• Journal of Positioning, Navigation, and Timing
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• 제11권4호
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• pp.287-295
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• 2022

The GNSS coordinate time series is used as important data for geophysical analysis such as terrestrial reference frame establishment, crustal deformation, Earth orientation parameter estimation, etc. However, various factors may cause discontinuity in the coordinate time series, which may lead to errors in the interpretation. In this paper, we describe the discontinuity in the coordinate time series due to the equipment replacement for domestic GNSS stations and discuss the change in movement magnitude and velocity vector difference in each direction before and after discontinuity correction. To do this, we used three years (2017-2019) of data from 40 GNSS stations. The average magnitude of the velocity vector in the north-south, east-west, and vertical directions before correction is -12.9±1.5, 28.0±1.9, and 4.2±7.6 mm/yr, respectively. After correction, the average moving speed in each direction was -13.0±1.0, 28.2±0.8, and 0.7±2.1 mm/yr, respectively. The average magnitudes of the horizontal GNSS velocity vectors before and after discontinuous correction was similar, but the deviation in movement size of stations decreased after correction. After equipment replacement, the change in the vertical movement occurred more than the horizontal movement variation. Moreover, the change in the magnitude of movement in each direction may also cause a change in the velocity vector, which may lead to errors in geophysical analysis.

• 대한토목학회논문집
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• 제30권3A호
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• pp.211-219
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• 2010

고속철도교량의 설계개념은 정적하중에 따른 충격계수를 고려하여 기존 교량 구조물의 강성을 증가시키기 위한 방안을 적용하고 있으며, 전반적인 구조설계 과정은 선진 외국기술에 의존하고 있는 실정이다. 그러나 고속철도(Korea Train eXpress: KTX)의 긴 연장(380 m)과 고속(300 km/h) 주행은 공진현상에 상당한 영향을 미치기 때문에 고속철도교량의 동적증폭계수(DAF) 및 동적성능 평가는 상세한 검토가 필수적으로 수행되어야 할 것이다. 따라서 이 연구에서는 전형적인 PSC 박스 거더 고속철도교량을 대상으로 동적성능을 효율적으로 검토하고자 하며, 합리적인 구조설계를 위한 기초자료를 제시하고자 한다. 이를 위해, 기존문헌을 토대로 KTX의 하중선도를 고려하여 정적해석을 수행하였다. 또한 다양한 해석변수를 고려하여 KTX의 이동하중을 시계열하중으로 변환하였으며, 변환된 시계열하중을 이용하여 시간이력해석을 합리적으로 평가하였다. 이때, 시계열하중을 산정하기 위한 변수는 KTX의 하중재하 절점간격, 시간증분 및 속도변화를 고려하였다. FE해석 결과를 바탕으로 PSC 박스 거더 고속철도교량의 동적성능을 체계적으로 검토하였으며, 국내외 관련규정에 따라 구조안전성을 정량적으로 평가하였다.

• 대한예방의학회:학술대회논문집
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• 대한예방의학회 2004년도 하계워크샵 및 전공의 연수교육 강의집
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• pp.21-37
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• 2004

• 디지털융복합연구
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• 제16권8호
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• pp.393-400
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• 2018

본 연구는 스포츠 이슈에 따른 집단 감정으로서의 스포츠 감정의 생성과 전개에 대한 감정 동학을 실증적으로 규명하고자 수영선수 박태환의 금지 약물 사건을 연구대상으로 선정하였다. 전체 전개 과정을 총 10개의 에피소드로 단순화 한 후, 각각의 신문기사와 댓글을 시계열 분석 하였다. 본 연구의 결론은 다음과 같다. 첫째, 스포츠 이슈와 스포츠 감정 간에는 명확한 인과 관계가 있었다. 둘째, 스포츠 감정은 사회적 과정과 긴밀한 상호작용을 주고받는 사회적 존재임을 확인할 수 있었다. 셋째, 스포츠 이슈에 따른 스포츠 감정, 집단 행위, 사회적 변화 간의 역동적인 상호작용으로 이루어진 스포츠 감정 동학의 기제는 타당한 것으로 분석되었다. 본 연구는 스포츠 감정 동학의 기제를 시계열 분석이라는 실증적 접근을 통해 검증하였으나, 추후 보다 계량적인 통계 기법을 통한 추가 연구가 필요할 것이다.

