• 한국정보통신학회논문지
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• 제13권12호
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• pp.2671-2676
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• 2009

EMD(Empirical mode decomposition) 방법은 시간-주파수 분석의 새로운 방법으로 적응적이며 효율적으로 신호를 분해한다. EMD는 신호 그 자체에 의해 정의된 IMFs(Intrinsic mode functions)로 명명되는 함수의 집합으로 분해되며, 분해된 IMFs는 원신호의 고유한 속성을 보존하므로 기저함수 및 필터로 사용될 수 있다. EMD 방법에 의한 분해는 신호의 지역적인 시간 스케일 특성에 기반을 두고 있으므로 비선형(non-linear) 비정상(non-stationary) 신호처리에 적합하며 ECG와 같은 생체 신호처리에 유용하다. 본 논문은 EMD 방법을 이용하여 ECG 신호를 분해하고 분해된 신호의 특성을 이용하여 잡음 제거 필터를 구현하였다. 전통적인 저주파 필터가 퓨리에 변환을 이용하여 주파수 영역에서 신호를 해석하는 것과 달리 EMD 방법은 시간 영역에서 필터링하여 신호의 속성을 유지한다. 영상 향상의 정도를 측정하기 위한 PRMD와 SSR 평가지수를 사용하여 제안된 기법과 전통적인 저주파 필터의 결과를 비교 제시하였다.

• ETRI Journal
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• 제38권4호
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• pp.695-702
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• 2016

A hierarchical approach for fast bi-dimensional empirical mode decomposition (B-EMD) is proposed. The presented approach utilizes an efficient window size determination scheme that enables the multi-level computation of the order statistics filter (OSF). Our detailed experiments show that the proposed OSF computation approach allows a significantly faster computation of an EMD without degrading the decomposition accuracy.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2014년도 제49차 동계학술대회논문집 22권1호
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• pp.89-92
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• 2014

This paper presents a secure and blind adaptive audio watermarking algorithm based on Empirical Mode Decomposition (EMD). The audio signal is divided into frames and each one is decomposed adaptively, by EMD, into several Intrinsic Mode Functions (IMFs). The watermark and the synchronization codes are then embedded into the extrema of the last IMF. The experimental results show that the proposed method has good imperceptibility and robustness against signal processing attacks.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.699-704
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• 2004

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The result by EMD method which has used only output vibration data is almost identical to the result by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• 한국소음진동공학회논문집
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• 제15권2호
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• pp.192-198
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• 2005

Empirical mode decomposition(EMD) method has been recently proposed to analyze non-linear and non-stationary data. This method allows the decomposition of one-dimensional signals into intrinsic mode functions(IMFs) and is used to calculate a meaningful multi-component instantaneous frequency. In this paper, it is assumed that each mode of damped vibration signal could be well separated in the form of IMF by EMD. In this case, we can have a new powerful method to calculate natural frequencies and dampings from damped vibration signal which usually has multiple modes. This proposed method has been verified by both simulation and experiment. The results by EMD method whichhas used only output vibration data are almost identical to the results by FRF method which has used both input and output data, thereby proving usefulness and accuracy of the proposed method.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.90-90
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• 2011

Long-term nonstationary oscillations (NSOs) are commonly observed in hydrological and climatological data series such as low-frequency climate oscillation indices and precipitation dataset. In this work, we present a stochastic model that captures NSOs within a given variable. The model employs a data-adaptive decomposition method named empirical mode decomposition (EMD). Irregular oscillatory processes in a given variable can be extracted into a finite number of intrinsic mode functions with the EMD approach. A unique data-adaptive algorithm is proposed in the present paper in order to study the future evolution of the NSO components extracted from EMD.

• 한국정보통신학회논문지
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• 제19권2호
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• pp.279-286
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• 2015

EMD는 미리 정의된 어떠한 기저함수도 사용하지 않으며 사용자에 의해 미리 정의된 파라미터값도 필요치 않은 완전히 데이터에 기반한 신호 처리의 특징을 갖는다. 그러나 유사한 스케일을 갖는 신호 모드로 분해하는 것을 방해하는 모드 혼합이 발생하는 단점이 있다. 이를 해결하기 위해 EEMD 알고리즘이 도입되었으며, EEMD는 처리하고자 하는 신호에 가우시안 백색 잡음을 혼합하여 앙상블 수만큼 신호를 만들어 EMD 방법을 적용함으로써 모드 혼합 문제를 해결한다. 그럼에도 EEMD는 잡음이 추가된 신호 분해 시 원 신호와 상이한 모드 수를 만들어 내며, 분해된 신호들을 원 신호로 재구성 시에도 레지듀 잡음이 포함된다. 본 논문은 개선된 EEMD알고리즘으로 EMD의 모드 혼합 문제를 해결하고 원신호를 정확히 재구성하며 EEMD 보다 적은 연산 비용으로 신호 모드 분리를 제안한다. 실험결과는 EEMD 방법과 비교하여 적은 체과정의 반복으로 빠른 모드 분리를 보여 주었으며 EEMD 방법의 20.87%의 비용만으로 완전한 신호 분해가 가능하였고, 신호 복원에 있어서도 EEMD 보다 우수한 성능을 보여주었다.

• Smart Structures and Systems
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• 제3권4호
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• pp.423-437
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• 2007

The empirical mode decomposition (EMD) method is well-known for its ability to decompose a multi-component signal into a set of intrinsic mode functions (IMFs). The method uses a sifting process in which local extrema of a signal are identified and followed by a spline fitting approximation for decomposition. This method provides an effective and robust approach for decomposing nonlinear and non-stationary signals. On the other hand, the IMF components do not automatically guarantee a well-defined physical meaning hence it is necessary to validate the IMF components carefully prior to any further processing and interpretation. In this paper, an attempt to use the EMD method to identify properties of nonlinear elastic multi-degree-of-freedom structures is explored. It is first shown that the IMF components of the displacement and velocity responses of a nonlinear elastic structure are numerically close to the nonlinear normal mode (NNM) responses obtained from two-dimensional invariant manifolds. The IMF components can then be used in the context of the NNM method to estimate the properties of the nonlinear elastic structure. A two-degree-of-freedom shear-beam building model is used as an example to illustrate the proposed technique. Numerical results show that combining the EMD and the NNM method provides a possible means for obtaining nonlinear properties in a structure.

• Structural Engineering and Mechanics
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• 제39권6호
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• pp.813-831
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• 2011

This paper presents an efficient version of Hilbert-Huang transform for nonlinear non-stationary systems analyses. An ensemble empirical mode decomposition (EEMD) is introduced to alleviate the problem of mode mixing between intrinsic mode functions (IMFs) decomposed by EMD. Yet the problem has not been fully resolved when a signal of a similar scale resides in different IMF components. Instead of using a trial and error method to select the "best" outcome generated by EEMD, a hybrid algorithm based on EEMD and EMD is proposed for multi-mode signal processing. The developed approach comprises the steps from a bandpass filter design for regrouping modes of the IMFs obtained from EEMD, to the mode extraction using EMD, and to the assessment of each mode in the marginal spectrum. A simulated two-mode signal is tested to demonstrate the efficiency and robustness of the approach, showing average relative errors all equal to 1.46% for various noise levels added to the signal. The developed approach is also applied to a real bridge structure, showing more reliable results than the pure EMD. Discussions on the mode determination are offered to explain the connection between modegrouping form on the one hand, and mode-grouping performance on the other.