary signals. However, it has the inherent problem of mode mixing. To overcome this, the Ensemble Empirical Mode Decomposition (EEMD) method incorporates noise with known variance, utilizes the ensemble nature of EMD and enhances the decomposition process. This paper proposes a novel method for extracting features using EEMD to make its parameters independent. The intrinsic mode functions (IMFs) extracted from EEMD may va

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B Shankar, B., and D. Jayadevappa. "Ensemble EMD based Time-Frequency Analysis of Continuous Adventitious Signal Processing." International Journal of Engineering & Technology 7, no. 4.10 (2018): 896. http://dx.doi.org/10.14419/ijet.v7i4.10.26783.

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The importance of lung sound analyses is increasing day by day very rapidly. In this paper, we present a new method for analysis of two classes of lung signals namely wheezes and crackles. The procedure used in this article is based on improved Empirical Mode Decomposition (EMD) called Ensemble Empirical Mode Decomposition (EEMD) to analyze and compare continuous and discontinuous adventitious sounds with EMD. These two proposed procedures decompose the lung signals into a set of instantaneous frequency components. Function (IMF). The continuous and discontinuous adventitious sounds are presen

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Xu, Y., B. Liu, J. Liu, and S. Riemenschneider. "Two-dimensional empirical mode decomposition by finite elements." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2074 (2006): 3081–96. http://dx.doi.org/10.1098/rspa.2006.1700.

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Empirical mode decomposition (EMD) is a powerful tool for analysis of non-stationary and nonlinear signals, and has drawn significant attention in various engineering application areas. This paper presents a finite element-based EMD method for two-dimensional data analysis. Specifically, we represent the local mean surface of the data, a key step in EMD, as a linear combination of a set of two-dimensional linear basis functions smoothed with bi-cubic spline interpolation. The coefficients of the basis functions in the linear combination are obtained from the local extrema of the data using a g

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SHEN, ZHIYUAN, NAIZHANG FENG, and YI SHEN. "RIDGE REGRESSION MODEL-BASED ENSEMBLE EMPIRICAL MODE DECOMPOSITION FOR ULTRASOUND CLUTTER REJECTION." Advances in Adaptive Data Analysis 04, no. 01n02 (2012): 1250013. http://dx.doi.org/10.1142/s1793536912500136.

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Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method to solve the problem of mode mixing caused by empirical mode decomposition (EMD). It is shown that the decomposition error tends to zero, as ensemble number increases to infinity in EEMD. In this paper, a novel EEMD-based ridge regression model (REEMD) is proposed, which solves the problem of mode mixing and achieves less decomposition error compared with the EEMD. When the ensemble number is small, the weights of outliers are constraint to zero to reduce the decomposition error in REEMD and the resu

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WU, QIN, and SHERMAN D. RIEMENSCHNEIDER. "BOUNDARY EXTENSION AND STOP CRITERIA FOR EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 02 (2010): 157–69. http://dx.doi.org/10.1142/s1793536910000434.

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In this paper, a new idea about the boundary extension has been introduced and applied to the Empirical Mode Decomposition (EMD) algorithm. Instead of the traditional mirror extension on the boundary, we propose a ratio extension on the boundary. We also adopt the stop criteria by Rilling et al. for B-Spline based EMD algorithm. Numerical experiments are used for empirically assessing performance of the modified EMD algorithm. The examples indicate that the ratio boundary extension indeed improves the result of the original EMD.

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Chen, Ye Qu, Wen Zheng, and Xie Ben Wei. "Application of EMD to Integrated Signal Trend Extraction." Advanced Materials Research 591-593 (November 2012): 2072–76. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2072.

