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Готові списки джерел за темами / Emperical Mode Decomposition (EMD) / Статті в журналах

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Статті в журналах з теми "Emperical Mode Decomposition (EMD)"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Pradika, Eky Fortuna, Achmad Widodo, and Ismoyo Haryanto. "Diagnosis Kerusakan Roda Gigi dengan Metode Ensemble Emperical Mode Decomposition (EEMD)." ROTASI 20, no. 4 (2019): 207. http://dx.doi.org/10.14710/rotasi.20.4.207-213.

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Анотація:

Perawatan mesin merupakan suatu kombinasi dari berbagai tindakan yang dilakukan untuk menjaga performa mesin atau memperbaikinya sampai pada suatu kondisi yang bisa diterima. Dalam dunia industri, pemeliharaan memberikan pengaruh yang sangat besar bagi kondisi mesin dan jumlah produksi. Penggunaan mesin dalam jangka waktu lama tentunya akan membutuhkan perawatan, salah satu metode perawatan adalah perawatan prediktif. Roda gigi adalah komponen yang sangat penting pada mesin, oleh karenanya roda gigi juga perlu diperhatikan kondisinya untuk menghindari kerusakan pada saat pengoperasian mesin. M

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2

Lv, Zhong Liang, Yi Lin Liu, Xian Wu Han, and Min Liu. "Study on Rolling Element Bearing Fault Diagnosis Methods Based on Ensemble Empirical Mode Decomposition." Applied Mechanics and Materials 457-458 (October 2013): 602–7. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.602.

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For rotating machinery, a fault diagnosis method is proposed on the basis of the EEMD (Ensemle Emperical Mode Decomposition) and Correlation Coefficient Method. In the Vibration Signals and rotate speed of rotating machinery, the fault diagnosis method is achieved by Spectrum Analysis and real-time monitoring. The original signals are decomposed into several IMF components. Each IMF contains the local feature of the signal. Correlation coefficient method is used to select the appropriate Intrinsic Mode Function. Extract the fault feature through its envelope diagram. Experiment proves the feas

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3

Eman, Alharbi, Rasheed Saim, and Buhari Seyed. "BRAIN Journal - Single Trial Classification of Evoked EEG Signals Due to RGB Colors." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 1 (2016): 29–41. https://doi.org/10.5281/zenodo.1044255.

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ABSTRACT Recently, the impact of colors on the brain signals has become one of the leading researches in BCI systems. These researches are based on studying the brain behavior after color stimulus, and finding a way to classify its signals offline without considering the real time. Moving to the next step, we present a single trial classification model for EEG signals evoked by RGB colors stimuli, which is not presented in previous studies. In this research, EEG signals were recorded from 7 subjects through BCI2000 toolbox. The Empirical Mode Decomposition (EMD) technique was used at the signa

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4

Safie, S. I., and Rahim Rosuhana. "Quality assessment on muscle locations for speech representation." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 2 (2020): 957–67. https://doi.org/10.11591/ijeecs.v17.i2.pp957-967.

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There are more than 68 muscles, which are activated either simultaneously or sequentially during speech production. To monitor the signals from all these muscles at once, involve a lot of sensors and such system is very expensive. In the Quran therapeutic treatment applications, the use of specific muscles is very important, for the production of correct Arabic pronunciation. The proper pronunciation will improve the reader's understanding of what is being read, thus assisting the effectiveness of the therapy process. The objective of this study is to identify the most optimal muscle locat

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5

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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6

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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Анотація:

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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7

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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8

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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Анотація:

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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9

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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10

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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11

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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12

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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Анотація:

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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13

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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14

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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Анотація:

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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15

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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16

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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17

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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18

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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19

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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20

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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Анотація:

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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21

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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22

UR REHMAN, NAVEED, CHEOLSOO PARK, NORDEN E. HUANG, and DANILO P. MANDIC. "EMD VIA MEMD: MULTIVARIATE NOISE-AIDED COMPUTATION OF STANDARD EMD." Advances in Adaptive Data Analysis 05, no. 02 (2013): 1350007. http://dx.doi.org/10.1142/s1793536913500076.

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A noise-assisted approach in conjunction with multivariate empirical mode decomposition (MEMD) algorithm is proposed for the computation of empirical mode decomposition (EMD), in order to produce localized frequency estimates at the accuracy level of instantaneous frequency. Despite many advantages of EMD, such as its data driven nature, a compact decomposition, and its inherent ability to process nonstationary data, it only caters for signals with a sufficient number of local extrema. In addition, EMD is prone to mode-mixing and is designed for univariate data. We show that the noise-assisted

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23

Tang, Wei, Yu Yang Lian, Xi Chen, Zhi Yong Pei, and Qi Wang. "A Secondary Iterative Sifting EMD Algorithm and its Application to Bearing Fault Diagnosis." Applied Mechanics and Materials 734 (February 2015): 451–58. http://dx.doi.org/10.4028/www.scientific.net/amm.734.451.

