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Опубліковано: 11 вересня 2023

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Статті в журналах з теми "ENSEMBLE EMPIRIAL MODE DECOMPOSITION"

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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2

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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Анотація:
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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3

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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Анотація:
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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4

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

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Анотація:
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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5

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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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 (April 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 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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Анотація:
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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Анотація:
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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Анотація:
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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Анотація:
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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Більше джерел

Дисертації з теми "ENSEMBLE EMPIRIAL MODE DECOMPOSITION"

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Furlaneto, Dennis Carnelossi. "An analysis of ensemble empirical mode decomposition applied to trend prediction on financial time series." reponame:Repositório Institucional da UFPR, 2017. http://hdl.handle.net/1884/49137.

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Анотація:
Orientador : Luiz Eduardo S. Oliveira
Coorientador : David Menotti
Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 20/07/2017
Inclui referências : f. 63-72
Resumo: As séries temporais financeiras são notoriamente difíceis de analisar e prever dada sua natureza não estacionária e altamente oscilatória. Nesta tese, a eficácia da técnica de decomposição não-paramétrica Ensemble Empirical Mode Decomposition (EEMD) é avaliada como uma técnica de extração de característica de séries temporais provenientes de índices de mercado e taxas de câmbio, características estas usadas na classificação, juntamente com diferentes modelos de aprendizado de máquina, de tendências de curto prazo. Os resultados obtidos em dois datasets de dados financeiros distintos sugerem que os resultados promissores relatados na literatura foram obtidos com a adição, inadvertida, de lookahead bias (viés) proveniente da aplicação desta técnica como parte do pré-processamento das séries temporais. Em contraste com as conclusões encontradas na literatura, nossos resultados indicam que a aplicação do EEMD com o objetivo de gerar uma melhor representação dos dados financeiração, por si só, não é suficiente para melhorar substancialmente a precisão e retorno cumulativo obtidos por modelos preditivos em comparação aos resultados obtidos com a utilização de series temporais de mudanças percentuais. Palavras-chave: Predição de Tendencias, Aprendizado de Máquina, Séries Temporais Financeiras.
Abstract: Financial time series are notoriously difficult to analyse and predict, given their nonstationary, highly oscillatory nature. In this thesis, the effectiveness of the Ensemble Empirical Mode Decomposition (EEMD) is evaluated at generating a representation for market indexes and exchange rates that improves short-term trend prediction for these financial instruments. The results obtained in two different financial datasets suggest that the promising results reported using EEMD on financial time series in other studies were obtained by inadvertently adding look-ahead bias to the testing protocol via pre-processing the entire series with EEMD, which do affect the predictive results. In contrast to conclusions found in the literature, our results indicate that the application of EEMD with the objective of generating a better representation for financial time series is not sufficient, by itself, to substantially improve the accuracy and cumulative return obtained by the same models using the raw data. Keywords: Trend Prediction, Machine Learning, Financial Time Series.
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2

Li, Zhendan. "An Ensemble Empirical Mode Decomposition Approach to Wear Particle Detection in Lubricating Oil Subject to Particle Overlap." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20313.

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Анотація:
With the development of mechanical fault diagnosis technology, complex mechanical systems do not need to be shut down periodically for the maintenance. The working condition of the mechanical systems can be monitored by analyzing the wear metal particles in the systems' lubricating oil. However, the output signals of the monitoring sensor are non-stationary. In some case the particle signals are overlapped with each other. The goal of this thesis is to find a method to decompose those overlapped particle signals, and then count the particle number in the lubricating oil. At the beginning EMD method was introduced in the experiment because of the character of the sensor signals. In this project, because EMD method is sensitive to the noise in the original signals, an improved version of EMD, EEMD method was implemented. Finally, a post processing method was used to get a better result.
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3

Alshahrani, Saeed Sultan. "Detection, classification and control of power quality disturbances based on complementary ensemble empirical mode decomposition and artificial neural networks." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15872.

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4

Lozano, García Manuel. "Multichannel analysis of normal and continuous adventitious respiratory sounds for the assessment of pulmonary function in respiratory diseases." Doctoral thesis, Universitat Politècnica de Catalunya, 2015. http://hdl.handle.net/10803/397706.

