Yao-San Lin

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Since only few examples can be obtained in the early stages in a manufacturing system and that fewer exemplars usually lead to a lower learning accuracy, this research uses intervalized kernel methods of Density Estimation to improve the small-data-set learning. Used techniques include the Intervalization Process to improve the kernel density estimation and(More)
Thin Film Transistor—Liquid Crystal Displays (TFT-LCDs) are widely used in TVs, monitors, and PDAs. The key process of producing a TFT-LCD is using alignment to combine a Thin Film Transistor (TFT) panel with a Color Filter (CF) panel, which is called “celling”. The defined cell vernier, which indicates the alignment error, is an important quality index in(More)
  • Yao-San Lin
  • 2016 5th IIAI International Congress on Advanced…
  • 2016
This article proposes a procedure for small sample regression, systematically using the concept of robust Bayesian inference and a contaminated prior. The approach explores the possible domain of population information and attempts to estimate regression parameters further. A data augmentation step included in the procedure works to enlarge the original(More)
Executing pilot runs before mass production is a common strategy in manufacturing systems. Using the limited data obtained from pilot runs to shorten the lead time to predict future production is this worthy of study. Since a manufacturing system is usually comprehensive, Artificial Neural Networks are widely utilized to extract management knowledge from(More)
Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination(More)
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