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Journal articles on the topic 'ENSEMBLE EMPIRIAL MODE DECOMPOSITION'

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Published: 11 September 2023

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1

Lang, Xun, Naveed ur Rehman, Yufeng Zhang, Lei Xie, and Hongye Su. "Median ensemble empirical mode decomposition." Signal Processing 176 (November 2020): 107686. http://dx.doi.org/10.1016/j.sigpro.2020.107686.

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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 (April 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 result of REEMD is asymptotic to that of EEMD, as the ensemble number increases. The proposed REEMD is suitable for tissue clutter rejection in color flow imaging system. Simulation shows that reasonable flow-frequency estimations can be achieved by REEMD and the estimation error limits to zero, as the flow frequency increases.
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CHANG, YU-MEI, ZHAOHUA WU, JULIUS CHANG, and NORDEN E. HUANG. "MODEL VALIDATION BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 04 (October 2010): 415–28. http://dx.doi.org/10.1142/s1793536910000550.

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We proposed a new model validation method through ensemble empirical mode decomposition (EEMD) and scale separate correlation. EEMD is used to analyze the nonlinear and nonstationary ozone concentration data and the data simulated from the Taiwan Air Quality Model (TAQM). Our approach consists of shifting an ensemble of white noise-added signal and treats the mean as the final true intrinsic mode functions (IMFs). It provides detailed comparisons of observed and simulated data in various temporal scales. The ozone concentration of Wan-Li station in Taiwan is used to illustrate the power of this new approach. Our results show that, at an urban station, the ozone concentration fluctuation has various cycles that include semi-diurnal, diurnal, and weekly time scales. These results serve to demonstrate the anthropogenic origin of the local pollutant and long-range transport effects were all important. The validation tests indicate that the model used here performs well to simulate phenomena of all temporal scales.
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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 improve the adaptive ability of morphological filter (MF). Then, the adaptive MF was introduced into EMD to remove the superfluous white noise components to improve the decomposition results. Based on the contributions of MF in a single EMD process, the MF-EEMD was proposed by combining EEMD with MF to suppress the mode mixing problems. Finally, an analog signal and a measured signal were used to verify the feasibility of MF-EEMD. The results show that MF-EEMD significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD.
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Zhang, Jian, Ruqiang Yan, Robert X. Gao, and Zhihua Feng. "Performance enhancement of ensemble empirical mode decomposition." Mechanical Systems and Signal Processing 24, no. 7 (October 2010): 2104–23. http://dx.doi.org/10.1016/j.ymssp.2010.03.003.

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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 (April 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 effect of selecting different stopping criteria and noise levels. Our results indicate that EEMD, in addition to slightly increasing the accuracy of the EMD output, substantially increases the robustness of the results and the confidence in the decomposition.
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7

Zhu, Jia Xing, Wen Bin Zhang, Ya Song Pu, and Yan Jie Zhou. "Purification of Axis Trace by Ensemble Empirical Mode Decomposition." Advanced Materials Research 791-793 (September 2013): 1006–9. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1006.

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Aiming at the purification of axis trace, a novel method was proposed by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a collection of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose intrinsic mode function components and reconstructed the signal. Finally the purification of axis trace was obtained. Simulation and practical results show the advantage of ensemble empirical mode decomposition. This method also has simple algorithm and high calculating speed; it provides a new method for purification of axis trace.
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8

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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9

TSUI, PO-HSIANG, CHIEN-CHENG CHANG, and NORDEN E. HUANG. "NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 01 (January 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 the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.
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10

Niu, Xiaoxu, Junwei Ma, Yankun Wang, Junrong Zhang, Hongjie Chen, and Huiming Tang. "A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction." Applied Sciences 11, no. 10 (May 20, 2021): 4684. http://dx.doi.org/10.3390/app11104684.