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Анотація:
Respiratory sounds (RS) are produced by turbulent airflows through the airways and are inhomogeneously transmitted through different media to the chest surface, where they can be recorded in a non-invasive way. Due to their mechanical nature and airflow dependence, RS are affected by respiratory diseases that alter the mechanical properties of the respiratory system. Therefore, RS provide useful clinical information about the respiratory system structure and functioning. Recent advances in sensors and signal processing techniques have made RS analysis a more objective and sensitive tool for measuring pulmonary function. However, RS analysis is still rarely used in clinical practice. Lack of a standard methodology for recording and processing RS has led to several different approaches to RS analysis, with some methodological issues that could limit the potential of RS analysis in clinical practice (i.e., measurements with a low number of sensors, no controlled airflows, constant airflows, or forced expiratory manoeuvres, the lack of a co-analysis of different types of RS, or the use of inaccurate techniques for processing RS signals). In this thesis, we propose a novel integrated approach to RS analysis that includes a multichannel recording of RS using a maximum of five microphones placed over the trachea and the chest surface, which allows RS to be analysed at the most commonly reported lung regions, without requiring a large number of sensors. Our approach also includes a progressive respiratory manoeuvres with variable airflow, which allows RS to be analysed depending on airflow. Dual RS analyses of both normal RS and continuous adventitious sounds (CAS) are also proposed. Normal RS are analysed through the RS intensity–airflow curves, whereas CAS are analysed through a customised Hilbert spectrum (HS), adapted to RS signal characteristics. The proposed HS represents a step forward in the analysis of CAS. Using HS allows CAS to be fully characterised with regard to duration, mean frequency, and intensity. Further, the high temporal and frequency resolutions, and the high concentrations of energy of this improved version of HS, allow CAS to be more accurately characterised with our HS than by using spectrogram, which has been the most widely used technique for CAS analysis. Our approach to RS analysis was put into clinical practice by launching two studies in the Pulmonary Function Testing Laboratory of the Germans Trias i Pujol University Hospital for assessing pulmonary function in patients with unilateral phrenic paralysis (UPP), and bronchodilator response (BDR) in patients with asthma. RS and airflow signals were recorded in 10 patients with UPP, 50 patients with asthma, and 20 healthy participants. The analysis of RS intensity–airflow curves proved to be a successful method to detect UPP, since we found significant differences between these curves at the posterior base of the lungs in all patients whereas no differences were found in the healthy participants. To the best of our knowledge, this is the first study that uses a quantitative analysis of RS for assessing UPP. Regarding asthma, we found appreciable changes in the RS intensity–airflow curves and CAS features after bronchodilation in patients with negative BDR in spirometry. Therefore, we suggest that the combined analysis of RS intensity–airflow curves and CAS features—including number, duration, mean frequency, and intensity—seems to be a promising technique for assessing BDR and improving the stratification of BDR levels, particularly among patients with negative BDR in spirometry. The novel approach to RS analysis developed in this thesis provides a sensitive tool to obtain objective and complementary information about pulmonary function in a simple and non-invasive way. Together with spirometry, this approach to RS analysis could have a direct clinical application for improving the assessment of pulmonary function in patients with respiratory diseases.
