• Ocean and Polar Research
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• v.41 no.3
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• pp.121-133
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• 2019

Natural variability associated with a variety of large-scale climate modes causes regional differences in sea level rise (SLR), which is particularly remarkable in the Pacific Ocean. Because the superposition of the natural variability and the background anthropogenic trend in sea level can potentially threaten to inundate low-lying and heavily populated coastal regions, it is important to quantify sea level variability associated with internal climate variability and understand their interaction when projecting future SLR impacts. This study seeks to identify the dominant modes of sea level variability in the tropical Pacific and quantify how these modes contribute to regional sea level changes, particularly on the two strong El $Ni{\tilde{n}}o$ events that occurred in the winter of 1997/1998 and 2015/2016. To do so, an adaptive data analysis approach, Ensemble Empirical Mode Decomposition (EEMD), was undertaken with regard to two datasets of altimetry-based and in situ-based steric sea levels. Using this EEMD analysis, we identified distinct internal modes associated with El $Ni{\tilde{n}}o$-Southern Oscillation (ENSO) varying from 1.5 to 7 years and low-frequency variability with a period of ~12 years that were clearly distinct from the secular trend. The ENSO-scale frequencies strongly impact on an east-west dipole of sea levels across the tropical Pacific, while the low-frequency (i.e., decadal) mode is predominant in the North Pacific with a horseshoe shape connecting tropical and extratropical sea levels. Of particular interest is that the low-frequency mode resulted in different responses in regional SLR to ENSO events. The low-frequency mode contributed to a sharp increase (decrease) of sea level in the eastern (western) tropical Pacific in the 2015/2016 El $Ni{\tilde{n}}o$ but made a negative contribution to the sea level signals in the 1997/1998 El $Ni{\tilde{n}}o$. This indicates that the SLR signals of the ENSO can be amplified or depressed at times of transition in the low-frequency mode in the tropical Pacific.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.555-561
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• 2012

The Hilbert Huang Transform (HHT) technigue with Empirical Mode Decomposition (EMD) is one of the time-frequency domain analysis methods and it has several advantages such that analyzing non-stationary and nonlinear signal is possible. However, there are shortcomings in detecting near-range of frequencies and added noise signals. In this paper, to analyze characteristics of each method, HHT and Short-Time Fourier Transform (STFT) effective in dealing with stationary signals are compared. And with thermoacoustic instabilities signals from a Rijke tube test, HHT and the improved HHT with Ensemble Empirical Mode Decomposition (EEMD) are compared. The results show that the improved HHT is more appropriate than the original HHT due to the relative insensitivity to noise. Therefore it will result in more accurate analysis.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.2
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• pp.169-177
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• 2017

In this paper, classification of spall and crack faults of gear teeth is studied by applying the ensemble empirical mode decomposition(EEMD) for the gear transmission error(TE). Finite element models of the gears with the two faults are built, and TE is obtained by simulation of the gears under loaded contact. EEMD is applied to the residuals of the TE which are the difference between the normal and faulty signal. From the result, the difference of spall and crack faults are clearly identified by the intrinsic mode functions(IMF). A simple test bed is installed to illustrate the approach, which consists of motor, brake and a pair of spur gears. Two gears are employed to obtain the TE for the normal, spalled, and cracked gears, and the type of the faults are separated by the same EEMD application process. In order to quantify the results, crest factors are applied to each IMF. Characteristics of spall and crack are well represented by the crest factors of the first and the third IMF, which are used as the feature signals. The classification is carried out using the Bayes decision theory using the feature signals acquired through the experiments.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.813-823
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• 2013

On-line monitoring system is important for high voltage vacuum circuit breakers (HVCBs) in operation condition assessment and fault diagnosis. A distributed multilayer system with client/server architecture is developed on rated voltage 10kV HVCB with spring operating mechanism. It can collect data when HVCB switches, calculate the necessary parameters, show the operation conditions and provide abundant information for fault diagnosis. Ensemble empirical mode decomposition (EEMD) is used to detect the singular point which is regarded as the contact moment. This method has been applied to on-line monitoring system successfully and its satisfactory effect has been proved through experiments. SVM and FCM are both effective methods for fault diagnosis. A combinative algorithm is designed to judge the faults of HVCB's operating mechanism. The system's precision and stability are confirmed by field tests.

• Journal of the Korean Geographical Society
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• v.49 no.5
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• pp.675-689
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• 2014

This study applied Ensemble Empirical Mode Decomposition(EEMD), a new methodology to define the timing of spring onset over the Republic of Korea and to examine its spatio-temporal change. Also this study identified the relationship between spring onet timing and some atmospheric variations, and figured out synoptic factors which affect the timing of spring onset. The averaged spring onset timing for the period of 1974-2011 was 11th, March in Republic of Korea. In general, the spring onset timing was later with higher latitude and altitude regions, and it was later in inland regions than in costal ones. The correlation analysis has been carried out to find out the factors which affect spring onset timing, and global annual mean temperature, Arctic Oscillation(AO), Siberian High had a significant correlation with spring onset timing. The multiple regression analysis was conducted with three indices which were related to spring onset timing, and the model explained 64.7%. As a result of multiple regression analysis, the effect of annual mean temperature was the greatest and that of AO was the second. To find out synoptic factors affecting spring onset timing, the synoptic analysis has been carried out. As a result the intensity of meridional circulation represented as the major factor affect spring onset timing.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.671-680
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• 2019

Many researches illustrated that the magnitude and frequency of hydrological event would increase in the future due to changes of hydrological cycle components according to climate change. However, few studies performed quantitative analysis and evaluation of future rainfall in North Korea, where the damage caused by extreme precipitation is expected to occur as in South Korea. Therefore, this study predicted the extreme precipitation change of North Korea in the future (2020-2060) compared to the current (1981-2017) using stationary and nonstationary frequency analysis. This study conducted nonstationary frequency analysis considering the external factors (mean precipitation of JFM (Jan.-Mar.), AMJ (Apr.-Jun.), JAS (Jul.-Sept.), OND (Oct.-Dec.)) of the HadGEM2-AO model simulated according to the Representative Concentration Pathway (RCP) climate change scenarios. In order to select external factors that have a similar tendency with extreme rainfall events in North Korea, the maximum annual rainfall data was obtained by using the ensemble empirical mode decomposition (EEMD) method. Correlation analysis was performed between the extracted residue and the external factors. Considering selected external factors, nonstationary GEV model was constructed. In RCP4.5, four of the eight stations tended to decrease in future extreme precipitation compared to the present climate while three stations increased. On the other hand, in RCP8.5, two stations decreased while five stations increased.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.61-68
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• 2019

Recently, climate indices represented by quantifying atmospheric-ocean circulation patterns have been widely used to predict hydrologic variables for considering long-term climate variability. Hydrologic forecasting models based on artificial neural networks have been developed to provide accurate and stable forecasting performance. Forecasts of hydrologic variables considering climate variability can be effectively used for long-term management of water resources and environmental preservation. Therefore, identifying significant indicators for hydrologic variables and applying forecasting models still remains as a challenge. In this study, we selected representative climate indices that have significant relationships with dam inflow time series in the Han-River basin, South Korea for applying the dam inflow forecasting model. For this purpose, the ensemble empirical mode decomposition(EEMD) method was used to identify a significance between dam inflow and climate indices and an artificial neural network(ANN) ensemble model was applied to overcome the limitation of a single ANN model. As a result, the forecasting performances showed that the mean correlation coefficient of the five dams in the training period is 0.88, and the test period is 0.68. It can be expected to come out various applications using the relationship between hydrologic variables and climate variability in South Korea.