• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.3
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• pp.227-232
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• 2020

This paper develops a risk index based on an indicator of risk assessment in terms of coastal activity location and accident type. The risk index is derived from a formula which adds the consequence of failure to a vulnerability value, then subtracts the mitigation value. Specifically, the consequence of failure is the number of casualties in coastal activity locations. An indicator of vulnerability refers to coastal environment elements and social elements. A pointer of mitigation includes managerial and organizational elements that indicate the capabilities of coastal activities. A risk rating of coastal activity location is found from a risk matrix consisting of the accident location and type. The purpose of this study is to prevent accidents at coastal activity locations by allowing the Coastal police guard to monitor effectively and inform visitors of potential risks.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.340-350
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• 2021

This paper proposes a decision support model for USVs to improve the accuracy of collision avoidance decision-making. It is formed by Navigation Safety Domain (NSD) and domain-based Collision Risk Index (CRI), capable of determining the collision stage and risk between multiple ships. The NSD is composed of a warning domain and a forbidden domain, which is constructed under the constraints of COLREGs (International Regulations for Preventing Collisions at Sea). The proposed domain based CRI takes the radius of NSD in various encounter situations as threshold parameters. It is found that the value of collision risk in any directions can be calculated, including actual value and risk threshold. A catamaran USV and 6 given vessels are taken as study objects to validate the proposed model. It is found that the judgment of collision stage is accurate and the azimuth range of risk exists can be detected, hence the ships can take direct and effective collision avoidance measures. According to the relation between the actual value of CRI and risk threshold, the decision support rules are summarized, and the specific terms of COLREGs to be followed in each encounter situation are given.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2759-2769
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• 2018

The behavioral finance view on the existence of asset pricing anomalies is based on two factors: investors' sentiment and limits to arbitrage. This paper tries to examine the effect of investors' sentiment on the stock price in the Korean stock market. In order to measure investors' sentiment, we constructed the sentiment index using principal component of five sentiment variables. By using sentiment index as an additional independent variable to three risk factors, impacts of the sentiment index on individual stocks and 25 portfolios sorted by BM-size are examined. Main results found are as follows: 1) not only all three risk factors show positive impacts on the return of individual stock, but also the sentiment index has a positive impact. SI alone explains 15% of individual return variation. 2) among four independent variables, the most important factor turned out to be the market risk factor and investors' sentiment has better explanatory power on stock price than the size effect. 3) after controlling the market risk factor, the coefficient of the sentiment index for the smallest size and highest book/market value portfolios is significantly positive. 4) all the coefficients of the sentiment index for 25 portfolios sorted by BM-size have significant positive value after controlling size or (and) value.

• Korean Journal of Construction Engineering and Management
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• v.23 no.5
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• pp.98-107
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• 2022

The purpose of this study is to suggest a model for evaluation of a risk based value index for modular design alternatives in plant construction projects. Accordingly, 1) Setting the basic project cost and the scope to apply the module, 2) Evaluating the importance, easiness, and effectiveness index for Engineering, Procurement, Fabrication, transportation, and construction work, 3) Estimating the total project cost by analyzing the risk reserve Step, 4) Comparing the effectiveness index and total project cost for each modular design alternative, it was composed of the steps of deriving RVI. To verify such a model, Plan-A, which applied a module to one process, and Plan-B, which applied a module to three processes, were composed to evaluate RVI.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.147-165
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• 2009

This study proposes a certain measure or investment strategy for decision making associated with seismic retrofitting. This strategy reduces the risk of a large-scale malfunction such as water supply loss under seismic risks. The authors developed a stochastic value index that will be used in the overall evaluation of social benefit, income gain, life cycle costs and failure compensation associated with existing lifeline systems damaged by an earthquake during the remaining service period. Optimal seismic disaster prevention investment of deteriorated lifeline systems is discussed. Finally, the present study provides a performance-based design method for seismic retrofitting strategies of existing lifelines which are carried out using the target probabilities of value loss and structural failure.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.171-180
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• 2016

Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.438-449
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• 2020

