The purpose of this study is to reconstruct the “aspect of vocabulary”, which is used in current grammar education, considering the overlaps between vocabulary categories. For this purpose, the existing discussion on the aspect of vocabulary and the way of implementing the curriculum and the textbooks were examined. As a result, it is confirmed that the vocabulary aspect education has not fully taken into consideration of “overlapping among the vocabulary categories”. In this study, we set up a category of vocabulary with multiple features and categorized these features using “+” and “-” depending on the case. In this way, aspect of vocabulary is restructured more coherently. More discussion on the future studies and education should be continued.

In industrial society, the core competency of company was depend on the productivity. However the knowledge information era of the 21st century, the market power moved to downstream, the core competency of company is moved from productivity to how to make the products meet the market. Inventory was the burden of the company management. Most of company trying to reduce the inventory. In this study, analyze the impact of inventory to company's operating profit and the impact of distribution center consolidation to total inventory of company.

Data mining and game sounds classification prerequisite to find a compact but effective set of features in the overall problem-solving process. As a preprocessing step of data mining, feature selection has tuned to be very efficient in reducing its dimensionality and removing irrelevant data at hand. In this paper we cast a feature selection problem on rough set theory and a conditional entropy in information theory and present an empirical study on feature analysis for classical instrument classification. An new definition of a significance of each feature using rough set theory based on rough entropy is proposed. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the musical instrument sound classification problem through Weka’s classifiers. The results show that the performance of the best 17 selected features among 37 features has 3.601 compared to 2.332 in standard deviation and 94.667 compared to 96.935 in average with four classifiers.