• Journal of the Ergonomics Society of Korea
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• v.2 no.1
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• pp.3-11
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• 1983

Anthropometric data is most basic materials in the all studies related with it. Therefore, in anthropometric data, not only consideration of the state of variance, but more various analysis is needed. This study selected the 13 parts that properly show a whole characteristics of human body and, anthropometric data were obtained through the actual measurements for male and female workers who were engaged in production factory. And, to interpret anthropometric data, principal component analysis of multivariate analysis methods was applied.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.311-324
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• 2006

The Support Vector Machine(SVM) is very functional and efficient classification method to any other classification analysis method. However, its optimal extension to more than two classes is not obvious. In this paper several multi-category SVM methods are introduced and compared using simulation and real data sets. Also comparison with traditional multi-category classification and SVM based methods is performed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2273-2286
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• 2018

This paper describes transformations between elemental image arrays and a RGBD image for three-dimensional integral imaging and transmitting systems. Two transformations are introduced and analyzed in the proposed method. Normally, a RGBD image is utilized in efficient 3D data transmission although 3D imaging and display is restricted. Thus, a pixel-to-pixel mapping is required to obtain an elemental image array from a RGBD image. However, transformations and their analysis have little attention in computational integral imaging and transmission. Thus, in this paper, we introduce two different mapping methods that are called as the forward and backward mapping methods. Also, two mappings are analyzed and compared in terms of complexity and visual quality. In addition, a special condition, named as the hole-free condition in this paper, is proposed to understand the methods analytically. To verify our analysis, we carry out experiments for test images and the results indicate that the proposed methods and their analysis work in terms of the computational cost and visual quality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.581-587
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• 2011

Reliability estimation methods using attribute data are widely used in reliability evaluation of various systems such as nuclear energy plants, food and drug, and space launch vehicles. This paper shows sensitivity analysis and comparison results of reliability estimation methods including a parametric estimation method in open literature with respect to both sample size and sampling points of time. And ways to improve accuracy of each reliability estimation method were proposed from the sensitivity analysis results.

• Molecules and Cells
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• v.22 no.3
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• pp.254-261
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• 2006

DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray experiments. Normalization is a critical step for obtaining data that are reliable and usable for subsequent analysis such as identification of differentially expressed genes and clustering. A variety of normalization methods have been proposed over the past few years, but no methods are still perfect. Various assumptions are often taken in the process of normalization. Therefore, the knowledge of underlying assumption and principle of normalization would be helpful for the correct analysis of microarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing on their principles and assumptions.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.271-280
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• 2005

Kriging methods as traditional spatial data analysis methods and geographical weighted regression models as statistical analysis methods are compared. In this paper, we apply data from the Ministry of Environment to spatial analysis for practical study. We compare these methods to performance with monthly carbon monoxide observations taken at 116 measuring area of air pollution in 1999.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.115-128
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• 2002

Hierarchical models are widely used for inference on correlated parameters as a compromise between underfitting and overfilling problems. In this paper, we take a Bayesian approach to analyzing hierarchical models and suggest a Markov chain Monte Carlo methods to get around computational difficulties in Bayesian analysis of the hierarchical models. We apply the method to a real data on smoking and lung cancer which are collected from cities in China.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.471-480
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• 2010

Cluster analysis is one of the useful methods to find out number of groups and member’s belongings. With the rapid development of computer application in statistics, variety of new methods in clustering analysis were studied such as EM algorism and Self organization maps. The goals of cluster analysis is finding the number of groupings that are meaningful to me. If data are analyzed perfectly with cluster analysis, we can get the same results from discernment analysis.

• Mass Spectrometry Letters
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• v.3 no.3
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• pp.63-67
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• 2012

The need of heavy and unconventional crude oil as an energy source is increasing day by day, so does the importance of petroleomics: the pursuit of detailed knowledge of heavy crude oil. Crude oil needs techniques with ultra-high resolving capabilities to resolve its complex characteristics. Therefore, ultra-high resolution mass spectrometry represented by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) has been successfully applied to the study of heavy and unconventional crude oils. The analysis of crude oil with high resolution mass spectrometry (FT-ICR MS) has pushed analysis to the limits of instrumental and methodological capabilities. Each high-resolution mass spectrum of crude oil may routinely contain over 50,000 peaks. To visualize and effectively study the large amount of data sets is not trivial. Therefore, data processing and visualization methods such as Kendrick mass defect and van Krevelen analyses and statistical analyses have played an important role. In this regard, it will not be an overstatement to say that the success of FT-ICR MS to the study of crude oil has been critically dependent on data processing methods. Therefore, this review offers introduction to peotroleomic data interpretation methods.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.373-384
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• 2011

This study aims at the examination of the relative characteristics of discharge and water quality in river basins using statistical methods. For it, water quality and discharge data was collected in observed stations of Nakdong river and carried out correlation analysis, regression analysis, factor analysis and cluster analysis. And it was investigated the applicability of water quality prediction using Nearest-neighbor method. As a result, it grasped a trenditional characteristics and mutual relations between discharge an water quality data. Therefore, this results were suggested the comprehensive data and methods for a management of water quality, effective operation and policy development in Nakdong river basin.