• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.6
• /
• pp.2086-2097
• /
• 2021

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.45 no.11
• /
• pp.1610-1616
• /
• 2016

This study aimed to establish an optimal extraction process and high performance liquid chromatography-ultraviolet (HPLC-UV) analytical method for determination of 3,5-dicaffeoylquinic acid (3,5-DCQA) as a part of materials standardization for the development of a xanthine oxidase inhibitor as a health functional food. The quantitative determination method of 3,5-DCQA as a marker compound was optimized by HPLC analysis using a Luna RP-18 column, and the correlation coefficient for the calibration curve showed good linearity of more than 0.9999 using a gradient eluent of water (1% acetic acid) and methanol as the mobile phase at a flow rate of 1.0 mL/min and a detection wavelength of 320 nm. The HPLC-UV method was applied successfully to quantification of the marker compound (3,5-DCQA) in Aster glehni extracts after validation of the method with linearity, accuracy, and precision. Ethanolic extracts of A. glehni (AGEs) were evaluated by reflux extraction at 70 and $80^{\circ}C$ with 30, 50, 70, and 80% ethanol for 3, 4, 5, and 6 h, respectively. Among AGEs, 70% AGE at $70^{\circ}C$ showed the highest content of 3,5-DCQA of $52.59{\pm}3.45mg/100g$ A. glehni. Furthermore, AGEs were analyzed for their inhibitory activities on uric acid production by the xanthine/xanthine oxidase system. The 70% AGE at $70^{\circ}C$ showed the most potent inhibitory activity with $IC_{50}$ values of $77.01{\pm}3.13{\sim}89.96{\pm}3.08{\mu}g/mL$. The results suggest that standardization of 3,5-DCQA in AGEs using HPLC-UV analysis would be an acceptable method for the development of health functional foods.