• Sim-seong Yeon-gu
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• v.25 no.2
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• pp.224-264
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• 2010

A brief story of the tale follows : Mokdoryung was a son of an arbor tree and a fairly. When the boy was 7-8 years old, mother-fairy returned to the sky. By using father-tree, Mokdoryung survived from the flood where he saved ants, mosquitos, and a boy with the same age. They arrived on top of the highest mountain, met an old woman with two daughters, worked as servants. With help of insects, Mokdoryung passed the trials, married to a wise daughter and 2 couples became the ancestor of the mankind. Interpretation of the tale starts with amplification of tree which symbolizes Self and Libido. As the son of the tree-spirit and a fairly from the sky, Mokdoryung is a kind of 'divine child' which represents a psychic possibility to understand archetypal nature of unconscious. Adversities of early childhood due to mother's absence regarded as necessary condition for 'divine child' to attain highest good. Flood can be compensation of absence of feminine as well as to bring a new life. The notion of father·tree becomes a kind of life-boat has to do with union of opposite(vertical phallic tree and horizontal feminine boat). Ants and mosquitoes represent upper and lower level of unconsciousness, they mediate divine power. Therefore respecting insects means respecting unconscious, and reward of insects means salvation come from unconscious. The boy saved from the flood presents emergence of psychic energy in its latent unconscious condition to create mental dynamism. The old woman is Great Mother or anima, the controller or guider of unconscious. Working as servants can be an active service for the divine marriage. Trials of separating millet from sand, and finding right direction relate to separatio, means one needs to be separated from unconscious before conunctio, union of opposite. Two sets of couple becoming ancestor of man-kind has to do with number 4 (quaternity) as well as regeneration. Although the tale includes both positive couple (Mokdoryung, wise daugther in east room). and negative couple(shadow side of Mokdoryung, step daughter in west room)as ancestors of mankind, "Good" seems to be more valued than "evil".

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.4
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• pp.305-314
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• 2007

We studied spatial and temporal distributions of the phytoplankton and their relationships to physico-chemical environmental factors in the western Jinhae Bay, Korea from November 2003 to August 2004. In most cases, physico-chemical environmental factors showed homogeneous distribution. The phytoplankton communities were composed of mainly diatoms and dinoflagellates, and their standing crops ranged from $16{\times}10^3\;cells\;l^{-1}\;to\;5,845{\times}10^3\;cells\;l^{-1}$ (with a mean value of $555{\times}10^3\;cells\;l^{-1}$). The bloom of phytoplankton was observed in Gohyun Port in the summer. Seasonal variation of phytoplankton standing crops was higher in winter and summer than in spring and autumn. The dominant species were Skeletonema costatum, Akashiwo sanguinea, Pseudo-nitzschia pungens, Dactyliosolen sp., Leptocylindrus danicus, cryptomonads and etc. Especially, S. costatum was predominant in the summer and A. sanguinea (spring and autumn), Pseudo-nitzschia sp. (summer), Guinardia striata (spring), unidentified flagellates (summer) and cryptomonads (spring) appeared to be an opportunistic species. Concentrations of Chl a ranged from $0.6{\mu}g{\cdot}l^{-1}\;to\;16.7{\mu}g{\cdot}l^{-1}$ (with a mean value of $3.4{\mu}g{\cdot}l^{-1}$). The results of the canonical correspondence analysis implies the study area was grouped into the 2 water masses (inner and outer waters of Gohyun Port) and inner waters had higher abundance and Chl a concentration than outer waters. Also, phytoplankton sanding crops were related with temperature, DO and nutrients ($SiO^2$, TN, TP and etc.) in inner waters. Inner water-mass of Gohyun Port expanded between Gacho Is. and Chilchon Is. during the winter.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.12
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• pp.1837-1847
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• 2008

The goal of this study is to express the image of chameleons-that change their colors by light, temperature and its mood-into the sexy styles of corresponding coquettish temperamental people in Wearable Art. The method used in this study was experimenting various production mediums, including creating the textured stretch fabric, in the process of expressing the conceptual characteristics of the chameleon in Wearable Art. The concept of the work was a concoction of 'tempting', 'splendid', 'brilliant', 'fascinating', etc. that highlighted the real disposition of the chameleon. The futuristic preference of the researcher was also implicated. "Comfortable" and "enjoyable" concepts via motions were improved with the its completeness. The point of the design and production is to express symbolically the chameleon in real life, analyzing its sleek body lines, conditional colors changing, outer skins and the cubic textures. The coquettish temperamental image, the conceptual image of the chameleon, was also expressed by implication into the whole work. The entire line of this work is body-conscious silhouette. It was symbolically selected to image the outline of the chameleon that has the slim and sleek body. The exposed back is intended to express symbolically the projected back bones of the chameleon. The hood of gentle triangle line expresses the smooth-lined head part. The irregular hemlines represent the elongated chameleon's tale. The chameleon with its colors of vivid tones is characterized the colors changing by its conditions. This point was importantly treated in the working process by trying the effects that the colors are seen slightly different according to the light and angles. The material was given the effect that its surface colors are seen different in lights and angles because of the wrinkles protruded lumpy-bumpy. The various stones of red and blue tones are very similar to the skin tones of the real chameleon, and their gradation makes the effect that the colors are visibly changed with each move. The textures of the chameleon were produced via the wrinkle effect of smoke-shape, which is the result of using the elastic threads on the basic mediums stitched with 50/50 chiffon and polyester along with velvet dot patterns. The stretching fabric by the impact of the elastic threads is as much suitable for making the body-conscious line. The stones are composed of acrylic cabochon and gemstone. They are symbolically expressed the lumpy and bumpy back skin of the chameleon and produced the effect of the colors visibly different. The primary technique used in this dress is the draping utilizing the biased grains. The front body piece is connected to the hood and joined to the back piece without any seam. For the irregular hemline flares, leaving the several rectangular pieces with bias grains, they were connected by interlocking. What defines the clothes is the person in action. Therefore, what decides the completeness of clothes might be its comfortable and enjoyable feeling by living and acting people. The chameleon dress could also reach its goal of comforting and pleasing Wearable Art in the process of studying the techniques and effects that visibly differentiate the colors. It is considered as a main point of the Wearable Art, which is a comfortable enjoyable clothing tempered with the artistic beauty.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.