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윤호섭 한국콩연구회 1990 韓國콩硏究會誌 Vol.7 No.2
Soybeans is one of the main source of proteins in Korean diets, and, therefore, is called $quot;meat produced at upland. Soybean is used not only for food consumption directly, but also far processing of tofu, sousauce, soybean-made milk, and so on. That is the use of soybean fear, food has been diversified with a development of soybean processing technology and expansion of processing industry, causing a steadily increasing trend of soybean demand since 1970s. The soybean demand for fedgrain has been also greatly increased with an increasing consumption of livestock and dairy products. Therefore, the objective of this study is to understand the mechanism of soybean market, and to forecast consumption of soybean and soybean-related products, production and impork demand. The basic approact used is the econometric estimation of simultaneous equation system which is based on the major behavioral and economic relations. The estimation result shows that soybean import demand continues to increase under any scenario, with an increasing trend of comsumptions.
윤호섭,왕민,민병우,Yoon, Ho-Sub,Wang, Min,Min, Byung-Woo 대한전자공학회 1996 전자공학회논문지-B Vol.b33 No.12
This paper describes facial component detection and skew correction algorithm for face recognition. We use a priori knowledge and models about isolated regions to detect eye location from the face image captured in natural office environments. The relations between human face components are represented by several rules. We adopt an edge detection algorithm using sobel mask and 8-connected labelling algorith using array pointers. A labeled image has many isolated components. initially, the eye size rules are used. Eye size rules are not affected much by irregular input image conditions. Eye size rules size, and limited in the ratio between gorizontal and vertical sizes. By the eye size rule, 2 ~ 16 candidate eye components can be detected. Next, candidate eye parirs are verified by the information of location and shape, and one eye pair location is decided using face models about eye and eyebrow. Once we extract eye regions, we connect the center points of the two eyes and calculate the angle between them. Then we rotate the face to compensate for the angle so that the two eyes on a horizontal line. We tested 120 input images form 40 people, and achieved 91.7% success rate using eye size rules and face model. The main reasons of the 8.3% failure are due to components adjacent to eyes such as eyebrows. To detect facial components from the failed images, we are developing a mouth region processing module.