• Journal of Fashion Business
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• v.6 no.5
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• pp.136-150
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• 2002

In this study, we proposed a method to be standing customers as the supporting system for the improvement of fashion garment industry which was the marginal growth getting into full maturity of market. As for the customer creation method of Fashion garment company is developing a marketing program to be standing customer as customer scoring to estimate a existing customer‘s buying power, and figure out minimum fixed sales of company to use a future purchasing predict. This study was a result of data from total sixty thousands data to be created for the 11 months from september. 2000 to July. 2001. The data is part of which the company leading the Korean fashion garment industry has a lot of a customer purchasing history data. But this study used only 48,845 refined purchased data to discriminate from sixty thousands data and 21,496 customer case with the exception of overlapping purchased data among of those. The software used to handle sixty thousands data was SAS e-miner. As the analysis process is put in to operation the analysis of the purchasing customer’s profile firstly, and the second come into basket analysis to consider the buying associations for Association goods, the third estimate the customer grade of Customer loyalty by 3 ways of logit regression analysis, decision tree, Artificial Neural Network. The result suggested a method to be estimate the customer loyalty as 3 independent variables, 2 coefficients. The 3 independent variables are total purchasing amount, purchasing items per one purchase, payment amount by one purchasing item. The 2 coefficients are royal and normal for customer segmentation. The result was that this model use a logit regression analysis was valid as the method to be estimate the customer loyalty.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.6 s.261
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• pp.522-530
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• 2007

The practical studies on the method of particle contamination control for yield enhancement in the cleanroom were carried out. The method of the contamination control was proposed, which are composed of data collection, data analysis, improvement action, verification, and implement control. The partition check method and the composition analysis for data collection and data analysis were respectively used in the main board and the cellular phone module production lines. And these methods were evaluated by the variation of yield loss between before and after improvement action. In case that the partition check method was applied, the critical process step was selected and yield loss reduction through improvement actions was observed. While in case that the composition analysis was applied, the critical sources were selected and yield loss reduction through improvement actions was also investigated. From these results, it is concluded that the partition check and the composition analysis are effective solutions for particle contamination control in the cleanroom production lines.

• Journal of Korea Multimedia Society
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• v.17 no.6
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• pp.671-680
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• 2014

Recently, with the remarkable increase of social network services, it is necessary to extract interesting information from lots of data about various individual opinions and preferences on SNS(Social Network Service). The sentiment information can be applied to various fields of society such as politics, public opinions, economics, personal services and entertainments. To extract sentiment information, it is necessary to use processing techniques that store a large amount of SNS data, extract meaningful data from them, and search the sentiment information. This paper proposes an efficient method to extract sentiment information from various unstructured big data on social networks using HDFS(Hadoop Distributed File System) platform and MapReduce functions. In experiments, the proposed method collects and stacks data steadily as the number of data is increased. When the proposed functions are applied to sentiment analysis, the system keeps load balancing and the analysis results are very close to the results of manual work.

• Journal of Korea Spatial Information System Society
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• v.9 no.2
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• pp.81-91
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• 2007

In this paper, we have proposed the non-duplication loading method for supporting spatio-temporal analysis in spatial data warehouse. SDW(Spatial Data Warehouse) extracts spatial data from SDBMS that support various service of different machine. In proposed methods, it extracts updated parts of SDBMS that is participated to source in SDW. And it removes the duplicated data by spatial operation, then loads it by integrated forms. By this manner, it can support fast analysis operation for spatial data and reduce a waste of storage space. Proposed method loads spatial data by efficient form at application of analysis and prospect by time like spatial mining.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.533-541
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• 2013

In this study, a simple principal component analysis using Lasso is proposed. This method consists of two steps. The first step is to compute principal components by the principal component analysis. The second step is to regress each principal component on the original data matrix by Lasso regression method. Each of new principal components is computed as the linear combination of original data matrix using the scaled estimated Lasso regression coefficient as the coefficients of the combination. This method leads to easily interpretable principal components with more 0 coefficients by the properties of Lasso regression models. This is because the estimator of the regression of each principal component on the original data matrix is the corresponding eigenvector. This method is applied to real and simulated data sets with the help of an R package for Lasso regression and its usefulness is demonstrated.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.485-486
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• 2010

In this research, a new analysis method which is able to examine the safety and to assess the serviceability of high-rise buildings from construction period to service life has been developed. The effect of both construction sequence and inelastic behavior of concrete has been considered in the developed analysis method in three dimensions. The more efficient analysis technique and modeling method for practical use were also suggested. For verification of the developed analysis method, the data measured in a high-rise building under construction was compared with the analysis results. Through comparison of the analysis results with the measured data, it was found that the analysis results generally simulated the trend of the measured data well in all cases.

• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• Korean Institute of Interior Design Journal
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• no.31
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• pp.56-63
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• 2002

This study intends to prepare a system that can be used, by applying digital technique, in analyzing complexity of architectural forms that have been visualized by the correlation based on the distribution chart made in accordance with profile lines. The profile lines are derived from the edge analysis of the architectural forms, simplified based on the visual theory. For the purpose, this study was conducted in the following ways: First, problems of the existing models for the elevation analysis were examined along with formal analysis based on visual recognition to consider the profile lines derived from the forms. Secondly, in elevation analysis, profile lines were derived by digital method to measure them qualitatively. To verify the objectivity of the measured data value, a survey was conducted based on the adjective cataloging method, and the correlation of the survey result and analyzed data was analyzed to verify the validity of the derived data. Thirdly, supplementation for the problems deducted from experiments and the possibility to use it in designing were suggested. Digital method has many advantages over the conventional analyzing system in deriving precise data value by excluding subjectivity. It also allows various analytical methods in analyzing numerous data repeatedly. Diversified models and methods of analysis considering numerous factors arising in the process of designing remain assignments to research in future.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2338-2343
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• 2007

The practical studies on the method of particle contamination control for yield enhancement in the cleanroom were carried out. The method of the contamination control was proposed, which are composed of data collection, data analysis, improvement action, verification, and implement control. The partition check method for data collection and data analysis was used in the cellular phone module production lines. And this method was evaluated by the variation of yield loss between before and after improvement action. In case that the partition check method was applied, the critical process step was selected and yield loss reduction through improvement actions was observed. From these results, it is concluded that the partition check method is effective solution for particle contamination control in the cleanroom production lines.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.263-266
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• 2007

This paper discusses the Bayesian statistical inference. This paper discusses the Bayesian inference, MCMC (Markov Chain Monte Carlo) integration, MCMC method, Metropolis-Hastings algorithm, Gibbs sampling, Maximum likelihood estimation, Expectation Maximization algorithm, missing data processing, and BMA (Bayesian Model Averaging). The Bayesian statistical inference is used to process a large amount of data in the areas of biology, medicine, bioengineering, science and engineering, and general data analysis and processing, and provides the important method to draw the optimal inference result. Lastly, this paper discusses the method of principal component analysis. The PCA method is also used for data analysis and inference.