• The Korean Journal of Quaternary Research
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• v.19 no.2
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• pp.74-79
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• 2005

Recently, several attempts have been made to provide reasonable information on unusual severe weather phenomena such as tolerant heavy rains and very wild typhoons. Quantitative precipitation forecasts and probabilistic quantitative precipitation forecasts (QPFs and PQPFs, respectively) might be one of the most promising methodologies for early warning on the flesh floods because those diagnostic precipitation models require less computational resources than fine-mesh full-dynamics non-hydrostatic mesoscale model. The diagnostic rainfall model used in this study is the named QPM(Quantitative Precipitation Model), which calculates the rainfall by considering the effect of small-scale topography which is not treated in the mesoscale model. We examine the capability of probabilistic diagnostic rainfall model in terms of how well represented the observed several rainfall events and what is the most optimistic resolution of the mesoscale model in which diagnostic rainfall model is nested. Also, we examine the integration time to provide reasonable fine-mesh rainfall information. When we apply this QPM directly to 27 km mesh meso-scale model (called as M27-Q3), it takes about 15 min. while it takes about 87 min. to get the same resolution precipitation information with full dynamic downscaling method (called M27-9-3). The quality of precipitation forecast by M27-Q3 is quite comparable with the results of M27-9-3 with reasonable threshold value for precipitation. Based on a series of examination we may conclude that the proosed QPM has a capability to provide fine-mesh rainfall information in terms of time and accuracy compared to full dynamical fine-mesh meso-scale model.

• Journal of Information Technology Applications and Management
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• v.16 no.3
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• pp.59-71
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• 2009

Hazard analysis identifies probability to hazard occurrence and its potential impact on business processes operated in organizations. This paper illustrates a quantitative approach of hazard analysis of information systems by measuring the degree of hazard to information systems using probabilistic risk analysis and activity based costing technique. Specifically the research model projects probability of occurrence by PRA and economic loss by ABC under each identified hazard. To verify the model, each computerized subsystem which is called a business process and hazards occurred on information systems are gathered through one private organization. The loss impact of a hazard occurrence is produced by multiplying probability by the economic loss.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.185-190
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• 2005

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. In order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The six landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The six factors consist of two topographic factors and four geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 86.5% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.488-493
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• 2013

Accurate initial cost estimates are essential to effective management of construction projects where many decisions are made in the course of project management by referencing the estimates. In practice, the initial estimates are frequently derived from historical actual cost data, for which standard distribution-based techniques are widely applied in the construction industry to account for risk associated with the estimates. This approach assumes the same probability distribution of estimate errors for any selected estimates. This assumption, however, is not always satisfied. In order to account for the probabilistic nature of estimate errors, an alternative method for measuring the risk associated with a selected initial estimate is developed by applying the Bayesian probability approach. An application example include demonstrates how the method is implemented. A hypothesis test is conducted to reveal the robustness of the Bayesian probability model. The method is envisioned to effectively complement cost estimating methods that are currently in use by providing benefits as follows: (1) it effectively accounts for the probabilistic nature of errors in estimates; (2) it is easy to implement by using historical estimates and actual costs that are readily available in most construction companies; and (3) it minimizes subjective judgment by using quantitative data only.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.463-478
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• 2019

The most important non-functional requirements for dependability of any Embedded Real-Time Safety Systems are safety, availability and reliability requirements. System architecture plays the primary role in achieving these requirements. Compliance with these non-functional requirements should be ensured early in the development cycle with appropriate considerations during architectural design. In this paper, we present an application of system architecture modeling for quantitative assessment of system dependability. We use probabilistic model checker (PRISM), for dependability analysis of the DTMC model derived from system architecture model. In general, the model checking techniques do not scale well for analyzing large systems, because of prohibitively large state space. It limits the use of model checking techniques in analyzing the systems of practical interest. We propose abstraction based compositional analysis methodology to circumvent this limitation. The effectiveness of the proposed methodology has been demonstrated using the case study involving the dependability analysis of safety system of a large Pressurized Water Reactor (PWR).