Los sonidos respiratorios (SR) se generan con el paso del flujo de aire a través de las vías respiratorias y se transmiten de forma no homogénea hasta la superficie torácica. Dada su naturaleza mecánica, los SR se ven afectados en gran medida por enfermedades que alteran las propiedades mecánicas del sistema respiratorio. Por lo tanto, los SR proporcionan información clínica relevante sobre la estructura y el funcionamiento del sistema respiratorio. La falta de una metodología estándar para el registro y procesado de los SR ha dado lugar a la aparición de diferentes estrategias de análisis de SR con ciertas limitaciones metodológicas que podrían haber restringido el potencial y el uso de esta técnica en la práctica clínica (medidas con pocos sensores, flujos no controlados o constantes y/o maniobras forzadas, análisis no combinado de distintos tipos de SR o uso de técnicas poco precisas para el procesado de los SR). En esta tesis proponemos un método innovador e integrado de análisis de SR que incluye el registro multicanal de SR mediante un máximo de cinco micrófonos colocados sobre la tráquea yla superficie torácica, los cuales permiten analizar los SR en las principales regiones pulmonares sin utilizar un número elevado de sensores . Nuestro método también incluye una maniobra respiratoria progresiva con flujo variable que permite analizar los SR en función del flujo respiratorio. También proponemos el análisis combinado de los SR normales y los sonidos adventicios continuos (SAC), mediante las curvas intensidad-flujo y un espectro de Hilbert (EH) adaptado a las características de los SR, respectivamente. El EH propuesto representa un avance importante en el análisis de los SAC, pues permite su completa caracterización en términos de duración, frecuencia media e intensidad. Además, la alta resolución temporal y frecuencial y la alta concentración de energía de esta versión mejorada del EH permiten caracterizar los SAC de forma más precisa que utilizando el espectrograma, el cual ha sido la técnica más utilizada para el análisis de SAC en estudios previos. Nuestro método de análisis de SR se trasladó a la práctica clínica a través de dos estudios que se iniciaron en el laboratorio de pruebas funcionales del hospital Germans Trias i Pujol, para la evaluación de la función pulmonar en pacientes con parálisis frénica unilateral (PFU) y la respuesta broncodilatadora (RBD) en pacientes con asma. Las señales de SR y flujo respiratorio se registraron en 10 pacientes con PFU, 50 pacientes con asma y 20 controles sanos. El análisis de las curvas intensidad-flujo resultó ser un método apropiado para detectar la PFU , pues encontramos diferencias significativas entre las curvas intensidad-flujo de las bases posteriores de los pulmones en todos los pacientes , mientras que en los controles sanos no encontramos diferencias significativas. Hasta donde sabemos, este es el primer estudio que utiliza el análisis cuantitativo de los SR para evaluar la PFU. En cuanto al asma, encontramos cambios relevantes en las curvas intensidad-flujo yen las características de los SAC tras la broncodilatación en pacientes con RBD negativa en la espirometría. Por lo tanto, sugerimos que el análisis combinado de las curvas intensidad-flujo y las características de los SAC, incluyendo número, duración, frecuencia media e intensidad, es una técnica prometedora para la evaluación de la RBD y la mejora en la estratificación de los distintos niveles de RBD, especialmente en pacientes con RBD negativa en la espirometría. El método innovador de análisis de SR que se propone en esta tesis proporciona una nueva herramienta con una alta sensibilidad para obtener información objetiva y complementaria sobre la función pulmonar de una forma sencilla y no invasiva. Junto con la espirometría, este método puede tener una aplicación clínica directa en la mejora de la evaluación de la función pulmonar en pacientes con enfermedades respiratorias
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5

