• Korean Journal of Heritage: History & Science
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• v.54 no.2
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• pp.78-97
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• 2021

Seojangdaeyajodo is a drawing of military night training on February 12th (lunar leap month), 1795. Focusing on the Seojangdaeyajodo, the characteristics and of the costumes worn by various types of officials were examined. There were 34 officials located near King Jeongjo in and around Seojangdae, with 27 Dangsanggwan and 7 Danghagwan. They wore three types of costumes, including armor, yungbok, and military uniforms. All of the twelve armor wearers and the five officials wearing yungbok were dangsanggwan, and the military uniform wearers included eleven dangsanggwan and six danghagwan. For the shape of the armor, the armor relics of General Yeoban, suitable for riding horses, and the armor painting of Muyedobotongji were referenced, and the composition of the armor was based on practicality. The armor consists of a helmet, a suit of armor, a neck guard, armpit guards, arm guards, and a crotch guard. The color of the armor was red and green, which are the most frequently used colors in Seojangdaeyajodo. The composition of yungbok was jurip, navy cheollik, red gwangdahoe, socks made of leather, and suhwaja. The composition of the military uniform was a lined jeolrip, dongdari, jeonbok, yodae, jeondae, and suhwaja. There were differences in the fabrics used in dangsanggwan and danghagwan military uniforms. Dangsanggwan used fabric with depictions of clouds and jewels, and danghagwan used unpatterned fabric. Moreover, jade, gold, and silver were used for detailed ornamental materials in dangsanggwan. The weapons included bows and a bow case, a sword, a rattan stick, wrist straps, and a ggakji. In the records of the King Jeongjo period, various colored heopsu were mentioned; the colors of the dongdari and jeonbok of dangsanggwan and danghagwan were referenced in various colors. It was presented as an illustration of costumes that could be used to produce objects accurately reflecting the above historical results. The basic principle of the illustration was to present the modeling standards for 3D content production. Samples of form, color, and material of the corresponding times and statuses were presented. The front, the side, and the back of each costume and its accessories were presented, and the colors were presented in RGB and CMYK.

• Korean Journal of Heritage: History & Science
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• v.56 no.1
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• pp.182-205
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• 2023

The use of royal artificial flowers was finally found through schematics and records in Wonhaeng Eulmyojeongri Uigwe, which organized the procession to Hwaseong in 1795. The results of classifying the uses of artificial flowers in the brthday banquet at Bongsudang Hall in 1795 and considering the shape, user, and usage are as follows. According to literature records, artificial flowers were made with high-quality materials such as gold, silver, and silk thread in the early period, but were mainly made of paper in the later period. Artificial flowers were used for decorating official hats, Bongsudang Hall, and banquet tables. The Sagwonhwa was used for decoration of the official hats of members of the royal family, and the one on the top was called Eosam-Sagwonhwa. At the birthday banquet inBongsudang Hall, King Jeongjo and Hyegyeonggung used the Eosam-Sagwonhwa and put it on the right side of the official hats. Officials put peach blossom with two petals on the left side of the official hats for decoration. The artificial flowers for decoration of the official hats of musicians and dancers were more expensive and flashier than the officials' ones. Depending on the dance, several artificial flowers were inserted into the official hats. When measuring the size of artificial flowers, the scale used was when making a ceremonial article. For artificial flowers for decoration of the banquet hall, red and white peach blossoms were placed in two jars with dragons painted on them and them placed on two red-painted tables, respectively. The table and jar with flowers were tied together with a red cotton string and fixed so as not to fall over. The artificial flowers for decoration of the banquet table of King Jeongjo, Hyegyeonggung, and the king's sisters were a large lotus, medium-sized lotus, peony, rose, and specially made peach flowers. The artificial flowers for decoration of the banquet table of guests and officials were small lotuses and peach blossoms. The artificial flowers used in the birthday banquet at Bongsudang Hall the most were peach blossoms, and peaches had the meaning of longevity and exorcism. It is expected that the above research results will be helpful in understanding the characteristics and usage of artificial flowers in the period of King Jeongjo and use in reproducing royal feasts and producing traditional cultural contents.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Economic and Environmental Geology
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• v.42 no.3
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• pp.177-193
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• 2009

The Samgwang deposit consists of eight massive mesothermal quartz veins that filled NE and NW-striking fractures along fault zones in Precambrian granitic gneiss of the Gyeonggi massif. The mineralogy and paragenesis of the veins allow two separate discrete mineralization episodes(stage I=quartz and calcite stage, stage II-calcite stage) to be recognized, temporally separated by a major faulting event. The ore minerals are contained within quartz and calcite associated with fracturing and healing of veins that occurred during both mineralization episodes. The hydrothermal alteration of stage I is sericitization, chloritization, carbonitization, pyritization, silicification and argillization. Sericitic zone occurs near and at quartz vein and include mainly sericite, quartz, and minor illite, carbonates and chlorite. Chloritic zone occurs far from quartz vein and is composed of mainly chlorite, quartz and minor sericite, carbonates and epidote. Fe/(Fe+Mg) ratios of sericite and chlorite range 0.45 to 0.50(0.48$\pm$0.02) and 0.74 to 0.81(0.77$\pm$0.03), and belong to muscovite-petzite series and brunsvigite, respectiveIy. Calculated $Al_{IV}$-FE/(FE+Mg) diagrams of sericite and chlorite suggest that this can be a reliable indicator of alteration temperature in Au-Ag deposits. Calculated activities of chlorite end member are $a3(Fe_5Al_2Si_3O_{10}(OH)_6$=0.0275${\sim}$0.0413, $a2(Mg_5Al_2Si_3O_{10}(OH)_6$=1.18E-10${\sim}$7.79E-7, $a1(Mg_6Si_4O_{10}(OH)_6$=4.92E-10${\sim}$9.29E-7. It suggest that chlorite from the Samgwang deposit is iron-rich chlorite formed due to decreasing temperature from high temperature(T>450$^{\circ}C$). Calculated ${\alpha}Na^+$, ${\alpha}K^+$, ${\alpha}Ca^{2+}$, ${\alpha}Mg^{2+}$ and pH values during wallrock alteration are 0.0476($400^{\circ}C$), 0.0863($350^{\circ}C$), 0.0154($400^{\circ}C$), 0.0231($350^{\circ}C$), 2.42E-11($400^{\circ}C$), 7.07E-10($350^{\circ}C$), 1.59E-12($400^{\circ}C$), 1.77E-11($350^{\circ}C$), 5.4${\sim}$6.4($400^{\circ}C$), 5.3${\sim}$5.7($350^{\circ}C$)respectively. Gain elements(enrichment elements) during wallrock alteration are $TiO_2$, $Fe_2O_3(T)$,CaO, MnO, MgO, As, Ag, Cu, Zn, Ni, Co, W, V, Br, Cs, Rb, Sc, Bi, Nb, Sb, Se, Sn and Lu. Elements(Ag, As, Zn, Sc, Sb, Rb, S, $CO_2$) represents a potential tools for exploration in mesothermal and epithermal gold-silver deposits.

• 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.