• Structural Engineering and Mechanics
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• v.6 no.5
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• pp.473-484
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• 1998

The first and second moments of response variables for SDOF systems with hysteretic nonlinearity are obtained by a direct linearization procedure. This adaptation in the implementation of well-known statistical linearization methods, provides concise, model-independent linearization coefficients that are well-suited for numerical solution. The method may be applied to systems which incorporate any hysteresis model governed by a differential constitutive equation, and may be used for zero or non-zero mean random vibration. The implementation eliminates the effort of analytically deriving specific linearization coefficients for new hysteresis models. In doing so, the procedure of stochastic analysis is made independent from the task of physical modeling of hysteretic systems. In this study, systems with three different hysteresis models are analyzed under various zero and non-zero mean Gaussian White noise inputs. Results are shown to be in agreement with previous linearization studies and Monte Carlo Simulation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.29
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• pp.30-36
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• 1994

This paper has provided a systematic technique, the evaluation of the distribution with the NPV ana the derivation of the IRR in the investment alternatives, for the cost estimating analysts. The proposals of investment alternatives are included the venture capital under risk and probabilities at each events, within the cash inflows are occuring at random timing. Therefore. we have considered the followings : 1) the first cash outflow is deterministic. 2) the cash inflows are random variables with known distributions. 3) the lengths of the time intervals between the cash inflows are independently distributed and independent of the cash inflows. In this paper. the first two moments of the distribution, the Laplace Transforms and the convolutions are computed for both independent cash inflows and mutually exclusive alternatives as in the case of quite correlated cash inflows.

• Journal of Korean Society for Quality Management
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• v.17 no.1
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• pp.82-88
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• 1989

This paper has provided a systematic technique, the evaluation of the distribution with the NPV and the derivation of the IRR in the investment alternatives, for the cost estimating analysts, The proposals of investment alternatives are included the venture capital under risk and probabilities at each events, within the cash inflows are occuring at random timing. Therefore, we have considered the followings ; 1) the first cash outflow is deterministic, 2) the cash inflows are random variables with known distributions, 3) the lengths of the time intervals between the cash inflows are independently distributed and independent of the cash inflows. In this paper, the first two moments of the distribution, the Laplace Transforms and the convolutions are computed for both independent cash inflows and mutually exclusive alternatives as in the case of quite correlated cash inflows.

• Journal of the Korean Mathematical Society
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• v.15 no.1
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• pp.59-61
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• 1978

Let $X_1$, $X_2$, ${\cdots}$, $X_n$ be i.i.d. uniform (0,1) random variables. Let $f_n(x)$ denote the probability density function (p.d.f.) of $T_n={\sum}^n_{i=1}X_i$. Consider a set S(x ; ${\delta}$) of lattice points defined by S(x ; ${\delta}$) = $x{\mid}x={\delta}+j$, j=0, 1, ${\cdots}$, n-1, $0{\leq}{\delta}{\leq}1$} The lattice distribution induced by the p.d.f. of $T_n$ is defined as follow: (1) $f_n^{(\delta)}(x)=\{f_n(x)\;if\;x{\in}S(x;{\delta})\\0\;otherwise.$. In this paper we show that $f_n{^{(\delta)}}(x)$ is a probability function thus we obtain a family of lattice distributions {$f_n{^{(\delta)}}(x)$ : $0{\leq}{\delta}{\leq}1$}, that the mean and variance of the lattice distributions are independent of ${\delta}$.

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.419-421
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• 1994

Recently Hong and Oh [5] provided a fairly general weak law for arrays in the following form: Let {(X/sub ni/, l ≤ i ≤ k/sub n/), n ≥ l}, k/sub n/ → ∞ as n → ∞, be an array of random variables on (Ω, F, P) and set F/sub nj/ = σ{X/sub ni/, 1 ≤ i ≤ j}, 1 ≤ j ≤ k/sub n/, n ≥ 1, and F/sub n0/ = {ø, Ω}, n ≥ 1. Suppose that (equation omitted) aP { X/sub ni/ /sup p/ ＞ a} → 0 as a → ∞ uniformly in n for some 0 < p < 2. Then S/sub n//(equation omitted) → 0 in probability as n → ∞ where S/sub n/ = (equation omitted)(X/sub ni/ - E(X/sib ni/I( X/sub ni/ /sub p/ ≤ k/sub n/) F/sub n,i-l/)). In this note, we will prove the following result under the same domination condition of Hong and Oh [5].(omitted)

