• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.364-364
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• 2012

수자원 연구의 주요 목적인 효과적인 홍수 및 가뭄관리를 하기 위해서는 그 연구의 기초가 되는 자료를 관측하고 정도(accuracy, 精度)를 향상시키는 연구 또한 매우 중요한 부분이라고 볼 수 있다. 이러한 점에서 수위-유량측정의 경우, 관측자의 숙련도와 계측기 오차에 따라 관측값에 미치는 영향이 큰 특징을 갖고 있어 유량측정의 정확성을 높이고자 진보된 계측기의 개발 및 분석 방법에 관한 연구는 꾸준히 진행되고 있다. 일반적으로 유량을 추정하기 위해서 특정 단면에서의 수위를 측정하여 이를 수위-유량 관계곡선을 통해서 유량으로 환산하고, 수위-유량 관계를 측정한 후 이를 회귀분석 방법으로 내삽 및 외삽을 실시하여 유량을 측정하게 된다. 그러나 수위-유량 관계곡선에서 저수위와 고수위를 하나의 곡선식으로 하게 되는 경우 정도가 낮아지게 되므로 많은 경우에 있어서 저수위, 고수위를 각각의 곡선으로 구하여 사용하고 있다. 문제는 이러한 경우 정량적으로 변곡점을 구하기보다는 경험적으로 저수위와 고수위를 구분하고 있으며, 수위-유량관계를 회귀식에 의해서 추정하게 되므로 이에 대한 불확실성이 발생하게 된다. 따라서 본 연구에서는 불확실성을 정량화시키기 위한 방법으로 Bayesian MCMC 기법을 활용하며 수위-유량 관계곡선식의 매개변수들의 사후분포를 추정하여 매개변수의 최적화 및 불확실성을 평가하였다. 앞서 언급되었듯이 저수위 및 고수위로 분리하여 수위-유량 곡선식을 도출하고 있으나 저수위 및 고수위를 분리하는 기준이 경험적이기 때문에 신뢰성이 저해되는 문제점이 발생한다. 본 연구에서는 수위-유량 곡선식의 매개변수들을 최적화 하는 동시에 Poisson 분포 기반의 변동점 분석이 연동되어 저수위 및 고수위를 분리할 수 있는 Bayesian 기반 통합 수위-유량 곡선 해석 방법을 개발하고자 한다.

• Journal of the Korean Geophysical Society
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• v.5 no.1
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• pp.29-39
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• 2002

Geophysical data sets from the Chojeong area in the Chungbok-Do are compositely studied in terms of multi-attribute interpretations for the subsurface mappings of shallow fracture zones, associated with groundwater reservoir. Utilizing a GIS software, the attribute data were implemented to a database; a lineament from the satellite image, electrical resistivities and its standard deviation, radioactivity, seismic velocity, and bedrock depth. In an attempt to interpret 1-D electrical sounding data in 3-D views, 1-D data are firstly performed horizontal and vertical inter- and extrapolation. Reconstruction of a resistivity volume is found to be an effective scheme for subsurface mapping of shallow fracture zones. Shallow fracture zones are located in the southeastern part of the study area, which are commonly correlated with the various exploration data.

• Journal of the Korean earth science society
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• v.32 no.6
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• pp.665-673
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• 2011

In order to estimate the hydrocarbon reserves, the porosity of the reservoir must be determined. The porosity of the area without a well is generally calculated by extrapolating the porosity logs measured at wells. However, if not only well logs but also seismic data exist on the same site, the more accurate pseudo porosity log can be obtained through artificial neural network technique by extracting the relations between the seismic data and well logs at the site. In this study, we have developed a module which creates pseudo porosity logs by using the polynomial neural network method. In order to obtain more accurate pseudo porosity logs, we selected the seismic attributes which have high correlation values in the correlation analysis between the seismic attributes and the porosity logs. Through the training procedure between selected seismic attributes and well logs, our module produces the correlation weights which can be used to generate the pseudo porosity log in the well free area. To verify the reliability and the applicability of the developed module, we have applied the module to the field data acquired from F3 Block in the North Sea and compared the results to those from the probabilistic neural network method in a commercial program. We could confirm the reliability of our module because both results showed similar trend. Moreover, since the pseudo porosity logs from polynomial neural network method are closer to the true porosity logs at the wells than those from probabilistic method, we concluded that the polynomial neural network method is effective for the data sets with insufficient wells such as F3 Block in the North Sea.

