• Fire Science and Engineering
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• v.24 no.3
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• pp.66-71
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• 2010

In order to evaluate the fire and explosion involved and to ensure the safe and optimized operation of chemical processes, it is necessary to know combustion properties. Explosion limit is one of the major combustion properties used to determine the fire and explosion hazards of the flammable substances. In this study, the lower explosion and upper explosion limits of esters were predicted by using the heat of combustion. The values calculated by the proposed equations agreed with literature data within a few percent. From the given results, using the proposed methodology, it is possible to predict the explosion limits of the other ester flammable substances.

• Fire Science and Engineering
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• v.19 no.4 s.60
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• pp.26-31
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• 2005

In order to evaluate the fire and explosion involved and to ensure the safe and optimized operation of chemical processes, it is necessary to know combustion properties. Explosion limit is one of the major combustion properties used to determine the fire and explosion hazards of the flammable substances. In this study, the explosion limits of alcohols were predicted by using the normal boiling points and the flash points based on a solution theory. The values calculated by the proposed equations agreed with literature data within a few percent. From the given results, using the proposed methodology; it is Possible to Predict the explosion limits of the other flammable substances.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.27-37
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• 2020

Recently, big data is rapidly emerging as a core technology in the 4th industrial revolution. Further, the utilization and the demand of drones are continuously increasing with the development of the 4th industrial revolution. However, as the drones usage increases, the risk of drones falling increases. Drones always have a risk of being able to fall easily even with small problems due to its simple structure. In this paper, in order to predict the risk of drone fall and to prevent the fall, ESC (Electronic Speed Control) is attached integrally with the drone's driving motor and the acceleration sensor is stored to collect the vibration data in real time. By processing and monitoring the data in real time and analyzing the data through big data obtained in such a situation using a Fast Fourier Transform (FFT) algorithm, we proposed a prediction system that minimizes the risk of drone fall by analyzing big data collected from drones.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.283-290
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• 2020

The purpose of this study was to evaluate the usefulness of the triglyceride and glucose(TyG) index to predict the risk of hyperuricemia in Korean adults. This study included 14,266 men and 9,033 women over 20 years old who underwent health screenings from 2017 to 2019 at a general hospital in Seoul. To confirm the risk of hyperuricemia and predictive ability of the TyG index, logistic regression analysis and ROC curves were obtained. The accuracy of the TyG index for predicting hyperuricemia was 0.68, 0.61 for men and 0.67 for women(respectively p<0.001). The risk of hyperuricemia in the TyG index was 1.69 times higher in the fourth quartile than in the first quartile, 2.03 times higher in men and 2.07 times higher in women(respectively p<0.05). Thus the TyG index was not of high diagnostic usefulness as a screening test for hyperuricemia, but it was related to the TyG index and hyperuricemia.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.139-169
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• 2009

This paper introduces the modified VaR which takes into account the asymmetry and fat-tails of financial asset distribution, and then compares its out-of-sample forecast performance with traditional VaR model such as historical simulation model and Riskmetrics. The empirical tests using stock indices of 6 countries showed that the modified VaR has the best forecast accuracy. At the test of independence, Riskmetrics and GARCH model showed best performances, but the independence was not rejected for the modified VaR. The Monte Carlo simulation using skew t distribution again proved the best forecast performance of the modified VaR. One of many advantages of the modified VaR is that it is appropriate for measuring VaR of the portfolio, because it can reflect not only the linear relationship but also the nonlinear relationship between individual assets of the portfolio through coskewness and cokurtosis. The empirical analysis about decomposing VaR of the portfolio of 6 stock indices confirmed that the component VaR is very useful for the re-allocation of component assets to achieve higher Sharpe ratio and the active risk management.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.559-575
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• 2021

Due to the increase in ground excavation work, the possibility of ground subsidence accidents is increasing. And it is very difficult to prevent these risk fundamentally through institutional reinforcement such as the special law for underground safety management. As for the various cases of urban ground excavation practice, the ground subsidence behavior characteristics which is predicted using various information before excavation showed a considerable difference that could not be ignored compared to the results real construction data. Changes in site conditions such as seasonal differences in design and construction period, changes in construction methods depending on the site conditions and long-term construction suspension due to various reasons could be considered as the main causes. As the countermeasures, the safety management system through various construction information is introduced, but there is still no suitable system which can predict the effect of excavation and risk assessment. In this study, a web-based system was developed in order to predict the degree of impact on the ground subsidence and surrounding structures in advance before ground excavation and evaluate the risk in the design and construction of urban ground excavation projects. A system was built using time series analysis technique that can predict the current and future behavior characteristics such as ground water level and settlement based on past field construction records with field monitoring data. It was presented as a geotechnical data visualization (GDV) technology for risk reduction and disaster management based on web-based system, Using this newly developed web-based assessment system, it is possible to predict ground excavation impact prediction and risk assessment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.311-313
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• 2014