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Aiming at the mode mixing problem caused by interpolation point selection of conventional EMD (Empirical mode decomposition) method, a secondary iterative sifting EMD method that can avoid mode mixing and achieve high-precision decomposition of HHT (Hilbert–Huang transformation) is proposed based on the theory of EMD. The simulation results show that the proposed method is superior to conventional EMD on the ability to split mixed signal. Finally, the proposed algorithm is applied to the fault diagnosis of rolling bearing and the test results have proved its effectiveness and advantages.

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24

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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25

Huang, Dishan, Xianglong Kong, and Yibing Xia. "Identification and Cancellation of Pseudo Mode Function in EMD based on Energy Conservation." Advances in Data Science and Adaptive Analysis 08, no. 01 (2016): 1650001. http://dx.doi.org/10.1142/s2424922x16500017.

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This paper introduces an effective method to identify and cancel a pseudo mode function in empirical mode decomposition (EMD) under the condition of insufficient sampling rate. The contents of this paper have three aspects: First, basic properties of the pseudo mode are accurately revealed. Second, a post-processing technique for EMD is developed. This new technique, called as mode function cancellation, can identify and kick out the pseudo mode from the decomposition results, and correct the decomposition error in the intrinsic mode function. As a result, it can help us to improve the decompo

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26

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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27

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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28

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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Анотація:

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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29

Qin, Shiqiang, Qiuping Wang, and Juntao Kang. "Output-Only Modal Analysis Based on Improved Empirical Mode Decomposition Method." Advances in Materials Science and Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/945862.

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The output-only modal analysis for bridge structures based on improved empirical mode decomposition (EMD) is investigated in this study. First, a bandwidth restricted EMD is proposed for decomposing nonstationary output measurements with close frequency components. The advantage of bandwidth restricted EMD to standard EMD is illustrated by a numerical simulation. Next, the modal parameters are extracted from intrinsic mode function obtained from the improved EMD by both random decrement technique and stochastic subspace identification. Finally, output-only modal analysis of a railway bridge is

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30

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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31

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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32

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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Анотація:

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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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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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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Anh, Duong Tuan, and Tran Van Xuan. "EMD combined with ensemble of machine learning predictors for foreign exchange rate forecasting." CTU Journal of Innovation and Sustainable Development 16, Special issue: ISDS (2024): 69–79. http://dx.doi.org/10.22144/ctujoisd.2024.324.

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Forecasting foreign exchange rates is a critical financial challenge. In this paper, we build on recent trends and address the limitations of prior research by proposing a novel approach. Our method combines empirical mode decomposition (EMD) with ensemble of machine learning predictors in foreign exchange rate forecasting. To demonstrate that our proposed method (called EMD-ML) is effective, we used the new approach to forecast six foreign exchange rate time series at a specific time. The first experiment was implemented to compare the proposed forecasting model EMD–LSTM, which combines empir

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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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Huang, Shi Qi, Bei He Wang, Yi Hong Li, and Bei Ge. "SAR Target Detection Method Based on Empirical Mode Decomposition." Advanced Engineering Forum 6-7 (September 2012): 496–500. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.496.

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Empirical mode decomposition (EMD) is a new signal processing theory, and it is very much fitting for non-stationary signal processing, such as radar signal. So this paper proposes the new synthetic aperture radar (SAR) image target detection algorithm after analyzing the characteristics of EMD and SAR images. The proposed method performs the EMD operation, feature extraction, election and fusion, which can reduce the affection of speckle. Experimental results show that the proposed method is very effective.

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Li, Zhen Tao, and Hui Li. "EMD and Envelope Spectrum Based Bearing Fault Detection." Advanced Materials Research 459 (January 2012): 233–37. http://dx.doi.org/10.4028/www.scientific.net/amr.459.233.

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A novel method to fault diagnosis of bearing based on empirical mode decomposition (EMD) and envelope spectrum is presented. EMD method is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original vibration signal in intrinsic oscillation modes, using the empirical mode decomposition. Then the envelope spectrum is applied to the selected intrinsic mode function that stands for the bearing faults. The basic principle is firstly introduced in detail. Then the EMD is applied in the research of the fault detection and diagnosis of the be

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Bian, Xihui, Zitong Shi, Yingjie Shao, Yuanyuan Chu, and Xiaoyao Tan. "Variational Mode Decomposition for Raman Spectral Denoising." Molecules 28, no. 17 (2023): 6406. http://dx.doi.org/10.3390/molecules28176406.

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As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two art

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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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Chen, Yan Long, and Pei Lin Zhang. "Bearing Fault Detection Based on SVD and EMD." Applied Mechanics and Materials 184-185 (June 2012): 70–74. http://dx.doi.org/10.4028/www.scientific.net/amm.184-185.70.

