• Journal of Korean Society for Quality Management
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• v.19 no.1
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• pp.28-42
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• 1991

For a series or parallel system, though the component lifetimes have the absolutely continuous bivariate exponential distributions(ACBVE) by Block and Basu(1974), the common assumption that the component lifetimes are independent is used. The purpose of this paper, in this case, is to investigate the magnitude of the error caused by erroneous assumption, using the measure proposed by Klein and Moeschberger(1986). Estimation of the measure is conducted by maximum likelihood estimator(MLE) and those estimators are compared with corresponding jackknifed MLE through the Monte Carlo study.

• The Korean Journal of Food And Nutrition
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• v.4 no.1
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• pp.99-107
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• 1991

Lignocellulose and lignolic compounds were absolutely given much weight In the biosphere, and their degradation was essential for continuous biological carbon circulation. Whereas aerobic cellulolytic microorganism dissolved the cellulose into their elements in the first stage, strict anaerobic cellulolytic microorganism's role was taken I increasing interest through the recent research. It was reviewed that anaerobic microbial degradation process of lignocellulose and its derivatives (cellulose, lignin, oligolignol and monoaromatic compound), and function of anaerobic microorganism on the. environmental ecology.

• The Pure and Applied Mathematics
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• v.11 no.2
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• pp.133-138
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• 2004

In this paper we derive a decomposition of the solution of Fredholm equations of the second kind in terms of reproducing kernels in the space ${W_2}^2$($\Omega$)

• Journal of the Chungcheong Mathematical Society
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• v.23 no.2
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• pp.245-250
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• 2010

We present characterizations of the Weibull distribution by the independent property of record values that F(x) has a Weibull distribution if and only if $\frac{X_{U(m)}}{X_{U(n)}}$ and $X_{U(n)}$ or $\frac{X_{U(n)}}{X_{U(n)}{\pm}X_{U(m)}}$ and $X_{U(n)}$ are independent for $1{\leq}m.

• Communications of the Korean Mathematical Society
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• v.16 no.2
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• pp.287-290
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• 2001

Let X$_1$, X$_2$, … be a sequence of independent and identically distributed random variables with continuous cumulative distribution function F(x). X(sub)j is an upper record value of this sequence if X(sub)j > max {X$_1$, X$_2$, …, X(sub)j-1}. We define u(n) = min {j│j > u(n-1), X(sub)j > X(sub)u(n-1), n $\geq$ 2} with u(1) = 1. Then F(x) = 1 - e(sup)-x/c, x > 0 if and only if E[X(sub)n(n+1) - X(sub)u(n)│X(sub)u(m) = y] = c or E[X(sub)u(n+2) - X(sub)u(n)│X(sub)u(m) = y] = 2c, n $\geq$ m+1.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.167-174
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• 2003

It is absolutely natural to be interested in durability and safety of the structure under shear behavior. To fulfill this desire, a comparison on the shear behavior between steel fiber reinforced concrete both simple and continuous beams is done to use in the field working. Several operations are conducted : First of all, plan for optimal combination is standardized. Second, resistance for shear has been generalized in that it is decided by combination of individual elements. Third, as the fracture of tensile bar leads to destruction of specimen, shear behavior of whole specimen is decided by stress working on tensile bar. It should be generalized for other specimens also. Forth, evidence of the softness of steel fiber reinforced concrete beam by experiment lead to application in the fields. Finally, numeral values of the steel fiber reinforced concrete are analyzed and the result is compared to those of experiments. With these consequences, this study was done for the application to dynamic structures such as bridges and the repair and rehabilitation.

• Communications of the Korean Mathematical Society
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• v.38 no.2
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• pp.431-450
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• 2023

The basic goal of quantization for probability distribution is to reduce the number of values, which is typically uncountable, describing a probability distribution to some finite set and thus to make an approximation of a continuous probability distribution by a discrete distribution. It has broad application in signal processing and data compression. In this paper, first we define the uniform distributions on different curves such as a line segment, a circle, and the boundary of an equilateral triangle. Then, we give the exact formulas to determine the optimal sets of n-means and the nth quantization errors for different values of n with respect to the uniform distributions defined on the curves. In each case, we further calculate the quantization dimension and show that it is equal to the dimension of the object; and the quantization coefficient exists as a finite positive number. This supports the well-known result of Bucklew and Wise [2], which says that for a Borel probability measure P with non-vanishing absolutely continuous part the quantization coefficient exists as a finite positive number.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.759-766
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• 2013

The paper examines several classes of probability distributions with heavy tails. An (asymptotic) expression for tail probability needs to be known to understand which class a given probability distribution belongs to. It is usually not easy to get expressions for tail probabilities since most absolutely continuous probability distributions are specified by probability density functions and not by distribution functions. The paper proposes a method to obtain asymptotic expressions for tail probabilities using only probability density functions. Some examples are given to illustrate the proposed method.

• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.441-446
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• 2003

Let { $X_{n}$ , n $\geq$ 1} be a sequence of independent and identically distributed random variables with a common continuous distribution function F(x) and probability density function f(x). Let $Y_{n}$ = max{ $X_1$, $X_2$, …, $X_{n}$ } for n $\geq$ 1. We say $X_{j}$ is an upper record value of { $X_{n}$ , n$\geq$1} if $Y_{j}$ > $Y_{j-1}$, j > 1. The indices at which the upper record values occur are given by the record times {u(n)}, n$\geq$1, where u(n) = min{j｜j>u(n-1), $X_{j}$ > $X_{u}$ (n-1), n$\geq$2} and u(1) = 1. We call the random variable X $\in$ Beta (1, c) if the corresponding probability cumulative function F(x) of x is of the form F(x) = 1-(1-x)$^{c}$ , c>0, 0$\leq$x$\leq$1. In this paper, we will give a characterization of the beta distribution of the first kind by considering conditional expectations of record values.s.

• Communications of the Korean Mathematical Society
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• v.18 no.1
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• pp.127-131
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• 2003

Let X$_1$, X$_2$,... be a sequence of independent and identically distributed random variables with continuous cumulative distribution function F(x). X$_j$ is an upper record value of this sequence if X$_j$ > max {X$_1$,X$_2$,...,X$_{j-1}$}. We define u(n)=min{j$\mid$j> u(n-1), X$_j$ > X$_{u(n-1)}$, n $\geq$ 2} with u(1)=1. Then F(x) = 1-x$^{\theta}$, x > 1, ${\theta}$ < -1 if and only if (${\theta}$+1)E[X$_{u(n+1)}$$\mid$X$_{u(m)}$=y] = ${\theta}E[X_{u(n)}$\mid$X_{u(m)}=y], (\theta+1)^2E[X_{u(n+2)}$\mid$X_{u(m)}=y] = \theta^2E[X_{u(n)}$\mid$X_{u(m)}=y], or (\theta+1)^3E[X_{u(n+3)}$\mid$X_{u(m)}=y] = \theta^3E[X_{u(n)}$\mid$X_{u(m)}=y], n $\geq$ M+1$.