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.

본 논문은 영상에 내재하는 얼굴영상에 대하여 보다 빠르고 강인하게 검출하기 위하여 퍼지측도를 이용하여 얼굴을 검출하는 방법을 제안한다. 먼저 여러 가지 조명환경과 인종의 피부색 모델을 이용해 피부영역을 검출한다. 그리고 영 역 라벨링과 필터링으로 매칭에 필요한 탬색범위를 줄이고, 에지를 이용한 템플릿 매칭을 탐색영역에 적용한다. 이를 위하여 퍼지적분에 해당하는 퍼지측도의 비퍼지화를 통한 퍼지 수학적 형태학적인 침식연산을 제안하였다. 각각의 부 분집합에 대한 각각의 퍼지측도의 포함정도를 측정하는 퍼지집합에 대하여 비퍼지화 과정을 적용한다. 또한 모든 부분 집합에 대하여 λ-퍼지 측도를 정의하여 이에 대한 마스크내의 영상에 대한 비퍼지화를 수행하여 퍼지적분의 결과로 대 치하였다. 결국 퍼지 측도를 기반으로 하여 침식에 대한 퍼지 형태학적 연산자를 정의하였다. 실험 결과는 제안된 방법 이 이질적인 템플릿을 이용할 때보다 얼굴색과 유사한 배경에서 얼굴을 강인하게 검출하였으며, 템플릿의 단계를 줄여 검출시간을 줄일 수 있었다.