This study is to improve the method of calculating the risk of liability that arise from release and dispersion of chemicals outside the plant in process industries such as chemical and petrochemical plants. To achieve this goal, the correlation factors with the risk of chemical release accident is derived by simulating release and dispersion of substances (14 types) designated by Ministry of Environment as preparation for accident, analyzing the cases of chemical release and effects of plant life damage. The method of calculating chemical liability risk was modified and supplemented based on the results obtained from the study. The correlation coefficient between the probit value of 14 chemical types and the liability risk by EURAM (European Union Risk Ranking Method) was -0.526, while the correlation coefficient with the modified chemical release accident risk was 0.319. Thus, the value from modified method shows that they appear to be correlated. According to modified calculating methodology, the correlation between ERPG-2 value and liability risk of 97 chemical types was -0.494 which is 19 times higher than existing liability risk correlation as absolute value. And the correlation coefficient of corrosion risk was 0.91. The standardized regression coefficients (β) value of correlation factors that affected the increase and decrease of risk were derived in order of Corrosion Index(0.713), ERPG-2 (0.400) and NFPA Health Index (0.0680) by values. It is expected that these findings this study result will also enable the calculation of reasonable chemical release liability risk for existing and new chemical, and will help use them as quantitative liability risk management indicators for chemical plant site.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.15-22
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• 2019

Highly pathogenic avian influenza (HPAI) is among the top infectious disease priorities in Korea and the leading cause of economic loss in relevant poultry industry. An understanding of the spatial epidemiology of HPAI outbreak is essential in assessing and managing the risk of the infection. Though previous studies have reported the majority of outbreaks occurred clustered in what are preferred to as densely populated poultry regions, especially in southwest coast of Korea, little is known about the spatial distribution of risk areas vulnerable to HPAI occurrence based on geographic information system (GIS). The main aim of the present study was to develop a GIS-based risk index model for defining potential high-risk areas of HPAI outbreaks and to explore spatial distribution in relative risk index for each 252 Si-Gun-Gu (administrative unit) in Korea. The risk index was derived incorporating seven GIS database associated with risk factors of HPAI in a standardized five-score scale. Scale 1 and 5 for each database represent the lowest and the highest risk of HPAI respectively. Our model showed that Jeollabuk-do, Chungcheongnam-do, Jeollanam-do and Chungcheongbuk-do regions will have the highest relative risk from HPAI. Areas with risk index value over 4.0 were Naju, Jeongeup, Anseong, Cheonan, Kochang, Iksan, Kyeongju and Kimje, indicating that Korea is at risk of HPAI introduction. Management and control of HPAI becomes difficult once the virus are established in domestic poultry populations; therefore, early detection and development of nationwide monitoring system through targeted surveillance of high-risk spots are priorities for preventing the future outbreaks.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.219-234
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• 2010

In index investing according to KOSPI, we estimate Value at Risk(VaR) from the extreme losses of the daily returns which are obtained from KOSPI. To this end, we apply Block Maxima(BM) model which is one of the useful models in the extreme value theory. We also estimate the extremal index to consider the dependency in the occurrence of extreme losses. From the back-testing based on the failure rate method, we can see that the model is adaptable for the VaR estimation. We also compare this model with the GARCH model which is commonly used for the VaR estimation. Back-testing says that there is no meaningful difference between the two models if we assume that the conditional returns follow the t-distribution. However, the estimated VaR based on GARCH model is sensitive to the extreme losses occurred near the epoch of estimation, while that on BM model is not. Thus, estimating the VaR based on GARCH model is preferred for the short-term prediction. However, for the long-term prediction, BM model is better.

• Journal of the Korean Society of Safety
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• v.32 no.5
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• pp.96-102
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• 2017

The highest fatal accident ratio was recorded in the construction industry. According to the industrial insurance premium rate & business type example, among the construction industry, the architectural work has the highest fatal and loss time accident ratio. Previous literature has investigated various aspects of accident occurrence and prevention in architectural work. However, those studied were limited in that they only focused on the fatal accident without considering the loss time accident. But non fatal accidents were recorded more than 50 times of fatal accidents. Therefore non fatal accidents must be controlled to lessen industrial accidents. Based on this, the goal of this study was to investigate the nature of the loss time accident and derive the risk index of work type in architectural work. In this study, opinions of safety experts were gathered and the risk index of work type was derived using AHP(Analytic Hierarchy Process). And verification was accomplished by comparing the results of this study with the risk index derived by analysis of accident records. Results showed that the risk index of work type was significantly higher in steel frame work, temporary installation work, earth & foundation work, facilities work, concrete work. And statistical analysis for verification showed that coefficient of Pearson correlation was 0.686 and P-value was 0.001.