• Korean Journal of Heritage: History & Science
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• v.47 no.2
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• pp.86-113
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• 2014

This study has researched the characteristics and elements of Chwibyeong (翠屛), a sort of trellis in the Joseon Dynasty through the old documents, and the original form of Chwibyeong at Juhapru (宙合樓) in Changdeuk Palace. The results were as follow. First, as the result of literatures analysis for Imwon-gyeongje-ji (林園經濟志) and Jeungbo-sallim-gyeongje (增補山林經濟), the plant screen was classified as kinds of support[frame] material, plants and methods of planting. It was found that the supports of Chwibyeong were made of bamboo or the material such as the Jinjangmok (眞長木: a stick of oak) and Giryu (杞柳: Salix purpurea var. japonica). The evergreen coniferous trees including Pinus densiflora, Taxus cuspidata and Thuja orientalis were mainly used for the plant material of Chwibyeong. The general planting method of Chwibyeong was to plant on the ground, but sometimes the container planting was also found on the artificial ground. Second, the term of 'Chwibyeong' in the literatures was used in only the screen made by evergreen trees, and the superordinate category term of it was indicated by 'byeong (屛)'. Therefore Chwibyeong was a compound word formed from 'chwi (翠)' which means the characteristics of evergreen and 'byeong' as tree screen which the support was made by bamboo. And Chwibyeong had semantic context which was combined with the literary symbolization to describe a landscape of green peak and Taoist ideology be inherent from 'twelve peaks of Musan[巫山十二峰]' in Sichuan sheng (四川省). Thirdly, the photograph of Chwibyeong at Juhapru taken by the 1880s, showed that Chwibyeong was made with coniferous trees and was almost 2 meters high. The Chwibyeong at Juhapru was removed during the Japanese colonial era, but a few yew trees(Taxus cuspidata) used for Chwibyeong are still remaining. And some Juniperus chinensis which the composition time is unclear, were cultivated while hung loose its branchs at the sides of Eosumun (魚水門). This Junipers were presumed to be planted by Japanese after Japanese annexation of Korea(1910), and it was judged that both of the roofs of Eosumun's side gates might have been transformed into Japanese style at the same time. Lastly, Chwibyeong at Juhapru was restored in 2008 but it was restored in wrong way from original form without precise research. Especially Chwibyeong was restored with Sasa boreralis which is damaged by frost, so it requires exertion that should revive the originals to plant original material as much as possible. And it needs the development of fabrication technique for Chwibyeong and the application to current landscape architecture.

• Korean Journal of Heritage: History & Science
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• v.49 no.3
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• pp.78-103
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• 2016

This research is a study on the side framework structure of the hipped and gable roof of Muryangsujeon at Buseoksa Temple. There is a record that Muryangsujeon was deconstructed and repaired in the period of Japanese Occupancy, and its authenticity has continuously been called into question because the structure of the hipped and gable side roofs, and the bonding of the rafters and eaves were not in good order and very different from those of Joseon Dynasty. Scholars date it differently. It is either dated at 13th century or 12th century. This study compares the non-planar configuration of the middle and front proportions of Muryangsujeon's hipped-and-gable-roof framework with those of the Tang(唐) and Song(宋) Dynasties in China. It concludes that the hipped-and-gable-roof framework of those architecture were built with the same technique. The style of architecture that side rafters directly touch the internal security (梁), like in Muryangsujeon, is not usual even among the hipped and garble roofs of the Tang(唐) and Song(宋) Dynasties. The technique of constructing the hipped roofs developed much further after the Tang Dynasty because they began to use garble eaves to build the side structure. The technique seems to have developed greatly by the period of Ming and Qing Dynasties. It also seems that the parallel-flat (平行輻射椽) rafter, which is the form of rafters used between the parallelrafter period and the half-flat-rafter period is very similar to the construction style of the current rafters of Muryangsujeon. However, the Muryangsujeon's eaves do not touch the corner rafter from the middle part. This seems to be a unique style, which is not common in China. In conclusion, the style of the side roof framework of Muryangsujeon at Buseoksa Temple is not of the China's southern regions, but of the northern regions of Tang(唐), Song(宋) and Liao(遼) Dynasties. And when considering the construction year and proportions of the middle front and side front on the same flat, this must be an ancient technique of the northeastern regions of Asia. Since it is likely that the structure of the side roof framework of Muryangsujeon at Buseoksa Temple has not been altered but is a unique style of hipped and gable roofs, this roof can serve as a good guide to restoring the hipped and garble roofs of the pre-Goyreo Dynasty period.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.306-318
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• 2018