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Huang’s data-driven technique of Empirical Mode Decomposition (EMD) is presented, and issues related to its effective implementation are discussed. Integrating signal directly will produce a trend, it will cause distortion and interfere with the calculation results. This paper discusses the reasons that cause the integrated signal trend, compares the different methods for extracting trend. The traditional steps use the linear fitting and a high-pass filter to remove low frequency signal to extract trend. This paper uses Empirical Mode Decomposition (EMD) method to extract integrated signals tr

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Hu, Hong Ying, and Chun Ming Kan. "Fully Nonparametric Probability Density Function Estimation Based on Empirical Mode Decomposition." Advanced Materials Research 460 (February 2012): 189–92. http://dx.doi.org/10.4028/www.scientific.net/amr.460.189.

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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 prov

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Shweta, A. Fulambarkar* Dr. D. V. Jadhav. "EMPIRICAL MODE DECOMPOSITION: A METHOD FOR ANALYZING NON-STATIONARY SIGNALS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 1186–90. https://doi.org/10.5281/zenodo.58642.

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This paper described the non-linear Technique called Empirical Mode Decomposition (EMD), a method to analyze non-stationary signal and representing them as a mono component called Intrinsic mode Function (IMF). Decomposition of a signal is done by using sifting process & the analyzed with Hilbert Transform. It gives the instantaneous frequency& instantaneous amplitude variation.

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NUNES, JEAN-CLAUDE, and ÉRIC DELÉCHELLE. "EMPIRICAL MODE DECOMPOSITION: APPLICATIONS ON SIGNAL AND IMAGE PROCESSING." Advances in Adaptive Data Analysis 01, no. 01 (2009): 125–75. http://dx.doi.org/10.1142/s1793536909000059.

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In this paper, we propose some recent works on data analysis and synthesis based on Empirical Mode Decomposition (EMD). Firstly, a direct 2D extension of original Huang EMD algorithm with application to texture analysis, and fractional Brownian motion synthesis. Secondly, an analytical version of EMD based on PDE in 1D-space is presented. We proposed an extension in 2D-case of the so-called "sifting process" used in the original Huang's EMD. The 2D-sifting process is performed in two steps: extrema detection (by neighboring window or morphological operators) and surface interpolation by spline

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Xiao, Feng, Gang S. Chen, Wael Zatar, and J. Leroy Hulsey. "Quantification of Dynamic Properties of Pile Using Ensemble Empirical Mode Decomposition." Advances in Civil Engineering 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/8379871.

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This paper investigated dynamical interactions between pile and frozen ground by using the ensemble empirical mode decomposition (EEMD) method. Unlike the conventional empirical mode decomposition (EMD) method, EEMD is found to be able to separate the mode patterns of pile response signals of different scales without causing mode mixing. The identified dynamic properties using the EEMD method are more accurate than those obtained from conventional methods. EEMD-based results can be used to reliably and accurately characterize pile-frozen soil interactions and help designing infrastructure foun

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RILLING, GABRIEL, and PATRICK FLANDRIN. "SAMPLING EFFECTS ON THE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 01, no. 01 (2009): 43–59. http://dx.doi.org/10.1142/s1793536909000023.

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Standard exposition of Empirical Mode Decomposition (EMD) is usually done within a continuous-time setting whereas, in practice, the effective implementation always operates in discrete-time. The purpose of this contribution is to summarize a number of results aimed at quantifying the influence of sampling on EMD. The idealized case of a sampled pure tone is first considered in detail and a theoretical model is proposed for upper bounding the approximation error due to finite sampling rates. A more general approach is then discussed, based on the analysis of the nonlinear operator that underli

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Germán-Salló, Zoltán. "Empirical Mode Decomposition in Discrete Time Signals Denoising." Acta Marisiensis. Seria Technologica 16, no. 1 (2019): 10–13. http://dx.doi.org/10.2478/amset-2019-0002.

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Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This

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Zhou, Chengjiang, Zenghui Xiong, Haicheng Bai, Ling Xing, Yunhua Jia, and Xuyi Yuan. "Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA." Sensors 22, no. 19 (2022): 7195. http://dx.doi.org/10.3390/s22197195.

