• Journal of IKEEE
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• v.16 no.2
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• pp.145-152
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• 2012

This paper is the Software Hazard Analysis (SWHA) which will study the managerial process and the technical methode and techniques inherent in the performance of software safety task within the Military Aircraft System Safety program. This SWHA identifies potential hazardous effects on the software intensive systems and provides a comprehensive and qualitative assessment of the software safety. The purpose of this paper is to identify safety critical functions of software in Military A/C. The identified software hazards associated with the design or function will be evaluated for risks and operational constraint to further improve the software design requirement, analysis and testing efforts for safety critical software. This common SWHA, the first time analysis in KOREA, was review all avionics OFP(Operational Flight Program), and focus only on software segments which are safety critical. This paper provides a important understanding between the customer and developer as to how the software safety for the Military A/C will be accomplished. It will also provide the current best solution which may as one consider the necessary step in establishing a credible and cost-effective software safety program.

• Journal of the Korean Statistical Society
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• v.25 no.3
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• pp.433-444
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• 1996

In contrast to the multiplicative risk model, the additive risk model specifies that the hazard function with covariates is the sum of, rather than product of, the baseline hazard function and the regression function of covariates. We, in this paper, propose a method for checking the adequacy of the additive risk model based on partial-sum of matingale residuals. Under the assumed model, the asymptotic properties of the proposed test statistic and approximation method to find the critical values of the limiting distribution are studied. Several real examples are illustrated.

• Proceedings of the Korean Reliability Society Conference
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• 2005.06a
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• pp.265-269
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• 2005

Cox's proportional hazards model (PHM) has been widely applied in the analysis of lifetime data, and it can be characterized by the baseline hazard function and covariates influencing systems' lifetime, where the covariates describe operating environments (e.g. temperature, pressure, humidity). In this article, we consider the constant baseline hazard function and a discrete random variable of a covariate. The estimation procedure is developed in a parametric framework when there are not only complete data but also incomplete one. The Expectation-Maximization (EM) algorithm is employed to handle the incomplete data problem. Simulation results are presented to illustrate the accuracy and some properties of the estimation results.

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1341-1364
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• 2023

This article introduces a method for estimating the conditional hazard function of a real-valued response variable based on a functional variable. The method uses local linear estimation of the conditional density and cumulative distribution function and is applied to a functional stationary ergodic process where the explanatory variable is in a semi-metric space and the response is a scalar value. We also examine the uniform almost complete convergence of this estimation technique.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.25 no.4
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• pp.525-533
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• 2015

Objectives: Alongside smoking, occupational exposure is an important risk factor for chronic obstructive pulmonary disease. The purpose of this study was to evaluate factors associated with occupational pulmonary function decline that can be used to create guidelines for the health management of shipyard workers Materials: This study analyzed spirometry from 10,597 male shipbuilding workers. Functional decline in spirometry was defined as FEV1/FVC <70% and logistic regression for work duration and occupational hazard exposure was performed Results: Among the subjects, 4.2% showed an obstructive pattern in pulmonary function. The odds ratios for hazard exposure were 1.67(indirect) and 3.54(direct), and for work duration 1.97(10-18 years), 2.29(19-27), and 5.02(28+). After adjusting for smoking and work-related factors, the odds ratios for work durations of over 10 years were 1.73(10-18 years), 1.99(19-27), and 4.09(28+), but for hazards exposure was 1.71(direct) alone after adjustment. Conclusions: Occupational COPD is insidious and chronic, and thus long-term hazard exposed(especially over 10 years) shipyard workers with functional decline in spirometry need to prevent and manage COPD. This study is important for establishing guidelines to manage hazard exposure among shipyard workers and prevent COPD.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.40 no.4
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• pp.201-209
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• 2007