Quinlan, John Mathew. "Investigation of driving mechanisms of combustion instabilities in liquid rocket engines via the dynamic mode decomposition." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54343.

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Анотація:
Combustion instability due to feedback coupling between unsteady heat release and natural acoustic modes can cause catastrophic failure in liquid rocket engines and to predict and prevent these instabilities the mechanisms that drive them must be further elucidated. With this goal in mind, the objective of this thesis was to develop techniques that improve the understanding of the specific underlying physical processes involved in these driving mechanisms. In particular, this work sought to develop a small-scale, optically accessible liquid rocket engine simulator and to apply modern, high-speed diagnostic techniques to characterize the reacting flow and acoustic field within the simulator. Specifically, high-speed (10 kHz), simultaneous data were acquired while the simulator was experiencing a 170 Hz combustion instability using particle image velocimetry, OH planar laser induced fluorescence, CH* chemiluminescence, and dynamic pressure measurements. In addition, this work sought to develop approaches to reduce the large quantities of data acquired, extracting key physical phenomena involved in the driving mechanisms. The initial data reduction approach was chosen based on the fact that the combustion instability problem is often simplified to the point that it can be characterized by an approximately linear constant coefficient system of equations. Consistent with this simplification, the experimental data were analyzed by the dynamic mode decomposition method. The developed approach to apply the dynamic mode decomposition to simultaneously acquired data located a coupled hydrodynamic/combustion/acoustic mode at 1017 Hz. On the other hand, the dynamic mode decomposition's assumed constant operator approach failed to locate any modes of interest near 170 Hz. This led to the development of two new data analysis techniques based on the dynamic mode decomposition and Floquet theory that assume that the experiment is governed by a linear, periodic system of equations. The new periodic-operator data analysis techniques, the Floquet decomposition and the ensemble Floquet decomposition, approximate, from experimental data, the largest moduli Floquet multipliers, which determine the stability of the periodic solution trajectory of the system. The unstable experiment dataset was analyzed with these techniques and the ensemble Floquet decomposition analysis found a large modulus Floquet multiplier and associated mode with a frequency of 169.6 Hz. Furthermore, the approximate Rayleigh criterion indicated that this mode was unstable with respect to combustion instability. Overall, based on the positive finding that the ensemble Floquet decomposition was able to locate an unstable combustion mode at 170 Hz when the operator's time period was set to 1 ms, suggests that the dynamic mode decomposition based 1017 Hz mode parametrically forces the 170 Hz mode, resulting in what could be characterized as a parametric combustion instability.
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6

Wei, Shao-Kuan, and 魏韶寬. "Ensemble Empirical Mode Decomposition with Clustering Analysis." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/82011189687957252619.

Повний текст джерела
Анотація:
碩士
國立臺灣師範大學
數學系
100
Ensemble Empirical Mode Decomposition (EEMD) is an adaptive time-frequency data analysis method. Time series or signals can be decomposed into a collection of intrinsic mode functions (IMFs). Nevertheless, there appears a multi-mode problem where signals with a similar time scale are decomposed into different IMFs. A possible solution to this problem is to combine the multi-modes into a proper single mode, but there is no general rule on how to combine IMFs in the literature. In this paper, we propose to modify EEMD algorithm using the statistical clustering analysis and to provide a framework to combine the IMFs into a condensed set of clustered intrinsic mode functions (CIMFs). The method is applied to two artificially synthesized signals, wind turbine signal at Chunan Miaoli, and a seismic signal during the earthquake at Chi-Chi in 1999. Especially, this seismic signal contains not only the main seismic information but also the seismic motion from a landslide in Tsaoling area. The present method can separate the two signal from different sources correctly, and these applications of other examples demonstrate that, the present method offers great improvement over EEMD for extracting useful information.
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7

Sheng-MaoWang and 王晟懋. "Automated program of Ensemble Empirical Mode Decomposition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2yg4c2.

Повний текст джерела
Анотація:
碩士
國立成功大學
航空太空工程學系
107
The ensemble empirical mode decomposition (EEMD) method is applied for wind data analysis in the current research. However, calculations could take a very long time. Therefore, an attempt is made to accelerate the calculations. MATLAB and Python are used to explore the characteristics of different programming language operations, and a user-friendly graphical interface is also developed, and the execution process will be operated automatically and continuously. The wind data analyzed were collected by the wind turbines located on campus of Case Western Reserve University in the United States. The wind data have been collecting since 2012 and the amount of data keeps growing. Thus, reducing the analyzing time is important. This study not only wants to use the graphics processor to try to shorten the time required for the operation process, but also finds the approximation trend in EEMD and refines the algorithm to shorten the operation time by about 65%.
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8

Yang, Sheng-Ning, and 楊勝寧. "Using Complementary Ensemble Empirical Mode Decomposition Method To Analyze Images." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/38533054079787149646.

