• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.787-790
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• 2008

The SWAT(Soil and Water Assesment Tool) is a relatively large scale model for the complicated watershed or river basin. The model was developed to predict the effect of land management practices on water, sediment and agricultural chemical yields in large complex watershed with varying soils, land use and management conditions over long periods of time. Usually streams are divided into urban stream and natural stream in accordance with the development level. In case of urban stream, according to urbanization, as impermeable areas are increasing due to the change of land use condition and land cover condition, dry stream phenomenon at urban stream is rapidly progressed. In this study, long term run-off simulations in urban stream are performed by using SWAT model. Especially, the model is applied in small scale water shed, Joman River basin. The optimization by the sensitivity analysis is also performed for the model parameter estimations.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.4
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• pp.112-118
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• 2013

The selection of appropriate ground motions and reasonable modification are becoming increasingly critical in reliable prediction on seismic performance of structures. A widely used amplitude scaling approach is not sufficient for robust structural evaluation considering a site specific seismic hazard because only one spectral value is matched to the design spectrum typically at the structural fundamental period. Hence alternative approaches for ground motion selection and modifications have been suggested. However, there is no means to evaluate such methodologies yet. In this study, it is focused to describe the main questions resided in the amplitude scaling approach and to propose a regression model for structural damage as point of comparison. Spectrum compatible approach whose resulting spectrum matches the design spectrum at the entire range of the structural period is considered as alternative to be compared to the amplitude scaling approach. The design spectrum is generated according to ASCE7-05.

• Journal of Korean Neurosurgical Society
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• v.53 no.6
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• pp.342-348
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• 2013

Objective : Several scales are currently used to assess occlusion rates of coiled cerebral aneurysms. This study compared these scales as predictors of recanalization. Methods : Clinical data of 827 patients harboring 901 aneurysms treated by coiling were retrospectively reviewed. Occlusion rates were assessed using angiographic grading scale (AGS), two-dimensional percent occlusion (2DPO), and volumetric packing density (vPD). Every scale had 3 categories. Followed patients were dichotomized into either presence or absence of recanalization. Kaplan-Meier analysis was conducted, and Cox proportional hazards analysis was performed to identify surviving probabilities of recanalization. Lastly, the predictive accuracies of three different scales were measured via Harrell's C index. Results : The cumulative risk of recanalization was 7% at 12-month, 10% at 24-month, and 13% at 36-month of postembolization, and significantly higher for the second and third categories of every scale (p<0.001). Multivariate-adjusted hazard ratios (HRs) of the second and third categories as compared with the first category of AGS (HR : 3.95 and 4.15, p=0.004 and 0.001) and 2DPO (HR : 4.87 and 3.12, p<0.001 and 0.01) were similar. For vPD, there was no association between occlusion rates and recanalization. The validated and optimism-adjusted C-indices were 0.50 [confidence (CI) : -1.09-2.09], 0.47 (CI : -1.10-2.09) and 0.44 (CI : -1.10-2.08) for AGS, 2DPO, and vPD, respectively. Conclusion : Total occlusion should be reasonably tried in coiling to maximize the benefit of the treatment. AGS may be the best to predict recanalization, whereas vPD should not be used alone.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.6-6
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• 2018