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As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and challenging due to the complex nonlinear characteristics of landslide monitoring data. Based on the principle of “decomposition and ensemble”, a three-step decomposition-ensemble learning model integrating ensemble empirical mode decomposition (EEMD) and a recurrent neural network (RNN) was proposed for landslide displacement prediction. EEMD and kurtosis criteria were first applied for data decomposition and construction of trend and periodic components. Second, a polynomial regression model and RNN with maximal information coefficient (MIC)-based input variable selection were implemented for individual prediction of trend and periodic components independently. Finally, the predictions of trend and periodic components were aggregated into a final ensemble prediction. The experimental results from the Muyubao landslide demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms the traditional decomposition-ensemble learning models (including EEMD-support vector machine, and EEMD-extreme learning machine). Moreover, compared with standard RNN, the gated recurrent unit (GRU)-and long short-term memory (LSTM)-based models perform better in predicting accuracy. The EEMD-RNN decomposition-ensemble learning model is promising for landslide displacement prediction.
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11

BARNHART, BRADLEY LEE, HONDA KAHINDO WA NANDAGE, and WILLIAM EICHINGER. "ASSESSING DISCONTINUOUS DATA USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 03, no. 04 (October 2011): 483–91. http://dx.doi.org/10.1142/s179353691100091x.

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This investigation presents an improved ensemble empirical mode decomposition (EEMD) algorithm that can be applied to discontinuous data. The quality of the algorithm is assessed by creating artificial data gaps in continuous data, then comparing the extracted intrinsic mode functions (IMFs) from both data sets. The results show that errors increase as the gap length increases. In addition, errors in the high-frequency IMFs are less than the low-frequency IMFs. The majority of the errors in the high-frequency IMFs are due to end-effect errors associated with under-defined interpolation functions near the gap endpoints. A method that utilizes a mirroring technique is presented to reduce the errors in the discontinuous decomposition. The improved algorithm provides a more locally accurate decomposition of the data amidst data gaps. Overall, this simple but powerful algorithm expands EEMD's ability to locally extract periodic components from discontinuous data.
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12

Tang, Ling, Wei Dai, Lean Yu, and Shouyang Wang. "A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting." International Journal of Information Technology & Decision Making 14, no. 01 (January 2015): 141–69. http://dx.doi.org/10.1142/s0219622015400015.

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To enhance the prediction accuracy for crude oil price, a novel ensemble learning paradigm coupling complementary ensemble empirical mode decomposition (CEEMD) and extended extreme learning machine (EELM) is proposed. This novel method is actually an improved model under the effective "decomposition and ensemble" framework, especially for nonlinear, complex, and irregular data. In this proposed method, CEEMD, a current extension from the competitive decomposition family of empirical mode decomposition (EMD), is first applied to divide the original data (i.e., difficult task) into a number of components (i.e., relatively easy subtasks). Then, EELM, a recently developed, powerful, fast and stable intelligent learning technique, is implemented to predict all extracted components individually. Finally, these predicted results are aggregated into an ensemble result as the final prediction using simple addition ensemble method. With the crude oil spot prices of WTI and Brent as sample data, the empirical results demonstrate that the novel CEEMD-based EELM ensemble model statistically outperforms all listed benchmarks (including typical forecasting techniques and similar ensemble models with other decomposition and ensemble tools) in prediction accuracy. The results also indicate that the novel model can be used as a promising forecasting tool for complicated time series data with high volatility and irregularity.
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13

KUO, CHIH-YU, SHAO-KUAN WEI, and PI-WEN TSAI. "ENSEMBLE EMPIRICAL MODE DECOMPOSITION WITH SUPERVISED CLUSTER ANALYSIS." Advances in Adaptive Data Analysis 05, no. 01 (January 2013): 1350005. http://dx.doi.org/10.1142/s1793536913500052.

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Ensemble empirical mode decomposition (EEMD) is a noise-assisted data analysis method which decomposes a signal into a collection of intrinsic mode functions (IMFs). There nevertheless appears a multi-mode problem where signals with a similar timescale are decomposed into different IMF components. A possible solution to this problem is to recombine the multi-mode IMF components into a proper single mode but as of yet, no general rules have been proposed in the literature. This paper presents the incorporation of a statistical cluster analysis to assist in the diagnosis of multi-mode IMFs and to recombine them based on the classified clusters. As a result, signals are reorganized into a condensed set of clustered intrinsic mode functions (CIMFs). The method is applied to two sets of artificially synthesized signals and two sets of practical signals: wind turbine noise and earthquake motion. These applications demonstrate that, with the additional cluster analysis, the multi-mode problem can be largely eliminated in a statistically reliable manner, and in situ applications can be improved.
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14

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 defect diagnosis.
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15

Peng, Yanfeng, Junhang Chen, Ruiqiong Luo, Xiaojun Xie, Xianyu Zhu, Yanfei Liu, QingHua Lu, and Kuanfang He. "Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis." Advances in Mechanical Engineering 12, no. 3 (March 2020): 168781402091053. http://dx.doi.org/10.1177/1687814020910537.

