• The Journal of Natural Sciences
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• v.7
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• pp.133-162
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• 1995

Paderborn's companies, Wameling and Cassau, produce the liturgical vestments, which have much traditional artistic merit. And Kevelaerer Fahnen + Paramenten GmbH, located in Kevelater which is a place of pilgrimage of the Virgin Mary, was known to Europe, Africa, America and the Scandinavia Peninsula as the "Hidden Company" of liturgical vesments maker up to now. Paderborn and Kevelaer were the place of the center of the religious world and the Catholic ceremony during a good few centries. The Catholic liturgical vestiments of these 3 companies use versatile design, color, shape and techniques. These have not only the symbolism of religion, but also can meet our's expectations of utilization of modern textile art, art clothing and wide-all division of design. These give the understanding of symbolic meanings and harmony according to liturgical vestments to the believers. And these have an influence on mental thinking and induction of religious belief to the non-believers as the recognition and concerns about the religious art. The liturgical vestments are clothes which churchmen put on at the all ceremonial function of a mass, a sacrament, performance and a parade according to rules of church. These show the represen-tation of "Holy God" in silence and distinguish between common people and churchmen. And these represent a status and dignity of churchmen and induce majesty and respect to churchmen. Common clothes of the beginning of the Greece and Rome was developed to Christian clothes with the tendency of religion. There were no special uniforms distinguished from commen people until the Christianity was recognized officially by the Roman Emperor Constantinus at A.D.313. The color of liturgical vestments was originally white and changed to special colors according to liturgical day and each time by the Pope Innocentius at 12th century. The color and symbolic meaning of the liturgical vestments of present day was originated by the Pope St. Pius(1566-1572). Wool and Linen was used as decorations and materials in the beginnings and the special materials like silk was used after 4th century and beautiful materials made of gold thread was used at 12th century. It is expected that there is no critical changes to the liturgical vestments of future. But the development of liturgical vestments will continues slowly by the command of conservative church and will change to simple and convenient formes according to the culture, the trend of the times and the fashion of clothes. The companies of liturgical vestments develop versatile design, embroidery technique and realization of creative design for distinction of the liturgical vestments of each company and artistic progress. The cooperation of companies, artists and church will make the bright future of these 3 companies. We expect that our country will be a famous producing center of the liturgical vestments through the research and development of companies, participation of artists in religeous arts and concerts of church.

• 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.

• Korean Journal of Mineralogy and Petrology
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• v.36 no.3
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• pp.185-197
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• 2023

The Gubong Au-Ag deposit consists of eight lens-shaped quartz veins. These veins have filled fractures along fault zones within Precambrian metasedimentary rock. This has been one of the largest deposits in Korea, and is geologically a mix of orogenic-type and intrusion-related types. Korea Mining Promotion Corporation drilled into a quartz vein (referred to as the No. 6 vein) with a width of 0.9 m and a grade of 27.9 g/t Au at a depth of -728 ML by drilling (No. 90-12) in the southern site of the deposit, To further investigate the potential redevelopment of the No. 6 vein, another drilling (No. 04-1) was carried out in 2004. In 2004, samples (wallrock, wallrock alteration and quartz vein) were collected from the No. 04-1 drilling core site to study the occurrence and chemical composition of Ti-bearing minerals (ilmenite, rutile). Rutile from mineralized zone at a depth of -275 ML occur minerals including K-feldspar, biotite, quartz, calcite, chlorite, pyrite in wallrock alteration zone. Ilmenite and rutile from ore vein (No. 6 vein) at a depth of -779 ML occur minerals including white mica, chlorite, apatite, zircon, quartz, calcite, pyrrhotite, pyrite in wallrock alteration zone and quartz vein. Based on mineral assemblage, rutile was formed by hydrothermal alteration (chloritization) of Ti-rich biotite in the wallrock. Chemical composition of ilmenite has maximum values of 0.09 wt.% (HfO₂), 0.39 wt.% (V₂O₃) and 0.54 wt.% (BaO). Comparing the chemical composition of rutile at a depth -275 ML and -779 ML, Rutile at a depth of -779 ML is higher contents (WO₃, FeO and BaO) than rutile at a depth of -275 ML. The substitutions of rutile at a depth of -275 ML and -779 ML are as followed : rutile at a depth of -275 ML Ba²⁺ + Al³⁺ + Hf⁴⁺ + (Nb⁵⁺, Ta⁵⁺) ↔ 3Ti⁴⁺ + Fe²⁺, 2V⁴⁺ + (W⁵⁺, Ta⁵⁺, Nb⁵⁺) ↔ 2Ti⁴⁺ + Al³⁺ + (Fe²⁺, Ba²⁺), Al³⁺ + V⁴⁺+ (Nb⁵⁺, Ta⁵⁺) ↔ 2Ti⁴⁺ + 2Fe²⁺, rutile at a depth of -779 ML 2 (Fe²⁺, Ba²⁺) + Al³⁺ + (W⁵⁺, Nb⁵⁺, Ta⁵⁺) ↔ 2Ti⁴⁺ + (V⁴⁺, Hf⁴⁺), Fe²⁺ + Al³⁺ + Hf ⁴⁺ + (W⁵⁺, Nb⁵⁺, Ta⁵⁺) ↔ 2Ti⁴⁺ + V⁴⁺ + Ba²⁺, respectively. Based on these data and chemical composition of rutiles from orogenic-type deposits, rutiles from Gubong deposit was formed in a relatively oxidizing environment than the rutile from orogenictype deposits (Unsan deposit, Kori Kollo deposit, Big Bell deposit, Meguma gold-bearing quartz vein).