• Journal of Conservation Science
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• v.28 no.1
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• pp.29-37
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• 2012

This study discusses a mercury amalgam gilding technique and examines how the color, surface and section of the gilt layer changes as the condition of heat treatment with mercury amalgam gilt object is changed. Some previous studies have mentioned reasons for various colors on gilt bronze artifacts depending on gilding manufacture and environment. However, reason for reddish color with gold on the artifacts' surface brought on high temperature corrosion has yet to be discussed and analyzed. A methodology was found in representing the mercury amalgam gilding technique and heat treatment test. According to the result of the heat treatment test, in the conditions of higher temperature and longer time, the oxidized layer on the gilt layer was distributed more widely and in the part when the oxide layer was eliminated, the gilt layer with a reddish color was observed. Moreover, in the surface observation of the specimen on which yellow and reddish colors were agitated, the changing aspects of its surface condition differed by colors. When investigated the section, it was observed that the void density and size became larger. After a test, the surface components changed; the temperature of heat treatment increased, component ratio of Hg and Au decreased gradually but component ratio of Cu increased. In regard to the gilt layer, as the time was longer and the temperature became higher for the heat treatment, the component ratio of Au and Cu by layers tended to change in inverse proportion. It is concluded that gilding techniques and the burial environment can make a difference in the surface color of the gilt layer on the gilt bronze artifacts, the high temperature corrosion that occurs by heat after they are manufactured is also one of the factors that affects their surface color.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.2
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• pp.221-226
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• 2012

For the investigation of a possibility as a useful functional material, 6 cultivars (Takinosawa Gold, Kawanakawase Hakuto, Madoka, Yumefuji, Nagasawa Hakuho, Hong Bak) of Prunus persica L. Batsch were studied at unripe stage to determine the physicochemical properties and chemical compositions. The cultivars were picked in late May, and all samples were analyzed for external properties, physicochemical properties, pH, Brix value, Hunter's color value, hardness, vitamin C, and reducing sugar. The size of the fruit from all six cultivars was compared, and it was determined that cultivars, fruit from Madoka was the largest, while that from Yumefuji was the smallest. Comparing fresh weight, the fruit from Yumefuji was lowest in moisture contents (89.13~89.96%), and that from Nagasawa Hakuho had significantly higher crude protein (1.02~1.62%). The contents of crude lipids (0.18~0.23%) and carbohydrates (8.00~9.35%) were not significantly different between cultivars and Madoka included higher crude ash contents (0.32~0.69%) than other cultivars. The pH of 6 cultivars from unripe peaches were significantly higher from Kawanakawase Hakuto, and the Brix value was also highest from Kawanakawase Hakuto, followed by Yumefuji, Madoka, Nagasawa Hakuho, Takinosawa Gold, and Hong Bak. In chromaticity, the L value, the indicator of brightness, was significantly higher in fruit from Nagasawa Hakuho. The a value, the indicator of redness, was the highest with Hong Bak and overall lower than -5. The b value, the indicator of yellowness, was the highest in fruit from Madoka and ranged from 16.51 to 18.33. In physical characteristics, the hardness of the unripe peaches was the highest in fruit from Hong Bak, and overall, white peaches have a higher hardness value than yellow peaches. The vitamin C content of the fruit didn't show any significant differences between cultivars, and the reducing sugar showed a higher percent than 6.34% in fruit from all cultivars. These results suggest that unripe peaches were commensurate with the development of natural pigment and as a functional foods.

• Journal of Haehwa Medicine
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• v.27 no.1
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• pp.9-20
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• 2018