Повний текст джерела
Анотація:
碩士
國立陽明大學
醫學工程研究所
99
In this study, by Huang NE, who proposed the complementary empirical mode decomposition method based on the empirical mode decomposition method. The algorithm inherits the empirical mode decomposition some of the benefits. For example it can solve nonlinear and nonstationary signals. Another Empirical mode decomposition method and the traditional numerical methods, the base need to know prior to analysis, the original signals can be based on the characteristics and nature of the original signal automatically a number of nature apart from the function and the remaining functions for analysis. Complementary empirical mode decomposition method also improved the empirical mode decomposition shortcomings. For example the intermittent signals, noise and the edge of the original signal processing have problems in Empirical mode decomposition method, so Complementary empirical mode decomposition method can solve problems by white noise. Another joining the white noise may affect the analysis of the original signal, so the Complementary empirical mode decomposition method are more general and increase the number of complementary concepts to minimize the impact of white noise, the results of analysis tends to positive solutions. Complementary empirical mode decomposition method can decompose the image into several graphic signals without mode mixing, linear and stable intrinsic mode functions, computing the signal waveform after the intrinsic mode functions to comply with the conditions. Intrinsic mode functions to establish original signal can understand some of original signal information, the remaining factor is the combination of the characteristics of the original image graph. One-dimensional empirical mode decomposition method have a lot of literature to proof of adaptability and it can be used in the ability of nonlinear and nonstationary signals. In this study, the two-dimensional empirical mode decomposition method, the application in image process can be obtained from the original number of information. Local feature in the original image signal area, the edge structure and light shading. Finally, to get the information to do with Bilateral Filter processing, a new method to improve noise issues.
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9

Chen-KuoChen and 陳振國. "The Study of Pile Integrity Test Using Ensemble Empirical Mode Decomposition." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/55168835560927508888.

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Анотація:
碩士
國立成功大學
土木工程學系
105
Non-destructive testing (NDT) has the advantages of economical, fast, and a wide range of applicability. Therefore it is a fairly effective testing method. The testing of piles without pile cap has been considered a well-developed technique. However, affected by complex boundaries conditions, testing of piles with pile cap is more difficult. This study use Impulse response (IR) method for the field test, then use ensemble empirical mode decomposition (EEMD) by Hilbert-Huang Transform to decompose the signals, finally use Fast Fourier Transform (FFT) to transform time domain data to frequency domain data. The purpose of this study is to separate the pile cap, pile bottom, and defect frequency and try to estimate the pile length and defect locations of piles with pile cap. The results show that the error of the estimation of pile length is less than 6%, and the errors of estimating top rectangular-defect were less than 3%. However, this method was less effective to detect circularity-defect and bottom rectangular-defect.
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10

Tsai, Shu-Yun, and 蔡舒韻. "A Novel Energy Detection Method Based on Ensemble Empirical Mode Decomposition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/mu66ak.

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Анотація:
碩士
國立東華大學
電機工程學系
100
Cognitive radio(CR) is a novel smart wireless communication technology. To improve spectrum utilization efficiency, CR can detect the communication environment features to automatically adjust the transmitting and receiving parameters of a system, such as power, frequency, and modulation mode etc. The spectrum sensing is a key technology in the CR. In this thesis, we use energy detection for spectrum sensing. Many researches on energy detection are performed on the basis of a precise knowledge of noise power environment to set threshold. In this thesis we use ensemble empirical mode decomposition(EEMD) to decompose signal into licensed users and noise in the noisy environment. The signal power of licensed users and noise power are estimated in the current channel environment. An adaptive threshold is set in an unknown noise environment to improve the performance of energy detector.
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Частини книг з теми "ENSEMBLE EMPIRIAL MODE DECOMPOSITION"

1

Shen, Yi, and Min Zhang. "Hyperspectral Image Classification Based on Ensemble Empirical Mode Decomposition." In Advances in Intelligent and Soft Computing, 529–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27329-2_72.

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Lin, Jinshan. "Improved Ensemble Empirical Mode Decomposition Method and Its Simulation." In Advances in Intelligent Systems, 109–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27869-3_14.

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Henzel, Norbert. "QRS Complex Detection Based on Ensemble Empirical Mode Decomposition." In Innovations in Biomedical Engineering, 286–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47154-9_33.

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Lin, Jinshan. "Fault Feature Extraction of Gearboxes Using Ensemble Empirical Mode Decomposition." In Communications in Computer and Information Science, 478–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23214-5_63.

