• Journal of Industrial Technology
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• v.20 no.A
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• pp.159-167
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• 2000

In the study rainfall frequency analysis attemped the many specific property data record duration it is differance from occur to error-term and probability ditribution of concern manifest. error-term analysis of method are fact sample data using method in other hand it is not appear to be fault that sample data of number to be small random variates. Therefore, day-rainfall data: to randomicity consider of this study sample data to the Monte Carlo method by randomize after data recode duration of form was choice method which compared an assumed maternal distribution from splitting frequency analysis consequence. In the conclusion, frequency analysis of chuncheon region rainfall appeared samll RMSE to the Gamma II distribution. In the rainfall frequency analysis estimate RMSE using random variates great transform, RMSE is appear that return period increasing little by little RMSE incresed and data number incresing to RMSE decreseing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.5
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• pp.46-52
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• 2002

Today's database system needs to collect huge amount of questionnaire that results from development of the information technology by the internet, so it has to be administrable. However, there are many difficulties concerned with finding analytic data or useful information in the high capacity-database. Data mining can solve these problems and utilize the database. Questionnaire analysis that uses data mining has drawn relevant patterns that did not look or was tended to overlook before. These patterns can be applied by a new business rule. The purpose of this research is to analyze the questionnaire results and to present the result that can help to make decision easily with data mining. Recognition and analysis about these techniques of data mining show suitable type of questionnaire survey. This research focus on the form of present composition and the model of suitable questionnaire to analyze the type of it. Also, the comparison between the actual questionnaire result and the conventional statistical analysis is examined.

• Journal of Microbiology and Biotechnology
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• v.16 no.7
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• pp.1139-1143
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• 2006

Various techniques and strategies have been developed for the identification of intracellular metabolic conditions, and among them, isotope balance-based flux analysis with gas chromatography/mass spectrometry (GC/ MS) has recently become popular. Even though isotope balance-based flux analysis allows a more accurate estimation of intracellular fluxes, its application has been restricted to relatively small metabolic systems because of the limited number of measurable metabolites. In this paper, a strategy for incorporating isotope balance-based flux data obtained for a small network into metabolic flux analysis was examined as a feasible alternative allowing more accurate quantification of intracellular flux distribution in a large metabolic system. To impose GC/MS based data into a large metabolic network and obtain optimum flux distribution profile, data reconciliation procedure was applied. As a result, metabolic flux values of 308 intracellular reactions could be estimated from 29 GC/ MS based fluxes with higher accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.56.6-56
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• 2001

The FDAS(Flight Data Analysis & Animation System) is a comprehensive analysis system designed for the improvement of flight safety. FDAS provides decoding, analysis and animation tools that can be used for investigation of data origination primarily from Flight Data Recoders(FDRs), Quick Access Recoders(QARs), Using FOQA(Flight Operation Quality Assurance) Data Analysis, an analyst can perform a variety of functions including data smoothing, interpolation, differentiation, integration, calculator function, flight path generation, performance routines, as well as user-programmed functions. Utilizing data captured and processed by our FDAS software module, FDAS provides high-fidelity 3-D aircraft views and instruments views. Multiple windows enable you to view the situation from a variety of perspectives, including out-of-window, chase plane ...

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.369-374
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• 2004

Web-based analysis programs for reliability assessment of machine tools were developed in this study. First, the reliability data analysis program was developed to search for failure rate using failure data and reliability test data of mechanical part. Second, failure mode analysis was developed through performance tests like circular movement test vibration test for machine tools. This analysis program shows correlation between failure mode and performance test result. Third, tool life was predicted by correlation between flank wear and cutting time, using the extended Taylor tool life equation in turning data and the equivalently converted equation in order to apply ball endmill data to Taylor tool life equation in milling data. All the information related to input and result data can be stored in theses programs.

• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.19-28
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• 2013

In this research, we presented an effective clustering method based on ICA for the analysis of huge Raman hyperspectral dental data. The hyperspectral dataset captured by HR800 micro Raman spectrometer at UMKC-CRISP(University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties), has 569 local points. Each point has 1,005 hyperspectal dentin data. We compared the clustering effectiveness and the clustering time for the case of using all dataset directly and the cases of using the scores after PCA and ICA. As the result of experiment, the cases of using the scores after PCA and ICA showed, not only more detailed internal dentin information in the aspect of medical analysis, but also about 7~19 times much shorter processing times for clustering. ICA based approach also presented better performance than that of PCA, in terms of the detailed internal information of dentin and the clustering time. Therefore, we could confirm the effectiveness of ICA for the analysis of Raman hyperspectral dental data.

• Communications for Statistical Applications and Methods
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• v.31 no.2
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• pp.191-202
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• 2024

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.35-45
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• 2005

Data fusion is method to combination data. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. It can offer variety and actually information because it can fuse image data and survey data for street fashion. Data fusion method exists exact matching method, judgemental matching method, probability matching method, statistical matching method, data linking method, etc. In this study, we use exact matching method. Our system can be visual information analysis of customer's viewpoint because it can analyze both each data and fused data for image data and survey data.

• Journal of Platform Technology
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• v.8 no.2
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• pp.3-9
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• 2020

Big Data is a new concept in the global and local area. This field has gained tremendous momentum in the recent years and has attracted attention of several researchers. Big Data is a data analysis methodology enabled by recent advances in information and communications technology. However, big data analysis requires a huge amount of computing resources making adoption costs of big data technology. Therefore, it is not affordable for many small and medium enterprises. We survey the concepts and characteristics of Big Data along with a number of tools like HADOOP, HPCC for managing Big Data. It also presents an overview of big data like Characteristics of Big data, big data technology, big data management tools etc. We have also highlighted on some challenges and opportunities related to the fields of big data.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1123-1128
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• 2011

Generally, there are many types of data, such as wear data or irregularity data, are detected for analyzing railway performance. The analysis of railroad wear or irregularity data is most effective method for the efficient railway maintenance. Especially, the track damaged directly by train, have as important position with the inspection data. This study suggest the Life-Cycle performance analysis method and process of railroad by analyzing two types of data, wear data and irregularity data. This analysis methods can be applied to suggest the Life-Cycle performance of railroad based on environmental factors or design factors, such as sleeper type, roadbed type, cant, the radius of curvature, speed, maintenance history and so on.