Objectives : Ten heavenly stems(10天干) and Twelve earthly branches(12地支) are symbols exposing change order in heaven and earth, and are a very important sign in studying oriental philosophy and oriental medicine. Especially, 10 heavenly stems(10天干) and 12 earthly branches(12地支) are indispensable for the study of Five Circuits And Six Qi(오운육기), and a deep study is needed. Methods : I have examined Yin Yang combination(음양배합), Five elements combination(오행배합), Six Qi 3Yin 3Yang combination(육기삼음삼양배합), viscera combination(장부배합), Mutual collision(상충), Six combination(육합), Three combination(삼합), etc. of 12 earthly branches(12지지) by referring to books such as "Yellow Emperor Internal Classic" ("黃帝內經") and "Principle of universe change" ("우주변화의 원리"). Results & Conclusions : Zi Yin Chen Wu Shen Xu(子 寅 辰 午 申 戌) become Yang(陽), Chou Mao Si Wei You Hai(丑 卯 巳 未 酉 亥) become Yin(陰), Zi Si Yin Mao Chen Si(子 丑 寅 卯 辰 巳) become Yang, and Wu Wei Shen You Xu Hai(午 未 申 酉 戌 亥) become Yin. Twelve earthly branches can be divided into five movements by its original meaning, where YinMao(인묘) is tree, SiWu(사오) is a fire, ShenYou(신유) is a gold, HaiZi(해자) is water, and ChenXuChouWei(진술축미) mediate in the middle of four movements So they become soil(土). SiHai(巳亥) is JueYin Wind Tree(궐음 풍목), ZiWu(子午) is ShaoYin Monarch Fire(소음 군화), ChouWei(丑未) is TaiYin Humid Soil(태음 습토), YinShen(寅申) is ShaoYang Ministerial Fire(소양 상화), MaoYou(卯酉) is YangMing Dry Gold(양명 조금), and ChenXu(辰戌) is TaiYang Cold Water(태양 한수). Viscera combination(장부배합) combines Zi(子) and Bile(膽), Chou(丑) and Liver(肝), Yin(寅) and Lung(肺), Mao(卯) and Large intestine(大腸), Chen(辰) and Stomach(胃), Si(巳) and Spleen(脾), Wu(午) and Heart(心), Wei(未) and Small intestine(小腸), Shen(申) and Bladder(膀胱), You(酉) and Kidney(腎), Xu(戌) and Pericardium(心包), Hai(亥) and Tri-energizer(三焦), Which means that the function of the viscera and channels is the most active at that time. Twelve earthly branches mutual collisions collide with Zi(子) and Wu(午), Chou(丑) and Wei(未), Yin(寅) and Shen(申), Mao(卯) and You(酉), Chen(辰) and Xu(戌), and Si(巳) and Hai(亥). The two colliding earthly branches are on opposite sides, facing each other and restricting each other by the relation of Yin-Yin and Yang-Yang it rejects each other so a collision occurs. Six Correspondences(六合) coincide with Zi(子) and Chou(丑), Yin(寅) and Hai (亥), Mao(卯) and Xu(戌), Chen(辰) and You(酉) and Si(巳) and Shen(申) Wu(午) and Wei(未). Three combination(三合) is composed of ShenZiChen(申子辰), SiYouChou(巳酉丑), YinWuXu(寅午戌), and HaiMaoWei(亥卯未). Three combination(三合) is composed of ShenZiChen(申子辰), SiYouChou(巳酉丑), YinWuXu(寅午戌), and HaiMaoWei(亥卯未). This is because the time Six Qi(六氣) shifts in these three years are the same.

• Korean Journal of Plant Resources
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• v.32 no.5
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• pp.486-498
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• 2019

This study was conducted to investigate the leaf growth and flowering characteristics of 85 Hosta cultivars. The 85 cultivars were grown in a pot in Useful Plant Resources Center in Yangpyeong, Korea. H. 'Abiqua Blue Crinkles', H. 'Abiqua Drinking Gourd', H. 'Dancing in the Rain', H. 'Elegance', H. 'Inniswood', and H. 'Venus' were classified as a large size group (> 50 cm), while 27 cultivars including H. 'Abby', H. 'Birchwood Parky's Gold', H. 'Blue Cadet', and H. 'Blue Edge' were classified as a small size group (< 20 cm). The others were classified as a medium size groups. 79% of Hosta cultivars had leaf variegation. Leaf variegation type was divided into 5 types (standard, marginata, mediovarigata, albomaculata, striata). Among them 31 cultivars including H. 'Abby', H. 'Abiqua Moonbeam', and H. 'Atlantis' has a variegation type of marginata in the leaf. 36 cultivars including H. 'Abby', H. 'Abiqua Drinking Gourd', and H.'Abiqua Moonbeam' bloomed in late May and 9 cultivars including H. 'Black Hills', H. 'Boeun', and H. 'Fragrant Bouquet' started to flower on late August. Most flowers were below 3.0 cm in length, while H. 'Avocado' was longest on 10.0 cm. Most flowers have a lavender color group (63.5%), and 14 cultivars of Hosta showed white color group (16.5%). 12 cultivars including H. 'Blue Mouse Ears', H. 'Captain Kirk', and H. 'Fragrant Bouquet' had the fragrance in their flowers. H. 'Cherry Berry' and H. 'Revolution' had a colorful stalk, red and yellow, respectively.

• Conservation Science in Museum
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• v.29
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• pp.1-32
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• 2023

The Paintings of a 60^th Wedding Anniversary Ceremony Created by an Unknown Painter (Deoksu 6375), housed by the National Museum of Korea, is a five-panel painting book depicting scenes from a wedding ceremony. Hoehonrye is a type of repeated wedding ceremony to commemorate a couple's 60th wedding anniversary with congratulations from the community. The paintings of the book record five scenes from the wedding: jeoninrye, a ceremony where the groom brings a wooden wild goose to the bride's house; gyoberye, the groom and the bride bowing to each other; heosurye, pouring liquor to toast to the couple's longevity; jeopbin, offering tea to guests; and a banquet to celebrates the couple's 60th wedding anniversary. The book describes figures, buildings and a variety of items in detail with delicate brushstrokes. The techniques were examined using microscopy, infrared, and X-ray irradiation and hyperspectral imaging analysis. The invisible parts were examined to identify the rough sketch and distinguish pigments and dyes used for each color. The components of the pigments were determined by X-ray fluorescence analysis, while the dyes were identified by UV-vis spectrometry. Microscope observation revealed that the fabric used for the paintings was raw silk thread with almost no fiber twist, and plain silk fabric. Hyperspectral imaging analysis, X-ray fluorescence analysis, and UV-vis spectrometry confirmed that the white pigment was white lead and the black was chinese ink. The red pigments were using red clay, cinnabar, and a mixture of cinnabar and minium. Brown was made using red clay and organic dyes, and yellow using gamboge. Green was identified as indigo, malachite, chrome green, barium sulfide, and blue as azurite, smalt, and indigo. The purple dye was estimated as a mixture of indigo and cochineal, and gold parts were used gold powder. Hyperspectral images were distinguished parts damaged and conservation treatment area.

