• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.197-205
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• 2016

Recently, natural hazards have occurred frequently due to climate change. The research need for predicting variability and tendency of precipitation and temperature has been increased. However, it is difficult to determine the characteristics of precipitation and temperature within a confidence range since they change due to complex factors with choppy and too many components. If their characteristics having more than one component are decomposed, then it can be useful for determining the variation of such characteristics more accurately. In this study, Korean precipitation and temperature were decomposed and their Intrinsic Mode Function (IMF) were extracted from Empirical Mode Decomposition (EMD). Finally, the characteristics of Korean precipitation and temperature data were analyzed in terms of periodicity and tendency.

• Journal of KIISE
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• v.41 no.12
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• pp.1050-1057
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• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.159-174
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• 2015

Dragomiretskiy and Zosso (2014) developed a new decomposition method, termed variational mode decomposition (VMD), which is efficient for handling the tone detection and separation of signals. However, VMD may be inefficient in the presence of missing data since it is based on a fast Fourier transform (FFT) algorithm. To overcome this problem, we propose a new approach based on a novel combination of VMD and hierarchical (or h)-likelihood method. The h-likelihood provides an effective imputation methodology for missing data when VMD decomposes the signal into several meaningful modes. A simulation study and real data analysis demonstrates that the proposed method can produce substantially effective results.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.207-215
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• 2016

In this study, it is analyzed how large scale climate variation has an effect on climate systems over Korea using correlation analysis between climate indices and Intrinsic Mode Functions (IMFs) of precipitation and temperature. For this purpose, the estimated IMFs of precipitation and temperature from the accompanying paper were used. Furthermore, cross correlation coefficients and lag time between climate indices and IMFs were calculated considering periodicities and tendencies. As results, more accurate correlation coefficients were obtained compared with those between climate indices and raw precipitation and temperature data. We found that the Korean climate is closely related with climate variations of $El-Ni{\tilde{n}}o$ in terms of periodicity and its tendency is followed with increasing sea surface temperature due to climate change.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.232-232
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• 2016

주어진 시계열 자료의 경향성을 분석하고 판별하는 것은 수문 자료의 분석에서 가장 우선적으로 수행되어야 할 절차이며 경향성의 유무에 따라 자료를 분석하는 방법이 달라지게 되므로 매우 중요한 부분이다. 일반적으로 국내에서 주로 사용되는 수문 시계열 자료의 경향성 분석 방법으로는 비매개변수적인 방법인 Mann-Kendall test, Spearman's rho test, Hotelling Pabst test, Sentest 등이 있으며 그 중에서도 국내외 수문 자료의 경향성 분석에는 비교적 높은 기각력을 보이는 Mann-Kendall test가 주된 방법으로 활용되어 오고 있다. Mann-Kendall test는 통계적 유의성을 바탕으로 한 경향성 판별 방법으로 시계열 자료 내에 존재하는 경향성의 형태를 분석하여 경향성 유무를 판별하는 것에는 한계가 있다. 경험적 모드분해법을 활용한 경향성 분석 방법은 체거름 과정을 통하여 주어진 시계열 자료를 내재모드함수로 분해한 후, 추출된 모든 요소를 제거하고 남은 잔여값의 형태를 이용하여 경향성 유무를 판별하는 방법으로 자료에 내재된 경향성의 형태를 확인할 수 있는 장점을 가지고 있다. 본 연구에서는 이러한 경험적 모드분해법을 이용한 경향성 분석 방법을 소개하고, 모의를 통한 시계열 자료를 이용하여 경향성 분석에 적용한 후 기존에 사용되어온 Mann-Kendall test와의 비교를 통해 적용성을 평가하였다.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.525-537
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• 2016

The paper considers a hybrid model to analyze and forecast time series data based on an empirical mode decomposition (EMD) that accommodates complex characteristics of time series such as nonstationarity and nonlinearity. We aggregate IMFs using the concept of cumulative energy to improve the interpretability of intrinsic mode functions (IMFs) from EMD. We forecast aggregated IMFs and residue with a hybrid model that combines the ARIMA model and an exponential smoothing method (ETS). The proposed method is applied to forecast KOSPI time series and is compared to traditional forecast models. Aggregated IMFs and residue provide a convenience to interpret the short, medium and long term dynamics of the KOSPI. It is also observed that the hybrid model with ARIMA and ETS is superior to traditional and other types of hybrid models.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.230-230
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• 2019