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.138-145
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• 2019

With the application of digital technology to safety-critical infrastructures, cyber-attacks have emerged as one of the new dangerous threats. In safety-critical infrastructures such as a nuclear power plant (NPP), a cyber-attack could have serious consequences by initiating dangerous events or rendering important safety systems unavailable. Since a cyber-attack is conducted intentionally, numerous possible cases should be considered for developing a cyber security system, such as the attack paths, methods, and potential target systems. Therefore, prior to developing a risk-informed cyber security strategy, the importance of cyber-attacks and significant critical digital assets (CDAs) should be analyzed. In this work, an importance analysis method for cyber-attacks on an NPP was proposed using the probabilistic safety assessment (PSA) method. To develop an importance analysis framework for cyber-attacks, possible cyber-attacks were identified with failure modes, and a PSA model for cyber-attacks was developed. For case studies, the quantitative evaluations of cyber-attack scenarios were performed using the proposed method. By using quantitative importance of cyber-attacks and identifying significant CDAs that must be defended against cyber-attacks, it is possible to develop an efficient and reliable defense strategy against cyber-attacks on NPPs.

• The Journal of Engineering Geology
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• v.14 no.2
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• pp.211-222
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• 2004

In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The seven landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The seven factors consist of two topographic factors and five geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 90.74% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.7
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• pp.544-551
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• 2011

The objective of this study was to analyze VIRULO model, a probabilistic quantitative model, which had been developed by US Environmental Protection Agency. The model could assess the viral attenuation capacity of soil as hydrogeologic barrier using Monte Carlo simulation. The governing equations used in the model were composed of unsaturated flow equations and viral transport equations. Among the model parameters, those related to water flow for 11 soil types were from UNDODA data, and those related to 5 virus species were from the literatures. The model compared the attenuation factor with threshold of attenuation to determine the probability of failure and presented the exceedances and Monte Carlo runs as output. The analysis indicated that among 11 USDA soil types, the viral attenuation capacity of loamy sand and sand were far lower than those of clay and silt soils. Also, there were differences in the attenuation in soil among 5 viruses with poliovirus showing the highest attenuation. The viral attenuation capacity of soil decreased sharply with increasing soil water content and increased nonlinearly with increasing soil barrier length. This study indicates that VIRULO model could be considered as a useful screening tool for viral risk assessment in subsurface environment.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.2
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• pp.274-281
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• 2019

Objective: The objective of this study was to estimate the risk of Campylobacter jejuni (C. jejuni) infection from various jerky products in Korea. Methods: For the exposure assessment, the prevalence and predictive models of C. jejuni in the jerky and the temperature and time of the distribution and storage were investigated. In addition, the consumption amounts and frequencies of the products were also investigated. The data for C. jejuni for the prevalence, distribution temperature, distribution time, consumption amount, and consumption frequency were fitted with the @RISK fitting program to obtain appropriate probabilistic distributions. Subsequently, the dose-response models for Campylobacter were researched in the literature. Eventually, the distributions, predictive model, and dose-response model were used to make a simulation model with @RISK to estimate the risk of C. jejuni foodborne illness from the intake of jerky. Results: Among 275 jerky samples, there were no C. jejuni positive samples, and thus, the initial contamination level was statistically predicted with the RiskUniform distribution [RiskUniform (-2, 0.48)]. To describe the changes in the C. jejuni cell counts during distribution and storage, the developed predictive models with the Weibull model (primary model) and polynomial model (secondary model) were utilized. The appropriate probabilistic distribution was the BetaGeneral distribution, and it showed that the average jerky consumption was 51.83 g/d with a frequency of 0.61%. The developed simulation model from this data series and the dose-response model (Beta Poisson model) showed that the risk of C. jejuni foodborne illness per day per person from jerky consumption was $1.56{\times}10^{-12}$. Conclusion: This result suggests that the risk of C. jejuni in jerky could be considered low in Korea.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.1
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• pp.32-39
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• 2018

Repairing timing and the extended service life with repairing are very important for cost estimation during operation. Conventionally used model for repair cost shows a step-shaped cost elevation without consideration of variability of extended service life due to repairing. In the work, RC(Reinforced Concrete) Column is considered for probabilistic evaluation of repairing number and cost. Two mix proportions are prepared and chloride behavior is evaluated with quantitative exterior conditions. The repairing frequency and cost are investigated with varying service life and the extended service life with repairing which were derived from the chloride behavior analysis. The effect of COV(Coefficient of Variation) on repairing frequency is small but the 1st repairing timing is shown to be major parameter. The probabilistic model for repairing cost is capable of reducing the number of repairing with changing the intended service life unlike deterministic model of repairing cost since it can provide continuous repair cost with time.