• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.343-351
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• 2008

By supervised learning with p predictors, we frequently obtain a prediction function of the form $y\;=\;f(x_1,...,x_p)$. When $p\;{\geq}\;3$, it is not easy to understand the inner structure of f, except for the case the function is formulated as additive. In this study, we propose to use p simple graphs for visual understanding of complex prediction functions produced by several supervised learning engines such as LOESS, neural networks, support vector machines and random forests.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.185-193
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• 2009

Machine learning methods such as support vector machines and random forests yield nonparametric prediction functions of the form y = $f(x_1,{\ldots},x_p)$. As a sequel to the previous article (Huh and Lee, 2008) for visualizing nonparametric functions, I propose more sensible graphs for visualizing y = $f(x_1,{\ldots},x_p)$ herein which has two clear advantages over the previous simple graphs. New graphs will show a small number of prototype curves of $f(x_1,{\ldots},x_{j-1},x_j,x_{j+1}{\ldots},x_p)$, revealing statistically plausible portion over the interval of $x_j$ which changes with ($x_1,{\ldots},x_{j-1},x_{j+1},{\ldots},x_p$). To complement the visual display, matching importance measures for each of p predictor variables are produced. The proposed graphs and importance measures are validated in simulated settings and demonstrated for an environmental study.

• Design Convergence Study
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• v.15 no.6
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• pp.195-214
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• 2016

Predictive analysis is based on a probabilistic learning algorithm called pattern recognition or machine learning. Therefore, if users want to extract more information from the data, they are required high statistical knowledge. In addition, it is difficult to find out data pattern and characteristics of the data. This study conducted statistical data analyses and visual data analyses to supplement prediction analysis's weakness. Through this study, we could find some implications that haven't been found in the previous studies. First, we could find data pattern when adjust data selection according as splitting criteria for the decision tree method. Second, we could find what type of data included in the final prediction model. We found some implications that haven't been found in the previous studies from the results of statistical and visual analyses. In statistical analysis we found relation among the multivariable and deducted prediction model to predict high box office performance. In visualization analysis we proposed visual analysis method with various interactive functions. Finally through this study we verified final prediction model and suggested analysis method extract variety of information from the data.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.4
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• pp.92-100
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• 2009

This paper presents a voltage sag assessment program. The program provides various functions for stochastic assessment of voltage sags such as short-circuit analysis, the determination of the area of vulnerability and the calculation of expected sag frequency(ESF). Effective data visualization functions based on computer graphics and animation were also implemented in the developed program. In this paper, the concept of voltage sag assessment and the assessment method considering generator scheduling and time-varying fault rates are presented. The influence of generator scheduling and time-varying fault rates on voltage sag prediction is also described by performing case studies using the developed program.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.37-46
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• 2007

In the practice of preloading method for soft ground improvement, field engineers need information of ground properties, construction works and field monitoring on ground behaviors of the site. So, integrating all these informations into one database can provide more efficient way for managing and utilizing the data for construction management. In this study, integrated system for construction management of ground improvement sites under preloading is developed. The developed system consists of database (DB) and application program. The database contains all collected data in a construction site and processed data in the system with their geographic information. All informations in the database are standardized from the result of data characterization. Application program performs various functions on managing and utilizing information in the database; pre- and post- data processing with graphic visualization of output, spatial data interpolation, and prediction of ground behavior using field measuring data. And by providing integrating informations and predictions over entire project area with comprehensible visual displays, the applicability and effectiveness of the developed system for construction management were confirmed.