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Beltrán-Castro, Juan, Juliana Valencia-Aguirre, Mauricio Orozco-Alzate, Germán Castellanos-Domínguez, and Carlos M. Travieso-González. "Rainfall Forecasting Based on Ensemble Empirical Mode Decomposition and Neural Networks." In Advances in Computational Intelligence, 471–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38679-4_47.

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6

Kwon, Sundeok, and Sangjin Cho. "Analysis of Acoustic Signal Based on Modified Ensemble Empirical Mode Decomposition." In Transactions on Engineering Technologies, 377–86. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9115-1_29.

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Srivastava, Ashita, Vikrant Bhateja, Deepak Kumar Tiwari, and Deeksha Anand. "AWGN Suppression Algorithm in EMG Signals Using Ensemble Empirical Mode Decomposition." In Intelligent Computing and Information and Communication, 515–24. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_50.

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Taralunga, Dragos Daniel, and G. Mihaela Neagu. "An Ensemble Empirical Mode Decomposition Based Method for Fetal Phonocardiogram Enhancement." In IFMBE Proceedings, 387–91. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-9038-7_73.

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Wang, Yuchen. "Real-Time Tsunami Detection Based on Ensemble Empirical Mode Decomposition (EEMD)." In Springer Theses, 63–76. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7339-0_4.

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Zhu, Bangzhu, and Julien Chevallier. "A Multiscale Analysis for Carbon Price with Ensemble Empirical Mode Decomposition." In Pricing and Forecasting Carbon Markets, 47–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57618-3_4.

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Тези доповідей конференцій з теми "ENSEMBLE EMPIRIAL MODE DECOMPOSITION"

1

Jin-Long Chen, Ya-Chen Chen, and Tzu-Chien Hsiao. "Recognizing thoracic breathing by ensemble empirical mode decomposition." In 2013 9th International Conference on Information, Communications & Signal Processing (ICICS). IEEE, 2013. http://dx.doi.org/10.1109/icics.2013.6782956.

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2

Roy, Sujan Kumar, Md Khademul Islam Molla, and Keikichi Hirose. "Robust Pitch Estimation using Ensemble Empirical Mode Decomposition." In 7th International Conference on Speech Prosody 2014. ISCA: ISCA, 2014. http://dx.doi.org/10.21437/speechprosody.2014-94.

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3

Lin, Xuze, Yanping Cai, Xinjun Wang, and Fang Wang. "Improved Ensemble Empirical Mode Decomposition and Its Application." In The 5th International Conference on Computer Engineering and Networks. Trieste, Italy: Sissa Medialab, 2015. http://dx.doi.org/10.22323/1.259.0022.

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4

Chang, Kang-Ming. "Ensemble empirical mode decomposition based ECG noise filtering method." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5581064.

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5

Chang, Li-Wen, Men-Tzung Lo, Nasser Anssari, Ke-Hsin Hsu, Norden E. Huang, and Wen-mei W. Hwu. "Parallel implementation of Multi-dimensional Ensemble Empirical Mode Decomposition." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946808.

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6

Torres, Maria E., Marcelo A. Colominas, Gaston Schlotthauer, and Patrick Flandrin. "A complete ensemble empirical mode decomposition with adaptive noise." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947265.

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Rezgui, Dhouha, and Zied Lachiri. "Detection of ECG beat using ensemble empirical mode decomposition." In 2015 7th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2015. http://dx.doi.org/10.1109/icmic.2015.7409389.

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Sadrawi, Muammar, Jiann-Shing Shieh, Koichi Haraikawa, Jen Chien Chien, Chien Hung Lin, and Maysam F. Abbod. "Ensemble empirical mode decomposition applied for PPG motion artifact." In 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2016. http://dx.doi.org/10.1109/iecbes.2016.7843455.

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9

Fontugne, Romain, Nicolas Tremblay, Pierre Borgnat, Patrick Flandrin, and Hiroshi Esaki. "Mining anomalous electricity consumption using Ensemble Empirical Mode Decomposition." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638662.

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10

Wang, Ming-Shu, and Tee-Ann Teo. "Hyperspectral data discrimination based on Ensemble Empirical Mode Decomposition." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5964294.

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