• Korean Journal of Heritage: History & Science
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• v.54 no.3
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• pp.68-89
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• 2021

In August 1837, a list of wedding gifts was given by Queen Sunwon (1789-1857) to her son-in-law, Namnyeong-wie, Yun Eui-Seon (1823-1887) at the wedding of Princess Deok-on (1822-1844). This Honsubalgi is now kept at the National Hangeul Museum. This text was used in the present study to examine the everyday clothes of the royal son-in-law in the early 19th century. First, the everyday clothes were organized into about 36 types. They were classified as tops, bottoms, hats, accessories, belts, pouches, fans and shoes. Second, the most important clothes were the ordinary formal attire, composed of the namgwangsa dopo and namgwangcho changui. As for the bottoms, the pants, the Chinese hemp leggings, two pairs of socks, the green silk belt, and a pair of light blue ankle ties were identified. Third, as for the head and accessories, there were heukrip, with the gemstone string and silk string, the jeong-ja-gwan and dong-pa-gwan, as well as tang-geon and bok-geon. And there were the sangtu-gwan, three types of donggos, and the mang-geon equipped with okgwanja. On the other hand, the jeong-ja-gwan and dong-pa-gwan are peculiar hats whose status has changed over time since the mid-18th century. The fact that the jeong-ja-gwan and dong-pa-gwan were given to Namnyeong-wie showed that the status of these hats improved in the early reign of King Heonjong. The belt was given with the sejodae that is suitable for the dangsang, the coral plates, and the silk bag containing a flint pouch. Fourth, there were the red-colored sejodae, a ssamji silk pouch for flint and the fan decorated with okseonchu, and shoes, such as unhye and danghye.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.264-277
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• 2021

This study analyzed the relationship between the total precipitable water vapor in the atmosphere and the moving speed of recent typhoons. This study used ground observation data of air temperature, precipitation, and wind speed from the Korea Meteorological Administration (KMA) as well as total rainfall data and Red-Green-Blue (RGB) composite images from the U.S. Meteorological and Satellite Research Institute and the KMA's Cheollian Satellite 2A (GEO-KOMPSAT-2A). Using the typhoon location and moving speed data provided by the KMA, we compared the moving speeds of typhoon Bavi, Maysak, and Haishen from 2020, typhoon Tapah from 2019, and typhoon Kong-rey from 2018 with the average typhoon speed by latitude. Tapah and Kong-rey moved at average speed with changing latitude, while Bavi and Maysak showed a significant decrease in moving speed between approximately 25°N and 30°N. This is because a water vapor band in the atmosphere in front of these two typhoons induced frontogenesis and prevented their movement. In other words, when the water vapor band generated by the low-level jet causes frontogenesis in front of the moving typhoon, the high pressure area located between the site of frontogenesis and the typhoon develops further, inducing as a blocking effect. Together with the tropical night phenomenon, this slows the typhoon. Bavi and Maysak were accompanied by copious atmospheric water vapor; consequently, a water vapor band along the low-level jet induced frontogenesis. Then, the downdraft of the high pressure between the frontogenesis and the typhoon caused the tropical night phenomenon. Finally, strong winds and heavy rains occurred in succession once the typhoon landed.

• Korean Journal of Breeding Science
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• v.43 no.4
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• pp.233-240
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• 2011

Food security has been a main global issue due to climate changes and growing world population expected to 9 billion by 2050. While biodiversity is becoming more highlight, breeders are confronting shortage of various genetic materials needed for new variety to tackle food shortage challenge. Though biotechnology is still under debate on potential risk to human and environment, it is considered as one of alternative tools to address food supply issue for its potential to create a number of variations in genetic resource. The new technology, phenomics, is developing to improve efficiency of crop improvement. Phenomics is concerned with the measurement of phenomes which are the physical, morphological, physiological and/or biochemical traits of organisms as they change in response to genetic mutation and environmental influences. It can be served to provide better understanding of phenotypes at whole plant. For last decades, high-throughput screening (HTS) systems have been developed to measure phenomes, rapidly and quantitatively. Imaging technology such as thermal and chlorophyll fluorescence imaging systems is an area of HTS which has been used in agriculture. In this article, we review the current statues of high-throughput screening system in phenomics and its application for crop improvement.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1165-1178
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• 2018