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Adaptive sparsest narrow-band decomposition is the most sparse solution to search for signals in the over-complete dictionary library containing intrinsic mode functions, which transform the signal decomposition into an optimization problem, but the calculation accuracy must be improved in the case of strong noise interference. Therefore, in combination with the algorithm of the complementary ensemble empirical mode decomposition, a new method of the complementary ensemble adaptive sparsest narrow-band decomposition is obtained. In the complementary ensemble adaptive sparsest narrow-band decomposition, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters. The analysis results of the simulation and experimental data show this method is superior to complementary ensemble empirical mode decomposition and adaptive sparsest narrow-band decomposition in inhibiting the mode confusion, endpoint effect, improving the component orthogonality and accuracy, and effectively identifying the gears fault types.
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Wang, Fang, Menggang Li, and Ruopeng Wang. "A New Forecasting Approach for Oil Price Using the Recursive Decomposition–Reconstruction–Ensemble Method with Complexity Traits." Entropy 25, no. 7 (July 12, 2023): 1051. http://dx.doi.org/10.3390/e25071051.

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The subject of oil price forecasting has obtained an incredible amount of interest from academics and policymakers in recent years due to the widespread impact that it has on various economic fields and markets. Thus, a novel method based on decomposition–reconstruction–ensemble for crude oil price forecasting is proposed. Based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique, in this paper we construct a recursive CEEMDAN decomposition–reconstruction–ensemble model considering the complexity traits of crude oil data. In this model, the steps of mode reconstruction, component prediction, and ensemble prediction are driven by complexity traits. For illustration and verification purposes, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data. The empirical result demonstrates that the proposed model has better prediction performance than the benchmark models. Thus, the proposed recursive CEEMDAN decomposition–reconstruction–ensemble model can be an effective tool to forecast oil price in the future.
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Chen, Jun, Meng-Shi Zhang, Yong-Fang Zhao, and Hong-Sheng Zhan. "Kinematic Data Smoothing Using Ensemble Empirical Mode Decomposition." Journal of Medical Imaging and Health Informatics 4, no. 4 (August 1, 2014): 540–46. http://dx.doi.org/10.1166/jmihi.2014.1281.

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18

Gyamfi, Y. O. Adu, Nii O. Attoh Okine, and A. Y. Ayenu Prah. "Pavement profile analysis using ensemble empirical mode decomposition." International Journal of Vehicle Systems Modelling and Testing 4, no. 4 (2009): 277. http://dx.doi.org/10.1504/ijvsmt.2009.032020.

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Lin, Shang-Ching, and Pai-Chi Li. "Automatic contrast enhancement using ensemble empirical mode decomposition." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 58, no. 12 (December 2011): 2680–88. http://dx.doi.org/10.1109/tuffc.2011.2130.

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20

Yao, Yuan, Stefano Sfarra, Clemente Ibarra-Castanedo, Renchun You, and Xavier P. V. Maldague. "The multi-dimensional ensemble empirical mode decomposition (MEEMD)." Journal of Thermal Analysis and Calorimetry 128, no. 3 (January 12, 2017): 1841–58. http://dx.doi.org/10.1007/s10973-016-6082-6.

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21

WU, ZHAOHUA, NORDEN E. HUANG, and XIANYAO CHEN. "THE MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION METHOD." Advances in Adaptive Data Analysis 01, no. 03 (July 2009): 339–72. http://dx.doi.org/10.1142/s1793536909000187.

