• Journal of Korea Water Resources Association
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• v.53 no.5
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• pp.383-393
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• 2020

The main objective of this study is to provide a robust model for estimating parameters of the Clark unit hydrograph (UH) using the observed rainfall-runoff data in the Soyangang dam basin. In general, HEC-1 and HEC-HMS models, developed by the Hydrologic Engineering Center, have been widely used to optimize the parameters in Korea. However, these models are heavily reliant on the objective function and sample size during the optimization process. Moreover, the optimization process is carried out on the basis of single rainfall-runoff data, and the process is repeated for other events. Their averaged values over different parameter sets are usually used for practical purposes, leading to difficulties in the accurate simulation of discharge. In this sense, this paper proposed a hierarchical Bayesian model for estimating parameters of the Clark UH model. The proposed model clearly showed better performance in terms of Bayesian inference criterion (BIC). Furthermore, the result of this study reveals that the proposed model can also be applied to different hydrologic fields such as dam design and design flood estimation, including parameter estimation for the probable maximum flood (PMF).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.564-570
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• 2019

Although some studies have been attempted to utilize 3D spatial information for fire safety and disaster management, it is still not enough to apply it to actual work. Especially, in case of multi-use facilities, many facilities are more vulnerable to rapid response in the event of a disaster due to complexity of facilities, diversity of usage, and specificity of users. In this paper, we propose a method to develop a 3D safety status information platform that combines 3D spatial information and time - varying safety status information for efficient disaster management of multi-use facilities. In detail, first, we analyze the use cases of existing disaster management platform and the needs of business users. Second, based on the analyzed results, target facilities were selected and possible scenarios were created. Finally, we developed platform architecture design and service development strategy. The research results will be used as a basis for future 3D safety status information platform development. This will contribute to improving the safety of multi-use facilities and minimizing damage to disaster vulnerable groups.

• Journal of the Korea Concrete Institute
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• v.19 no.1
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• pp.91-102
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• 2007

Through lessons in recent earthquakes, the bridge engineering community recognizes the need for new seismic design methodologies based on the inelastic structural performance of RC bridge structures. This study represents results of performance-based fragility analysis of reinforced concrete (RC) bridge. Monte carlo simulation is performed to study nonlinear dynamic responses of RC bridge. Two-parameter log-normal distribution function is used to represent the fragility curves. These two-parameters, referred to as fragility parameters, are estimated by the traditional maximum likelihood procedure, which is treated each event of RC bridge pier damage as a realization of Bernoulli experiment. In order to formulate the fragility curves, five different damage states are described by two practical factors: the displacement and curvature ductility, which are mostly influencing on the seismic behavior of RC bridge piers. Five damage states are quantitatively assessed in terms of these seismic ductilities on the basis of numerous experimental results of RC bridge piers. Thereby, the performance-based fragility curves of RC bridge pier are provided in this paper. This approach can be used in constructing the fragility curves of various bridge structures and be applied to construct the seismic hazard map.

• Journal of Distribution Science
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• v.11 no.5
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• pp.43-53
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• 2013

Purpose - The purpose of this study is to analyze the effectiveness of customer relationship management (CRM) support policies for facilitating traditional markets, especially with respect to customer acquisition and maintenance, and to investigate the factors affecting CRM. Research design, data, and methodology - We analyzed the CRM efficiency of traditional markets in 16 cities and provinces in Korea on the basis of DEA analysis and Malmquist productivity analysis. The DEA model calculates a ratio of the weighted mean of various inputs to the weighted mean of various outputs and measures the efficiency of a specific decision making unit (DMU), which is compared to the reference group that has a similar input-output structure. The input variables are coupon, event, parcel service, premiums, while is the number of customers per day. Further, through regression analysis, we analyzed CRM-related factors affecting traditional markets' customer appeal and revenue growth. Results - We obtained the results of the efficiency of traditional markets in 16 provinces. The traditional markets in Seoul, Busan, and Jeju were found to be efficient in a model CCR that used the number of customers per day as an output variable, while Chungbuk, Jeonbuk Province, and According to the results of the DEA analysis and Malmquist productivity analysis, large cities such as Seoul, Busan, and Jeju showed efficiency in CRM-related investment businesses in traditional markets for attracting customers. The Malmquist analysis results confirmed that the productivity of traditional markets increased from 2008 to 2010. The results of the regression analysis revealed that the "customer acquisition/maintenance factor" and the "offering of customer convenience facility factor" were significant to the daily average number of customers, which is a dependent variable. The results of the test with the mediating variable, "number of customers," and the final dependent variable, "sales revenue," were rejected. However, the variable "customer acquisition /maintenance" was found to affect sales revenue positively. Conclusions - It is necessary to enhance the business not only for promotional activities to attract customers, but also to strengthen customer relationships among CRM businesses, such as through the management of key customers. The regression analysis results showed that CRM businesses have yet to produce an increase in sales revenues in traditional markets. Therefore, to help customers who visit traditional markets to keep buying products, it is necessary to prepare various investment methods and provide support to improve "customer loyalty." This study has a limitation in terms of CRM-related statistics. Therefore, in the future, it is necessary to conduct a survey of customers who use traditional markets to analyze the markets by type and size as well as the CRM-related factors. Based on the analysis, we will try to perform a variety of statistical analyses, including structural equations.