Collapse of an Antarctic ice shelf and its flow velocity changes has the potential to reduce the restraining stress to the seaward flow of the Antarctic Ice Sheet, which can cause sea level rising. In this study, variations in ice velocity from 2000 to 2017 for the Nansen Ice Shelf in East Antarctica that experienced a large-scale collapse in April 2016 were analyzed using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images. To extract ice velocity, image matching based on orientation correlation was applied to the image pairs of blue, green, red, near-infrared, panchromatic, and the first principal component image of the Landsat multispectral data, from which the results were combined. The Landsat multispectral image matching produced reliable ice velocities for at least 14% wider area on the Nansen Ice Shelf than for the case of using single band (i.e., panchromatic) image matching. The ice velocities derived from the Landsat multispectral image matching have the error of $2.1m\;a^{-1}$ compared to the in situ Global Positioning System (GPS) observation data. The region adjacent to the Drygalski Ice Tongue showed the fastest increase in ice velocity between 2000 and 2017. The ice velocity along the central flow line of the Nansen Ice Shelf was stable before 2010 (${\sim}228m\;a^{-1}$). In 2011-2012, when a rift began to develop near the ice front, the ice flow was accelerated (${\sim}255m\;a^{-1}$) but the velocity was only about 11% faster than 2010. Since 2014, the massive rift had been fully developed, and the ice velocity of the upper region of the rift slightly decreased (${\sim}225m\;a^{-1}$) and stabilized. This means that the development of the rift and the resulting collapse of the ice front had little effect on the ice velocity of the Nansen Ice Shelf.

• Korean Journal of Heritage: History & Science
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• v.37
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• pp.121-150
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• 2004

As genre of Buddhist paintings that express generally mountains and waters, there are Eight Scenes from Life of the Buddha(八相圖), Eternal Life Painting(甘露圖), Avalokitesvara Painting(觀音菩薩圖) includes Sixteen Lohans painting(十六羅漢圖), and Hermit Painting(獨聖圖), or Mountain God Painting(山神圖) which is especially appeared in late Chosun Period. These Buddhist paintings had various backgrounds including mountains and waters, the tradition of Water Ink Painting still remains after 18th century, however the trends got complicated to express various landscapes including splendid color, waters and mountains, and it appeared to have historical trend with introduction of factors of Minhwa(民畵) so called in 19th century. Sixteen Lohans painting painted from late 19th to early 20th century, still contains the traditional factors in terms of describing background among above trends, however the main factors of expressing the background are different from other Buddhist painting which reflects historical art trends in colors and its materials by drawing various background distinctively. That is, Sixteen Lohans painting is distinct at describing the background of blue & green colored mountains and waters that is appeared in trend of Minhwa(民畵) and the royal which were popular at that time It also shows broad acceptance with introduction of new background expressions such as Sipjangsang(十長生, Picture of 10 different things of Sun, Mountain, Water, Stone, Cloud, Pine, Plant of eternal youth, tortoise, Crane, and deer to hope the eternal life) Unryoung(雲龍, Dragon Cloud), Mangho(猛虎, Wild Tiger), Gweseck(怪石, Oddly shaped stone), Hwajo(花鳥, Flowers and Birds), Chaekgoeri(冊巨里, The books and bookshaves). In terms of its materials, positive representations of eternal life, wealth and luck were mainly appeared, this is closely related with Self-Search of Buddhist which was the trend at that time that Buddhist turned into the popularized religion in Chosun Period, especially the cooperation of popular belief with Taoism. This is appeared on various Taoists that is expressed in Buddhist paintings of Sixteen Lohans painting at that time. It would provide some clauses to infer the painted years of existing Minhwa at the fixed type of folk story paintings appeared on Sixteen Lohans painting painted mainly from late 19th to early 20th Century. There is also a possibility of the active participation of Buddhist painters(佛畵僧) as painters of Minhwa by request and demands from common people. Inquiry into factors of folk story paintings among Buddhist paintings started from similarity of the materials and shapes, however it doesn't seem to have dramatically expressed comic or exceptional techniques. But, the fact that there are similar types of decorative pictures in the Royal Court rather seemed to be possible for Buddhist paintings to have functions as religion.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1607-1622
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• 2022

