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검색어 | Robust parameter design

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김희영 산업및시스템공학과 2012 1, 2, 3, 2, 4, 7, 6, 4, 8, 3, 4, 7, 8, 3, 2025
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Application of kernel principal component analysis to multi-characteristic parameter design problems ANNALS OF OPERATIONS RESEARCH, v.263, no.1-2, pp.69 - 91, 2018-04
SCOPUS 15

Keywords Kernel principal component analysis Multiple performance characteristics Robust parameter design SN ratio Taguchi method

Application of kernel principal component analysis to multi-characteristic parameter design problems
Soh, Woo Jin ; Kim, Heeyoung ; Yum, Bong-Jin

초록	The Taguchi method for robust parameter design traditionally deals with single characteristic parameter design problems. Extending the Taguchi method to the case of multi-characteristic parameter design (MCPD) problems requires an overall evaluation of multiple characteristics, for which the principal component analysis (PCA) has been frequently used. However, since the PCA is based on a linear transformation, it may not be effectively used for the data with complicated nonlinear structures. This paper develops a kernel PCA-based method that allows capturing nonlinear relationships among multiple characteristics in constructing a single aggregate performance measure. Applications of the proposed method to simulated and real experimental data show the advantages of the kernel PCA over the original PCA for solving MCPD problems.
서지사항	ANNALS OF OPERATIONS RESEARCH, v.263, no.1-2, pp.69 - 91, 2018-04
ISSN	0254-5330
주제분야	Operations Research & Management Science
저자 키워드	Kernel principal component analysis Multiple performance characteristics Robust parameter design SN ratio Taguchi method
DOI	10.1007/s10479-015-1889-2
자료유형	Article
기관	KAIST

Robust Parameter Design of Derivative Optimization Methods for Image Acquisition Using a Color Mixer JOURNAL OF IMAGING, v.3, no.3, 2017-09
SCOPUS 4

Keywords derivative optimization light control multi-color source RGB mixer robust parameter design Taguchi method

Robust Parameter Design of Derivative Optimization Methods for Image Acquisition Using a Color Mixer
Kim, HyungTae ; Cho, KyeongYong ; Kim, Jongseok ; Jin, KyungChan ; Kim, SeungTaek

초록	A tuning method was proposed for automatic lighting (auto-lighting) algorithms derived from the steepest descent and conjugate gradient methods. The auto-lighting algorithms maximize the image quality of industrial machine vision by adjusting multiple-color light emitting diodes (LEDs)-usually called color mixers. Searching for the driving condition for achieving maximum sharpness influences image quality. In most inspection systems, a single-color light source is used, and an equal step search (ESS) is employed to determine the maximum image quality. However, in the case of multiple color LEDs, the number of iterations becomes large, which is time-consuming. Hence, the steepest descent (STD) and conjugate gradient methods (CJG) were applied to reduce the searching time for achieving maximum image quality. The relationship between lighting and image quality is multi-dimensional, non-linear, and difficult to describe using mathematical equations. Hence, the Taguchi method is actually the only method that can determine the parameters of auto-lighting algorithms. The algorithm parameters were determined using orthogonal arrays, and the candidate parameters were selected by increasing the sharpness and decreasing the iterations of the algorithm, which were dependent on the searching time. The contribution of parameters was investigated using ANOVA. After conducting retests using the selected parameters, the image quality was almost the same as that in the best-case parameters with a smaller number of iterations.
서지사항	JOURNAL OF IMAGING, v.3, no.3, 2017-09
ISSN	2313-433X
주제분야	Imaging Science & Photographic Technology
저자 키워드	derivative optimization light control multi-color source RGB mixer robust parameter design Taguchi method
DOI	10.3390/jimaging3030031
자료유형	Article
기관	KAIST

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