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A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi-dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition (EEMD) to slices of data in each and every dimension involved. The final reconstruction of the corresponding intrinsic mode function (IMF) is based on a comparable minimal scale combination principle. For two-dimensional spatial data or images, f(x,y), we consider the data (or image) as a collection of one-dimensional series in both x-direction and y-direction. Each of the one-dimensional slices is decomposed through EEMD with the slice of the similar scale reconstructed in resulting two-dimensional pseudo-IMF-like components. This new two-dimensional data is further decomposed, but the data is considered as a collection of one-dimensional series in y-direction along locations in x-direction. In this way, we obtain a collection of two-dimensional components. These directly resulted components are further combined into a reduced set of final components based on a minimal-scale combination strategy. The approach for two-dimensional spatial data can be extended to multi-dimensional data. EEMD is applied in the first dimension, then in the second direction, and then in the third direction, etc., using the almost identical procedure as for the two-dimensional spatial data. A similar comparable minimal-scale combination strategy can be applied to combine all the directly resulted components into a small set of multi-dimensional final components. For multi-dimensional temporal-spatial data, EEMD is applied to time series of each spatial location to obtain IMF-like components of different time scales. All the ith IMF-like components of all the time series of all spatial locations are arranged to obtain ith temporal-spatial multi-dimensional IMF-like component. The same approach to the one used in temporal-spatial data decomposition is used to obtain the resulting two-dimensional IMF-like components. This approach could be extended to any higher dimensional temporal-spatial data.
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ZHANG, MIN, and YI SHEN. "ENSEMBLE EMPIRICAL MODE DECOMPOSITION FOR HYPERSPECTRAL IMAGE CLASSIFICATION." Advances in Adaptive Data Analysis 04, no. 01n02 (April 2012): 1250003. http://dx.doi.org/10.1142/s1793536912500033.

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Ensemble empirical mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. This paper presents the utilization of EEMD for hyperspectral images to extract signals from them, generated in noisy nonlinear and nonstationary processes. First, EEMD is applied to each hyperspectral image band and defines the true intrinsic mode function (IMF) components as the mean of an ensemble of trials, each consisting of the signal plus a white noise of finite amplitude. After EEMD is performed to each band, new bands are reconstructed as the sum of IMFs and the trend, and classification is executed over these new bands. Finally, the hyperspectral image with new bands was classified with support vector machine (SVM) to show the classification performance of the proposed approach. Experimental results show that the utilization of the EEMD significantly increases the classification accuracy compared to the dataset processed by empirical mode decomposition (EMD) and the original dataset.
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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 (July 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 reducing the number of ensemble trials was thus proposed in this paper by over-sampling the signal to be decomposed. Numerical simulations were conducted to demonstrate proposed approach.
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Xing, Guangyuan, Shaolong Sun, and Jue Guo. "A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting." Discrete Dynamics in Nature and Society 2020 (March 22, 2020): 1–11. http://dx.doi.org/10.1155/2020/6019826.

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In this study, we focus our attention on the forecasting of daily PM2.5 concentrations. According to the principle of “divide and conquer,” we propose a novel decomposition ensemble learning approach by integrating ensemble empirical mode decomposition (EEMD), artificial neural networks (ANNs), and adaptive particle swarm optimization (APSO) for forecasting PM2.5 concentrations. Our proposed decomposition ensemble learning approach is formulated exclusively to deal with difficulties in quantitating meteorological information with high volatility, irregularity, and complicacy. This decomposition ensemble learning approach mainly consists of three steps. First, we utilize EEMD to decompose original time series of PM2.5 concentrations into a specific amount of independent intrinsic mode functions (IMFs) and residual term. Second, the ANN, whose connection parameters are optimized by APSO algorithm, is employed to model IMFs and residual terms, respectively. Finally, another APSO-ANN is applied to aggregate the forecast IMFs and residual term into a collection as the final forecasting results. The empirical results show that the forecasting of our decomposition ensemble learning approach outperforms other benchmark models in terms of level accuracy and directional accuracy.
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Hadiyoso, Sugondo, Inung Wijayanto, Achmad Rizal, and Suci Aulia. "Biometric systems based on ECG using ensemble empirical mode decomposition and Variational Mode decomposition." Journal of Applied Engineering Science 18, no. 2 (2020): 181–91. http://dx.doi.org/10.5937/jaes18-26041.

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Mokhtari, MA, and M. Sabzehparvar. "Identification of spin maneuver aerodynamic nonlinear model by applying ensemble empirical mode decomposition and extended multipoint modeling." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 5 (April 6, 2018): 1865–78. http://dx.doi.org/10.1177/0954410018764937.