The purpose of this study is to determine the actual performance of cool roof in preventing absorbed solar radiation. The spatial correlation between surface temperature and absorbed solar radiation is the method by which the performance of a cool roof can be understood and evaluated. The research area of this study is the vicinity of Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where an actual cool roof is applied. FLIR Vue Pro R thermal infrared sensor, Micasense Red-Edge multi-spectral sensor and DJI H20T visible spectral sensor was used for aerial photography, with attached to the drone DJI Matrice 300 RTK. To perform the spatial correlation analysis, thermal infrared orthomosaics, absorbed solar radiation distribution maps were constructed, and land cover features of roof were extracted based on the drone aerial photographs. The temporal scope of this research ranged over 9 points of time at intervals of about 1 hour and 30 minutes from 7:15 to 19:15 on July 27, 2021. The correlation coefficient values of 0.550 for the normal roof and 0.387 for the cool roof were obtained on a daily average basis. However, at 11:30 and 13:00, when the Solar altitude was high on the date of analysis, the difference in correlation coefficient values between the normal roof and the cool roof was 0.022, 0.024, showing similar correlations. In other time series, the values of the correlation coefficient of the normal roof are about 0.1 higher than that of the cool roof. This study assessed and evaluated the potential of an actual cool roof to prevent solar radiation heating a rooftop through correlation comparison with a normal roof, which serves as a control group, by using high-resolution drone images. The results of this research can be used as reference data when local governments or communities seek to adopt strategies to eliminate the phenomenon of urban heat islands.

• Korean Journal of Environmental Agriculture
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• v.38 no.4
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• pp.225-236
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• 2019

BACKGROUND: Hydroponics is one of the methods for evaluating plant production using the inorganic nutrient solutions, which is applied under the artificial light conditions of plant factory system. However, the application of the conventional inorganic nutrients for hydroponics caused several environmental problems: waste from culture mediums and high nitrate concentration in plants. Organic nutrients are generally irrigated as a supplementary fertilizer for plant growth promotion under field or greenhouse conditions. Hydroponic culture using organic nutrients derived from the agricultural by-products such as dumped stems, leaves or immature fruits is rarely considered in plant factory system. Effect of organic or conventional inorganic nutrient solutions on the growth and nutrient absorption pattern of green and red leaf lettuces was investigated in this experiment under fluorescent lamps (FL) and mixture Light-Emitting Diodes (LEDs). METHODS AND RESULTS: Single solution of tomatoes (TJ) and kales (K) deriving from agricultural by-products including leaves or stems and its mixed solution (mixture ration 1:1) with conventional inorganic Yamazaki (Y) were supplied for hydroponics under the plant factory system. The Yamazaki solution was considered as a control. 'Jeockchima' and 'Cheongchima' lettuce seedlings (Lactuca sativa L.) were used as plant materials. The seedlings which developed 2~3 true leaves were grown under the light qualities of FL and mixed LED lights of blue plus red plus white of 1:2:1 mixture in energy ratio for 35 days. Light intensity of the light sources was controlled at 180 μmol/㎡/s on the culture bed. The single and mixture nutrient solutions of organic and/or inorganic components which controlled at 1.5 dS/m EC and 5.8 pH were regularly irrigated by the deep flow technique (DFT) system on the culture gutters. Number of unfolded leaves of the seedlings grown under the single or mixed nutrient solutions were significantly increased compared to the conventional Y treatment. Leaf extension of 'Jeockchima' under the mixture LED radiation condition was not affected by Y and YK or YTJ mixture treatments. SPAD value in 'Jeockchima' leaves exposed by FL under the YK mixture medium was approximately 45 % higher than under conventional Y treatment. Otherwise, the maximum SPAD value in the leaves of 'Cheongchima' seedlings was shown in YK treatment under the mixture LED lights. NO₃-N contents in Y treatment treated with inorganic nutrient at the end of the experiment were up to 75% declined rather than increased over 60 % in the K and TJ organic treatment. CONCLUSION: Growth of the seedlings was affected by the mixture treatments of the organic and inorganic solutions, although similar or lower dry weight was recorded than in the inorganic treatment Y under the plant factory system. Treatment Y containing the highest NO₃-N content among the considered nutrients influenced growth increment of the seedlings comparing to the other nutrients. However effect of the higher NO₃-N content in the seedling growth was different according to the light qualities considered in the experiment as shown in leaf expansion, pigmentation or dry weight promotion under the single or mixed nutrients.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Korean Journal of Heritage: History & Science
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• v.46 no.3
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• pp.230-250
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• 2013