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In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshopper optimization algorithm (IGOA). The method not only improved the local optimal problem of GOA, but could also determine the bandwidth threshold and B-spline order of TVF-EMD adaptively. Firstly, a nonlinear decreasing strategy was introduced in this paper to adjust the decreasing coefficient of GOA dynamically. Then, energy entropy mutual informat

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Lei, Zhufeng, Wenbin Su, and Qiao Hu. "Multimode Decomposition and Wavelet Threshold Denoising of Mold Level Based on Mutual Information Entropy." Entropy 21, no. 2 (2019): 202. http://dx.doi.org/10.3390/e21020202.

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The continuous casting process is a continuous, complex phase transition process. The noise components of the continuous casting process are complex, the model is difficult to establish, and it is difficult to separate the noise and clear signals effectively. Owing to these demerits, a hybrid algorithm combining Variational Mode Decomposition (VMD) and Wavelet Threshold denoising (WTD) is proposed, which involves multiscale resolution and adaptive features. First of all, the original signal is decomposed into several Intrinsic Mode Functions (IMFs) by Empirical Mode Decomposition (EMD), and th

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Li, Yifan, Jianxin Liu, and Yan Wang. "Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition." Shock and Vibration 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4879283.

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This study explores the capacity of the improved empirical mode decomposition (EMD) in railway wheel flat detection. Aiming at the mode mixing problem of EMD, an EMD energy conservation theory and an intrinsic mode function (IMF) superposition theory are presented and derived, respectively. Based on the above two theories, an improved EMD method is further proposed. The advantage of the improved EMD is evaluated by a simulated vibration signal. Then this method is applied to study the axle box vibration response caused by wheel flats, considering the influence of both track irregularity and ve

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Wu, Zhiyuan, Changbo Jiang, Mack Conde, Bin Deng, and Jie Chen. "Hybrid improved empirical mode decomposition and BP neural network model for the prediction of sea surface temperature." Ocean Science 15, no. 2 (2019): 349–60. http://dx.doi.org/10.5194/os-15-349-2019.

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Abstract. Sea surface temperature (SST) is the major factor that affects the ocean–atmosphere interaction, and in turn the accurate prediction of SST is the key to ocean dynamic prediction. In this paper, an SST-predicting method based on empirical mode decomposition (EMD) algorithms and back-propagation neural network (BPNN) is proposed. Two different EMD algorithms have been applied extensively for analyzing time-series SST data and some nonlinear stochastic signals. The ensemble empirical mode decomposition (EEMD) algorithm and complementary ensemble empirical mode decomposition (CEEMD) alg

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Tellala, I., N. Amardjia та A. Kesmia. "Α Modified EMD-ACWA Denoising Scheme using a Noise-only Model". Engineering, Technology & Applied Science Research 10, № 2 (2020): 5470–76. https://doi.org/10.5281/zenodo.3748352.

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This paper describes a modified denoising approach combining Empirical Mode Decomposition (EMD) and Adaptive Center-Weighted Average (ACWA) filter. The Intrinsic Mode Functions (IMFs), resulting from the EMD decomposition of a noisy signal, are filtered by the ACWA filter, according to the noise level estimated in each IMF via a noise-only model. The noise levels of IMFs are estimated by the characteristics of fractional Gaussian noise through EMD. It is found that this model provides a better estimation of noise compared to the absolute median deviation of the signal used in the conventional

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Zhang, Pan, Tai Yong Wang, Lu Liu, Lu Yang Jin, and Jin Xiang Fang. "Approach to Weak Signal Extraction Based on Empirical Mode Decomposition and Stochastic Resonance." Advanced Materials Research 819 (September 2013): 216–21. http://dx.doi.org/10.4028/www.scientific.net/amr.819.216.

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The empirical mode decomposition (EMD) of weak signals submerged in a heavy noise was conducted and a method of stochastic resonance (SR) used for noisy EMD was presented. This method used SR as pre-treatment of EMD to remove noise and detect weak signals. The experiment result prove that this method, compared with that using EMD directly, not only improve SNR, enhance weak signals, but also improve the decomposition performance and reduce the decomposition layers.

