• Korean Journal of Environmental Agriculture
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• v.11 no.2
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• pp.146-154
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• 1992

DDVP, malathion and diazinon ECs which differ in chemical compositions and moisture contents were formulated with nine emulsifiers, three solvents(xylene, cyclohexanone and DMF) and epichlorohydrin. For the studies of decomposition characteristics, these technicals and ECs were subjected to the test under elevated temperature at $54^{\circ}C$ for 15 days and $38^{\circ}C$ for 90 days respectively. DDVP technical was rapidly decomposed in early stage of thermoaccelerated test at $54^{\circ}C$, but the decomposition rate slowed down with time. As for malathion and diazinon technicals, the longer they were incubated, the more decomposed. The decomposed AI in ECs increased with solvent polarity. The increment of moisture content in ECs accelerated the decomposition of AI, and that was remarkable especially in diazinon ECs. Addition of emulsifiers increased the moisture content to be accelerated the decomposition of AI, but the decomposition of AI was more affected by the kind of emulsifier than by the moisture content of emulsifier, Stabilizing effect by epichlorohydrin was distingished in malathion and diazinon ECs, but there was no effect in other solvent-based formulation except xylene.

• Journal of Korean Tunnelling and Underground Space Association
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• v.9 no.2
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• pp.109-120
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• 2007

Auxiliary support systems such as the reinforced protective umbrella method have been applied before tunnel excavation to increase ground stiffness and to prevent the large deformation. However, determination procedure of geotechnical parameters along the construction sequence contains various errors. This study suggests a method to characterize the time-dependent behavior of pre-reinforced zones around the tunnel using elastic waves. Experimental results show that shear strength as well as elastic wave velocities increase with the curing time. Shear strength and strength parameters can be uniquely correlated to elastic wave velocities. Obtained results from the laboratory tests are applied to numerical simulation of tunnel considering its construction sequences. Based on numerical analysis, initial installation part of pre-reinforcement and portal of tunnel are critical for tunnel stability. Result of the time-dependent condition is similar to the results of for $1{\sim}2$ days of the constant time conditions. Finally, suggested simple analysis method combining experimental and numerical procedure which considering time-dependent behavior of pre-reinforced zone on tunnel would provide reliable and reasonable design and analysis for tunnel.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.113-122
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• 2016

Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failure time has been applied on several works. In this paper, we extend it to the case where longitudinal data involve informative observation time process as well as competing risks survival times. We use a likelihood approach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggested joint model allows us to make inferences for three components: longitudinal outcome, observation time process and competing risk failure time. In addition, we can test the association among these components. In this paper, liver cirrhosis patients' data is analyzed. The relationship between prothrombin times measured at irregular visiting times and drop outs is investigated with a joint model.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1459-1473
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• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.

• Communications for Statistical Applications and Methods
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• v.5 no.2
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• pp.431-437
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• 1998

본 논문의 목적은 계층적 베이즈 일반화 선형모형을 이용하여 경시적 자료를 분석하는 것이다. 구체적으로 계층적 베이즈 변량효과 모형을 소개하고 무정보적 사전분포 하에서 사후분포가 진(proper)인지에 대한 충분조건을 찾는다 또한, 깁스(Gibbs) 표본자를 사용하여 제안된 계층적 베이즈 절차의 수행에 관해 논의한다. 현실자료를 사용하여 제안된 계층적 베이즈 분석을 예시하고, 이에 대응하는 경험적 베이즈 분석과 비교한다.

• Journal of Navigation and Port Research
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• v.26 no.1
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• pp.106-111
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• 2002

The estimation of pile bearing capacity is important since the design details are determined from the result. There are numerous ways of determining the pile design load, but only few of them are chosen in the actual design. According to the recent investigation in Korea, the formulas proposed by Meyerhof based on the SPT N values are most frequently chosen in the design stage. In the study, various static and dynamic formulas have been used in predicting the allowable bearing capacity of a pile. Further, the reliability of these formulas has been verified by comparing the perdicted values with the static and dynamic load test measurements. Also, in most cases, these methods of pile bearing capacity determination do not take the time effect consideration, the actual allowable load as determined from pile load test indicates severe deviation from the design value. The principle results of this study are summarized as follows : As a result of estimate the reliability in criterion of the Davisson method, t was showed that Terzaghi & Peck >Chin>Meyerhof > Modified Meyerhof method was the most reliable method for the prediction of bearing capacity. Comparisons of the various pile-driving formulas showed that Modified Engineering News was the most reliable method. However, a significant error happened between dynamic bearing capacity equation was judged that uncertainty of hammer efficiency, characteristics of variable, time effect etc... was not considered. As a result of considering time effect increased skin friction capacity higher than end bearing capacity. It was found out that it would be possible to increase the skin friction capacity 1.99 times higher than a driving. As a result of considering 7 day's time effect, it was obtained that Engineering news, Modified Engineering News, Hiley, Danish, Gates, CAPWAP(CAse Pile Wave Analysis Program) analysis for relation, repectively, $Q_{u(Restrike)} / Q_{u(EOID)} = 0.98t_{0.1}$ , $0.98t_{0.1}$, $1.17t_{0.1}$, $0.88t_{0.1}$, $0.89t_{0.1}$, $0.97t_{0.1}$.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1263-1272
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• 2014

Longitudinal categorical data commonly occur from medical, health, and social sciences. In these data, the correlation of repeated outcomes is taken into account to explain the effects of covariates exactly. In this paper, we introduce marginalized random effects models that are used for the estimation of the population-averaged effects of covariates. We also review how these models have been developed. Real data analysis is presented using the marginalized random effects.

• Proceedings of the Korean Association for Survey Research Conference
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• 2005.12a
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• pp.47-54
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• 2005

• Cement Symposium
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• no.25
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• pp.110-116
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• 1997

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.335-342
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• 2015

Joint hierarchical generalized linear models proposed by Molas et al. (2013) extend the simple longitudinal model into multiple models fitted jointly. It can easily handle the correlation of multivariate longitudinal data. In this paper, we apply this method to analyze KoGES cohort dataset. Fixed unknown parameters, random effects and variance components are estimated based on a standard framework of h-likelihood theory. Furthermore, based on the conditional Akaike information criterion the correlated covariance structure of random-effect model is selected rather than an independent structure.