possessed by Dong-A University Museum is designated as Treasure No. 595, and has been known for a more exquisite, delicate and realistic expression and a colorful three-dimensional structure compared to the 'grass and insect painting' work and its value in art history. However, it has not been analyzed and studied in fabric craft despite it being an embroidered work. This study used scientific devices to examine and analyze the Screen's fabric, thread colors, and embroidery techniques to clarify its patterns and fabric craft characteristics for its value in the history of fabric craft. As a result, consists of eight sides and its subject matters and composition are similar to those of the general paintings of grass and insects. The patterns on each side of the 'grass and insect painting' include cucumber, cockscomb, day lily, balsam pear, gillyflower, watermelon, eggplant, and chrysanthemums from the first side. Among these flowers, the balsam pear is a special material not found in the existing paintings of grass and insect. The eighth side only has the chrysanthemums with no insects and reptiles, making it different from the typical forms of the paintings of grass and insect. The fabric of the Screen uses black that is not seen in other decorative embroideries to emphasize and maximize various colors of threads. The fabric used the weave structure of 5-end satin called Gong Dan [non-patterned satin]. The threads used extremely slightly twisted threads that are incidentally twisted. Some threads use one color, while other threads use two or mixed colors in combination for three-dimensional expressions. Because the threads are severely deterioration and faded, it is impossible to know the original colors, but the most frequently used colors are yellow to green and other colors remaining relatively prominently are blue, grown, and violet. The colors of day lily, gillyflower, and strawberries are currently remaining as reddish yellow, but it is anticipated that they were originally orange and red considering the existing paintings of grass and insects. The embroidery technique was mostly surface satin stitch to fill the surfaces. This shows the traditional women's wisdom to reduce the waste of color threads. Satin stitch is a relatively simple embroidery technique for decorating a surface, but it uses various color threads and divides the surfaces for combined vertical, horizontal, and diagonal stitches or for the combination of long and short stitches for various textures and the sense of volume. The bodies of insects use the combination of buttonhole stitch, outline stitch, and satin stitch for three-dimensional expressions, but the use of buttonhole stitch is particularly noticeable. In addition to that, decorative stitches were used to give volume to the leaves and surface pine needle stitches were done on the scouring rush to add more realistic texture. Decorative stitches were added on top of gillyflower, strawberries, and cucumbers for a more delicate touch. is valuable in the history of paintings and art and bears great importance in the history of Korean embroidery as it uses outstanding technique and colors of Korea to express the Shin Sa-im-dang's 'Grass and Insect Painting'.

• 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.