대규모순환패턴과 같은 기후시스템에서의 상태와 변화를 정량화하여 나타낸 기상인자는 수문기상학적 변수와 밀접한 연관이 있는 것으로 알려져 있으며, 이에 따라 비정상성 빈도해석의 수행에 있어서 확률분포모형의 매개변수에 대한 공변량으로 널리 활용되고 있다. 본 연구에서는 비정상성 강우빈도해석 시 매개변수의 공변량으로 우리나라의 극치강우의 장기경향성을 잘 반영할 수 있는 기상인자를 선정하고자 한다. 먼저, 시계열자료를 주기성을 가지는 내재모드함수와 장기경향성을 나타내는 잔여값으로 분해할 수 있는 앙상블 경험적 모드분해법을 이용하여 우리나라 전역에 분포된 61개 지점에서 관측된 연 최대치 강우자료의 평균 및 분산에 대한 잔여값을 추출하였다. 다음으로 11개의 월 단위 기상인자에 대한 계절별 연 평균 시계열과 추출된 평균 및 분산의 잔여값과의 상관계수를 산정하였다. 그 결과, 11개의 기상인자 중 Atlantic Meridional Mode (AMM), Atlantic Multi-decadal Oscillation (AMO), North Atlantic Oscillation (NAO)가 우리나라 연 최대치 강우자료의 평균 및 분산에 대한 장기경향성과 높은 상관성이 있는 것으로 나타났다. 계절적으로는 AMM과 AMO의 경우 이전 년도 가을철 평균이 전 지점 평균 약 0.6, NAO는 이전 년도 여름철 평균이 전 지점 평균 0.3 이상의 유의한 상관계수를 가지는 것으로 나타났다.

• The Korean Journal of Applied Statistics
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• v.19 no.2
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• pp.319-330
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• 2006

A signal in real world usually composes of multiple signals having different scales of frequencies. For example sun-spot data is fluctuated over 11 year and 85 year. Economic data is supposed to be compound of seasonal component, cyclic component and long-term trend. Decomposition of the signal is one of the main topics in time series analysis. However when the signal is subject to nonstationarity, traditional time series analysis such as spectral analysis is not suitable. Huang et. at(1998) proposed data-adaptive method called empirical mode decomposition (EMD) . Due to its robustness to nonstationarity, EMD has been applied to various fields. Huang et. at, however, have not considered denoising when data is contaminated by error. In this paper we propose efficient denoising method utilizing cross-validation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.38 no.3
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• pp.35-43
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• 2001

A method is newly presented to synthesize the modal impedances satisfying the desired coupling factor of a reflective (or hack ward) coupled-line. The synthesis is achieved by optimal perturbations of repeating null points of lobes inherent to the solution of the first order nonlinear differential equation for coupling. It is based on the synthesis method of nonlinear source distribution functions for the prescribed space factor pattern in the one-dimensional array antenna. Here, the conventional synthesis method for the even distribution function is extended to the odd case. Resulting modal impedances will have continuously varying profiles. The design procedure of asymmetrical and symmetrical couplers corresponding to the even and odd distribution functions, is examplified to show the generalization and the simplicity of the proposed method.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.15-26
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• 2015

In this paper, amplitude and phase difference patterns for theta and alpha bands of the Evoked Potential(EP) in relation to perform a task at visual stimulus were analyzed using the Empirical mode decomposition(EMD). The EMD is applied to decompose EP signals with task-related sub-frequency band signals. Intrinsic mode function was implied in Hilbert transform and instantaneous amplitude and phase differences of theta and alpha were derived from Hilbert transformed EP. In a task status, large amplitude for both bands was observed at P2, N2, and P3 points as well as maximum phase difference was observed at N1 and P2. We confirmed that both bands are associated with a task at visual stimulus, and less associated with fixation. The proposed method enhances the time and frequency resolution in comparison with band-pass filter method which observed different phase results according to conditions.