In order to evaluate the seismic capacity of massive vertical type breakwaters which have intensively been deployed along the coast of South Korea over the last two decades, we carry out the preliminary numerical simulation against the PoHang, GyeongJu, Hachinohe 1, Hachinohe 2, Ofunato, and artificial seismic waves based on the measured time series of ground acceleration. Numerical result shows that significant sliding can be resulted in once non-negligible portion of seismic energy is shifted toward the longer period during its propagation process toward the ground surface in a form of shear wave. It is well known that during these propagation process, shear waves due to the seismic activity would be amplified, and non-negligible portion of seismic energy be shifted toward the longer period. Among these, the shift of seismic energy toward the longer period is induced by the viscosity and internal friction intrinsic in the soil. On the other hand, the amplification of shear waves can be attributed to the fact that the shear modulus is getting smaller toward the ground surface following the descending effective stress toward the ground surface. And the weakened intensity of soil as the number of attacking shear waves are accumulated can also contribute these phenomenon (Das, 1993). In this rationale, we constitute the numerical model using the model by Hardin and Drnevich (1972) for the weakened shear modulus as shear waves go on, and shear wave equation, in the numerical integration of which $Newmark-{\beta}$ method and Modified Newton-Raphson method are evoked to take nonlinear stress-strain relationship into account. It is shown that the numerical model proposed in this study could duplicate the well known features of seismic shear waves such as that a great deal of probability mass is shifted toward the larger amplitude and longer period when shear waves propagate toward the ground surface.

• (The) Research of the performance art and culture
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• no.39
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• pp.549-580
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• 2019

There are many festivals in the region that mainly use traditional performing arts. Traditional performing arts has a story that incorporates the history of the area, and it is easy to build a brand that can be distinguished from other areas through traditional performing arts, so it has conditions that are easy to grow in the local festival it can. In this paper, I researched the relationship between the significance and regionality of regional performing arts, referring to the case where traditional performing arts, which is a joint cultural heritage of East Asia, are transmitted as a festival. The performance art with strong locality has grown into a local festival, and the Kanto Festival (竿燈) held in Akita prefecture in northeastern Japan was selected as a representative example of that area. Along with the Aomori Nebuta Festival and the Sendai Tanabata Festival, the Kanto Festival is called the 3 major festivals in the northeastern part of Japan. It was designated as an important intangible folk cultural property in 1980 and became more famous. It visited Seoul several times after the 2005 Japan-Korea Exchange Festival event. It is widely known as a regional festival that represents Japan. The Kanto Festival, which was a participatory event on a village basis, has faced problems such as the migration and aging of young people since the 1970s. In order to solve this, they led the participation of schools, educational institutions, and various groups beyond the village unit and persuaded the participation to the local companies. They have been steadily promoting free lectures on technical skills and school visit events that induce children's interest. As a result, the number of moths mobilized in the current festival has reached 250, and the Kanto tournament also shows great popularity every year.

• Journal of the Daesoon Academy of Sciences
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• v.46
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• pp.1-32
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• 2023

The purpose of this article is to critically reflect on the 2022 national curriculum on religions. The perspective of this reflection is that since the religious curriculum is meant to be a national curriculum, it should be applicable to all high school students, be shareable, and function as a place for meta-reflection regarding the proper use of the category of religion. For this purpose, I reviewed the form and content of the 2022 curriculum on religions in Section 2. The form of the 2022 curriculum on religions looks similar to the previously utilized curriculum. However, the main change is that the subject of religions was arbitrarily placed into the category of 'subjects for choosing a career.' And the 2022 curriculum on religions has two characteristics in terms of content: the orientation of 'making religious people (spiritual formation)' and the reemergence of the concept of 'pseudo-religion.' In Section 3, I delved into the orientation of 'making religious people through religious reflection' among the characteristics of the 2022 curriculum on religions. In this process, I discovered that the concept of 'reflection as a metacognitive technology,' which was the core of the prior curriculum and school education, was transformed into the concept of 'religious reflection,' and the concepts of spirituality and religiosity were also added. In Section 4, I delved into the dichotomy of 'religion and pseudo-religion.' 'Pseudo-religion' is a new focus in the 2022 curriculum on religions. In this process, I revealed that the concept of 'pseudo-religion' is a combination of an outdated administrative term of the Japanese Government-General of Korea during Japan's occupation of Korea, and as such, the term is inherently value-laden and harmful. I also revealed that determining 'pseudo-religion' in school education regenerates the colonial Japanese Government-General's biased attitudes toward Korean religions and forces teachers to 'invent' (detect or personally appraise) modern day pseudo-religions through arbitrary judgements. The 'curriculum to emphasize religious reflection and detect pseudo-religions in order to create religious people' can distort the subject of religion in the national curriculum as into a 'subject for religion (promotion or degradation).' If this distortion continues, the appropriateness of curriculum on religions existing within the national curriculum will eventually become a subject of debate.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.689-694
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• 2003

