• Proceedings of the Korean Society for Quality Management Conference
• /
• 2010.04a
• /
• pp.226-234
• /
• 2010

In this thesis, we present process that remove mistake of data before statistical analysis. If field data which is not simple examination about validity of data, we cannot believe analyzed statistics information. As statistical analysis information is produced based on data to be input in statistical analysis process, the data to be input should be free of error. In this paper, we study the application of statistical analysis road map that can enhance application on site by organizing basic theory and approaching on initial data exploratory phase, essential step before conducting statistical analysis. Therefore, access to statistical analysis can be enhanced and reliability on result of analysis can be secured by conducting correct statistical analysis.

• The Journal of Bigdata
• /
• v.8 no.1
• /
• pp.35-47
• /
• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

• Korean Journal of Computational Design and Engineering
• /
• v.15 no.2
• /
• pp.85-93
• /
• 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 the Korean Institute of Intelligent Systems
• /
• v.25 no.6
• /
• pp.592-599
• /
• 2015

In big data environment, we need new approach for big data analysis, because the characteristics of big data, such as volume, variety, and velocity, can analyze entire data for inferring population. But traditional methods of statistics were focused on small data called random sample extracted from population. So, the classical analyses based on statistics are not suitable to big data analysis. To solve this problem, we propose an approach to efficient big data analysis. In this paper, we consider a big data analysis using principal component analysis, which is popular method in multivariate statistics. To verify the performance of our research, we carry out diverse simulation studies.

• Journal of Wind Energy
• /
• v.4 no.1
• /
• pp.20-29
• /
• 2013

Load measurement, which is performed based on IEC 61400-13, consists of three stages: the stage of collecting huge amounts of load measurement data through a measurement campaign lasting for several months; the stage of processing the measured data, including data validation and classification; and the stage of analyzing the processed data through time series analysis, load statistics analysis, frequency analysis, load spectrum analysis, and equivalent load analysis. In this research, we pursued the development of an analysis software in MATLAB to save labor and to secure exact and consistent performance evaluation data in processing and analyzing load measurement data. The completed analysis software also includes the functions of processing and analyzing power performance measurement data in accordance with IEC 61400-12. The analysis software was effectively applied to process and analyse the load measurement data from a demonstration research for a 750 kW direct-drive wind turbine generator system (KBP-750D), performed at the Daegwanryeong Wind Turbine Demonstration Complex. This paper describes the details of the analysis software and its processing and analysis stages for load measurement data and presents the analysis results.

• Journal of information and communication convergence engineering
• /
• v.21 no.3
• /
• pp.244-251
• /
• 2023

Engineering or humanities data are stored in databases and are often used for search services. While the latest deep-learning technologies, such like BART and BERT, are utilized for data analysis, humanities data still rely on traditional databases. Representative analysis methods include n-gram and lexical statistical extraction. However, when using a database, performance limitation is often imposed on the result calculations. This study presents an experimental process using MariaDB on a PC, which is easily accessible in a laboratory, to analyze the impact of the database on data analysis performance. The findings highlight the fact that the database becomes a bottleneck when analyzing large-scale text data, particularly over hundreds of thousands of records. To address this issue, a method was proposed to provide real-time humanities data analysis web services by leveraging the open source database, with a focus on the Seungjeongwon-Ilgy, one of the largest datasets in the humanities fields.

• Journal of the Korean Statistical Society
• /
• v.30 no.2
• /
• pp.219-230
• /
• 2001

How do you carry out data analysis\ulcorner There are few texts and little theory. One approach could be to use a pattern language, an idea which has been successful in field as diverse as town planning and software engineering. Patterns for data analysis are defined and discussed, illustrated with examples.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.1
• /
• pp.53-62
• /
• 2006

DNA microarray data analysis is a new technology to investigate the expression levels of thousands of genes simultaneously. Since DNA microarray data structures are various and complicative, the data are generally stored in databases for approaching to and controlling the data effectively. But we have some difficulties to analyze and control the data when the data are stored in the several database management systems or that the data are stored to the file format. The existing analysis tools for DNA microarray data have many difficult problems by complicated instructions, and dependency on data types and operating system. In this paper, we design and implement network-based analysis tool for obtaining to useful information from DNA microarray data. When we use this tool, we can analyze effectively DNA microarray data without special knowledge and education for data types and analytical methods.

• Journal of the Ergonomics Society of Korea
• /
• v.16 no.1
• /
• pp.119-126
• /
• 1997

Most of human factors experiments in nuclear industry domain produe lots of experimental data, thus much time is reauired to analyze the data. DAEXESS was developed to reduce resource demands necessary for the analysis work through systematic data analysis requirements and automated data processing based on computer technology. Physilolgical data, human behavior recording data, system log data and verbal protocl can be collected, synthesized and easily analyzed with with respect to time domain in DAEXESS so that analyser is able to look into inte- grated information on operating context. DAEXESS assists analyser to carry out qualitative and quantitative data analysis easily.

• Communications for Statistical Applications and Methods
• /
• v.28 no.2
• /
• pp.135-150
• /
• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.