• Smart Structures and Systems
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• 제21권5호
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• pp.705-713
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• 2018

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

• 응용통계연구
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• 제29권5호
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• pp.907-920
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• 2016

본 논문의 목표는 분위수 자기회귀모형을 활용하여 시계열 자료에서 특이치를 발견하는 알고리즘을 제안하고, 기존의 방법들과 그 성능을 비교하여 실제 주가 조작 사례에 적용해 보는 것이다. 지금까지의 특이치 발견 연구는 대부분 일반적인 데이터 형태에서만 있어왔기 때문에 시계열 데이터에서의 연구는 미미한 편이다. 또한 모수적인 방법에만 제한되었는데, 모수적 모형은 복잡할 뿐만 아니라 소요되는 분석 시간도 길기 때문에 편리하지 않다. 따라서 본 연구에서는 분위수 자기회귀모형을 활용한 특이치 발견 알고리즘을 새롭게 제시하고, 다양한 경우의 모의실험을 통해 기존 알고리즘과 비교하도록 한다. 특히 시계열 자료에서의 특이치 발견은 주가 조작을 적발하는 데에 유용하게 활용될 수 있다. 시간에 따라 관측되던 주가가 갑자기 그 동안의 흐름에서 벗어나 특이치로 발견되었다면 혹시 인위적인 개입으로 조작된 것은 아닌지 의심해 볼 수 있기 때문이다. 따라서 실제 주가 조작 사례에 적용해 봄으로써 얼마나 빠른 시일 내에 주가 조작을 적발해 낼 수 있는지 살펴보았다.

• 품질경영학회지
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• 제24권1호
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• pp.137-145
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• 1996

We propose a model which is useful for the analysis of the spatial time series data. The proposed model utilized the linear dependences across the spatial units as well as over time. Three stage model fitting procedures are suggested and the real data is analyzed.

• Journal of Welding and Joining
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• 제18권5호
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• pp.77-83
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• 2000

This study proposes the analysis and evaluation method of time series ultrasonic signal using the attractor analysis. Features extracted from time series signal analyze quantitatively characteristics of weld defects. For this purpose, analysis objective in this study is fractal dimension and attractor quadrant feature. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange even though the types of defects are identified. These difference in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hole) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimensions. Proposed attractor analysis and DAC in this study can enhance the precision rate of ultrasonic evaluation for defect signals of rail weld zone such as side hole and crack.

• 한국대기환경학회지
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• 제4권2호
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• pp.72-81
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• 1988

The typical ARIMA model which was developed by Box and Jenkins, was applied to the monthly $SO_2$ data collected at Seoungsoo and Oryudong in metropolitan area over five years, 1982 to 1986. To find out the changing pattern of $SO_2$ concentration, autocorrelation and partial autocorrelation analysis were undertaken. The three steps of time series model building were followed and the residual series was found to be a random white noise. The results of this study is summarized as follows. 1) The monthly $SO_2$ series was found to be a non-stationary series which which has a periodicity of 12 months. After eliminating the periodicity by differencing, the monthly $SO_2$ series became a stationary series. 2) The ARIMA seasonal model of the $SO_2$ was determined to be ARIMA $(1, 0, 0)(0, 1, 0,)_{12}$ model. 3) The model equations based on the prediction were: for Seoungsoodong: $Y_t = 0.5214Y_{t-1} + Y_{t-12} - 0.5214Y_{t-13} + a_t$ for Oryudong: $Y_t = 0.8549Y_{t-1} + Y_{t-12} - 0.8549Y_{t-13} + a_t$ 4) The validity of the model identified was checked by compairing the measured $SO_2$ values and one-month-ahead predicted values. The result of correlation and regression analysis is as follows. Seoungsoodong: $Y = 0.8710X + 0.0062 r = 0.8768$ Oryudong : $Y = 0.8758X + 0.0073 r = 0.9512$

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.