Weight records of Hanwoo cows from birth to 36 months of age collected in Daekwanryeong branch, National Livestock Research Institute(NLRI) were fitted to Gompertz, von Bertalanffy and Logistic functions. For the growth curve parameters fitted on individual records using Gompertz model, the mean estimates of mature weight(A), growth ratio(b) and growth rate(k) were 383.42 ${\pm}$ 97.29kg, 2.374 ${\pm}$ 0.340 and 0.0037 ${\pm}$ 0.0012, respectively, and mean estimates of body weight, age and daily gain rate at inflection were 141.05 ${\pm}$ 35.79kg, 255.63 ${\pm}$ 109.09 day and 0.500 ${\pm}$ 0.123kg, respectively. For von BertalanfTy model, the mean estimates of A, b and k were 410.47 ${\pm}$ 117.98kg, 0.575${\pm}$0.057 and 0.003 ${\pm}$ 0.001, and mean estimates of body weight, age and daily gain at inflection were 121.62 ${\pm}$ 34.94kg, 211.02 ${\pm}$ 105.53 and 0.504 ${\pm}$ O.l24kg. For Logistic model, the mean estimates of A, b and k were 347.64 ${\pm}$ 97.29kg, 6.73 ${\pm}$ 0.34 and 0.006 ${\pm}$ 0.0018, and mean estimates of body weight, age and daily gain at inflection were 173.82 ${\pm}$ 37.25kg, 324.47 ${\pm}$ 126.85 and 0.508 ${\pm}$ 0.131kg. Coefficients of variation for the A, b and k parameter estimates were 25.3%, 14.3% and 32.4%, respectively, for Gompertz model, 28.70/0, 9.9% and 33.3% for von Bertalanffy model, and 27.9°/0, 5.0% and 30.0% for Logistic model.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.115-127
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• 2018

This study was attempted to find out the value of cultural assets through the clear diagnosis and prescription of the dead and weakness factors of the Population of Retusa Fringe Trees in Pyeongji-ri, Jinan(Natural Monument No. 214), The results are as follows. First, Since the designation of 13 natural monuments in 1968, since 1973, many years have passed since then. In particular, despite the removal of some of the buried soil during the maintenance process, such as retreating from the fence of the primary school after 2010, Second, The first and third surviving tree of the designated trees also have many branches that are dead, the leaves are dull, and the amount of leaves is small. vitality of tree is 'extremely bad', and the first branch has already been faded by a large number of branches, and the amount of leaves is considerably low this year, so that only two flowers are bloomed. The second is also in a 'bad'state, with small leaves, low leaf density, and deformed water. The largest number 1 in the world is added to the concern that the s coverd oil is assumed to be paddy soils. Third, It is found that the composition ratio of silt is high because it is known as '[silty loam(SiL)]'. In addition, the pH of the northern soil at pH 1 was 6.6, which was significantly different from that of the other soil. In addition, the organic matter content was higher than the appropriate range, which is considered to reflect the result of continuous application for protection management. Fourth, It is considered that the root cause of failure and growth of Jinan pyeongji-ri Population of Retusa Fringe Trees group is chronic syndrome of serious menstrual deterioration due to covered soil. This can also be attributed to the newly planted succession and to some of the deaths. Fifthly, It is urgent to gradually remove the subsoil part, which is estimated to be the cause of the initial damage. Above all, it is almost impossible to remove the coverd soil after grasping the details of the soil, such as clayey soil, which is buried in the rootstock. After removal of the coverd soil, a pestle is installed to improve the respiration of the roots and the ground with Masato. And the dead 4th dead wood and the 5th and 6th dead wood are the best, and the lower layer vegetation is mown. The viable neck should be removed from the upper surface, and the bark defect should undergo surgery and induce the development of blindness by vestibule below the growth point. Sixth, The underground roots should be identified to prepare a method to improve the decompression of the root and the respiration of the soil. It is induced by the shortening of rotten roots by tracing the first half of the rootstock to induce the generation of new roots. Seventh, We try mulching to suppress weed occurrence, trampling pressure, and soil moisturizing effect. In addition, consideration should be given to the fertilization of the foliar fertilizer, the injection of the nutrients, and the soil management of the inorganic fertilizer for the continuous nutrition supply. Future monitoring and forecasting plans should be developed to check for changes continuously.

• 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.