As the hangover relieving drink market has been expanded, there have been efforts to produce traditional drinks that can be easily taken through reducing material costs after finding materials with high alcohol-degrading activity from agricultural products and manufacturing drinks in a way to produce sikhye. Studies were made to know quality, the ability to relieve hangover and preference of drinks for which seven kinds of agricultural products, including shiitake mushrooms, were added in the forms of fresh juice, extracts and powder. Farm products showed the highest acetaldehyde dehydrogenase (ALDH) when they were added in the form of powder before saccharification and shiitake mushrooms showed the highest enzyme activity when they were added in the form of powder. When added in the form of powder, people showed the lowest preference except when radish and hericium erinaceum were added. When bear puree was added to a mixture of powdered shiitake mushrooms, radish, bean sprouts and dadagi cucumber, enzyme activity got higher and people showed higher preference. An experiment of animal behaviors showed that the mixture had a high ability to relieve hangover in one or two hours after 10% or 40% alcohol was orally administered to mice due to long traveling distance of mice.

The process of Apparel Product Development requires several major steps such as Pattern Digitizing, Grading, Marker Making, and Cutting Out. The current operation for Pattern Digitizing is conducted operators who amend apparel patterns on a computer after digitizing them based on a point-input method. The current process is laborious, time-consuming, and expensive due to error-prone work that leaves much to be desired in solution expansion. These studies develop an automated system for apparel pattern input based on vectorising technique. Supporting industrial standardization (the detailed targets for the system development) minimizes the involvement of operators and include: automatic recognition of pattern domain, automatic recognition of sample shape, and inner part recognition performing apparel characteristic function. The following picture shows an example using our system developed with a women bra pattern. Figure 2 represents women a bra pattern Figure 3 represents data implemented on the automated system we developed The results of our study : 1. Analytical speed: under 4 seconds in resolution 150 dpi, image size A3. 2. Length error: under 0. 2mm. 3. Outline: 100% extract of outlines cutting and marker pattern. 4. Pattern elements: classification automatically and editing available by user. 5. Pattern division: try one acting scanning various pattern. 6. Data entry/output: converting of DXF, AAMA, TIP, DIGIT. We developed an automated system based on vectorising software for implementation of apparel patterns. The cost of effective system repeatedly processes digitizing tasks with high quality in a few minutes. The system will contribute to the apparel industry field by implementing automatic steps for recognition and classification pattern.