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Анотація:

While bearing fault signals are strongly interferenced by noise, diagnosis using EMD directly for bearings fault becomes incorrect. A scheme based on Singular Value Decomposition(SVD) and Empirical Mode Decomposition(EMD) is proposed for solving this problem. Aiming at bearing fault signal characteristics, SVD preprocesses sampled signals to denoise. Then preprocessed signals are analyzed by EMD. Fault characteristic frequency can be obtained by spectrum analysis for Intrinsic Mode Functions(IMFs). This method is useful to detect fault of bearings and a comparison is made between it and EMD. T

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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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PAO, SUN-HUA, CHIEH-NENG YOUNG, CHIEN-LUN TSENG, and NORDEN E. HUANG. "SMOOTHING EMPIRICAL MODE DECOMPOSITION: A PATCH TO IMPROVE THE DECOMPOSED ACCURACY." Advances in Adaptive Data Analysis 02, no. 04 (2010): 521–43. http://dx.doi.org/10.1142/s1793536910000616.

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Hilbert-Huang Transformation (HHT) is designed especially for analyzing data from nonlinear and nonstationary processes. It consists of the Empirical Mode Decomposition (EMD) to generate Intrinsic Mode Function (IMF) components, from which the instantaneous frequency can be computed for the time-frequency Hilbert spectral Analysis. Currently, EMD, based on the cubic spline, is the most efficient and popular algorithm to implement HHT. However, EMD as implemented now suffers from dependence on the cubic spline function chosen as the basis. Furthermore, due to the various stoppage criteria, it i

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Pan, Shing-Tai, Ching-Fa Chen, and Wen-Sin Tseng. "Efficient robust speech recognition with empirical mode decomposition using an FPGA chip with dual core." International Journal of Reconfigurable and Embedded Systems (IJRES) 9, no. 2 (2020): 109. http://dx.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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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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Анотація:

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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McDonald, A. J., A. J. G. Baumgaertner, G. J. Fraser, S. E. George, and S. Marsh. "Empirical Mode Decomposition of the atmospheric wave field." Annales Geophysicae 25, no. 2 (2007): 375–84. http://dx.doi.org/10.5194/angeo-25-375-2007.

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Abstract. This study examines the utility of the Empirical Mode Decomposition (EMD) time-series analysis technique to separate the horizontal wind field observed by the Scott Base MF radar (78° S, 167° E) into its constituent parts made up of the mean wind, gravity waves, tides, planetary waves and instrumental noise. Analysis suggests that EMD effectively separates the wind field into a set of Intrinsic Mode Functions (IMFs) which can be related to atmospheric waves with different temporal scales. The Intrinsic Mode Functions resultant from application of the EMD technique to Monte-Carlo simu

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Wu, Jing, Li Wu, Miao Sun, Ya-ni Lu, and Yan-hua Han. "Application of Boundary Local Feature Scale Adaptive Matching Extension EMD Endpoint Effect Suppression Method in Blasting Seismic Wave Signal Processing." Shock and Vibration 2021 (August 13, 2021): 1–9. http://dx.doi.org/10.1155/2021/2804539.

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The intrinsic endpoint effect of empirical mode decomposition (EMD) will lead to serious divergence of the intrinsic mode function (IMF) at the endpoint, which will lead to the distortion of IMF and affect the decomposition accuracy of EMD. In view of this phenomenon, an EMD endpoint effect suppression method based on boundary local feature scale adaptive matching extension was proposed. This method can consider both the change trend of the signal at the endpoint and the change rule of the signal inside. The simulation results showed that the proposed method had better suppression effect on th

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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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Kumar, Prem Shankar, Lakshmi Annamalai Kumaraswamidhas, and Swarup Kumar Laha. "Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis." Transactions of the Institute of Measurement and Control 41, no. 7 (2018): 1923–32. http://dx.doi.org/10.1177/0142331218790788.

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Анотація:

Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are data-driven self-adaptive signal processing methods to decompose a complex signal into different modes of separate spectral bands, in to a number of Intrinsic Mode Functions (IMFs). While the EMD extracts modes recursively and empirically, the VMD extracts modes non-recursively and concurrently. In this paper, both the EMD and the VMD have been applied to examine their efficacy in fault diagnosis of rolling element bearing. However, all the IMFs do not contain necessary information regarding fault characteristic si

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León-Bejarano, Fabiola, Miguel Ramírez-Elías, Martin O. Mendez, Guadalupe Dorantes-Méndez, Ma del Carmen Rodríguez-Aranda, and Alfonso Alba. "Denoising of Raman spectroscopy for biological samples based on empirical mode decomposition." International Journal of Modern Physics C 28, no. 09 (2017): 1750116. http://dx.doi.org/10.1142/s0129183117501169.

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Raman spectroscopy of biological samples presents undesirable noise and fluorescence generated by the biomolecular excitation. The reduction of these types of noise is a fundamental task to obtain the valuable information of the sample under analysis. This paper proposes the application of the empirical mode decomposition (EMD) for noise elimination. EMD is a parameter-free and adaptive signal processing method useful for the analysis of nonstationary signals. EMD performance was compared with the commonly used Vancouver algorithm (VRA) through artificial data (Teflon), synthetic (Vitamin E an

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