• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.1-5
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• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2333-2337
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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 considered, which is composed of data collection, data analysis, improvement action, verification, and implement control. The composition analysis for data collection and data analysis was used in the cellular phone module packaging lines. And this method was evaluated by the variation of yield loss between before and after improvement action. 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 composition analysis is effective solutions for particle contamination control in the cleanroom.

• Korean Journal of Computational Design and Engineering
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• v.15 no.2
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• pp.85-93
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• 2010

This paper proposes a CAE data translation and visualization technique that can verify time-varying continuous analysis simulation in a virtual reality (VR) environment. In previous research, the use of CAE analysis data has been problematic because of the lack of any interactive simulation controls for visualizing continuous simulation data. Moreover, the research on post-processing methods for real-time verification of CAE analysis data has not been sufficient. We therefore propose a scene graph based visualization method and a post-processing method for supporting interoperability of continuous CAE analysis data. These methods can continuously visualize static analysis data independently of any timeline; it can also continuously visualize dynamic analysis data that varies in relation to the timeline. The visualization system for continuous simulation data, which includes a CAE middleware that interfaces with various formats of CAE analysis data as well as functions for visualizing continuous simulation data and operational functions, enables users to verify simulation results with more realistic scenes. We also use the system to do a performance evaluation with regard to the visualization of continuous simulation data.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.94-101
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• 2016

Satellite data for remote sensing technology has limitations, especially with visible range sensor, cloud and/or other environmental factors cause missing data. In this study, using land surface temperature data from the MODerate resolution Imaging Spectro-radiometer(MODIS), we developed retrieving methods for satellite missing data and developed three methods; mean bias, regression analysis and local variation method. These methods used the previous day data as reference data. In order to validate these methods, we selected a specific measurement ratio using artificial missing data from 2014 to 2015. The local variation method showed low accuracy with root mean square error(RMSE) more than 2 K in some cases, and the regression analysis method showed reliable results in most cases with small RMSE values, 1.13 K, approximately. RMSE with the mean bias method was similar to RMSE with the regression analysis method, 1.32 K, approximately.

• Proceedings of the KIEE Conference
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• 2006.07c
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• pp.1535-1537
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• 2006

Recently, the noise discrimination method using PD pulse waveshape analysis has been suggested to be very effective method which can improve the reliability of the on-site PD measurement. In this method, the data clusters due to PD pulses or noises can be distinguished on the PA map. And for the automatic recognition of the PD clusters, it is necessary to adopt the adaptable pattern recognition method. In this study, as for the algorithm which can recognize data clusters, the data mining method has been adopted and the result of the analysis has been reported.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.46-51
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• 2007

In this paper, we propose a new data clustering method using local probability and hypothesis theory. To cluster the test data set we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set using mean standard deviation and variance etc. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. For evaluating, the proposed classification method is compared to the conventional fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm. The simulation results show more accuracy than results of fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.13 no.4
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• pp.211-217
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• 2010

As the vehicle components are various, we confront unexpected problems in the development and application of them. also warranty expenses occur in the result of unconfirmed warranty.in this paper, to solve the problems of disconnection of damper Strut cable, we applied the optimum conditions through taguchi method for improvement of durability. and we made standard of reliability by weibull analysis of the field data. we acquired reliability standard by correlation with lab data and confirmed improved components satisfying the target of reliability. The analysis of reliability by field data is very useful and we need to apply this method to other components, the correlation between field data and Lab Test has influence on satisfying the target of reliability.this method would be utilized for current mass production components and upcoming developed components. the reliability of durability should be continuously used in the basis of primary technique in cope with competitive automotive companies.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.75-84
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• 2021

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

• Genomics & Informatics
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• v.20 no.2
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• pp.16.1-16.12
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• 2022

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

• Journal of the Korea Safety Management & Science
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• v.3 no.2
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• pp.145-154
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• 2001

This paper deals with Activity Analysis Method that is important procedure in Activity Based Costing System Implementation. There are many existing Activity Analysis Method, for example Interview, Questionnaire, Specialist Discussion and Work Measurement. Activity Analysis Data gained through this method has high reliability but this method bring about high cost. In case that certain company needs a strategic costing system, Activity Analysis Method which has high reliability will be need. But, if companies want the costing system as a internal decision making tool only, they need to design the ABC system fast and cheaply. This paper explains that Activity Analysis using existing finn material is good alternatives. So, this paper show the feasibility of Activity Analysis using existing firm material with comparing between job description, job specification information and Activity Analysis information.