• Journal of Korean Institute of Industrial Engineers
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• v.27 no.4
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• pp.366-373
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• 2001

Economic component screening procedures for multi-component assembly products supplied with warranty are proposed. It is assumed that the screening variable is continuous and the performance variable is dichotomous. Logistic and normal models are considered; the proportion of conforming items is a logistic function of the screening variable in the logistic model and the screening variable given the performance variable is normally distributed in the normal model. Cost models are constructed which involve three cost components; screening inspection cost for each component, cost due to disposing a rejected component, and warranty cost for an assembly product. Methods of finding the optimal screening procedures are presented and numerical examples are given.

• KSTLE International Journal
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• v.2 no.2
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• pp.114-119
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• 2001

The distribution of magnetic flux density of electro-magnetic chucks may clarify the clamping characteristics, which is strongly related to the machining efficiency and machining accuracy in surface grinding machine. Therefore the distribution of the normal and the tangential components of magnetic flux density have been analyzed theoretically. It appears that the normal component of magnetic flux density increases and the tangential component of magnetic flux density increases as the ratio of the separator width to the pitch, e/p decreases. The results seem to increase the stability and uniformity of normal component of magnetic flux density for the decreased e/p.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.

• Korea-Australia Rheology Journal
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• v.17 no.4
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• pp.149-156
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• 2005

A simple constitutive model for viscoelastic suspensions is discussed in this paper. The model can be used to predict the rheological properties (relative viscosity and all stresses) for viscoelastic suspensions in shear and elongational flow, and the constitutive equations combine a 'viscoelastic' behaviour component and a 'Newtonian' behaviour component. As expected, the model gives a prediction of positive first normal stress difference and negative second normal stress difference; the dimensionless first normal stress difference strongly depends on the shear rate and decreases with the volume fraction of solid phase, but the dimensionless second normal stress difference (in magnitude) is nearly independent of the shear rate and increases with the volume fraction. The relative viscosities and all the stresses have been tested against available experimental measurements.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.113-123
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• 2006

Normal mixture model(NMM) frequently used to cluster genes on microarray gene expression data. In this paper some of component means of NMM are modelled by a linear regression model so that its design matrix presents the pattern between sample classes in microarray matrix. This modelling for the component means by given design matrices certainly has an advantage that we can lead the clusters that are previously designed. However, it suffers from 'overfitting' problem because in practice genes often are highly dimensional. This problem also arises when the NMM restricted by the linear model for component-means is fitted. To cope with this problem, in this paper, the use of the factor analyzer NMM restricted by linear model is proposed to cluster genes. Also several design matrices which are useful for clustering genes are provided.

• Progress in Superconductivity
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• v.1 no.2
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• pp.99-104
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• 2000

The e.m.f. induced by a normal spot moving in a superconducting foil has been investigated by using the simulation and the experiment of a simple superconducting Power Supply. The induced e.m.f. has been derived theoretically from the magnetic field distribution within the spot. It is the sum of a DC component induced constantly by the Faraday's law during the spot's movement and a pulse component induced periodically by the flux conservation law at every electrical degrees 2$\pi$ radians. The DC component of the output voltage appears slightly nonlinear to the rotating speed, having values greater than the linear approximation values. The theoretical interpretation has been verified through experiment.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.760-767
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• 2006

Underestimation of rock joint shear strength comes from an inadequate consideration of roughness mobilization behavior, which is changed by asperity size as well applied normal load. In this study, we performed rock joint shear tests, and studied the roughness mobilization characteristics related with the scale of normal stress and asperities. Test specimens with artificial triangular asperities were manufactured. The specimens consisted of 3 types, and each type represented unevenness, waviness and total roughness(superposition of unevenness and waviness). The experimental results show that the roughness mobilization characteristics are varied by the scale of normal stress and asperities. Furthermore, the investigation shows that the rate of geometrical component and mechanical component in the total roughness is also varied by the scale of normal stress and asperities. These results suggest that we should consider the roughness mobilization characteristics for the roughness quantification and the shear strength modelling.

• Progress in Medical Physics
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• v.18 no.1
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• pp.20-26
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• 2007

It is expected that synaptic construction and electrical characteristics In degenerate retina might be different from those In normal retina. Therefore, we analyzed the retinal waveform recorded with multielectrode array in normal and degenerate retina using principal component analysis (PCA) and Independent component analysis (ICA) and compared the results. PCA Is a well established method for retinal waveform while ICA has not tried for retinal waveform analysis. We programmed ICA toolbox for spatiotemporal analysis of retinal waveform. In normal mouse, the MEA spatial map shows a single hot spot perfectly matched with PCA-derived ON or OFF ganglion cell response. However In rd/rd mouse, the MEA spatial map shows numerous hot and cold spots whose underlying interactions and mechanisms need further Investigation for better understanding.

• IE interfaces
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• v.22 no.3
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• pp.214-222
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• 2009

In the early design stage, system reliability must be estimated from life testing data at the component level. Previously, a point estimate of system reliability was obtained from the unbiased estimate of the component reliability after assuming that the number of failed components for a given time followed a binomial distribution. For deriving the confidence interval of system reliability, either the lognormal distribution or the normal approximation of the binomial distribution was assumed for the estimator of system reliability. In this paper, a new estimator is used for the component level reliability, which is biased but has a smaller mean square error than the previous one. We propose to use the beta distribution rather than the lognormal or approximated normal distribution for developing the confidence interval of the system reliability. A numerical example based on Monte Carlo simulation illustrates advantages of the proposed approach over the previous approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2709-2729
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• 2016

Representation based classification, kernel method and sparse representation have received much attention in the field of face recognition. In this paper, we proposed an improved kernel principal component analysis method based on sparse representation to improve the accuracy and robustness for face recognition. First, the distances between the test sample and all training samples in kernel space are estimated based on collaborative representation. Second, S training samples with the smallest distances are selected, and Kernel Principal Component Analysis (KPCA) is used to extract the features that are exploited for classification. The proposed method implements the sparse representation under ℓ₂ regularization and performs feature extraction twice to improve the robustness. Also, we investigate the relationship between the accuracy and the sparseness coefficient, the relationship between the accuracy and the dimensionality respectively. The comparative experiments are conducted on the ORL, the GT and the UMIST face database. The experimental results show that the proposed method is more effective and robust than several state-of-the-art methods including Sparse Representation based Classification (SRC), Collaborative Representation based Classification (CRC), KCRC and Two Phase Test samples Sparse Representation (TPTSR).