• Journal of Korea Society of Industrial Information Systems
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• v.20 no.4
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• pp.75-87
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• 2015

A redundant structure typically consists of primary component and standby component taking over the function of the primary component when the primary component fails. In this research, we consider a redundant structure in which a standby component can take over the function of more than one primary component when primary components fail. And we assume that the system has multi-state according to the states of components while all components have two states. This system is called as the multi-state redundant system with a multi-functional standby component. This type of redundant structure is frequently adapted by the system such as an aircraft in which the weight is an important design factor. In this paper, we propose new reliability model for this multi-state redundant system with a multi-functional standby component in order for evaluating the reliability of the system. Under the assumption that all components have constant failure rate, we evaluate the reliability of the system by applying Markov analysis method. And we investigate the effect of the multi-functional standby component by comparing reliabilities of the parallel system with multi-functional standby component and a simple parallel system and a parallel system with redundant structure.

• Communications for Statistical Applications and Methods
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• v.27 no.1
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• pp.149-161
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• 2020

In this paper we extend the functional principal component analysis for real-valued random functions to the case of Hilbert-space-valued functional random objects. For this, we introduce an autocovariance operator acting on the space of real-valued functions. We establish an eigendecomposition of the autocovariance operator and a Karuhnen-Loève expansion. We propose the estimators of the eigenfunctions and the functional principal component scores, and investigate the rates of convergence of the estimators to their targets. We detail the implementation of the methodology for the cases of compositional vectors and density functions, and illustrate the method by analyzing time-varying population composition data. We also discuss an extension of the methodology to multivariate cases and develop the corresponding theory.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.431-445
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• 2006

In this paper we review some methods for analyzing functional data and illustrate real application of functional data analysis. Representing methods for functional data by using basis function, analyzing functional variation by functional principal component analysis and functional linear models are reviewed. For a real application, we use temperature and precipitation data measured in Korea from the January of 1970 to the May of 2004. We apply functional principal component analysis for each data and test the significance of regional division done by using shining hours. We also estimate functional regression model for temperature and precipitation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.138-147
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• 2017

As the functions and structure of the system are complicated and elaborated, various types of structures are emerging to increase reliability in order to cope with a system requiring higher reliability. Among these, standby systems with standby components for each major component are mainly used in aircraft or power plants requiring high reliability. In this study, we consider a standby system with a multi-functional standby component in which one standby component simultaneously performs the functions of several major components. The structure of a parallel system with multifunctional standby components can also be seen in real aircraft hydraulic pump systems and is very efficient in terms of weight, space, and cost as compared to a basic standby system. All components of the system have complete operation, complete failure, only two states, and the system has multiple states depending on the state of the component. At this time, the multi-functional standby component is assumed to be in a non-operating standby state (Cold Standby) when the main component fails. In addition, the failure rate of each part follows the Weibull distribution which can be expressed as increasing type, constant type, and decreasing type according to the shape parameter. If the Weibull distribution is used, it can be applied to various environments in a realistic manner compared to the exponential distribution that can be reflected only when the failure rate is constant. In this paper, Markov chain analysis method is applied to evaluate the reliability of multi-functional multi-state standby system. In order to verify the validity of the reliability, a graph was generated by applying arbitrary shape parameters and scale parameter values through Excel. In order to analyze the effect of multi-functional multi-state standby system using Weibull distribution, we compared the reliability based on the most basic parallel system and the standby system.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.885-893
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• 2015

It is important to improve the forecasting accuracy of one-year-ahead seasonal factors in order to produce seasonally adjusted series of the following year. In this paper, seasonal factors of 8 monthly Korean economic time series are examined and forecast based on the functional principal component regression. One-year-ahead forecasts of seasonal factors from the functional principal component regression are compared with other forecasting methods based on mean absolute error (MAE) and mean absolute percentage error (MAPE). Forecasting seasonal factors via the functional principal component regression performs better than other comparable methods.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.1
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• pp.24-28
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• 2009

