• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.1-14
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• 1997

This paper considers maximum likelihood (ML) estimation of lifetime distribution under stress bounded ramp tests in which the stress is increased linearly from used condition stress to the stress u, pp.r bound. The following assumptions are used: exponential lifetime distribution under a constant stress, an inverse power law relationship between stress and mean of exponential lifetime distribution, and a cumulative exposure model for the effect of changing stress. Likelihood equations for the parameters involved in the model and asymptotic distribution of the estimators are obtained, and a numerical example is given.

• Journal of Environmental Science International
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• v.25 no.8
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• pp.1107-1114
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• 2016

This research, sponsored by the Korean Ministry of Environment in 2014, was the first epidemiological study in Korea that investigated the health impact assessment of radon exposure. Its purpose was to construct a model that calculated the annual mean cumulative radon exposure concentrations, so that reliable conclusions could be drawn from environment-control group research. Radon causes chronic lung cancer. Therefore, the long-term measurement of radon exposure concentration, over one year, is needed in order to develop a health impact assessment for radon. Hence, based on the seasonal correction model suggested by Pinel et al.(1995), a predictive model of annual mean radon concentration was developed using the year-long seasonal measurement data from the National Institute of Environmental Research, the Korea Institute of Nuclear Safety, the Hanyang University Outdoor Radon Concentration Observatory, and the results from a 3-month (one season) survey, which is the official test method for radon measurement designated by the Korean Ministry of Environment. In addition, a model for evaluating the effective annual dose for radon was developed, using dosimetric methods. The model took into account the predictive model for annual mean radon concentrations and the activity characteristics of the residents.

• Journal of Preventive Medicine and Public Health
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• v.33 no.3
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• pp.343-348
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• 2000

Objectives : This study was conducted to integrate the results of studies assessing the association between chronic noise exposure and blood pressure. Methods : Using a MEDLINE search with noise exposure, blood pressure and hypertension as key words, we retrieved articles from the literature that were published from 1980 to December 1999. The criteria for quality evaluation were as follows: 1) the study subjects must have been workers employed at a high noise level area 2) The paper should use average and cumulative noise exposure as method for exposure evaluation. 3) Blood pressure in each article should be reported in a continuous scale Among the 77 retrieved articles, six studies were selected for quantitative meta-analysis. Before the integration of the regression coefficients for the association between blood pressure and noise level, homogeneity tests were conducted. Results : All studies were a cross-sectional design and the study subjects were industrial workers. Five papers used a time-weighted average for noise exposure and only one paper calculated the cumulative noise exposure level. The measurement of blood pressure in the majority of studios were accomplished in a resting stale, and used an average of two or more readings. The homogeneity of studies was rejected in a fixed effect model, so we used the results in a random effect model. The results of the quantitative meta-analysis, the weighted regression coefficient of noise associated with systolic blood pressure and diastolic blood pressure were 0.05 (95% confidence interval [CI]: -0.03, 0.13) and 0.06 (95% CI: -0.01, 0.13), respectively. Conclusions : Our results suggested that chronic exposure to industrial noise does not cause elevated blood pressure.

• Journal of Korean Academy of Nursing
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• v.53 no.4
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• pp.453-467
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• 2023

Purpose: This study investigated the cumulative effects of depressive symptoms on cognitive function over time in community-dwelling older adults. Methods: Data were investigated from 2,533 community-dwelling older adults who participated in the Korean Longitudinal Study of Aging (KLoSA) from the 5th (2014) to the 8th wave (2020). The association between cumulative depressive symptoms and cognitive function was identified through multiple regression analysis. Results: When the multiple regression analysis was conducted from each wave, the current depressive symptoms scores and cognitive function scores were negatively associated, regardless of the waves (B5th = - 0.26, B6th = - 0.26, B7th = - 0.26, and B8th = - 0.27; all p < .001). Further, when all the previous depressive symptoms scores were added as explanatory variables in the 8th wave, the current one (B8th = - 0.09, p < .001) and the previous ones (B5th = - 0.11, B6th = - 0.09, and B7th = - 0.13; all p < .001) were also negatively associated with the cognitive function score. The delta R², which indicates the difference between the model's R² with and without the depressive symptoms scores, was greater in the model with all the previous and current depressive symptoms scores (6.4%) than in the model with only the current depressive symptoms score (3.6%). Conclusion: Depressive symptoms in older adults have a long-term impact. This results in an accumulated adverse effect on the cognitive function. Therefore, to prevent cognitive decline in older adults, we suggest detecting their depressive symptoms early and providing continuous intervention to reduce exposure to long-term depressive symptoms.