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Identification of the “aircraft aerodynamic model” in some unusual flight conditions such as spin maneuver provides critical information to the flight controller to retake the “dynamic stability” after it has been disturbed by the systematic, natural or environmental oscillations. Hence, a method for identifying the appropriate aerodynamic model in spin maneuvers is presented in this paper. We present an innovative systematic method for aerodynamic modeling of spin maneuvers, which combines the ensemble empirical mode decomposition technique and extended multipoint modeling approach, using flight data. In ensemble empirical mode decomposition, in addition to having all the empirical mode decomposition features, the original signal is collected with the white noise, and by using its statistical characteristics, the ensemble empirical mode decomposition solves the mode mixing problem. By applying the ensemble empirical mode decomposition to the flight parameter data, their intrinsic mode frequencies are extracted and are used as inputs to the extended multipoint modeling model. The extended multipoint modeling structure includes some parameters describing the distribution of aerodynamic forces and moments along each of the aircraft components. Moreover, this method allows coupling between the forces and moments. Unlike conventional methods, which consider the average forces obtained by plane surfaces relative to the center of mass, in the extended multipoint modeling technique, the force generated by each plane of the aircraft is allowed to appear independently in the motion equations. For identifying the aerodynamic model with extended multipoint modeling structure, the equation error method is used with a maximum likelihood optimizer inside. The obtained algorithm has been applied to two sets of spin maneuver flight data which were recorded in actual spin flight. The results demonstrate that the proposed method is able to reproduce the aerodynamic forces and moments for the second spin flight inputs with high accuracy by using a model which is derived from the first spin data identification.
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Barnova, Katerina, Radana Kahankova, Rene Jaros, Martina Litschmannova, and Radek Martinek. "A comparative study of single-channel signal processing methods in fetal phonocardiography." PLOS ONE 17, no. 8 (August 19, 2022): e0269884. http://dx.doi.org/10.1371/journal.pone.0269884.

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Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.
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Shen, Chengwu, Zhiqian Wang, Chang Liu, Qinwen Li, Jianrong Li, and Shaojin Liu. "Analysis of Vehicle Platform Vibration Based on Empirical Mode Decomposition." Shock and Vibration 2021 (February 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/8894959.

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Vehicle platform vibration (VPV) directly affects the measurement accuracy of precise measuring instrument (PMI) fixed on it. In order to reduce the influences of VPV on measurement accuracy, it is necessary to perform vibration isolation between vehicle platform and PMI. Analysis of vibration characteristics is a prerequisite for vibration isolation. However, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) reveal that there is obvious mode mixing phenomenon in the collected VPV signals. In this paper, a noise stretch ensemble empirical mode decomposition (NSEEMD) method is proposed to suppress mode mixing, and the specific operation process of NSEEMD is expounded. By NSEEMD, mode mixing of the collected platform vibration data is well suppressed, and the principal component of platform vibration can be obtained.
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Mohguen, W., and S. Bouguezel. "Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition." Engineering, Technology & Applied Science Research 11, no. 5 (October 12, 2021): 7536–41. http://dx.doi.org/10.48084/etasr.4302.

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In this paper, a novel electrocardiogram (ECG) denoising method based on the Ensemble Empirical Mode Decomposition (EEMD) is proposed by introducing a modified customized thresholding function. The basic principle of this method is to decompose the noisy ECG signal into a series of Intrinsic Mode Functions (IMFs) using the EEMD algorithm. Moreover, a modified customized thresholding function was adopted for reducing the noise from the ECG signal and preserve the QRS complexes. The denoised signal was reconstructed using all thresholded IMFs. Real ECG signals having different Additive White Gaussian Noise (AWGN) levels were employed from the MIT-BIH database to evaluate the performance of the proposed method. For this purpose, output SNR (SNRout), Mean Square Error (MSE), and Percentage Root mean square Difference (PRD) parameters were used at different input SNRs (SNRin). The simulation results showed that the proposed method provided significant improvements over existing denoising methods.
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Chang, Kang-Ming. "Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition." Sensors 10, no. 6 (June 17, 2010): 6063–80. http://dx.doi.org/10.3390/s100606063.

