Seoung Bum Kim

Korea University, Sŏul, Seoul, South Korea

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Publications (89)99.06 Total impact

  • Ji Hoon Kang · Jaehong Yu · Seoung Bum Kim
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    ABSTRACT: Multivariate statistical process control techniques have been widely used to improve processes by reducing variation and preventing defects. In modern manufacturing, because of the complexity and variability of processes, traditional multivariate control charts such as Hotelling's T2 cannot efficiently handle situations in which the patterns of process observations are nonlinear, multimodal, and time varying. In the present study, we propose a nonparametric control chart, which is capable of adaptively monitoring time-varying and multimodal processes. Experiments with simulated and real process data from a thin film transistor-liquid crystal display (TFT-LCD) demonstrate the effectiveness and accuracy of the proposed method.
    No preview · Article · Jan 2016 · Journal of Process Control
  • Wan Sik Nam · Seoung Bum Kim

    No preview · Article · Dec 2015
  • Sung Won Han · Kyu Jong Lee · Hua Zhong · Seoung Bum Kim
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    ABSTRACT: Spatiotemporal surveillance, especially in detection of emerging outbreaks is of particular importance. When an outbreak spreads across some areas, the incidence rate at the center of the outbreak area might be expected to be much higher than the rate at its edge. However, to the best of our knowledge, all existing methods assume a uniformly increasing rate across the entire area of the outbreak. The purpose of this study is to compare the performance of the spatiotemporal surveillance methods such as multivariate cumulative sum (MCUSUM) or multivariate exponentially weighted moving average (MEWMA) when the changes in size are nonhomogeneous. Monte Carlo simulations were conducted to examine the properties of these spatiotemporal surveillance methods and compared them in terms of the detection speed and the identification rate under various scenarios. The results showed that when nonhomogeneous change sizes are involved, the MCUSUM method taking into account spatial nonhomogeneity of increase rates yields a better identification than the method ignoring such change size pattern although the detection speeds are similar. Further, a case study for the detection of male thyroid cancer data in New Mexico in the United States was performed to demonstrate the applicability of these methods.
    No preview · Article · Nov 2015 · Communication in Statistics- Simulation and Computation
  • Ji Hoon Kang · Seoung Bum Kim
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    ABSTRACT: Control charts have been widely used to improve manufacturing processes by reducing variations and defects. In particular, multivariate control charts have been effectively applied with monitoring processes that contain many correlated variables. Most existing multivariate control charts are vulnerable to misclassification errors that originate because of the hypothesis tests. In particular, these often cause the generation of a large number of false alarms. In this paper, we propose a procedure to reduce false alarms by combining a multivariate control chart and data mining algorithms. Simulation and real case studies demonstrate that the proposed method effectively reduces the false alarm rate.
    No preview · Article · Nov 2015 · Journal of Process Control

  • No preview · Article · Nov 2015 · Intelligent Systems, IEEE
  • Sugon Cho · Seoung Bum Kim

    No preview · Article · Nov 2015 · Intelligent Systems, IEEE
  • Jaehong Yu · Seoung Bum Kim
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    ABSTRACT: Clustering analysis can facilitate the extraction of implicit patterns in a dataset and elicit its natural groupings without requiring prior classification information. Numerous researchers have focused recently on graph-based clustering algorithms because their graph structure is useful in modeling the local relationships among observations. These algorithms perform reasonably well in their intended applications. However, no consensus exists about which of them best satisfies all the conditions encountered in a variety of real situations. In this study, we propose a graph-based clustering algorithm based on a novel density-of-graph structure. In the proposed algorithm, a density coefficient defined for each node is used to classify dense and sparse nodes. The main structures of clusters are identified through dense nodes and sparse nodes that are assigned to specific clusters. Experiments on various simulation datasets and benchmark datasets were conducted to examine the properties of the proposed algorithm and to compare its performance with that of existing spectral clustering and modularity-based algorithms. The experimental results demonstrated that the proposed clustering algorithm performed better than its competitors; this was especially true when the cluster structures in the data were inherently noisy and nonlinearly distributed.
    No preview · Article · Nov 2015 · Neurocomputing
  • Su Gon Cho · Jaehee Cho · Seoung Bum Kim

    No preview · Article · Oct 2015
  • Woo-Sik Choi · Seoung Bum Kim

    No preview · Article · Aug 2015
  • Jin Soo Park · Seoung Bum Kim

    No preview · Article · Aug 2015
  • Tae Woo Joo · Seoung Bum Kim
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    ABSTRACT: Forecasting time series data is one of the most important issues involved in numerous applications in real life. Time series data have been analyzed in either the time or frequency domains. The objective of this study is to propose a forecasting method based on wavelet filtering. The proposed method decomposes the original time series into the trend and variation parts and constructs a separate model for each part. Simulation and real case studies were conducted to examine the properties of the proposed method under various scenarios and compare its performance with time series forecasting models without wavelet filtering. The results from both simulated and real data showed that the proposed method based on wavelet filtering yielded more accurate results than the models without wavelet filtering in terms of mean absolute percentage error criterion.
    No preview · Article · May 2015 · Expert Systems with Applications
  • Younghoon Kim · Kevin A. Schug · Seoung Bum Kim
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    ABSTRACT: Successful identification of the significant features in complex mass spectral fingerprints is a crucial task in discriminating states or differences in natural systems (e.g., diseased vs. healthy, treated vs. untreated, and male vs. female) that are visualized using mass spectrometry technology. In this study, we present an ensemble regularization method that combines three regularization regression models to generate more robust results. Specifically, the coefficients from each of three regularization models were bootstrapped and the means and standard deviations of these coefficients were calculated. After obtaining these estimated statistics of the coefficients, we performed a hypothesis test for each feature. Finally, we determined the significant features that were simultaneously selected by the three hypothesis tests. Mass spectral data from six different extracts of mosquito cuticles were used to evaluate the performance of the proposed method. The purpose of this spectral analysis was to determine the major features needed to differentiate married-female mosquitoes having the potential to cause malaria infection from others. In addition, we compared the proposed ensemble feature selection method with random forest, a widely used feature selection algorithm. We found that the proposed method outperformed random forest in terms of feature selection efficiency.
    No preview · Article · May 2015 · Chemometrics and Intelligent Laboratory Systems
  • Jieun Son · Seoung Bum Kim · Hyunjoong Kim · Sungzoon Cho

