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Chi-Hyuck Jun
Researcher at Pohang University of Science and Technology
Publications - 314
Citations - 8971
Chi-Hyuck Jun is an academic researcher from Pohang University of Science and Technology. The author has contributed to research in topics: Control chart & EWMA chart. The author has an hindex of 35, co-authored 309 publications receiving 7442 citations. Previous affiliations of Chi-Hyuck Jun include Electronics and Telecommunications Research Institute & University of Veterinary and Animal Sciences.
Papers
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Journal ArticleDOI
A simple and fast algorithm for K-medoids clustering
Hae-Sang Park,Chi-Hyuck Jun +1 more
TL;DR: Experimental results show that the proposed algorithm takes a significantly reduced time in computation with comparable performance against the partitioning around medoids.
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Performance of some variable selection methods when multicollinearity is present
Il-Gyo Chong,Chi-Hyuck Jun +1 more
TL;DR: The nature of the VIP method is explored and it is compared with other methods through computer simulation experiments considering four factors–the proportion of the number of relevant predictor, the magnitude of correlations between predictors, the structure of regression coefficients, andThe magnitude of signal to noise.
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Variables sampling plans for Weibull distributed lifetimes under sudden death testing
TL;DR: Variables single, and double sampling plans are proposed for the lot acceptance of parts whose life follows a Weibull distribution with known shape parameter, which are different from the existing ones in that the lotaccept criteria do not depend on the estimated scale parameter.
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Multiple dependent state sampling plans for lot acceptance based on measurement data
TL;DR: In this article, a multiple dependent (or deferred) state sampling plan by variables for the inspection of normally distributed quality characteristics is proposed, where the decision upon the acceptance of the lot is based on the states of the preceding lots (dependent state plan) or on the state of the forthcoming lots (deferred state plan).
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A new loss function-based method for multiresponse optimization
TL;DR: The proposed method introduces predicted future responses in a loss function, which accommodates robustness and quality of predictions as well as bias in a single framework to give more reasonable results than the existing methods.