• Journal of the Society of Cosmetic Scientists of Korea
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• v.45 no.2
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• pp.161-173
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• 2019

In this study, the antioxidative and inhibitory activity of tyrosinase and collagenase for the solvent extract and silica column fractions of Artocarpus nitidus were evaluated. The activities were quantified using the 4 parameter logistic. LC/MS analysis showed that the major component of the fractions was polyphenol and the total polyphenol content of the extract was $48.1{\pm}2.6mg\;GAE/g$. The radical scavenging activities ($SC_{50}$) for 1,1-diphenyl-2-picrylhydrazyl of the extract, fraction-1 and fraction-2 were 16.7, 42.0 and $10.1{\mu}g/mL$, respectively. The value for fraction-2 was the closest to ascorbic acid ($1.5{\mu}g/mL$). The tyrosinase inhibitory activity of the extracts and the fractions showed $IC_{50}$ of 64.9, 0.9 and $1.2{\mu}g/mL$, respectively, and overall activity was higher than that of kojic acid ($7.4{\mu}g/mL$) and arbutin ($119.0{\mu}g/mL$). In the experiment by zebrafish embryo, the whitening activity of fraction-2 (27.5%) was higher than that of kojic acid (18.6%), and there was no adverse effect up to $500{\mu}g/mL$ of fraction-2. For the collagenase inhibitory activity, the samples showed $IC_{50}$ of 139.8, 20.6, and $16.8{\mu}g/mL$, respectively, which were competitive to 1, 10-Phenanthroline ($55.4{\mu}g/mL$). The extract and fraction-2 showed $IC_{50}$ of 61.8 and $67.1{\mu}g/mL$ for elastase. These results suggest that A. nitidus extract can be used as a cosmetic material useful for antioxidant, whitening, and prevention of skin aging without adverse effects.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.253-265
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• 2020

As the risk of landslide is recently increasing due to the typhoons and localized heavy rains, effective techniques for the landslide damage detection are required to support the establishment of the recovery planning. This study describes the analysis of landslide-damaged areas using ISODATA(Iterative Self-Organizing Data Analysis Technique Algorithm) with Sentinel-2 image, regarding the case of Gokseong in August 7, 2020. A total of 4.75 ha of landslide-damaged areas was detected from the Sentinel-2 image using spectral characteristics of red, NIR(Near Infrared), and SWIR(Shortwave Infrared) bands. We made sure that the satellite remote sensing is an effective method to detect the landslide-damaged areas and support the establishment of the recovery planning, followed by the field surveys that require a lot of manpower and time. Also, this study can be used as a reference for the landslide management for the CAS500-1/2(Compact Advanced Satellite) scheduled to launch in 2021 and the Korean Medium Satellite for Agriculture and Forestry scheduled to launch in 2024.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.