Line transect sampling in a sighting survey is one of most widely used methods for assessing animal abundance. This study applied distance data, collected from three sighting surveys using line transects for finless porpoise that were conducted in 2004 and 2005 off the west coast of Korea, to four models (hazard-rate, uniform, half-normal and exponential) that can use a variety of detection functions, g (x). The hazard-rate model, a derived model for the detection function, should have a shoulder condition chosen using the AIC (Akaike Information Criterion), as the most suitable model. However, it did not describe a shoulder shape for the value of g(x) near the track tine and underestimated g (x), just as the exponential model did. The hazard-rate model showed a bias toward overestimating the densities of finless porpoises with a higher coefficient of variation (CV) than the other models did. The uniform model underestimated the densities of finless porpoise but had the lowest CV. The half-normal model described a detection function with a shape similar to that of the uniform model. The half-normal model was robust for finless porpoise data and should be able to avoid density underestimation. The estimated abundance of finless porpoise was 3,602 individuals (95% CI=1,251-10,371) inshore in 2005 and 33,045 individuals (95% CI=24,274-44,985) offshore in 2004.

• Structural Engineering and Mechanics
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• v.69 no.3
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• pp.317-326
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• 2019

This research aims to assess the tight seismic risk curve of the intake tower at Geumgwang reservoir by considering the recorded historical earthquake data in the Korean Peninsula. The seismic fragility, a significant part of risk assessment, is updated by using Bayesian inference to consider the uncertainties and computational efficiency. The reservoir is one of the largest reservoirs in Korea for the supply of agricultural water. The intake tower controls the release of water from the reservoir. The seismic risk assessment of the intake tower plays an important role in the risk management of the reservoir. Site-specific seismic hazard is computed based on the four different seismic source maps of Korea. Probabilistic Seismic Hazard Analysis (PSHA) method is used to estimate the annual exceedance rate of hazard for corresponding Peak Ground Acceleration (PGA). Hazard deaggregation is shown at two customary hazard levels. Multiple dynamic analyses and a nonlinear static pushover analysis are performed for deriving fragility parameters. Thereafter, Bayesian inference with Markov Chain Monte Carlo (MCMC) is used to update the fragility parameters by integrating the results of the analyses. This study proves to reduce the uncertainties associated with fragility and risk curve, and to increase significant statistical and computational efficiency. The range of seismic risk curve of the intake tower is extracted for the reservoir site by considering four different source models and updated fragility function, which can be effectively used for the risk management and mitigation of reservoir.

• International Journal of Reliability and Applications
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• v.1 no.1
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• pp.15-26
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• 2000

This paper considers an aperiodic preventive maintenance (PM) model for repairable systems, in which the time intervals between two consecutive preventive maintenances are unequal. To propose such an aperiodic PM model, we assume that each PM reduces the current hazard rate by a certain amount which depends on the number of PMs performed previously. If the system fails between PMs, the minimal repair is performed and the hazard rate remains unchanged after the repair. We give the exact expressions for the hazard rate function for the aperiodic PM model. Based on the proposed aperiodic PM model, we suggest the maximum likelihood method to estimate the parameters characterizing the model and apply the method to the case of Weibull distribution. Numerical examples for estimating the parameters are presented for the purpose of illustration.

• Journal of the Korean earth science society
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• v.24 no.5
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• pp.428-439
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• 2003

Landslides, as a geological hazard, have caused extensive damage to property and sometimes result in loss of life. Thus, it is necessary to assess vulnerable areas for future possible landslides in order to mitigate the damage they cause. For this purpose, spatial data integration has been developed and applied to landslide hazard mapping. Among various models, this paper investigates and discusses the effectiveness of the Bayesian spatial data integration approach to landslide hazard mapping. In this study, several data sets related to landslide occurrences in Jangheung, Korea were constructed using GIS and then digitally represented using the likelihood ratio function. By computing the likelihood ratio, we obtained quantitative relationships between input data and landslide occurrences. The likelihood ratio functions were combined using the Bayesian combination rule. In order for predicted results to provide meaningful interpretations with respect to future landslides, we carried out validation based on the spatial partitioning of the landslide distribution. As a result, the Bayesian approach based on a likelihood ratio function can effectively integrate various spatial data for landslide hazard mapping, and it is expected that some suggestions in this study will be helpful to further applications including integration and interpretation stages in order to obtain a decision-support layer.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.59-68
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• 1996

In this paper, we consider how to estimate New Better Than Used (NBU) survival curves from randomly right censored data. We propose several possible NBU estimators and study their properties. Numerical studies indicate that the proposed estimators are appropriate in practical use. Some useful examples are presented.