최근 집중 호우로 인한 내수침수 피해가 도시화와 기후변화로 늘어나고 있다. 내수침수 피해로 인한 복구비용과 시간이 증가하고 있으며 향후에는 이보다 더 크게 늘어날 것으로 예상된다. 이러한 문제를 해결하기 위하여 충분한 선행시간을 가지고 내수 침수 구역을 제시할 수 있어야 한다. 기존의 물리적 모델은 정확하고 정교한 결과를 제공하지만, 시뮬레이션을 준비하고 마치는 데에 시간이 많이 소요된다. 그 이유로서는 강우량, 지형적 특성, 배수관망 시스템, 수문학적 매개변수 등의 다양한 데이터도 필요하기 때문이다. 이는 도시유역에 대한 내수침수의 실시간 예측이 어렵게 되었으며, 충분한 선행시간을 확보하지 못하는 원인이 되었다. 본 연구에서는 이 문제에 대한 해결책으로 결정론적 방법과 확률론적 방법을 자료지향형 모형으로 결합하여 해결책을 제시하고자 하며, 특정 강우 조건하에 도시유역에서의 내수침수에 영향을 미치는 맨홀에 대한 정보를 제공하고자 한다. 위와 같은 과정을 수행하기 위하여 입력자료 조합에 대한 비선형 분석을 실시하였으며, 그 결과로 특정 강우 조건에 대하여 각 맨홀에 대한 누적월류량을 예측할 수 있는 비선형 인공신경망을 구축할 수 있었다. 본 연구에서 제시된 방법론은 국내의 강남 배수분구에 대하여 적용이 되었으며, 내수침수 예측결과와 2차원 해석결과를 비교하고자 하였다. 본 연구에서는 위 과정을 통하여 1차원 도시유출해석을 위한 입력 자료를 준비하는 시간을 절약하고, 다양한 강우 조건과 내수침수지도 사이의 연관성을 학습하는 예측 모형을 이용하여 도시유역의 내수침수에 대한 충분한 선행시간을 확보하고자 한다. 결론적으로, 이 연구의 결과는 도시유역에 대한 비구조적 대책 수립에 도움을 줄 것으로 확인이 되며 도시 유역 내에 맨홀 위치들을 고려한 위험지구를 파악하는 데에 유용할 것으로 판단된다.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Korea Real Estate Review
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• v.28 no.3
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• pp.21-35
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• 2018

The recent performance of residential mortgages demonstrated how default risk operated separately from prepayment risk. In this study, we investigated the determinants of the borrowers' decisions pertaining to early termination through default from the mortgage performance data released by Freddie Mac, involving securitized mortgage loans from January 2011 to September 2013. We estimated a Cox-type, proportional hazard model with a single risk on fundamental factors associated with default options for individual mortgages. We proposed a mortgage default model that included two specifications of delinquency: one using a delinquency binary variable, while the other using a delinquency probability. We also compared the results obtained from two specifications with respect to goodness-of-fit proposed in the spirit of Vuong (1989) in both overlapping and nested models' cases. We found that a model with our proposed delinquency probability variable showed a statistically significant advantage compared to a benchmark model with delinquency dummy variables. We performed a default prediction power test based on the method proposed in Shumway (2001), and found a much stronger performance from the proposed model.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.135-140
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• 2019

Upon chemical agent release, it is of importance to study the characteristic persistence and evaporation of chemical agents from surfaces for the prediction of dispersion hazard. We have recently developed a fast and near real-time wind tunnel system proving the controlled environment(air flow, temperature, and humidity), continuously collects agent vapor and analyzes it. A thermal sorber/desorber is unnecessary to collect the vapor in the system we have developed. Instead, a tandem thermal sorber collects the vapor, which is then directly transferred to a fast gas chromatography(GC) for analysis. As a proof of concept, the evaporation of sulfur mustard agent(HD) was studied from glass, sand and concrete. The results were in an excellent agreement with those obtained from the conventional wind tunnel system.

• Journal of Lipid and Atherosclerosis
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• v.7 no.2
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• pp.110-121
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• 2018