• Polymer(Korea)
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• v.36 no.6
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• pp.712-720
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• 2012

Thermal degradation for glass fiber-reinforced polyamide 66 composite (PA 66) with respect of thermal exposure time has been investigated using optical microscopy, scanning electron microscopy and Fourier transform infrared spectroscopy. As the thermal exposure time was prolonged, a slight increase in tensile strength for only initial stage and afterward, a proportional decrease of tensile strength was observed. These results can be explained by the increase of crystallinity, followed by the increase of crosslinking density, chain scission and the decrease in chain mobility, due to thermal oxidation with the exposure time. Fourier transform infrared spectroscopy results showed the increase of ketone peak and silica peak on the surface of thermally exposed PA 66. In addition, the thermal decomposition kinetics of PA 66 was analyzed using thermogravimetric analysis at three different heating rates. The relationship between activation energy and lifetime-prediction of PA 66 was investigated by several methodologies, such as statistical tool, UL 746B, Ozawa and Kissinger. The activation energy determined by thermogravimetric analysis had a relatively large value compared with that from the accelerated test. This may result in over-estimating the lifetime of PA 66. In this study, a master curve of exponential fitting has been developed to extrapolate the activation energy at various service temperatures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.5
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• pp.463-474
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• 2009

Double symmetric patch repair of existing structures always causes membrane action only, however, in many cases this technique is not practical. On the other hand, the bending stiffness of the patch and the skin increases as tensile loading is increased and affects the bending deformation significantly in the case of single-sided patch repair. In this study, the p-convergent full layerwise model has been proposed to determine the stress concentration factor in the vicinity of a circular hole as well as across the thickness of plates with single-sided patch repair. In assumed displacement field, the strain-displacement relations and 3-D constitutive equations of a layer are obtained by the combination of 2-D and 3-D hierarchical shape functions. The transfinite mapping technique has been used to represent a circular boundary and Gauss-Lobatto numerical integration is implemented in order to directly obtain stresses occurred at the nodal points of each layer without other extrapolation techniques. The accuracy and simplicity of the present model are verified with comparison of the previous results in literatures using experiment and conventional 3-D finite element. Also, the bending effect has been investigated with various patch types like square, circular and annular shape.

• Journal of Korea Water Resources Association
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• v.48 no.10
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• pp.807-816
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• 2015

In general, the water stage-discharge relationship curve is established based on the assumptions of linearity and homoscedasticity. However, the relationship between the water stage and discharge is affected from geomorphological factors, which violates the basic assumptions of the water stage-discharge relationship curve. In order to reduce the error due to the violations, the curve is divided into several sections based on the manager's judgement considering change of cross-sectional shape. In this research, the objective-splitting criteria of the curve is proposed based on the measured data without the subjective decision. First, it is assumed that the coefficient of variation follows the normal distribution. Then, if the newly calculated coefficient of variation is outside of the 95% confidential interval, the curve is divided. Namely, the groups is divided by the characteristics of the coefficient of variation and the reasonable criteria is provided for establishing a multi-segmented rating curve. To validate the proposed method, it was applied to the data generated by three artificial power functions. In addition, to confirm the applicability of the proposed method, it is applied to the water stage and discharge data of the Muju water stage gauging station and Sangegyo water stage gauging station. As a result, it is found that the automatically divided rating curve improves the accuracy and extrapolation accuracy of the rating curve. Finally, through the residual analysis using Shapiro-Wilk normality test, it is confirmed that the residual of water stage-discharge relationship curve tends to follow the normal distribution.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.29-41
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• 2017

Design rainfalls are one of the most important hydrologic data for river management, hydraulic structure design and risk analysis. The design rainfalls are first estimated by a point frequency analysis and the IDF (intensity-duration-frequency) curve is then constructed by a nonlinear regression to either interpolate or extrapolate the design rainfalls for other durations which are not used in the frequency analysis. It has been widely recognised that the more reliable approaches are required to better account for uncertainties associated with the model parameters under circumstances where limited hydrologic data are available for the watershed of interest. For these reasons, this study developed a hierarchical Bayesian based GLM (generalized linear model) for a regional frequency analysis in conjunction with a scaling function of the parameters in probability distribution. The proposed model provided a reliable estimation of a set of parameters for each individual station, as well as offered a regional estimate of the parameters, which allow us to have a regional IDF curve. Overall, we expected the proposed model can be used for different aspects of water resources planning at various stages and in addition for the ungaged basin.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.587-601
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• 2020

The ocean circulation was simulated in the East Sea and Ulleungdo-Dokdo region using ROMS (Regional Ocean Modeling System) model. By adopting the East Sea 3 km model and the HYCOM 9 km data, Ulleungdo 1 km model and Ulleungdo-Dokdo 300 m model were constructed with one-way grid nesting method. During the model development, a correction method was proposed for the distortion of the open boundary data which may be caused by the bathymetry data difference between the mother and child models and the interpolation/extrapolation method. Using this model, a super-high resolution ocean circulation with a horizontal resolution of 300 m near the Ulleungdo and Dokdo region was simulated for year 2018. In spite of applying the same conditions except for the initial and boundary data, the numerical models result indicated significantly different characteristics in the study area. Therefore, these results were compared and verified by using the surface current data estimated by satellites altimeter data and temperature data from NIFS (National Institute of Fisheries Science). They suggest that in general, the improvement of the one-way grid nesting with the HYCOM data on RMSE, Mean Bias, Pattern correlation and Vector correlation is greater in 300 m model than in the 1 km model. However, the nesting results of using East Sea 3 km model showed that simulations of the 1 km model were better than 300 m model. The models better resolved distinct ridge/trough structures of isotherms in the vertical sections of water temperature when using the higher horizontal resolution. Furthermore, Karman vortex street was simulated in Ulleungdo-Dokdo 300 m model due to the terrain effect of th islands that was not shown in the Ulleungdo 1 km model.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.