    No preview · Article · Apr 2015
  • Kyu Jong Lee · Ji Hoon Kang · Jae Hong Yu · Seoung Bum Kim
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    ABSTRACT: Multivariate control charts have been widely recognised as efficient tools for detection of abnormal behaviour in multivariate processes. However, these charts provide only limited information about the contribution of any specific variable to an out-of-control signal. To address this limitation, some fault identification methods have been developed to identify contributors to an abnormality. In real situations, however, a couple of tasks should be further considered with these contributors to improve their applicability and to facilitate interpretation of faults. This study presents a rank sum-based summarisation technique and a decision tree algorithm to facilitate the interpretation of fault identification results. Experimental results with real data from the manufacturing process for a thin-film transistor-liquid crystal display (TF-LCD) demonstrate the applicability and effectiveness of the proposed methods.
    No preview · Article · Jan 2015 · European J of Industrial Engineering
  • Source
    Younghoon Kim · Seoung Bum Kim · Sangho Shim
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    ABSTRACT: Multicollinearity is the most challenging problem caused by tendency that inde-pendent variables in regression analysis are highly correlated. The multicollinearity reduces the reliability of estimated regression coefficients. In this study, we intro-duce a way of deciding the threshold of correlation which indicates the severity of multicollinearity. The way is to draw a conflict graph, which is the minimum vertex cover of multicollinear variables. The simulation results demonstrate that our pro-posed algorithm can provide an appropriate threshold for reducing large amounts of uncertainty of estimated regression coefficients.
    Full-text · Conference Paper · Dec 2014
  • Chan Hee Park · Seoung Bum Kim
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    ABSTRACT: Feature selection based on an ensemble classifier has been recognized as a crucial technique for modeling high-dimensional data. Feature selection based on the random forests model, which is constructed by aggregating multiple decision tree classifiers, has been widely used. However, a lack of stability and balance in decision trees decreases the robustness of random forests. This limitation motivated us to propose a feature selection method based on newly designed nearest-neighbor ensemble classifiers. The proposed method finds significant features by using an iterative procedure. We performed experiments with 20 datasets of microarray gene expressions to examine the property of the proposed method and compared it with random forests. The results demonstrated the effectiveness and robustness of the proposed method, especially when the number of features exceeds the number of observations.
    No preview · Article · Nov 2014 · Expert Systems with Applications
  • Gulanbaier Tuerhong · Seoung Bum Kim
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    ABSTRACT: Control charts are widely used in various industries to improve product quality. One recent trend in developing control charts is based on novelty score algorithms that can effectively describe reality and reflect the unique characteristics of the data being monitored. In this study, we compared eight novelty score algorithms—the T2, Local T2, Dmax, Dmean, K2, the k-nearest neighbor data description, the local density outlier factor, and the hybrid novelty score (HNS)—in terms of their average run length performance. A rigorous simulation was conducted to compare the novelty score-based multivariate control charts under both normal and non-normal scenarios. The simulation showed that in both normal and lognormal scenarios, Dmax-based control charts produced the most promising results. In skewed distribution with high kurtosis non-normal scenarios, HNS- and K2-based control charts performed best. In symmetric with kurtosis non-normal scenarios, local T2-based control charts outperformed the others.
    No preview · Article · Oct 2014 · Communication in Statistics- Simulation and Computation
  • Sungho Park · Seoung Bum Kim

    No preview · Article · Oct 2014
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    ABSTRACT: In novelty detection, support vector data description (SVDD) is a one-class classification technique that constructs a boundary to differentiate novel from normal patterns. However, boundaries constructed by SVDD do not consider the density of the data. Data points located in low density regions are more likely to be novel patterns because they are remote from their neighbors. This study presents a density-focused SVDD (DFSVDD), for which its boundary considers both shape and the dense region of the data. Two distance measures, the kernel distance and the density distance, are combined to construct the DFSVDD boundary. The kernel distance can be obtained by solving a quadratic optimization, while support vectors are used to obtain the density distance. A simulation study was conducted to evaluate the performance of the proposed DFSVDD and was then compared with the traditional SVDD. The proposed method performed better than SVDD in terms of the area under the receiver operating characteristic curve. Copyright © 2014 John Wiley & Sons, Ltd.
    No preview · Article · Oct 2014 · Quality and Reliability Engineering
  • Jieun Son · Seoung Bum Kim

    No preview · Article · Aug 2014

Publication Stats

544 Citations
99.06 Total Impact Points

Institutions

  • 2006-2015
    • Korea University
      • Department of Information Management Engineering
      Sŏul, Seoul, South Korea
  • 2006-2011
    • University of Texas at Arlington
      • • Department of Chemistry and Biochemistry
      • • Department of Industrial and Manufacturing Systems Engineering
      Arlington, Texas, United States
  • 2003-2006
    • Georgia Institute of Technology
      • School of Industrial and Systems Engineering
      Atlanta, Georgia, United States