• Transactions of the Korean Society of Mechanical Engineers
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• v.9 no.1
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• pp.127-134
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• 1985

On two type regenerator which is represented by their parameters f(t), f(x,t) for generalized regenerating heat exchanger, the temperature distributions are studied analytically in this paper. For f(x,t) regenerator type, particularly, we are able to induce the simplified temperature distribution and convection heat transfer coefficient in heating which apply on condition that regenerator having short transfer period from above theoretical analysis.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.99-112
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• 2016

Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.

• Convergence Security Journal
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• v.7 no.3
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• pp.51-60
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• 2007

Infinite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, exponential distribution and Rayleigh distribution model was reviewed, proposes the mixture reliability model, which made out efficiency substituted for situation for failure time Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using S27 data set for the sake of proposing shape parameter of the mixture distribution was employed. This analysis of failure data compared with the mixture distribution model and the existing model(using arithmetic and Laplace trend tests, bias tests) is presented.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.4
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• pp.460-466
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• 2018

With the development of information technology, the scale of computer software system is constantly expanding. Reliability and cost of software development have a great impact on software quality. In this study, based on the software failure interval time data, a comparative analysis was performed on the characteristics of the software development cost model based on the lifetime distribution following the Type-2 Gumbel and Erlang distribution in the NHPP model. As a result, the trends of the cost curves for the Go-Okumoto model and the proposed Erlang model and the Type-2 Gumble model both decreased in the initial stage and gradually increased in the latter half of the failure time. Also, Comparing the Erlang model with the Type-2 Gumble model, we found that the Erlang model is faster and more cost-effective at launch. Through this study, Software operators should remove possible defects from the testing phase rather than the operational phase to reduce defects after the software release date, it is expected to be able to study the prior information needed to understand the characteristic of software development cost.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.9-18
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• 2006

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this paper, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the exponentiated exponential distribution reliability model, which maked out efficiency substituted for gamma and Weibull model(2 parameter shape illustrated by Gupta and Kundu(2001) Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE, AIC statistics and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using NTDS data set for the sake of proposing shape parameter of the exponentiated exponential distribution was employed. This analysis of failure data compared with the exponentiated exponential distribution model and the existing model (using arithmetic and Laplace trend tests, bias tests) is presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.514-522
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• 2007

Finite failure NHPP models presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. In this parer, Goel-Okumoto and Yamada-Ohba-Osaki model was reviewed, proposes the Burr distribution reliability model, which making out efficiency application for software reliability. Algorithm to estimate the parameters used to maximum likelihood estimator and bisection method, model selection based on SSE, AIC statistics and Kolmogorov distance, for the sake of efficient model, was employed. Analysis of failure using real data set for the sake of proposing shape parameter of the Burr distribution was employed. This analysis of failure data compared with the Burr distribution model and the existing model(using arithmetic and Laplace trend tests, bias tests) is presented.

• Journal of IKEEE
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• v.9 no.2 s.17
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• pp.143-151
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• 2005

Finite failure NHPP models proposed in the literature exhibit is either constant, monotonic increasing or monotonic decreasing failure occurrence rates per fault. For the sake of proposing shape parameter of the Gamma family distribution, used the special pattern. Data set, where the underlying failure process could not be adequately described by the knowing models, which motivated the development of the Gamma or Weibull model and Gompertz model. Analysis of failure data set that led us to the Gamma or Weibull model and Gompertz model using arithmetic and Laplace trend tests, bias tests was presented in this Paper.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.69-84
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• 2020

Based on differential quadrature method (DQM) and nonlocal strain gradient theory (NSGT), forced vibrations of a porous functionally graded (FG) scale-dependent beam in thermal environments have been investigated in this study. The nanobeam is assumed to be in contact with a moving point load. NSGT contains nonlocal stress field impacts together with the microstructure-dependent strains gradient impacts. The nano-size beam is constructed by functionally graded materials (FGMs) containing even and un-even pore dispersions within the material texture. The gradual material characteristics based upon pore effects have been characterized using refined power-law functions. Dynamical deflections of the nano-size beam have been calculated using DQM and Laplace transform technique. The prominence of temperature rise, nonlocal factor, strain gradient factor, travelling load speed, pore factor/distribution and elastic substrate on forced vibrational behaviors of nano-size beams have been explored.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.498-504
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• 2002

Vibration beat phenomenon is theoretically investigated on a slightly asymmetric cylindrical shell, which is a simplified model of Korean bell. Mode pairs of the slightly asymmetric shell are obtained by receptance analysis and impulse response of the shell is derived using modal expansion and Laplace transform. Based on the impulse response model, beat mapping method is proposed to explain the reason that the beat of a bell vibration shows periodic distribution on the circumference. Beat characteristics of King Song-Dok Bell are explained in detail using the beat map and the measured modal data.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.905-912
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• 2009

We propose a doubly penalized kernel machine (DPKM) which uses heteroscedastic location-scale model as basic model and estimates both mean and volatility functions simultaneously by kernel machines. We also present the model selection method which employs the generalized approximate cross validation techniques for choosing the hyperparameters which affect the performance of DPKM. Artificial examples are provided to indicate the usefulness of DPKM for the mean and volatility functions estimation.