The color image of the brand comes first and is an important visual element that leads consumers to the consumption of the product. To express more effectively what the brand wants to convey through design, the printing market is striving to print accurate colors that match the intention. In ‘offset printing’ mainly used in printing, colors are often printed in CMYK (Cyan, Magenta, Yellow, Key) colors. However, it is possible to print more accurate colors by making ink of the desired color instead of dotting CMYK colors. The resulting ink is called ‘spot color’ ink. Spot color ink is manufactured by repeating the process of mixing the existing inks. In this repetition of trial and error, the manufacturing cost of ink increases, resulting in economic loss, and environmental pollution is caused by wasted inks. In this study, a deep learning algorithm to predict printed spot colors was designed to solve this problem. The algorithm uses a single DNN (Deep Neural Network) model to predict printed spot colors based on the information of the paper and the proportions of inks to mix. More than 8,000 spot color ink data were used for learning, and all color was quantified by dividing the visible light wavelength range into 31 sections and the reflectance for each section. The proposed algorithm predicted more than 80% of spot color inks as very similar colors. The average value of the calculated difference between the actual color and the predicted color through ‘Delta E’ provided by CIE is 5.29. It is known that when Delta E is less than 10, it is difficult to distinguish the difference in printed color with the naked eye. The algorithm of this study has a more accurate prediction ability than previous studies, and it can be added flexibly even when new inks are added. This can be usefully used in real industrial sites, and it will reduce the attempts of the operator by checking the color of ink in a virtual environment. This will reduce the manufacturing cost of spot color inks and lead to improved working conditions for workers. In addition, it is expected to contribute to solving the environmental pollution problem by reducing unnecessarily wasted ink.

국민의 건강과 웰빙에 대한 관심 증대 및 소득 증가로 한약재 소비를 비롯한 식품과 화장품 원료로서의 산업적 가치가 커지면서 약용작물의 수요는 점차 증가할 것으로 예상하며 한, 중 FTA 체결로 인해 약용작물 시장개방의 수입증가는 위협요인으로 작용할 것이다. 그러한 측면에 서 약용작물을 포함한 농식품산업을 미래 성장 동력산업 으로 육성하기 위해서는 종자품종육성과 생산유통기반의 정비와 확충, 산지이용규제 완화 등의 다양한 대책이 마련되어야 한다. 본 연구 결과의 활용방안을 살펴보면 다음과 같다. 첫째, 국가의 약용산업 육성 중장기 전략과 종자관리 체계의 구축에 필요한 약용작물의 데이터베이스를 구축 하고 향후 개발해야 할 중점기술을 제시해야 한다. 둘째, 우수한 약용작물 품종 개발 및 보급으로 농업생산성을 높여야 한다. 셋째, 약용작물 종자생산 및 보급에 필요한 제도정비가 필요하다. 넷째, 우량종자의 표준화 및 규격화 로 약용작물의 생산유통체계를 확립해야 한다. 현재 약용 작물의 소비행태는 직접적인 섭취보다 화장품, 의약품, 식품의 형태로 소비되는 패턴으로 바뀌고 있으며 규모가 점점 확대될 것으로 전망된다. 그러므로 향후 약용작물의 생산에서는 이러한 산업화 추세와 변화에 대응한 정책개 발과 제도적인 개선이 뒷받침되어야 한다.

This study was conducted to identify the effect of shading and pinching on growth and acanthoside-D content of Acanthopanax divaricatus var. albeofructus and A. koreanum Nakai. Different pinching heights showed no significant differences in terms of plant growth and acanthoside-D content but higher values showed that pinching A. divaricatus at 60 cm and A. koreanum at 30 cm favored good growth and higher fresh weight in the shoots. The content of acanthoside-D was not significantly affected by pinching heights. Also, no significant difference in acanthoside-D content was found between the lower and upper part of plant in the first year. However it was much higher in the lower part than the upper part in the second year, which indicated that the content of acanthoside-D was comparatively high in the lower part where lignification is much advanced. Shading showed benefits in terms of growth of A. divaricatus while only 50%-shading was favorable for A. koreanum to achieve superior growth. Overall, results indicated that shading had favorably affected the growth of the 2 Acanthopanax species while no-shading is better if we opt to achieve higher acathoside D content.