This study was conducted to evaluate the growth characters and yield components of 18 collected sesame cultivars to get basic information on the variation for the sesame breeding using principal component analysis. All characters except days to flowering, days to maturity and 1,000 seed weight showed significantly different. Seed weight per 10 are showed higher coefficient of variance. Capsule bearing stem length and liter weight showed positive correlation with seed yield per 10 are. The principal components analysis grouped the estimated sesame cultivars into four main components which accounted for 83.7% of the total variation at the eigenvalue and its contribution to total variation obtained from principal component analysis. The first principal component ($Z_1$) was applicable to increase plant height, capsule bearing stem length and 1,000-seed weight. The second principal component ($Z_2$) negatively correlated with days to flowering and maturity by which it was applicable to shorten flowering and maturity date of sesame. At the scatter diagram, Yangbaek, Ansan, M1, M2, M4, M7 and M9 were classified as same group, but M10, Yanghuk, Kanghuk, M5, M6, M12 and M13 were classified as different group. This results would be helpful for sesame breeder to understand genetic relationship of some agronomic characters and select promising cross lines for the development of new sesame variety.

• Elastomers and Composites
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• v.57 no.3
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• pp.100-106
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• 2022

Waterborne polyurethane-acrylate(WPUA) dispersions were prepared by surfactant-free emulsion polymerization in a two-step process. In the first step, polytetrahydrofuran, isophorone diisocyanate, dimethylol proponic acid, and 2-hydroxyethyl methacrylate were used to synthesize a vinyl-terminated polyurethane prepolymer. In the second step, styrene, methyl methacrylate, butyl acrylate, and different multi-functional crosslinkers were copolymerized. 1,6-hexanediol diacrylate, trimethylolpropane triacrylate, and pentaerythritol tetraacrylate were used as the crosslinkers, and their effect on the mechanical and thermal properties of WPUA was investigated. Overall, as the number of functional groups of the cross-linker increased, the gel fraction improved to 79.26%, the particle size increased from 75.9 nm to 148.7 nm, and the tensile strength was improved from 5.86 MPa to 12.40 MPa. In thermal properties, the glass transition temperature and decomposition temperature increased by 9.9℃ and 18℃, respectively. The chemical structures of the WPUA dispersions were characterized by Fourier-transform infrared spectroscopy. The synthesized WPUA has high potential for applications such as coatings, leather coatings, adhesives, and wood finishing.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.103-125
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• 2022

In this paper, we analyze the time series data of the case and death counts of COVID-19 that broke out in China in December, 2019. The study period is during the lockdown of Wuhan. We exploit functional data analysis methods to analyze the collected time series data. The analysis is divided into three parts. First, the functional principal component analysis is conducted to investigate the modes of variation. Second, we carry out the functional canonical correlation analysis to explore the relationship between confirmed and death cases. Finally, we utilize a clustering method based on the Expectation-Maximization (EM) algorithm to run the cluster analysis on the counts of confirmed cases, where the number of clusters is determined via a cross-validation approach. Besides, we compare the clustering results with some migration data available to the public.

• Journal of Applied Reliability
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• v.15 no.4
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• pp.233-240
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• 2015

We analyse reliability of multi-state UH-60 helicopter hydraulic pump system with a multi-functional standby component using Markov analysis method. The system consists of seven components: 2 main pumps, 1 standby pump, 2 primary servos, and 2 tail rotor servos. The standby pump can take over when one more than components fail. Therefore the standby pump is multi-functional standby component. The system has four states: good, deteriorated, dangerous, and failed. The components have 2 states: working and failed. We assume the system is unrepairable when the components fail. We estimate failure distributions and rates using collected failure time data in field. And we classify multi-state of the system according to emergency procedure of UH-60A student handout. We obtain the reliabilities of multi-state system using Visual Basic program because the differential equations is extremely complicated and tedious to solve.