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Zhang, Xu, and Ping Zhou. "Filtering of surface EMG using ensemble empirical mode decomposition." Medical Engineering & Physics 35, no. 4 (April 2013): 537–42. http://dx.doi.org/10.1016/j.medengphy.2012.10.009.

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32

Agarwal, Megha, and Richa Priyadarshani. "Denoising in Biomedical signals using Ensemble Empirical Mode Decomposition." IOSR Journal of Electronics and Communication Engineering 9, no. 6 (2014): 80–86. http://dx.doi.org/10.9790/2834-09638086.

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NEUBAUER, A., A. M. TOMÉ, A. KODEWITZ, J. M. GÓRRIZ, C. G. PUNTONET, and E. W. LANG. "BIDIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION OF FUNCTIONAL BIOMEDICAL IMAGES." Advances in Adaptive Data Analysis 06, no. 01 (January 2014): 1450004. http://dx.doi.org/10.1142/s1793536914500046.

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Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful classifiers trained to group these textures into several classes depending on the problem at hand. The study investigates the potential use of 2D EEMD in combination with proper classifiers to form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from a dementia are taken to illustrate this ability.
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BREAKER, LAURENCE C. "ENERGY PRODUCTION TREND EXTRACTION USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION." International Journal of Energy and Statistics 01, no. 03 (September 2013): 195–204. http://dx.doi.org/10.1142/s2335680413500130.

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Shi, Feng, Bao Yang, L. von Gunten, Chun Qin, and Zhangyong Wang. "Ensemble empirical mode decomposition for tree-ring climate reconstructions." Theoretical and Applied Climatology 109, no. 1-2 (December 30, 2011): 233–43. http://dx.doi.org/10.1007/s00704-011-0576-8.

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Siregar, Rosinar, Rukun Santoso, and Puspita Kartikasari. "PERAMALAN INDEKS HARGA SAHAM MENGGUNAKAN ENSEMBLE EMPIRICAL MODE DECOMPOSITION (EEMD)." Jurnal Gaussian 10, no. 2 (May 31, 2021): 211–20. http://dx.doi.org/10.14710/j.gauss.v10i2.29919.

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Stock price fluctuations make investors tend to hesitate to invest in stock markets because of an uncertain situation in the future. One method that can solve these problems is to use forecasting about the stock prices in the future. Generally, the huge size of data non linear and non stationary, and it is difficult to be interpreted in concrete. This problem can be solved by performing the decomposition process. One of decomposition method in time series data is Ensemble Empirical Mode Decomposition (EEMD). EEMD is process decomposition data into several Intrinsic Mode Function (IMF) and the IMF residue. In this research, this concept applied to data Stock Price Index in Property, Real Estate, and Construction from July 1, 2019 to July 30, 2020 as many as 272 data. Based on the results of data processing, as many as 6 IMF and IMF remaining were used as IMF forecasting and the IMF remaining in the future. The forecast was performed by choosing the best model of each IMF component and IMF remaining, used ARIMA and polynomial trend. Keywords: Time Series Data, Stock Price Index, EEMD, ARIMA, Polynomial Trend.
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Karijadi, Irene, and Ig Jaka Mulyana. "Application of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction." Jurnal Teknik Industri 22, no. 1 (May 30, 2020): 11–16. http://dx.doi.org/10.9744/jti.22.1.11-16.

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Improving accuracy of wind power prediction is important to maintain power system stability. However, wind power prediction is difficult due to randomness and high volatility characteristics. This study applies a hybrid algorithm that combines ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to develop a prediction model for wind power prediction. Ensemble empirical mode decomposition is employed to decompose original data into several Intrinsic Mode Functions (IMF). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of wind power Numerical testing demonstrated that the proposed method can accurately predict the wind power in Belgian.
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Hou, Sizu, and Wei Guo. "Faulty Line Selection Based on Modified CEEMDAN Optimal Denoising Smooth Model and Duffing Oscillator for Un-Effectively Grounded System." Mathematical Problems in Engineering 2020 (April 6, 2020): 1–21. http://dx.doi.org/10.1155/2020/5761642.