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Liu, Guangda, Xinlei Hu, Enhui Wang, Ge Zhou, Jing Cai, and Shang Zhang. "SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression Extension in PPG Signal Denoising." Computational and Mathematical Methods in Medicine 2019 (December 12, 2019): 1–10. http://dx.doi.org/10.1155/2019/5363712.

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Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and nonstationary characteristics, which have brought difficulties for the denoising of PPG signals. Ensemble empirical mode decomposition known as EEMD, which has made great progress in noise processing, is a noise-assisted nonlinear and nonstationary time series analysis method based on empirical mode decomposition (EMD). The EEMD method solves the “

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Hai, Zhichao, Xueliang Chen, and Qiurui Li. "Application of Feature Extraction Method Based on Empirical Fourier Decomposition in Rotor Rub-impact Fault." Journal of Physics: Conference Series 2418, no. 1 (2023): 012043. http://dx.doi.org/10.1088/1742-6596/2418/1/012043.

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Abstract This paper provides a feature extraction technique primarily by using empirical Fourier decomposition (EFD) to reveal the time-frequency features of rotor rub-impact fault. The Hilbert transform estimates the instantaneous frequency (IF) and instantaneous amplitude (IA) of the signal generated by EFD. Finally, high-quality time-frequency representation (TFR) is reconstructed by the adaptive time-frequency spectrum (ATFS). An example of the rotor rub-impact fault is utilized to demonstrate efficiency of this technique, and the results are contrasted with those of empirical mode decompo

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Shing-Tai, Pan, Chen Ching-Fa, and Tseng Wen-Sin. "Efficient robust speech recognition with empirical mode decomposition using an FPGA chip with dual core." International Journal of Reconfigurable and Embedded Systems 9, no. 2 (2020): 109–15. https://doi.org/10.11591/ijres.v9.i2.pp109-115.

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The purpose of this paper is to accelate the computing speed of Empirical Mode Decomposition (EMD) based on multi-core embedded systems for robust speech recognition. A reconfigurable chip, Field Programmable Gate Array (FPGA), is used for the implementation of the designed system. This paper applies EMD to discompose some noised speech signals into several Intrinsic Mode Functions (IMFs). These IMFs will be combined to recover the original speech by multiplying their corresponding weights which were trained by Genetic Algorithms (GA). After applying Empirical Mode Decomposition (EMD), we obta

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Sarika Nyaramneni. "SDN Traffic Prediction using Empirical Mode Decomposition." Journal of Information Systems Engineering and Management 10, no. 24s (2025): 56–64. https://doi.org/10.52783/jisem.v10i24s.3874.

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Internet traffic prediction is essential for effective network management, resource allocation, and ensuring efficient quality of service. Network resources can be dynamically managed by forecasting future traffic using past traffic patterns. Network traffic prediction enables the dynamic resource allocation to avoid the congestion and conflicts in the network. An Empirical mode decomposition (EMD) based machine learning models were proposed in this paper for the prediction of Software Defined Networks (SDN) traffic. SDN is a modern network architecture which separates the data plane from the

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Song, Chao, and Xiaohong Chen. "Performance Comparison of Machine Learning Models for Annual Precipitation Prediction Using Different Decomposition Methods." Remote Sensing 13, no. 5 (2021): 1018. http://dx.doi.org/10.3390/rs13051018.

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It has become increasingly difficult in recent years to predict precipitation scientifically and accurately due to the dual effects of human activities and climatic conditions. This paper focuses on four aspects to improve precipitation prediction accuracy. Five decomposition methods (time-varying filter-based empirical mode decomposition (TVF-EMD), robust empirical mode decomposition (REMD), complementary ensemble empirical mode decomposition (CEEMD), wavelet transform (WT), and extreme-point symmetric mode decomposition (ESMD) combined with the Elman neural network (ENN)) are used to constru

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