• 천문학회지
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• 제53권6호
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• pp.139-147
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• 2020

The sunspot area is a critical physical quantity for assessing the solar activity level; forecasts of the sunspot area are of great importance for studies of the solar activity and space weather. We developed an innovative hybrid model prediction method by integrating the complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM). The time series is first decomposed into intrinsic mode functions (IMFs) with different frequencies by CEEMD; these IMFs can be divided into three groups, a high-frequency group, a low-frequency group, and a trend group. The ELM forecasting models are established to forecast the three groups separately. The final forecast results are obtained by summing up the forecast values of each group. The proposed hybrid model is applied to the smoothed monthly mean sunspot area archived at NASA's Marshall Space Flight Center (MSFC). We find a mean absolute percentage error (MAPE) and a root mean square error (RMSE) of 1.80% and 9.75, respectively, which indicates that: (1) for the CEEMD-ELM model, the predicted sunspot area is in good agreement with the observed one; (2) the proposed model outperforms previous approaches in terms of prediction accuracy and operational efficiency.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• Structural Monitoring and Maintenance
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• 제8권4호
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• pp.379-402
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• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Ocean and Polar Research
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• 제41권3호
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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.

• 응용통계연구
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• 제27권3호
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• pp.431-444
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• 2014

본 논문에서는 시간-진동수 영역에서 시계열을 여러 구성 성분으로 분해하는 방법인 경험적모드분해법(Empirical Mode Decomposition)을 소개하고, 이를 이용하여 한국의 주요 거시 경제 지표를 대상으로 순환변동과 추세 성분을 추출하고 예측에 활용한다. 그 효율성을 살펴보기 위하여, 추출된 구성 성분들의 변동성, 동행성, 지속성, 인과성, 비정상성 및 예측력을 계산하고, 가장 보편적으로 널리 사용되고 있는 Hodrick-Prescott 필터에 의한 결과와 비교한다.

• Communications for Statistical Applications and Methods
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• 제22권2호
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• pp.159-172
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• 2015

This paper considers the problem of reconstructing an underlying signal from noisy data. This paper presents a reconstruction method based on synchrosqueezed wavelet transform recently developed for multiscale representation. Synchrosqueezed wavelet transform based on continuous wavelet transform is efficient to estimate the instantaneous frequency of each component that consist of a signal and to reconstruct components. However, an objective selection method for the optimal number of intrinsic mode type functions is required. The proposed method is obtained by coupling the synchrosqueezed wavelet transform with cross-validation scheme. Simulation studies and musical instrument sounds are used to compare the empirical performance of the proposed method with existing methods.

• 정보과학회 논문지
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• 제41권12호
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• pp.1050-1057
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• 2014

뇌전도 기반의 뇌-컴퓨터 인터페이스는 향후 손 또는 발과 같은 신체를 대체하거나 사용자의 편의성을 제고하는 등의 다양한 목적으로 여러 산업에서 사용이 될 수 있는 기술이다. 본 논문에서는 경험 모드 분해와 고속푸리에 변환을 통해 동작 상상 뇌전도 신호를 분해하고 특징을 추출하는 방법을 제안한다. 뇌전도 신호 분류 과정은 다음과 같이 3단계로 구성된다. 신호 분해에서는 경험모드분해를 이용하여 뇌전도 신호에 대한 내재모드함수를 생성한다. 특징 추출에서는 파워 스펙트럼 밀도를 이용하여 생성된 내재모드함수의 주파수 대역을 확인한 뒤, 뮤파 대역을 포함하고 있는 내재모드함수에 고속푸리에 변환을 적용하여 움직임 상상에 대한 특징을 추출한다. 특징 분류에서는 서포트 벡터 머신을 사용하여 동작 상상 뇌전도 신호에 대한 특징을 분류하고, 10-교차검증을 통해 분류기의 일반화 성능을 추정한다. 제안하는 방법은 다른 방법들과 비교하여 84.50%의 분류 정확도를 보여주었다.

• Structural Engineering and Mechanics
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• 제36권6호
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• pp.697-714
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• 2010

This paper presents an improved version of the Hilbert-Huang transform (HHT) for the modal evaluation of structural systems or signals. In this improved HHT, a well-designed bandpass filter is used as preprocessing to separate and determine each mode of the signal for solving the inherent modemixing problem in HHT (i.e., empirical mode decomposition, EMD, associated with the Hilbert transform). A screening process is then applied to remove undesired intrinsic mode functions (IMFs) derived from the EMD of the signal's mode. A "best" IMF is selected in each screening process that utilizes the orthogonalization coefficient between the signal's mode and its IMFs. Through mode-by-mode signal filtering, parameters such as the modal frequency can be evaluated accurately when compared to the theoretical value. Time history of the identified modal frequency is available. Numerical results prove the efficiency of the proposed approach, showing relative errors 1.40%, 2.06%, and 1.46%, respectively, for the test cases of a benchmark structure in the lab, a simulated time-varying structural system, and of a linear superimposed cosine waves.

• 응용통계연구
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• 제29권3호
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• pp.525-537
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• 2016

본 연구에서는 시계열 자료의 비정상성과 비선형성과 같은 복잡성을 효과적으로 포용할 수 있는 경험적모드분해법(empirical mode decomposition; EMD)을 토대로 시계열 자료의 분석 및 예측을 위한 혼합(hybrid) 모형을 연구한다. EMD에 의하여 생성되는 내재모드함수(intrinsic mode function; IMF)는 해석 및 예측의 편리성을 개선하기 위하여 누적에너지의 개념을 사용하여 그룹화하였으며, 그룹화된 IMF 및 residue의 성분들은 그 성질에 따라서 ARIMA 모형 및 지수평활법과 결합된 혼합 모형으로 예측된다. 제안된 방법은 일별 코스피 지수의 예측을 위해서 적용하였다. 다양한 형태의 혼합 모형을 사용하여 코스피 지수를 예측하였으며 전통적인 예측 방법과 비교하였다. 분석 결과, 그룹화된 성분들은 코스피 지수의 움직임을 단기적, 중기적, 장기적으로 해석하는데 편리함을 주었으며, 그룹화된 IMF 및 residue를 각각 ARIMA 모형과 지수평활법으로 조합한 혼합 모형이 우수한 예측력을 보여주었다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2009년도 추계학술대회 논문집
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• pp.99-105
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• 2009

Crack detection method of a rotating blade was suggested in this paper. A rotating blade was modeled with a cantilever beam connected to a hub undergoing rotating motion. The existence and the location of crack were able to be recognized from the vertical response of end tip of a rotating cantilever beam by employing Discrete Hidden Markov Model (DHMM) and Empirical Mode Decomposition (EMD). DHMM is a famous stochastic method in the field of speech recognition. However, in recent researches, it has been proved that DHMM can also be used in machine health monitoring. EMD is the method suggested by Huang et al. that decompose a random signal into several mono component signals. EMD was used in this paper as the process of extraction of feature vectors which is the important process to developing DHMM. It was found that developed DHMMs for crack detection of a rotating blade have shown good crack detection ability.