• The Journal of Information Systems
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• v.29 no.2
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• pp.287-306
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• 2020

Purpose Youth unemployment is a social problem that continues to emerge in Korea. In this study, we create a model that predicts the employment of college graduates using decision tree, random forest and artificial neural network among machine learning techniques and compare the performance between each model through prediction results. Design/methodology/approach In this study, the data processing was performed, including the acquisition of the college graduates' vocational path survey data first, then the selection of independent variables and setting up dependent variables. We use R to create decision tree, random forest, and artificial neural network models and predicted whether college graduates were employed through each model. And at the end, the performance of each model was compared and evaluated. Findings The results showed that the random forest model had the highest performance, and the artificial neural network model had a narrow difference in performance than the decision tree model. In the decision-making tree model, key nodes were selected as to whether they receive economic support from their families, major affiliates, the route of obtaining information for jobs at universities, the importance of working income when choosing jobs and the location of graduation universities. Identifying the importance of variables in the random forest model, whether they receive economic support from their families as important variables, majors, the route to obtaining job information, the degree of irritating feelings for a month, and the location of the graduating university were selected.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.873-876
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• 2000

The information-theoretic approach to face recognition is based on the compact coding where face images are decomposed into a small set of basis images. Most popular method for the compact coding may be the principal component analysis (PCA) which eigenface methods are based on. PCA based methods exploit only second-order statistical structure of the data, so higher- order statistical dependencies among pixels are not considered. Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. ICA exploits high-order statistical structure of the data that contains important information. In this paper we employ the ICA for the efficient feature extraction from face images and show that ICA outperforms the PCA in the task of face recognition. Experimental results using a simple nearest classifier and multi layer perceptron (MLP) are presented to illustrate the performance of the proposed method.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.269-274
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• 2004

Noisy speech recognition is one of most important problems in speech recognition. In this paper, a method which efficiently removes the mixed noise with speech, is proposed. The proposed method is based on the ICA to separate the mixed noise. ICA(Independent component analysis) is a signal processing technique, whose goal is to express a set of random variables as linear combinations of components that are statistically as independent from each other as possible.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1121-1131
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• 2015

Logistic models with a random intercept are useful to analyze longitudinal binary data. Traditionally, the random intercept of the logistic model is assumed to be parametric (such as normal distribution) and is also assumed to be independent to variables. Such assumptions are very strong and restricted for application to real data. Recently, Garcia and Ma (2015) derived semiparametric efficient estimators for logistic model with a random intercept without these assumptions. Their estimator shows the consistency where we do not assume any parametric form for the random intercept. In addition, the method is computationally simple. In this paper, we apply this method to analyze toenail infection data. We compare the semiparametric estimator with maximum likelihood estimator, penalized quasi-likelihood estimator and hierarchical generalized linear estimator.

• Structural Engineering and Mechanics
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• v.6 no.6
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• pp.659-671
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• 1998

Exercise of complete control on all aspects of any manufacturing / fabrication process is very difficult, leading to uncertainties in the material properties and geometric dimensions of structural components. This is especially true for laminated composites because of the large number of parameters associated with its fabrication. When the basic parameters like elastic modulus, density and Poisson's ratio are random, the derived response characteristics such as deflections, natural frequencies, buckling loads, stresses and strains are also random, being functions of the basic random system parameters. In this study the basic elastic properties of a composite lamina are assumed to be independent random variables. Perturbation formulation is used to model the random parameters assuming the dispersions small compared to the mean values. The system equations are analyzed to obtain the mean and the variance of the plate natural frequencies. Several application problems of free vibration analysis of composite plates, employing the proposed method are discussed. The analysis indicates that, at times it may be important to include the effect of randomness in material properties of composite laminates.