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As the un-effectively grounded system fails, the zero-sequence current contains strong noise and nonstationary features. This paper proposes a novel faulty line selection method based on modified complete ensemble empirical mode decomposition with adaptive noise (MCEEMDAN) and Duffing oscillator. Here, based on multiscale permutation entropy, fuzzy c-means clustering, and general regression neural network for abnormal signal detection, the MCEEMDAN is proposed. The endpoint mirror method is used to suppress the endpoint effect problem in the decomposition stage. The proposed algorithm is able to decompose the original signal into a series of intrinsic mode functions, which can complete the first filtering. The research shows that it can efficiently suppress the mode confusing phenomenon of empirical mode decomposition (EMD) and is also more complete and orthogonal than ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD). The optimal denoising smooth model is established for choosing optimal intrinsic mode functions to complete the second filtering. It can ensure that the reconstructed filtered signal has better smoothness and similarity. The optimal denoising smooth model of MCEEMDAN can not only keep useful details of the original signal but also reduce the noise and smooth signal. The bifurcation characteristic of the chaotic oscillator is applied in weak signal detection. The zero-sequence current’s denoising result is extracted as the input signal of the Duffing system. The faulty line could be selected by observing the phase diagram of the system. The research results verify the usability and effectiveness of the proposed method.
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Qin, Yu Qiang, and Xue Ying Zhang. "EEMD-Based Speaker Emotional Analysis for Speech Signal." Applied Mechanics and Materials 121-126 (October 2011): 815–19. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.815.

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Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.
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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 (November 26, 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 mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors.
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Jonak, Kamil, and Arkadiusz Syta. "Gearbox damage identification using Ensemble Empirical Decomposition method." MATEC Web of Conferences 252 (2019): 06005. http://dx.doi.org/10.1051/matecconf/201925206005.

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In this article, we have conducted a comparative analysis of vibration signals from helicopter aircraft propulsion transmissions, registered on an industrial research stand. We compared acceleration vibrations in the case of gears without physical damage and gears with one tooth missing. Based on recorded signals, we determined the values of indicators based on the statistical properties of signals and compared them with each other. For a more exact comparison, the distribution of the tested signals to the empirical modes using the EEMD (Ensemble Empirical Mode Decomposition) method was performed. This allows to treat individual modes as components of a signal at specific frequencies, and also prevents mixing of modes in individual components, which may take place in the classic EMD analysis. It should be noted that individual modes may correspond to characteristic frequencies for the operation of the transmission. When comparing the values of the most frequently used indicators, modes/frequencies in which the damage was most visible were indicated.
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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 (August 9, 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 developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD.
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43

Li, Yi-zhen, and Chun-fang Yue. "Prediction and analysis of non-stationary runoff extreme sequence based on ESMD combination prediction model." Water Supply 20, no. 4 (April 13, 2020): 1439–52. http://dx.doi.org/10.2166/ws.2020.058.

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Abstract With increasingly severe climate changes and intensified human activities, it is more and more difficult to predict the non-stationary extreme runoff series accurately. In this research, based on the ‘decomposition-prediction-reconstruction’ model, an instantaneous frequency distribution map was used to measure the effect of empirical mode decomposition (EMD), ensemble empirical mode decomposition, complete ensemble empirical mode decomposition and extreme-point symmetric mode decomposition (ESMD) in dealing with mode mixing; appropriate prediction methods for each component were selected to form a combined prediction model; and the advantages of a combined prediction model based on ESMD were compared and analyzed with the following results acquired: (1) ESMD can address the mode mixing problem with EMD; (2) particle swarm optimization-least squares support vector machine, autoregressive model (1) and random forest are suitable for high-/medium-/low-frequency components and the residual components R; (3) the results of the combined prediction model are better than those of the single ones; and (4) the prediction effect of the combined prediction model is the best under ESMD decomposition, and the prediction errors of the runoff extreme value sequence can be reduced by about 58–80% compared with the three other decomposition methods. Moreover, as demonstrated in this study, the combined prediction model based on ESMD can effectively predict the non-stationary extreme runoff series, while providing reference for forecasting other non-stationary time series.
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44

WU, ZHAOHUA, and NORDEN E. HUANG. "ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD." Advances in Adaptive Data Analysis 01, no. 01 (January 2009): 1–41. http://dx.doi.org/10.1142/s1793536909000047.

