• Journal of the Korean Institute of Gas
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• v.17 no.3
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• pp.1-7
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• 2013

The influence of the over-pressure caused by Explosion in gas station was calculated by using the Hopkinson's scaling law and injury effect by accident to buildings and human bodies was estimated by applying the probit model. As a result, the injury estimation was conducted by using the probit model for leakage 10% of 20ton storage tank. The separate distances from LPG station for building(damage) and human(lung hemorrhage to death) are 260 and 30 meters, respectively.

• Journal of the Korean Institute of Gas
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• v.12 no.1
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• pp.42-47
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• 2008

The demand of gas as an eco-friendly energy source has being increased. With the demand of gas, the use of gas is also increased, so injury and loss of life by the explosion and fire have been increasing every year. Hence the influence on over-pressure caused by Vapor Cloud Explosion in enclosure of experimental booth was calculated by using the Hopkinson's scaling law and damage effect by the accident to a human body was estimated by applying the probit model. As a result of the damage estimation conducted by using the probit model, both the damage possibility of explosion overpressure to human over 3 meters away and that of overpressure to tympanum rupture over 25 meters away from the explosion shows nothing.

• Journal of the Korean Institute of Gas
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• v.14 no.2
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• pp.15-21
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• 2010

The demand of gas as an eco-friendly energy source has being increased. With increasing the LPG demand, the number of LPG filling station. In this work, the influence on over-pressure caused by Vapor Cloud Explosion in gas station was calculated by using the Hopkinson's scaling law and injury effect by the accident to a human body was estimated by applying the probit model. As a result of the injury estimation conducted by using the probit model for leakage 10% of 20ton storage tank. The distances from LPG station for death and tympanum rupture are 36.5 and 290 meters, respectively.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.101-115
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• 2008

This study reports UTIS(Urban Traffic Information System), which has been generalized in developed countries through brisk research and development and is being promoted for introduction by National Police Agency and Road Traffic Authority to reduce the astronomical amount of social expenses including traffic congestion expenses. Also this study investigates the proper charges for using by the preestimate of demand and contentment according to methods of payment after the service is introduced. The results of this study are as follows. First, demand forecast model is constructed by Binary Logit Model. Second, forecast models of using aspects of UTIS service according to methods of payment are established by Ordered Probit Model. Third, the proper charges for using of UTIS service according to methods of payment are presented to the supplier in the aspects of users. For this, preferences by using aspects and methods of payment are captured. And unit elasticity of coefficient of utilization is understood through responsiveness analysis according to methods of payment.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2002.11a
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• pp.490-501
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• 2002

기업의 생산성향상과 이익률에 영향을 줄 수 있는 지식이 경제 전반에 확산되어 나가는 과정은 한 나라의 경제발전속도에 영향을 미치는 중요한 요인이다. 기업 측면에서는 도입하려는 기술이 도입 후에 그 기업의 이익을 높여 줄 수 있다면 도입하지 않을 이유가 없다. 하지만 미래 수요의 불확실성이나 기술발전 방향의 불확실성 등으로 해서 기업으로서는 도입 후의 이익을 정확히 사전적으로 측정하기는 어렵다. 본 논문에서는 학계에서 일반적으로 사용되고 있는 두 가지 지식확산 모델을 설명하고자 한다. 그 하나는 하나의 새로운 기술이나 상품이 시간이 흐름에 따라 어떻게 전체 사용 가능자(population)에게 확산되는 지를 보여주는 1) Epidemic Diffusion Model (흔히 5자형 - Sigmoid - 모델이라고도 한다. )과 어떤 도입자가 어느 시점에서 대상이 된 새로운 기술을 도입할 것인지 아닌지를 결정하는 모델로서 2) Probit Diffusion Model (프로빗 모델)을 중심으로 한다 그리고 이러한 지식확산과정과 속도에 영향을 줄 수 있는 기업 내부적 요인으로서 도입하고자 하는 기업의 누적된 경험이 중요하다는 것과 기업 외부적 요인으로서 네트웍 효과와 같은 요인들을 설명하였다.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.490-501
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• 2002