전동기는 산업 전 분야에 걸쳐 다양하게 사용되는 회전기기로서, 소형화, 경량화, 고속화하는 추세에 있다. 이는 전동기 프레임의 구조강성을 약화시키고, 축계 위험속도를 낮춤으로써 진동에 취약한 구조를 가지게 된다. 회전체 진동 관련 규정 중 API 684 에서는 베어링 지지강성이 베어링 강성에 비해 3.5 배 이하인 경우 베어링 지지강성이 위험 속도 해석 모델에 포함되어야 함을 명시하고 있다. 산업 현장에서는 베어링 지지강성을 정확하게 산출하기 어려워 이를 고려하지 않고 회전체를 설계하는 경우가 많아 실제 조건에서 예측하지 못한 진동 문제가 발생할 가능성이 있다. 본 논문에서는 전동기 베어링 하우징 및 프레임에 대한 가진 시험을 통해 얻은 주파수 응답함수의 실수부를 분석하여 베어링 지지강성을 추출하는 방법을 제시하였다. 이를 바탕으로 유한요소 해석모델을 이용하여 베어링 지지강성을 해석적으로 예측하는 기법을 정립하였다. 추출된 베어링 지지강성을 축계 해석 모델에 포함하여 베어링 지지강성 포함 유무에 따른 축계 위험속도 및 안정성을 비교하였다. 그 결과 베어링 지지강성을 포함한 경우, 보다 정확한 위험속도 및 진동응답 수준을 예측할 수 있음을 확인하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.179-179
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• 2017

우리나라는 국토면적의 약 64%가 산지로 이루어져 있으며 동고서저의 지형을 이루고 있다. 강원도 영동지방의 경우는 고도가 높으며 경사가 급한 특징을 지니고 있으며 이러한 지형적 특징으로 태풍 및 집중호우 시, 산지재해에 취약할 수밖에 없다. 더욱이 최근, 기후변화로 인한 이상기후 현상에 의해 태풍 및 집중호우가 빈번해 산지재해의 발생빈도도 높아지고 있는 실정이다. 그에 따라 대규모의 인적, 물적 등의 피해 또한 증가하고 있다. 산지재해 같은 경우, 예측이 어려우나 그러한 피해를 줄이기 위해서는 산지재해의 발생예상 지역, 피해정도 및 규모에 대한 예측 자료가 필요하다. 재해지도는 그에 따른 예측 자료로써 대상 지역의 위험요인과 잠재적인 영향 등을 표시하여 재해를 예방하는 데에 목적을 두고 있다. 이러한 재해지도를 작성하기 위해 사용되는 기법으로는 정량적 기법의 대표적인 방법으로 결정론적 기법(SHALATAB, SINMAP, GEOtop-FS), 확률론적 기법(빈도비분석법, 우도비, 증거가중법 등), 통계적 기법(로지스틱 회귀분석, 인공신경망 기법)을 사용하고 있다. 본 연구에서는 정량적 기법 중 하나인 결정론적 기법을 활용하여 위험지역을 분석하고 실제 위험지역과 비교하였다. 추후에 확률론적 기법과 통계적인 기법을 활용하여 위험지역을 분석하고자 한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1255-1262
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• 2021

The proportion of students dropping out of universities is increasing year by year, and they are trying to identify risk factors and eliminate them in advance to prevent dropouts. However, there is a problem in the management of students at risk of dropping out and the forecast is inaccurate because crisis students are managed through the univariable analysis of specific risk factors. In this paper, we identify risk factors for university dropout and analyze multivariables through machine learning method to predict university dropout. In addition, we derive the optimization method by evaluation performance for various prediction methods and evaluate the correlation and contribution between risk factors that cause university dropout.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.383-390
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• 1999

본 연구의 목적은 이원화된 위험을 분류하는데 사용된 여러 가지 데이터마이닝(datamining) 기법들의 성과를 측정·비교하는데 있어서, 표본추출(sampling error)의 영향, 표본의 구성비 영향, 기존의 전통적 위험예측치의 문제점등을 살펴보고, 새로운 위험예측치를 제시하여 실증적으로 비교, 검증하는 것을 연구의 주목적으로 하고 있다.