• Journal of Astronomy and Space Sciences
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• v.34 no.4
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• pp.289-302
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• 2017

The key risk analysis technologies for the re-entry of space objects into Earth's atmosphere are divided into four categories: cataloguing and databases of the re-entry of space objects, lifetime and re-entry trajectory predictions, break-up models after re-entry and multiple debris distribution predictions, and ground impact probability models. In this study, we focused on reentry prediction, including orbital lifetime assessments, for space situational awareness systems. Re-entry predictions are very difficult and are affected by various sources of uncertainty. In particular, during uncontrolled re-entry, large spacecraft may break into several pieces of debris, and the surviving fragments can be a significant hazard for persons and properties on the ground. In recent years, specific methods and procedures have been developed to provide clear information for predicting and analyzing the re-entry of space objects and for ground-risk assessments. Representative tools include object reentry survival analysis tool (ORSAT) and debris assessment software (DAS) developed by National Aeronautics and Space Administration (NASA), spacecraft atmospheric re-entry and aerothermal break-up (SCARAB) and debris risk assessment and mitigation analysis (DRAMA) developed by European Space Agency (ESA), and semi-analytic tool for end of life analysis (STELA) developed by Centre National d'Etudes Spatiales (CNES). In this study, various surveys of existing re-entry space objects are reviewed, and an efficient re-entry prediction technique is suggested based on STELA, the life-cycle analysis tool for satellites, and DRAMA, a re-entry analysis tool. To verify the proposed method, the re-entry of the Tiangong-1 Space Lab, which is expected to re-enter Earth's atmosphere shortly, was simulated. Eventually, these results will provide a basis for space situational awareness risk analyses of the re-entry of space objects.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.541-559
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• 2015

VaR is now widely used as an important tool to evaluate and manage financial risks. In particular, it is important to select an appropriate volatility model for the rate of return of financial assets. In this study, both univariate and multivariate models are considered to evaluate VaR of the portfolio composed of KOSPI, Hang-Seng, Nikkei indexes, and their performances are compared through back testing techniques. Overall, multivariate models are shown to be more appropriate than univariate models to estimate the portfolio VaR, in particular DCC and ADCC models are shown to be more superior than others.

• Nuclear Engineering and Technology
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• v.46 no.5
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• pp.595-604
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• 2014

SILER (Seismic-Initiated event risk mitigation in LEad-cooled Reactors) is a Collaborative Project, partially funded by the European Commission in the $7^{th}$ Framework Programme, aimed at studying the risk associated to seismic-initiated events in Generation IV Heavy Liquid Metal reactors, and developing adequate protection measures. The project started in October 2011, and will run for a duration of three years. The attention of SILER is focused on the evaluation of the effects of earthquakes, with particular regards to beyond-design seismic events, and to the identification of mitigation strategies, acting both on structures and components design. Special efforts are devoted to the development of seismic isolation devices and related interface components. Two reference designs, at the state of development available at the beginning of the project and coming from the $6^{th}$ Framework Programme, have been considered: ELSY (European Lead Fast Reactor) for the Lead Fast Reactors (LFR), and MYRRHA (Multi-purpose hYbrid Research Reactor for High-tech Applications) for the Accelerator-Driven Systems (ADS). This paper describes the main activities and results obtained so far, paying particular attention to the development of seismic isolators, and the interface components which must be installed between the isolated reactor building and the non-isolated parts of the plant, such as the pipe expansion joints and the joint-cover of the seismic gap.

• Journal of the Korean Institute of Gas
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• v.22 no.5
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• pp.107-113
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• 2018

While accident data are used to show alertness to accidents or to review similar cases, the analysis of nature of accident data its association with surrounding environment is very insufficient. Therefore, it is very necessary to demonstrate the possibility of an accident for a particular region by developing analysis techniques with the related accident data. The purpose of this study is to develop an analysis model and implement a system that produces regional accident probability based on historical weather information data and accident and reporting data. In other words, the system is designed and developed to create models by k-NN and decision tree algorithms with optional user-environment variables based on the probability between weather and accidents about many particular region of Korea. In the future, the models developed in this study are intended to be used to analyze and calculate the risk of a more narrow area.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1261-1266
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• 2021

Existing information security risk assessment methods focus on evaluating the vulnerability of information assets. However, when the form of information assets changes and new types of information assets emerge, there is a limitation in that the evaluation standards for them are also added or deleted. Existing methods have insufficient research on the path through which cyber threats are introduced. In particular, there is very little research on blocking the inflow path for web-based information systems with public IPs. Therefore, this paper introduces the main research contents of the BDLA (Blockade and Defense Level Analysis)-based information security risk assessment model. In addition, by applying the BDLA-based information security risk assessment model, the information security risk level was studied by measuring the blockade level and security equipment level of 17 public institutions.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.663-671
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• 2020