• Journal of Preventive Medicine and Public Health
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• v.29 no.1 s.52
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• pp.27-41
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• 1996

This study described methods to predict human health risk associated with exposure to environmental carcinogens using animal bioassay data. Also, biological assumption for various dose-response models were reviewed. To illustrate the process of risk estimate using relevant dose-response models such as Log-normal, Mantel-Bryan, Weibull and Multistage model, we used four animal carcinogenesis bioassy data of chloroform and chloroform concentrations of tap water measured in large cities of Korea from 1987 to 1995. As a result, in the case of using average concentration in exposure data and 95% upper boud unit risk of Multistge model, excess cancer risk(RISK I) was about $1.9\times10^{-6}$, in the case of using probability distribution of cumulative exposure data and unit risks, those risks(RISK II) which were simulated by Monte-Carlo analysis were about $2.4\times10^{-6}\;and\;7.9\times10^{-5}$ at 50 and 95 percentile, respectively. Therefore risk estimated by Monte-Carlo analysis using probability distribution of input variables may be more conservative.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.3
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• pp.459-471
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• 1996

This paper considers ramp tests for exponential lifetime distribution when there are limitations on test stress and test time. The inverse power law and a cumulative exposure model are assumed. Maximum likelihood (ML) estimators of model parameters and their asymptotic covariance matrix are obtained. The optimum ramp test plans are also found which minimize the asymptotic variance of the ML estimator of the log mean life at design constant stress. For selected values of the design parameters, tables useful for finding optimal test plans are given. The effect of the pre-estimates of design parameters is studied.

• International Journal of Reliability and Applications
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• v.18 no.2
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• pp.45-63
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• 2017

In this paper, we have formulated optimum Accelerated Life Test (ALT) plan for a parallel system with two independent components using masked data with ramp-stress loading scheme and Type-I censoring. Consider a system of two independent and non-identical components connected in parallel. Such a system fails whenever all of its components has failed. The exact component that causes the system to fail is often unknown due to cost and time constraint. For each parallel system at test, we observe its system's failure time and a set of component that includes the component actually causing the system to fail. The stress-life relationship is modelled using inverse power law, and cumulative exposure model is assumed to model the effect of changing stress. The optimal plan consists in finding out the optimum stress rate using D-optimality criterion. The method developed has been explained using a numerical example and sensitivity analysis carried out.

• Journal of Korean Society for Quality Management
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• v.23 no.1
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• pp.15-28
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• 1995

We consider a step-stress life testing which is devised for a two-component parallel system with considerably long life time. To describe such a system, we use an exponential distribution as the survival function. The lift distribution is assumed between the log mean life time and the stress with the cumulative exposure model. The criterion for optimality is to minimize the sum of the variances of the maximum likelihood estimators of the mean life times of each part under the normal stress.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.415-433
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• 1999

This paper considers the design of step-stress accelerated life tests for the Weibull distribution with a nonconstant shape parameter under Type I censoring. It is assumed that scale and shape parameters are log-linear functions of (possibly transformed) stress and that a cumulative exposure model holds for the effect of changing stress. The asymptotic variance of the maximum likelihood estimator of a stated quantile at design stress is used as an optimality criterion. The optimum three step-stress plans are presented for selected values of design parameters and the effects of errors in pre- estimates of the design parameters are investigated.

• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.208-219
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• 2004

This paper considers a nonparametric estimation of lifetime distribution for ramp stress tests with stress bound under intermittent inspection. The test items are inspected only at specified time points an⊂1 so the collected observations are grouped data. Under the cumulative exposure model, two nonparametric estimation methods of estimating the lifetime distribution at use condition stress are proposed for the situation which the time transformation function relating stress to lifetime is a type of the inverse power law. Each of items is initially put on test under ramp stress and then survivors are put on test under constant stress, where all failures in the Inspection interval are assumed to occur at the midi)oint or the endpoint of that interval. Two proposed estimators of quantile from grouped data consisting of the number of items failed in each inspection interval are numerically compared with the maximum likelihood estimator(MLE) based on Weibull distribution.