• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.133-140
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• 2009

In the paper, we proposed the systematical and quantitative software risk management methodology based on risk analysis model. A software risk management consists of the basic risk management method(BRIMM) and the detailed risk management method(DRIMM). BRIMM is applied to unimportant phases or the phase which also the risk factor does not heavily influence to project. DRIMM is used from the phase which influences highly in project success or the phase where the risk factor is many. Fulfilling risk management combined two methods, we can reduce project's budget, term and resource's usage, and prevent risk with the optimum measures obtained by the exact risk analysis.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.363-364
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• 2023

The tunnel construction projects is demanded more efficient risk management measures and loss forecasts to prepare for risk losses from an increase in the trend of tunnel construction. This study aims to analyze the risk factors that caused the loss of material in actual tunnel construction and to develop a quantified predictive loss model, based on the past loss record of tunnel construction projects.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.61-74
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• 2004

Risk analysis model is systematic and structural process that considers internal security problems and threat factors of the information systems to find optimal level of security control. But, the risk analysis model is just only defined conceptually and there are not so many empirical studies. This research used structural equation modeling(SEM) research methodology with rigorously validated research instrument. Based on results of this study, risk analysis methodology was proved to be practically useful in e-commerce environment. Factors like threat and control were significantly related to risk. In conclusion, the results of this study can be applied to general situation or environment of information security for analyzing and managing the risk and providing new approach to comprehend concept of risk in e-commerce environment.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.369-375
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• 2001

The statistical regression model is one of the most frequently used clinical analysis methods. It has basic assumption of linearity, additivity and normal distribution of data. However, most of biological data in medical field are nonlinear and unevenly distributed. To overcome the discrepancy between the basic assumption of statistical model and actual biological data, we propose a new analytical method based on artificial neural network. The newly developed multilayer perceptron(MLP) is trained with 120 data set (60 normal, 60 patient). On applying test data, it shows the discrimination power of 0.76. The diabetic risk factors were also identified from the MLP neural network model and the logistic regression model. The signigicant risk factors identified by MLP model were post prandial glucose level(PP2), sex(male), fasting blood sugar(FBS) level, age, SBP, AC and WHR. Those from the regression model are sex(male), PP2, age and FBS. The combined risk factors can be identified using the MLP model. Those are total cholesterol and body weight, which is consistent with the result of other clinical studies. From this experiment we have learned that MLP can be applied to the combined risk factor analysis of biological data which can not be provided by the conventional statistical method.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2014.11a
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• pp.212-213
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• 2014

The purpose of this study is to suggest analysis model of RCB construction in nuclear power plant. For the objective, This study drew the risk factors of RCB construction from existing literature. The results of the study proposed analysis model made hierarchy in rebar, form, and concrete work. These will be baseline data for risk management in construction project of nuclear power plant.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.1518-1523
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• 2008

Train collision and derailment are types of accident with low probability of occurrence, but they could lead to disastrous consequences including loss of lives and properties. The development of the risk assessment model has been called upon to predict and assess the risk for a long time. Nevertheless, the risk assessment model is recently introduced to the railway system in Korea. The classification of the hazardous events and causes is the commencement of the risk assessment model. In previous researches related to the classification, the hazardous events and causes were classified by centering the results. That classification was simple, but might not show the root cause of the hazardous events. This study has classified the train collision and derailment based on the relevant hazardous event including faults of the train related the accidents, and investigates the causes related to the hazardous events. For the risk assessment model, FTA (fault tree analysis) and ETA (event tree analysis) methods are introduced to assess the risk.

• Fashion & Textile Research Journal
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• v.13 no.3
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• pp.390-397
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• 2011

This study investigates the influences of shopping enjoyment and risk reduction on customers' attitude and the behavioral intention in the Internet shopping mall using a moving virtual model. For this study, we produced a moving virtual model to present a fashion product. The virtual model walks for about one minute on the stage. After respondents viewed it, they completed a questionnaire. The questionnaire consists of online shopping enjoyment, risk reduction, customers' attitude and behavioral intention. Respondents are limited to females aged in their 20s and 30s, who have experienced Internet shopping and are highly interested in fashion products. 411 samples were used for the final analysis. Cronbach's alpha, factor analysis, and multiple regression analysis were conducted. The results are as follows. Online shopping enjoyment and risk reduction influenced the behavioral intention directly as well as through the attitude. However, the size of the influence indicated that online shopping enjoyment is larger than risk reduction. Therefore, Internet malls should utilize the moving virtual model to provide customers with enjoyment and risk reduction, which will increase customers' favorable attitudes and the behavioral intention such as purchase intention and word of mouth.

• Journal of Industrial Convergence
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• v.2 no.2
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• pp.127-147
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• 2004

This paper examines the time-series relations among expected return, risk, and book-to-market(B/M) at the portfolio level. The time-series analysis is a natural alternative to cross-sectional regressions. An alternative feature of the time-series regressions is that they focus on changes in expected returns, not on average returns. Using the time-series analysis, we can directly test whether the three-factor model explains time-varying expected returns better than the characteristic-based model. These results should help distinguish between the risk and mispricing stories. We find that B/M is strongly associated with changes in risk, as measured by the Fama and French(1993) three-factor model. After controlling for changes in risk, B/M contains little additional information about expected returns. The evidence suggests that the three-factor model explains time-varying expected returns better than the characteristic-based model.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.261-285
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• 2019

Nowadays in offshore industry there are emerging hazards with vague property such as act of terrorism, act of war, unforeseen natural disasters such as tsunami, etc. Therefore industry professionals such as offshore energy insurers, safety engineers and risk managers in order to determine the failure rates and frequencies for the potential hazards where there is no data available, they need to use an appropriate method to overcome this difficulty. Furthermore in conventional risk based analysis models such as when using a fault tree analysis, hazards with vague properties are normally waived and ignored. In other word in previous situations only a traditional probability based fault tree analysis could be implemented. To overcome this shortcoming fuzzy set theory is applied to fault tree analysis to combine the known and unknown data in which the pre-combined result will be determined under a fuzzy environment. This has been fulfilled by integration of a generic bow-tie based risk analysis model into the risk assessment phase of the Risk Management (RM) cycles as a backbone of the phase. For this reason Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) are used to analyse one of the significant risk factors associated in offshore terminals. This process will eventually help the insurers and risk managers in marine and offshore industries to investigate the potential hazards more in detail if there is vagueness. For this purpose a case study of offshore terminal while coinciding with the nature of the Caspian Sea was decided to be examined.

• Food Science of Animal Resources
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• v.39 no.4
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• pp.565-575
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• 2019

This study evaluated Campylobacter jejuni risk in ground meat products. The C. jejuni prevalence in ground meat products was investigated. To develop the predictive model, survival data of C. jejuni were collected at $4^{\circ}C-30^{\circ}C$ during storage, and the data were fitted using the Weibull model. In addition, the storage temperature and time of ground meat products were investigated during distribution. The consumption amount and frequency of ground meat products were investigated by interviewing 1,500 adults. The prevalence, temperature, time, and consumption data were analyzed by @RISK to generate probabilistic distributions. In 224 samples of ground meat products, there were no C. jejuni-contaminated samples. A scenario with a series of probabilistic distributions, a predictive model and a dose-response model was prepared to calculate the probability of illness, and it showed that the probability of foodborne illness caused by C. jejuni per person per day from ground meat products was $5.68{\times}10^{-10}$, which can be considered low risk.