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서효정(Hyo-Jeong Suh),윤형식(Hyung-Sik Yoon) 한국식품영양과학회 1989 한국식품영양과학회지 Vol.18 No.4
Dietary fiber(DF) content and their properties were analyzed in some common vegetables such as Squash(leaf), Water cress, Garland chrysanthemum, Leek and sweet potato(stem).<br/> DF was analyzed by the method of detergent fractionation. Values for neutral detergent fiber(NDF), acid detergent fiber(ADF), lignin, hemicellulose were obtained. NDF for most samples was 13.83%~24.31%, ADF was 11.99%~21.03%, lignin was around 0.56%~3.00%, hemicellulose was about 1.33%~3.88%, cellulose was 9.08%~14.87%, except sweet potato(leaf) (20.47%).<br/> This paper describes two properties of the fiber-of commonly eaten vegetables ; water-holding capacity(WHC) and metal binding. The capacity of the acetone dried powder (ADP) to hold water was estimated. The WHC measurements differed from 7.4g per 9 of ADP^+ for Water cress to 11.0g per g of ADP for Leek. The binding of Fe, Zn in NDF, ADF obtained from five fiber sources was examined under two pH conditions : 1) pH2.1, 2) pH6.1. Fe, Zn binding increased with higher pH and differed from DF sources.
Super Resolution을 통한 건설현장 CCTV 고해상도 복원 및 Object Detection 성능 향상
김국빈 ( Kim Kug-bin ),서효정 ( Suh Hyo-jeong ),김하림 ( Kim Ha-rim ),유위성 ( Yoo Wi-sung ),조훈희 ( Cho Hun-hee ) 한국건축시공학회 2023 한국건축시공학회 학술발표대회 논문집 Vol.23 No.1
As technology improves with the 4th industrial revolution, smart construction is becoming a key part of safety management in the architecture and civil engineering. By using object detection technology with CCTV data, construction sites can be managed efficiently. In this study, super resolution technology based on deep learning is proposed to improve the accuracy of object detection in construction sites. As the resolution of a train set data and test set data get higher, the accuracy of object detection model gets better. Therefore, according to the scale of construction sites, different object detection models can be considered.