• Korean Journal of Heritage: History & Science
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• v.52 no.4
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• pp.38-55
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• 2019

At Otani University in Kyoto, Japan, there is a rubbed copy of Tripitaka Koreana, presumably printed in 1381. According to the postscript of the copy, written by Saek Lee himself, the rubbed copy was made at Haeinsa temple in 1381 and was kept at Sinluksa temple in Yeuju. The copy was delivered as a gift to Japan in 1414 and now is kept at the Library of Otani University. Although an approximate summary of the content of the copy was reported in the early 2000s after a basic survey, details of the copy, including the concrete format and packaging paper, are not known yet. In this paper a detailed survey of the copy is conducted on the 109 pages. The copy is divided into two parts: the wrapping and the inner pages. The wrapping paper is divided into yellow and brown colors depending on the material of the paper. The yellow colorwrapping paper was possibly made in 1381 at the time of the rubbed printing, and the brown wrapping paper was repaired after being moved to Japan. Using funds collected in February 1380, the copy of Gyeong(經), Yul(律), and Ron(論) chapters was printed in April 1381. Binding of the copy was completed in September, and the wrapping paper with the title in gold was made in October 1380. The box for keeping Buddhist scriptures was manufactured in November 1380. The copy was moved to Sinluksa temple in April 1382 and delivered to Japan in 1414. At Otani University, the copy is stored in separate rectangular boxes 32.1×25.3cm in size with a height of 23.6cm. The rectangular plate on the four sides is red in external color but black colorinside. The box for keeping Buddhist scriptures was probably made in 1381, but a partial repair was made later. Because of the difficulty of executing a detailed survey of the box for Buddhist scriptures, it is hard to find out its nation and period of production. We look forward to studying the copy as well as the box for Buddhist scriptures in future.

• Korean Journal of Heritage: History & Science
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• v.52 no.4
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• pp.226-245
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• 2019

Beopjusa Gwaebultaeng (Large Buddhist Painting), designated as Treasure No. 1259, was painted in 1766 and featured Yeorae (Buddha) at the center in the style of a single figure. It is the longest existing buddhist painting and was created by Duhun, a painter who was representative of 18th century Korean artists. His other remaining work is Seokgayeorae Gwaebultaeng (1767) in Tongdosa Temple. Considering their same iconography, they are assumed to have used the same underdrawing. Duhun had a superb ability to maintain a consistent underdrawing, while most painters changed theirs within a year. The Beopjusa painting carries significance because it was not only painted earlier than the one in Tongdosa, but also indicates possible relevance to the royal family through its records. Beopjusa Temple is also the site of Seonhuigung Wondang, a shrine housing the spirit tablet of Lady Yi Youngbin, also known as Lady Seonhui. Having been built only a year before Beopjusa Gwaebultaeng was painted, it served as a basis for the presumption that it has a connection to the royal family. In particular, a group of unmarried women is noticeable in the record of Beopjusa painting. The names of some people, including Ms. Lee, born in the year of Gyengjin, are recorded on the Bonginsa Temple Building, the construction of which Lady Yi Youngbin and Princess Hwawan donated money to. In this regard, they are probably court ladies related to Lady Yi Youngbin. The connection of Beopjusa Gwaebultaeng with the royal family is also verified by a prayer at the bottom of the painting, reading "JusangJusamJeonhaSumanse (主上主三殿下壽萬歲, May the king live forever)." While looking into the historical characteristics of this art, this study took an approach based on scientific analysis. Damages to Beopjusa Gwaebultaeng include: bending, folding, wrinkles, stains due to moisture, pigment spalling, point-shaped pigment spalling, and pigment penetration to the lining paper at the back. According to the results of an analysis of the painting pigments, white lead was used as a white pigment, while an ink stick and indigo were used for black. For red, cinnabar and minium were used independently or were combined. For purple, organic pigments seem to have been used. For yellow, white lead and gamboge were mixed, or gamboge was painted over white lead, and gold foil was adopted for storage. As a green pigment, atacamite or a mixture of atacamite and malachite was used. Azurite and smalt were used separately or together as blue pigments.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.