• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.2
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• pp.41-51
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• 2013

The tidal creek dependent mass transport characteristic in Gyeong-Gi Bay (west coast of Korea) was studied using field measured data and numerical model. Gyeong-Gi Bay consists of 3 main tidal channels and contains a well-developed vast tidal flat. This region is famous for its large tidal difference and strong current. We aim to study the effect of tidal creek in the tidal flat on the mass exchange between the estuary and the ocean. For numerical application, the application of unstructured grid feature is essential, since the tidal creek has complicated shape and form. For this purpose, the FVCOM is applied to the study area and simulation is performed for 2 different cases. In case A, geographic characteristics of the tidal creek is ignored in the numerical grid and in case B, the tidal creek are constructed using unstructured grid. And these 2 cases are compared with the field measured cross-channel mass transport data. The cross-channel mass transport at the Yeomha waterway mouth and Incheon harbor was measured in June, 9~10 (Spring tide) and 17~18 (Neap tide), 2009. CTD casting and ADCP cross-channel transect was conducted 13 times in one tidal cycle. The observation data analysis results showed that mass transport has characteristic of the ebb dominance Line 1 (Yeomha waterway mouth), on the other hand, a flood dominant characteristic is shown in Line 2 (Incheon harbor front). By comparing the numerical model (case A & B) with observation data, we found that the case B results show much better agreement with measurement data than case A. It is showed that the geographic feature of tidal creek should be considered in grid design of numerical model in order to understand the mass transport characteristics over large tidal flat area.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.427-438
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• 2021

Two-stage sampling allows us to estimate population characteristics by both unit and cluster level together. Given a complex auxiliary information, integrated calibration weighting would better reflect the level-wise characteristics as well as multivariate characteristics between levels. This paper explored the integrated calibration weighting methods by Estevao and Särndal (2006) and Kim (2019) through a simulation study, where the efficiency of those weighting methods was compared using an artificial population data. Two weighting methods among others are shown efficient: single step calibration at the unit level with stacked individualized auxiliary information and iterative integrated calibration at each level. Under both methods, cluster calibrated weights are defined as the average of the calibrated weights of the unit(s) within cluster. Both were very good in terms of the goodness-of-fit of estimating the population totals of mutual auxiliary information between clusters and units, and showed small relative bias and relative mean square root errors for estimating the population totals of survey variables that are not included in calibration adjustments.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.156-156
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• 2021

본 연구는 지역빈도해석을 기반으로한 인공신경망 모델과 기존에 널리 사용되는 방법인 홍수지수법의 성능을 몬테카를로 시뮬레이션을 이용하여 평가하였다. 컴퓨터 기술이 발달함에 따라 인공지능에 대한 접근성이 좋아지며 수문학을 포함한 다양한 분야에 적용되고 있다. 인공지능을 이용하여 강수량 및 유량 등 다양한 수문자료에 대한 예측이 이루어지고 있으나 빈도해석에 관한 연구는 비교적 적다. 본 연구에서 사용된 인공 지능 모델은 대상 지점의 지형학적 자료와 수문학적 자료를 이용하여 인공신경망을 통해 지점의 확률강우량(QRT-ANN) 및 확률분포형의 매개변수 (PRT-ANN)를 추정한다. 지형학적 자료로는 위도, 경도 그리고 고도가 사용되었으며 수문학적 자료로는 대상 지점의 최근 30년 일일연최대강우량을 사용하였다. 지역빈도해석의 정확도는 지역 내 통계적 특성이 비슷한 지점들이 포함되면 될수록 높아진다. 통계적 특성으로는 불일치 척도, 이질성 척도, 적합성 척도가 있으며 다양한 조건의 통계적 특성에 따른 세 개의 지역빈도해석 방법의 성능을 평가하고자 하였다. 대상 지역 내 n개의 지점이 있다고 가정하였을 때, 홍수지수법의 경우 n-1개의 지점으로 추정한 지역 성장곡선을 이용하여 나머지 1개 지점의 확률강우량을 산정할 수 있으며 인공신경망 모델들 또한 n-1개 지점들의 자료를 이용하여 모델을 구축한 뒤 나머지 지점의 확률강우량 및 확률분포형의 매개변수를 예측할 수 있다. PRT-ANN의 경우 예측된 매개변수를 이용하여 확률강우량을 산정하며 시뮬레이션 시행마다 발생시킨 자료의 지점빈도해석 결과에 대한 나머지 세 방법의 평균 제곱근 상대오차 (Relative root mean square error, RRMSE)를 계산하였다. 몬테카를로 시뮬레이션을 이용한 성능 분석을 통하여 관측값의 다양한 통계적 특성에 맞는 지역빈도해석 방법을 제시할 수 있을 것으로 판단된다.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.127-133
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• 2014

The purpose of this study is to evaluate the accuracy of Network-RTK(VRS) survey for applying to Ground Control Points(GCPs) survey required for mapping aerial photographs. Network-RTK has been serviced by National Geographic Information Institute since 2007. On the basis of the global coordinates system(ITRF2000), the coordinates of GCPs determined by Static GNSS survey with relative positioning techniques were regarded as accurate values. The coordinates of GCPs were also determined by Network-RTK survey using two kinds of receivers, and then they were converted into the global coordinates system(ITRF2000) by applying suitable geoid model and coordinate transformation. These coordinates of GCPs were compared with those from Static GNSS survey. The root mean squares error (RMSE) of coordinate differences between Network-RTK and Static GNSS was ${\pm}2.0cm$ in plane and ${\pm}7.0cm$ in height. Therefore, Network-RTK survey that enables single GNSS receiver to measure positions in short time is a practical alternative in positioning GCPs to either RTK survey that uses more than two sets of GNSS receivers or Static GNSS survey that requires longer observation time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.1813-1818
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• 2014

This paper presents the stability analysis of the haptic system including a human impedance using the Routh-Hurwitz criterion. The reflective force is computed from a virtual spring model and is transferred to a human operator using the first-order-hold method. The stability boundary conditions are induced and the relation among a virtual spring ($K_w$), the mass ($M_h$), the damping ($B_h$) and the stiffness ($K_h$) of a human impedance is analyzed. Hence the stability boundary of the virtual spring ($K_w$) is proposed as $K_w{\leq}54413{\sqrt{(M_h+M_d)(B_h+B_d)}}-0.486K_h$ when the sampling time is 1 ms. The average relative error is about 0.5% when the mathematical analysis results are compared with the results of the stability boundary model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.11
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• pp.5338-5343
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• 2013

In a haptic system, a virtual wall is modeled as a virtual spring. The larger the stiffness of the virtual spring is, the more improved the reality of the virtual wall is, but the more unstable the haptic system becomes. This paper shows how to increase the stiffness of the virtual spring while the stability of the haptic system is guaranteed and shows the effects of a mass (Md) and a damper (Bd) of a haptic device on the stability when first-order hold method is applied and a virtual wall is modeled as a virtual spring (Kw). The simulation results show the boundary of the virtual spring is proportional to the square root of the mass (Md) and the damper (Bd) while maintaining the stability. The relation among the virtual spring (Kw), the mass (Md) and the damper (Bd) of the haptic device, and sampling time (T) is inferred as $K_w{\leq}{1.611M_d}^{0.50}{B_d}^{0.50}T^{-1.51}$, by using the simulation results. The maximum available stiffness of the virtual spring in first-order hold method is larger than in zero-order hold method. So the reality of the virtual wall can be improved.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.