Self-help troubleshooting by Q-KE-CLD based on a fuzzy bayes model

Abstract

The previous study [9], [10] showed the fuzzy Bayes model successfully predicted print defects with a 50% hit rate at the first top prediction and an 80% hit rate within the top five predictions. However, the previous study was limited to English. In this study, Korean and English descriptions in predicting print defects by Korean subjects were evaluated based on fuzzy Bayes models. For the study, Korean descriptions were collected in Korea, and Bayes models were developed and evaluated. The result shows that Korean subjects much more accurately predicted print defects when they used Korean descriptions than English descriptions. Afterwards, English descriptions by US subjects will be collected, and both Korean and English lexicon data will be compared. Finally, the study will investigate a Korean-English cross language diagnosis (Q-KE-CLD) system to identify print defects based on the fuzzy Bayes model. © Springer-Verlag Berlin Heidelberg 2007.