기업의 생산성향상과 이익률에 영향을 줄 수 있는 지식이 경제 전반에 확산되어 나가는 과정은 한 나라의 경제발전속도에 영향을 미치는 중요한 요인이다. 기업 측면에서는 도입하려는 기술이 도입 후에 그 기업의 이익을 높여 줄 수 있다면 도입하지 않을 이유가 없다. 하지만 미래 수요의 불확실성이나 기술발전 방향의 불확실성 등으로 해서 기업으로서는 도입 후의 이익을 정확히 사전적으로 측정하기는 어렵다. 본 논문에서는 학계에서 일반적으로 사용되고 있는 두 가지 지식확산 모델을 설명하고자 한다. 그 하나는 하나의 새로운 기술이나 상품이 시간이 흐름에 따라 어떻게 전체 사용 가능자(population)에게 확산되는 지를 보여주는 1) Epidemic Diffusion Model (흔히 S자형 - Sigmoid - 모델이라고도 한다.)과 어떤 도입자가 어느 시점에서 대상이 된 새로운 기술을 도입할 것인지 아닌지를 결정하는 모델로서 2) Probit Diffusion Model (프로빗 모델)을 중심으로 한다. 그리고 이러한 지식확산과정과 속도에 영향을 줄 수 있는 기업 내부적 요인으로서 도입하고자 하는 기업의 누적된 경험이 중요하다는 것과 기업 외부적 요인으로서 네트웍 효과와 같은 요인들을 설명하였다.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.225-230
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• 2017

To generate mechanical movements in one-shot devices such as missiles and space launch vehicles, pyrotechnic mechanical device(PMD) such as pin pullers using pyrotechnic charge has been widely used. Reliability prediction of pin pullers is crucial to successfully execute target missions for the one-shot devices. Because the pin pullers require destructive tests to evaluate their reliability, one would need about 3,000 samples of success to guarantee a reliability of 99.9 % with a confidence level of 95 %. This paper suggests the application of a probit model using the charge amount as a functional parameter for estimation of functional reliability of pin puller. To guarantee target reliability, we propose estimation methods of the lower bound of functional reliability by applying the probit model. Given lower bound of functional reliability, we quantitatively show that the optimum amount of charge increases as the number of samples decreases. Along with a variety of simulations the validity of our new model via real test results is confirmed.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.81-94
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• 2022

In the service sectors, the qualitative evaluation method in the form of a survey is widely used as a major assessment tool to evaluate the quality of service. However, the results obtained from a survey can involve the subjective judgment of the respondent. In this study, we propose a method to secure objectivity by excluding subjectivity that may be included in the qualitative evaluation results. In particular, we deal with a situation where the same type of qualitative evaluation tool is used repeatedly by several service providers. To this end, by utilizing both the Ordered Probit model and third-party evaluation results, we determine whether subjectivity is involved in the results. After correcting subjectivity, the final results are obtained through statistical analysis. The application analyzed in this study is the medical service area. With the actual evaluation results supplied by the service providers, we explain how objectivity can be secured from the assessment data by applying our proposed approach.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.95-102
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• 2011

This paper presents a Driving Risk Model (DRM) based on driving speed choices using an Ordered Response Probit (ORP) model. The DRM is conceptualized based on the relation between speed deviation and the occurrence of crashes found by Solomon. The impacts of various driving risk factors are revealed by applying the DRM to evaluate the effectiveness of In-Vehicle Traffic Warning Information (IVTWI) in expressway driving. Regarding driving risk, the results show that: (1) the risk is lower among male drivers, those with more driving experience and those with less accident history, (2) the risk is higher when driving takes place on wet road surface, in the afternoon, and under conditions of low traffic volume, and (3) the risk is also higher on both downgraded and long curve sections. Additionally, the results provide evidence that provision of IVTWI can decrease the driving risk. The proposed DRM provides a solution for assessing the traffic safety impacts of countermeasures on roadways when there is a shortage of traffic accidents data.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.47-59
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• 2020

In this paper, we present the longitudinal Markov binary regression model with t-link function when its transition order is known or unknown. It is assumed that logit or probit models are considered in binary regression models. Here, t-link function can be used for more flexibility instead of the probit model since the t distribution approaches to normal distribution as the degree of freedom goes to infinity. A Markov regression model is considered because of the longitudinal data of each individual data set. We propose Bayesian method to determine the transition order of Markov regression model. In particular, we use the deviance information criterion (DIC) (Spiegelhalter et al., 2002) of possible models in order to determine the transition order of the Markov binary regression model if the transition order is known; however, we compute and compare their posterior probabilities if unknown. In order to overcome the complicated Bayesian computation, our proposed model is reconstructed by the ideas of Albert and Chib (1993), Kuo and Mallick (1998), and Erkanli et al. (2001). Our proposed method is applied to the simulated data and real data examined by Sommer et al. (1984). Markov chain Monte Carlo methods to determine the optimal model are used assuming that the transition order of the Markov regression model are known or unknown. Gelman and Rubin's method (1992) is also employed to check the convergence of the Metropolis Hastings algorithm.