• Journal of the Earthquake Engineering Society of Korea
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• v.6 no.5
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• pp.59-66
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• 2002

Long-rails in railways and high-speed railway are subjected to additional stresses resulted from the displacements inconsistence between upper structures, and this phenomenon is more remarkable in continuous bridges than in simple bridges. For the sake of safety, railways have to guarantee trains to stop safely without derailment even in the event of earthquake. The influences of acceleration, braking, and temperature were analyzed by static nonlinear method. But earthquake loads that require dynamic nonlinear analysis are not considered in these methods. Because linear relation between relative displacements of decks and rail stresses is not guaranteed at the nonlinear systems such as long rails on the bridges, it is required compute to rail stresses considering both braking and earthquake load by nonlinear dynamic analysis method. In this study, dynamic analysis method with material non-linearity for rails on continuous bridges according to the Taiwan High Speed Railway(THSR) Design Specification volume 9 was developed. And additional stresses and displacements of long rails for acceleration, braking, and earthquake loads were analyzed by this method.

• Nuclear Engineering and Technology
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• v.47 no.3
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• pp.240-252
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• 2015

In the original licensing application for the prototype fast-breeder reactor, MONJU, the event progression during an unprotected loss of flow (ULOF), which is one of the technically inconceivable events postulated beyond design basis, was evaluated. Through this evaluation, it was confirmed that radiological consequences could be suitably limited even if mechanical energy was released. Following the Fukushima-Daiichi accident, a new nuclear safety regulation has become effective in Japan. The conformity of MONJU to this new regulation should hence be investigated. The objectives of the present study are to conduct a preliminary evaluation of ULOF for MONJU, reflecting the knowledge obtained after the original licensing application through CABRI experiments and EAGLE projects, and to gain the prospect of in-vessel retention for the conformity of MONJU to the new regulation. The preliminary evaluation in the present study showed that no significant mechanical energy release would take place, and that thermal failure of the reactor vessel could be avoided by the stable cooling of disrupted-core materials. This result suggests that the prospect of in-vessel retention against ULOF, which lies within the bounds of the original licensing evaluation and conforms to the new nuclear safety regulation, will be gained.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3068-3084
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• 2021

Investigations of large commercial aircraft impact effect on nuclear power plant (NPP) buildings have been drawing extensive attentions, particularly after the 9/11 event, and this paper aims to experimentally assess the damage and vibrations of NPP buildings subjected to aircraft crash. In present Part I, two shots of reduce-scaled model test of aircraft impacting on NPP building were carried out. Firstly, the 1:15 aircraft model (weighs 135 kg) and RC NPP model (weighs about 70 t) are designed and prepared. Then, based on the large rocket sled loading test platform, the aircraft models were accelerated to impact perpendicularly on the two sides of NPP model, i.e., containment and auxiliary buildings, with a velocity of about 170 m/s. The strain-time histories of rebars within the impact area and acceleration-time histories of each floor of NPP model are derived from the pre-arranged twenty-one strain gauges and twenty tri-axial accelerometers, and the whole impact processes were recorded by three high-speed cameras. The local penetration and perforation failure modes occurred respectively in the collision scenarios of containment and auxiliary buildings, and some suggestions for the NPP design are given. The maximum acceleration in the 1:15 scaled tests is 1785.73 g, and thus the corresponding maximum resultant acceleration in a prototype impact might be about 119 g, which poses a potential threat to the nuclear equipment. Furthermore, it was found that the nonlinear decrease of vibrations along the height was well reflected by the variations of both the maximum resultant vibrations and Cumulative Absolute Velocity (CAV). The present experimental work on the damage and dynamic responses of NPP structure under aircraft impact is firstly presented, which could provide a benchmark basis for further safety assessments of prototype NPP structure as well as inner systems and components against aircraft crash.

• Journal of the Mineralogical Society of Korea
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• v.22 no.3
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• pp.207-216
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• 2009

Three distinct types of fluid inclusions in amethyst and quartz crystals are associated with metamorphic events in the Korea Amethyst deposit from Uljin-Gun, Gyeongbuk Province. The amethyst displays bimodal grain size distribution in fine-grained, strain-free equigranular quartz with coarse-grained quartz grains with kink bands and undulose extinction. Type I inclusions are liquid-rich and salinity is 0~7 wt% NaCl and the homogenization temperatures ($T_h$) $91{\sim}231^{\circ}C$ with eutectic temperatures ($T_e$) $-52{\sim}-20^{\circ}C$. Type II inclusions are vapor-rich (80~90 vol%). The salinity and $T_h$ ranges 3~6 wt% NaCl and $230{\sim}278^{\circ}C$, respectively with $T_e$ $-56{\sim}-23^{\circ}C$. Type III inclusions contain a daughter mineral other than NaCl. The salinity ranges 32~36 wt% NaCl and $T_h$ $210{\sim}271^{\circ}C$. The textural and fluid inclusion evidences suggest that the host Buncheon granite gneiss and Amethyst pegmatite experienced dynamic recrystallization and the studied fluid inclusions are metamorphic in origin. The metamorphic event possibly occurred at higher temperature than $271{\sim}278^{\circ}C$. The amethysts from Uljin Korea Amethyst can be distinguished from the synthetic amethyst on basis of the distinctive two and three-phases fluid inclusions. Furthermore, it is noticeable that Korea amethyst do not contain NaCl-bearing and $CO_2$-rich fluid inclusions unlike those compared to those from Eonyang and Samcheonpo deposits related to unmetamorphosed granitic rocks.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.