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A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose; therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method.
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45

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 foundations under permafrost condition.
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CHENG Zhi, 程知, 何枫 HE Feng, 靖旭 JING Xu, 张巳龙 ZHANG Si-long, and 侯再红 HOU Zai-hong. "Denoising Lidar Signal Based on Ensemble Empirical Mode Decomposition and Singular Value Decomposition." ACTA PHOTONICA SINICA 46, no. 12 (2017): 1201003. http://dx.doi.org/10.3788/gzxb20174612.1201003.

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47

Shi, Peiming, Cuijiao Su, and Dongying Han. "Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD." Shock and Vibration 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/9278581.

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An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.
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48

Han, Jiajun, and Mirko van der Baan. "Empirical mode decomposition for seismic time-frequency analysis." GEOPHYSICS 78, no. 2 (March 1, 2013): O9—O19. http://dx.doi.org/10.1190/geo2012-0199.1.

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Time-frequency analysis plays a significant role in seismic data processing and interpretation. Complete ensemble empirical mode decomposition decomposes a seismic signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. Analysis on synthetic and real data demonstrates that this method promises higher spectral-spatial resolution than the short-time Fourier transform or wavelet transform. Application on field data thus offers the potential of highlighting subtle geologic structures that might otherwise escape unnoticed.
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Liu, Wei, Siyuan Cao, and Yangkang Chen. "Applications of variational mode decomposition in seismic time-frequency analysis." GEOPHYSICS 81, no. 5 (September 2016): V365—V378. http://dx.doi.org/10.1190/geo2015-0489.1.

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We have introduced a novel time-frequency decomposition approach for analyzing seismic data. This method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can further decrease redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) and improved CEEMD (ICEEMD). Moreover, VMD is an adaptive signal decomposition technique, which can nonrecursively decompose a multicomponent signal into several quasi-orthogonal intrinsic mode functions. This new tool, in contrast to empirical mode decomposition (EMD) and its variations, such as EEMD, CEEMD, and ICEEMD, is based on a solid mathematical foundation and can obtain a time-frequency representation that is less sensitive to noise. Two tests on synthetic data showed the effectiveness of our VMD-based time-frequency analysis method. Application on field data showed the potential of the proposed approach in highlighting geologic characteristics and stratigraphic information effectively. All the performances of the VMD-based approach were compared with those from the CEEMD- and ICEEMD-based approaches.
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Qurban, Maria, Mohammed M. A. Almazah, Hafiza Mamona Nazir, Ijaz Hussain, Muhammad Ismail, Faud S. Al-Duais, Sana Amjad, and Mohammed N. Murshed. "Improvement towards Prediction Accuracy of Principle Mineral Resources Using Threshold." Mathematical Problems in Engineering 2022 (March 12, 2022): 1–18. http://dx.doi.org/10.1155/2022/5991311.

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The production data of mineral resources are noisy, nonstationary, and nonlinear. Therefore, some techniques are required to address the problem of nonstationarity and complexity of noises in it. In this paper, two hybrid models (EMD-CEEMDAN-EBT-MM and WA-CEEMDAN-EBT-MM) flourish to improve mineral production prediction. First, we use empirical mode decomposition (EMD) and wavelet analysis (WA) to denoise the data. Second, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition (CEEMDAN) are used for the decomposition of nonstationary data into intrinsic mode function (IMF). Then, empirical Bayesian threshold (EBT) is applied on noise dominant IMFs to consolidate noises, which are further used as input in the data-driven model. Next, other noise-free IMFs are used in the stochastic model as input for the prediction of minerals. At last, the predicted IMFs are ensemble for final prediction. The proposed strategy is exemplified using Pakistan's four major mineral resources. To measure the prediction performance of all the models, three methods, that is, mean relative error, mean square error, and mean absolute percentage error, are used. Our proposed framework WA-CEEMDAN-EBT-MM has shown improvement with minimum mean absolute percentage error value compared to other existing models in prediction accuracy for all four minerals. Therefore, our proposed strategy can predict the noisy and nonstationary time-series data with an efficient mechanism. Hence, it will be helpful to the policymakers for making policies and planning in mineral resource management.
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