Recently, there have been many natural disasters in Korea, not only in forest areas but also in urban areas, and the national requirements for them are increasing. In particular, there is no pre-disaster information system that can systematically manage the collapse of the slope of the national highway. In this study, big data analysis was conducted on the factors causing slope collapse based on the detailed investigation report on the slope collapse of national roads in Gangwon-do and Gyeongsang-do areas managed by the Cut Slope Management System (CSMS) and the basic survey of slope failures. Based on the analysis results, a slope collapse risk prediction model was established through Adaboost, a classification-based machine learning model, reflecting the collapse slope location and weather information. It also developed a visualization map for the risk of slope collapse, which is a visualization program, to show that it can be used for preemptive disaster prevention measures by identifying the risk of slope due to changes in weather conditions.

• Journal of Korean Public Health Nursing
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• v.33 no.1
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• pp.47-58
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• 2019

Purpose: Regarding the rapid growth of the obesity in Korea's youths, environmental factors as well as biological tendencies have been major focus of years of study. Methods: This study, as a secondary analysis research, bases its source upon the raw data from the 13th on-line survey of the Korean adolescents' health behaviors. In particular, it is a descriptive research for clarifying the relevance of the Korean youths' smart phone use with their health behaviors and obesity. Results: In logistic analysis, variables showing a significant difference are as follows: soft drinks frequency(p<.001), sweet drinks intake frequency(p<.001), fast food intake frequency(p<.001) and smoking status (p=.005). Based on the amount of time the teenagers using smart phones, there have been different results in the risk of obesity. Four to six hours a week of smart phone use (p=.002) has a 1.132 times higher risk, six to eight hours(p<.001) are 1.212 times higher, over eight hours(p=.020) are 1.132 times higher, than less than two hours'. Conclusion: This study has significance in that it has emphasized the new lifestyle, teenagers' smart phone use, as an additional important factor for the increased risk of obesity.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.23-29
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• 2023

This study analyzed the fire status of a rubber product manufacturing factory based on 19 years of fire data. Through the analysis of the current state of fire, electrical fires accounted for 58.19%, and among electrical fires, motor fires were the highest at 26.21%. For the motor fire occurrence process, the curing process accounted for the highest rate of 51.9%. Therefore, the installation environment was investigated for the motor in the curing process, and it was confirmed that the motor's maximum ambient temperature exceeded 40℃. In particular, in the case of the motor for curing operation, the motor was installed in a separate motor room, so the average indoor temperature was 48.10℃ and the motor frame's maximum temperature was 72.80℃. In this study, the risk of motor fire was confirmed through a field survey, and a safety management plan was derived by finding a process with high fire risk and conducting an experiment on the motor's installation environment and electrical characteristics in that process.

• Journal of Navigation and Port Research
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• v.36 no.3
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• pp.261-271
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• 2012

The purpose of the present study is to empirically examine factors that affect the information security awareness and perceived information security risk of employees of port companies. In particular, in order to identify factors that affect the perceived information security risks, we investigated the relation of assets, threats, and vulnerabilities to it, using the risk analysis methodology. With A total of 252 valid questionnaires, we also performed the structural equation modeling analysis using AMOS. It was found that first, there was no meaningful relationship between the information assets and the perceived information security risk in the case of employees of port companies. Second, threats and vulnerabilities turned out to have positive influences on the perceived information security risk. Finally, there was a positive relationship not only between the information security awareness and the information security education, but also between the information security awareness and the intention of information security. However, there was no meaningful relationship between the information security concern and the information security awareness.

• Journal of Technology Innovation
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• v.6 no.2
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• pp.159-177
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• 1998

Although each company is trying to develop an economic analysis model with its own particular style or format, the appropriate method is not yet developed because there are many problems to be solved such as uncertainity of outcomes and intangible benefits of technology. The purpose of tris paper therefore is to suggest an economic analysis methodology, which reflects the complexity and the risk of R&D investment, through a case study on the development of a superconductor fault current limiter. A self-developed Monte Carlo simulation program utilized as a main tool in this paper was very useful for risk analysis of R&D investment which could not be solved in the previous DCF(Discounted Cash Flow) model. We also introduce learning effect to consider the intangible benefits such as Know-How obtained from R&D execution. The expected value and its probability distribution for R&D investment can be obtained by combining the Monte Carlo method with the decision tree approach. This result is helpful in judging the priority and the resource-allocation of R&D projects. It is however necessary to develop more precise model for quantifying the technology stock and the simulation program using the continuous probability distribution in expected values to improve the reliability of economic analysis on R&D projects.