• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.131-146
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• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

• Transactions of the Korean Society of Mechanical Engineers
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• v.11 no.3
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• pp.454-464
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• 1987

The objective of this research is to analyze and evaluate the dynamic flow curve of metals under impact loading at both high strain rate (.epsilon.=1/h dh/dt > 10$\^$3/m/s/m) and large strain (.epsilon.=In h/h$\_$0/ > 1.0). A test method for dynamic compression of metal disc is described. The velocity of the striker face and the force on the anvil are measured during the impact period. From these primitive data the axial stress, strain, and strain rate of the disc are obtained. The Strain rate is determined by the striker velocity divided by the specimen height. This gives a slightly increasing strain rate over most of the deformation period. Strain rates of 100 to 10,000 per second are achieved. Attainable final strains are 150%. A discussion of several problem areas is presented. The friction on the specimen surfaces, the determination of the frictional coefficient, the influence of the specimen geometry (h$\_$0//d$\_$0/ ratio) on the friction effect, the lock-up condition for a given configuration, the friction correction factor, and the evaluation of several lubricants are given. The flow function(stress verus strain) is dependent on the material condition(e.g., prior cold work), specimen geometry, strain rate, and temperature.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.13-21
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• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.