• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.2
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• pp.81-87
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• 2004

This paper deals with a new approach to tolerance optimization problems. Optimal tolerance allotment problems can be formulated as stochastic optimization problems. Most schemes to solve the stochastic optimization problems have been found to exhibit difficulties in multivariate integration of the probability density function. As a typical example of stochastic optimization the optimal tolerance allotment problem has the same difficulties. In this stochastic model, manufacturing system is represented by Gauss-Markov stochastic process and the manufacturing unit availability is characterized for realistic optimization modeling. The new algorithm performed robustly for a large deviation approximation. A significant reduction in computation time was observed compared to the results obtained in previous studies.

• Journal of the Semiconductor & Display Technology
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• v.11 no.3
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• pp.19-25
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• 2012

The accuracy of Shape From Focus (SFF) technique depends on the quality of the focus measurements which are computed through a focus measure operator. In this paper, we introduce a new approach to estimate 3D shape of an object based on Gaussian process regression. First, initial depth is estimated by applying a conventional focus measure on image sequence and maximizing it in the optical direction. In second step, input feature vectors consisting of eginvalues are computed from 3D neighborhood around the initial depth. Finally, by utilizing these features, a latent function is developed through Gaussian process regression to estimate accurate depth. The proposed approach takes advantages of the multivariate statistical features and covariance function. The proposed method is tested by using image sequences of various objects. Experimental results demonstrate the efficacy of the proposed scheme.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.1
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• pp.39-55
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• 2011

Process monitoring has been emphasized for the monitoring of complex system such as chemical processing industries to achieve the efficiency enhancement, quality management, safety improvement. Recently, ICA (Independent Component Analysis) based MSPC (Multivariate Statistical Process Control) was widely used in process monitoring approaches. Moreover, DICA (Dynamic ICA) has been introduced to consider the system dynamics. However, the existing approaches show the limitation that their performances are strongly dependent on the statistical distributions of control variables. To improve the limitation, we propose a novel approach for process monitoring by integrating DICA and LOF (Local Outlier Factor). In this paper, we aim to improve the fault detection rate with the proposed method. LOF detects local outliers by using density of surrounding space so that its performance is regardless of data distribution. Therefore, the proposed method not only can consider the system dynamics but can also assure robust performance regardless of the statistical distributions of control variables. Comparison experiments were conducted on the widely used benchmark dataset, Tennessee Eastman process (TE process), and showed the improved performance than existing approaches.

• Journal of Korean Society for Quality Management
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• v.31 no.1
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• pp.132-141
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• 2003

As the technology has improved and demands of customers have varied, a lot of products are getting diverse and intricate. Consequently, the enterprise that produce products have to simultaneously consider the various variables for the very products. There are some scheme, such as Multivariate control chart and Demerit control chart, designed to simultaneously monitor the variables in the process. In this paper, we present an effective method for process control using the Demerit-CUSUM control chart in the process where nonconforming units or nonconformities are occured by various types. In addition, we show interpretation method for abnormal signal in order to quickly detect the assignable causes as Demerit-CUSUM control chart signals abnormality. we compare performance of Demerit control chart and Demerit-CUSUM control chart using example again used in the existing studies, and present result of performance accoriding to changing sample size and parameter.

• Communications of the Korean Mathematical Society
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• v.21 no.4
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• pp.779-786
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• 2006

Let $\mathbb{X}_t$ be an m-dimensional linear process defined by $\mathbb{X}_t=\sum{_{j=0}^\infty}\;A_j\;\mathbb{Z}_{t-j}$, t = 1, 2, $\ldots$, where $\mathbb{Z}_t$ is a sequence of m-dimensional random vectors with mean 0 : $m\times1$ and positive definite covariance matrix $\Gamma:m{\times}m$ and $\{A_j\}$ is a sequence of coefficient matrices. In this paper we give sufficient conditions so that $\sum{_{t=1}^{[ns]}\mathbb{X}_t$ (properly normalized) converges weakly to Wiener measure if the corresponding result for $\sum{_{t=1}^{[ns]}\mathbb{Z}_t$ is true.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.99-108
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• 2003

In early year 2003, the previous Urban Planning Law and National Land Use Management Law were abolished and integrated into the Law regarding National Land Planning and Land Use Management. Accordingly, previous Urban Planning Information System is reformed into National Land Use Information System. This paper intends to develop indexes supporting decision-making process and to construct theme map using digital map that can be utilized for the National Land Use Information System. As a base for the index development, necessary database(DB) was established by utilizing the socio-economic information of towns and villages in Busan and a layer of public facilities. Based on the DB, multivariate analyses were performed to examine and analyze the spatial structure of Busan. It was then expressed and constructed as a theme map. The facility theme map was also constructed based on a layer of public facilities and the shortest distance between towns and villages.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.293-308
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• 1997

Sliced inverse regression is a method for reducing the dimension of the explanatory variable X without going through any parametric or nonparametric model fitting process. This method explores the simplicity of the inverse view of regression; that is, instead of regressing the univariate output varable y against the multivariate X, we regress X against y. In this article, we propose bivariate sliced inverse regression, whose method regress the multivariate X against the bivariate output variables $y_1, Y_2$. Bivariate sliced inverse regression estimates the e.d.r. directions of satisfying two generalized regression model simultaneously. For the application of bivariate sliced inverse regression, we decompose the output variable y into two variables, one variable y gained by projecting the output variable y onto the column space of X and the other variable r through projecting the output variable y onto the space orthogonal to the column space of X, respectively and then estimate the e.d.r. directions of the generalized regression model by utilize two variables simultaneously. As a result, bivariate sliced inverse regression of considering the variable y and r simultaneously estimates the e.d.r. directions efficiently and steadily when the regression model is linear, quadratic and nonlinear, respectively.

• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.20-29
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• 2022

To stem the ever-prevalent occurrence of industrial accidents in the construction industry, which is emerging as a social problem, efforts must be invested by various stakeholders. Specifically, among stakeholders, the orderer is at the top of a project's decision-making structure. Therefore, the orderer's awareness of safety and health directly affects the process of securing the safety of the overall construction site. In this light, the present study aims to identify differences in the perceptions of each stakeholder regarding the obligatory safety and health measures for clients that have recently been introduced. In addition, it suggests specific implementation plans in the Korean context. The data used for analysis were collected through a survey targeting stakeholders such as orderers, safety managers, and site managers, and the collected data were quantitatively reviewed by using multivariate analysis methods such as analysis of variance. As a result of the analysis, the introduction of safety and health obligations for the owner was found to be necessary, and the designation and operation of safety and health experts as an action plan was deemed reasonable. The authors expect that the results of this study can be used as basic data for revising the related regulations in Korea. Moreover, as a further study, a review of the effectiveness after improving regulations would contribute strongly to the domain.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.311-314
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• 2004

To identify the effect of geology and land use, the hydrogeochemical and multivariate statitstical analysis were executed for stream water collected in headwater region of Daecheong reservoir. Hydrogeochemical analysis was showed the effect of weathering process such as dissolution of calc-silicate minerals to hydrochemistry of stream water with contrasting geology. Cluster and principal components analysis can also help to identify the source of dissolved components in stream water.

• Bulletin of the Korean Mathematical Society
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• v.53 no.3
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• pp.733-749
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• 2016

We aim to compute discretely monitored geometric Asian option prices under the Heston model. This method involves explicit formula for multivariate generalized Fourier transform of volatility process and their integrals over different time intervals using a recursive method. As numerical results, we illustrate efficiency and accuracy of our method. In addition, we simulate scenarios which show evidently practical importance of our work.