• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.84-94
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• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.619-631
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• 2018

Functional data analysis continues to attract interest because advances in technology across many fields have increasingly permitted measurements to be made from continuous processes on a discretized scale. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California remain among the highest in the United States. The relatively high frequency of particulate matter sampling enables us to regard the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of this underlying data structure. In addition, our study shows that certain aspects of size and temporal variation of the underlying PM10 structure are associated with changes in large-scale climate indices that quantify variations of sea surface temperature and atmospheric circulation patterns.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.501-510
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• 1999

We have seen a rapid development of multimedia intustry as computer evolves and the internet has changed our way of life dramatically in these days. There we several attempts to teach elementary statistics on the web but most of them are based on commercial products. The need for statistical data analysis and decision making based on those analysis is growing. In this article we try to show one way of reaching that goal by using a server side scripting language PHP3 toghether with extra graphical module and statistical distribution module on the web. We showed some elementary exploratory graphical data analysis and statistical inferences. There are plenty of room of improvements to make it a full blown statistical analysis tool on the web in the new future. All the programs and databases used in our article we public programs. The main engine PHP3 is included as an apache web server module so it is very light and fast. It will be much better when the PHP4(ZEND) will be officially out in terms of processing speed.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.191-198
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• 2012

Statistical analysis is a process which produces information based on data gathering and summary for final decision. In various application fields, we obtain information which supports final decision using statistical analysis. But statistical software program in PC (personal computer) is restricted by time and space. So web based system which can be used in web browser has been developed to minimize these restrictions. To overcome these restrictions, we have developed web based system for statistical analysis without a particular software.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.117-120
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• 1996

The objective of this study is to suggest modified Covariance Structure Analysis that combine with existing Multivariate Statistical Method which is used Casual Analysis Method in Management Information. For this purpose, we'll consider special feature and limitation about Correlation Analysis, Regression Analysis, Path Analysis and connect Covariance Structure Analysis with Statistical Factor Analysis so that theoretical casual model compare with variables structure in collecting data. A example is also presented to show the practical applicability of this approach.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.325-327
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• 2024

• Korean Management Science Review
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• v.13 no.2
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• pp.61-71
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• 1996

In this paper, we propose an interface design for helping users to utilize a statistical analysis program efficiently. This interface design is useful to suggest which statistical method to use and to provide the result by linking data file to the statistical analysis program in a user friendly way. In chapter 2, we explain the overall structure and modules of the interface.

• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.333-354
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• 1997

Computational algorithms to calculate M-estimators and rank estimators of regression parameters from left-truncated and right-censored data are developed herein. In the case of M-estimators, new statistical methods are also introduced to incorporate leverage assements and concomitant scale estimation in the presence of left truncation and right censoring on the observed response. Furthermore, graphical methods to examine the residuals from these data are presented. Two real data sets are used for illustration.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.131-140
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• 2006

There are many statistical information sites as the use of internet is increased quickly in recent years. In this paper, we explore and analyze internet sites for statistical information such as statistical survey system, education, database, and terminology. And then we classify these sites to apply statistical information to some particular spheres easily. In so doing, this study result aims at enhancing our understanding of internet sites for statistical information.

• 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.