Objective: We developed a new equation for predicting coronary heart disease (CHD) risk in Korean diabetic patients using a hospital-based cohort and compared it with a UK Prospective Diabetes Study (UKPDS) risk engine. Methods: By considering patients with type 2 diabetes aged ${\geq}30years$ visiting the diabetic center in Boramae hospital in 2006, we developed a multivariable equation for predicting CHD events using the Cox proportional hazard model. Those with CHD were excluded. The predictability of CHD events over 6 years was evaluated using area under the receiver operating characteristic (AUROC) curves, which were compared using the DeLong test. Results: A total of 732 participants (304 males and 428 females; mean age, $60{\pm}10years$; mean duration of diabetes, $10{\pm}7years$) were followed up for 76 months (range, 1-99 month). During the study period, 48 patients (6.6%) experienced CHD events. The AUROC of the proposed equation for predicting 6-year CHD events was 0.721 (95% confidence interval [CI], 0.641-0.800), which is significantly larger than that of the UKPDS risk engine (0.578; 95% CI, 0.482-0.675; p from DeLong test=0.001). Among the subjects with <5% of risk based on the proposed equation, 30.6% (121 out of 396) were classified as ${\geq}10%$ of risk based on the UKPDS risk engine, and their event rate was only 3.3% over 6 years. Conclusion: The UKPDS risk engine overestimated CHD risk in type 2 diabetic patients in this cohort, and the proposed equation has superior predictability for CHD risk compared to the UKPDS risk engine.

• Journal of Gastric Cancer
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• v.21 no.2
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• pp.155-168
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• 2021

Purpose: Previous studies have demonstrated the usefulness of the controlling nutritional status (CONUT) score in nutritional assessment and survival prediction of patients with various malignancies. However, its value in advanced gastric cancer (GC) treated with neoadjuvant chemotherapy and curative gastrectomy remains unclear. Materials and Methods: The CONUT score at different time points (pretreatment, preoperative, and postoperative) of 272 patients with advanced GC were retrospectively calculated from August 2004 to October 2015. The χ² test or Mann-Whitney U test was used to estimate the relationships between the CONUT score and clinical characteristics as well as short-term outcomes, while the Cox proportional hazard model was used to estimate long-term outcomes. Survival curves were estimated by using the Kaplan-Meier method and log-rank test. Results: The proportion of moderate or severe malnutrition among all patients was not significantly changed from pretreatment (13.5%) to pre-operation (11.7%) but increased dramatically postoperatively (47.5%). The pretreatment CONUT-high score (≥4) was significantly associated with older age (P=0.010), deeper tumor invasion (P=0.025), and lower pathological complete response rate (CONUT-high vs. CONUT-low: 1.2% vs. 6.6%, P=0.107). Pretreatment CONUT-high score patients had worse progression-free survival (P=0.032) and overall survival (OS) (P=0.026). Adjusted for pathologic node status, the pretreatment CONUT-high score was strongly associated with worse OS in pathologic node-positive patients (P=0.039). Conclusions: The pretreatment CONUT score might be a straightforward index for immune-nutritional status assessment, while being a reliable prognostic indicator in patients with advanced GC receiving neoadjuvant chemotherapy and curative gastrectomy. Moreover, lower pretreatment CONUT scores might indicate better chemotherapy responses.

• Geomechanics and Engineering
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• v.37 no.1
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• pp.65-72
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• 2024

Water ingress poses a common and intricate geological hazard with profound implications for tunnel construction's speed and safety. The project's success hinges significantly on the precision of estimating water inflow during excavation, a critical factor in early-stage decision-making during conception and design. This article introduces an optimized model employing the gene expression programming (GEP) approach to forecast tunnel water inflow. The GEP model was refined by developing an equation that best aligns with predictive outcomes. The equation's outputs were compared with measured data and assessed against practical scenarios to validate its potential applicability in calculating tunnel water input. The optimized GEP model excelled in forecasting tunnel water inflow, outperforming alternative machine learning algorithms like SVR, GPR, DT, and KNN. This positions the GEP model as a leading choice for accurate and superior predictions. A state-of-the-art machine learning-based graphical user interface (GUI) was innovatively crafted for predicting and visualizing tunnel water inflow. This cutting-edge tool leverages ML algorithms, marking a substantial advancement in tunneling prediction technologies, providing accuracy and accessibility in water inflow projections.