• Journal of Korean Theatre Studies Association
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• no.52
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• pp.319-357
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• 2014

Formation of 'body schema' is the start for actor to create role and becomes the root and the foundation of existing as a role on the stage. For this, an actor needs to form 'scheme of role' with escaping from own 'body schema.' 'Schema of role' is formed by acquiring through synthesizing daily basic actions, namely, walking, standing, sitting, hand stretching, bending, and touching. The body schema, which was made with simple and usual actions, has fundamental significance in a sense of becoming the body in which the past traces in a role are habituated while energy as a role flows. As for the process of forming body schema, an actor first needs to obtain the visualized materials like photo, magazine, picture and image available for seeing a role specifically and clearly based on what analyzed a character. An actor needs to have three-dimensional image available for always recalling it in the head during acting. To do this, image data available for fundamentally capturing routine actions along with body structure are still more useful. Next, the body schema is formed by interaction with environment. Thus, there is a need of passing through the two-time process of forming body schema. Firstly, the body schema is made on routine actions in a role as physical condition of a role in actor's own everyday life. Secondly, the body schema is made on routine actions available for moving efficiently and economically in line with the environment of performance. A theatrical stage is the temporal space of rhythm and rule different from routine space. What forms body schema immediately in the second phase without body schema in the first phase ultimately becomes what exists as actor's own body, not the body of a role. The body schema, which was formed as the second process, is what truly has identity as a role in the ontological aspect, comes to experience the oppositional force in muscle, a qualitative change in energy, and emotional agitation in the physical aspect, and experiences perception, thinking, volition, and even consciousness with the entire body in the cognitive dimension. Thus, the formation of body schema can be known to be just a method of changing even spiritual and emotional layer. Body schema cannot be made if there is no process of embodiment and habit. Embodiment and habit are not simply the repeated, empty and mechanical action in the body. But, habit itself has very important meanings for forming body schema for role creating. First, habit allows the body itself to learn and understand a meaning. Second, habit relies upon environment, thereby allowing an actor of making the habituated body schema to recognize environment. Third, habit makes the mind. The habituated body schema is just the mind and the ego of a person who possesses the body schema. Fourth, habit comes to experience the expansion in energy and the expansion in existence. It may be experienced through interrelation among actor's body, tool, and environment. Fifth, habit makes identity of the body. Hence, this just becomes what secures identity of a role. These implications of habit are the formation of body schema, which is maintained with the body of being remembered firmly through being closely connected with the process of neural adaptation. Finally, it sought for possibility of practice as one method of forming body schema for role creating through Deleuze's '-becoming' theory. As 'actual animal-becoming' is real '-becoming' of forming structural transformation in the physical dimension, it meets with what the formation of body schema pursues actuality and reality. This was explained with a concept as saying of 'all '-becoming' molecular' by Deleuze/Guattari. 'Animal of having imitated animal's characteristic- becoming' is formed by which the body schema relies upon environment. In this way, relationship among the body, tool and environment has influence even upon a change in consciousness, thinking, and emotion, thereby being able to be useful for forming body schema in a sense of possibly experiencing ultimately expansion in role, namely, expansion in existence.

• Journal of Dental Rehabilitation and Applied Science
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• v.34 no.4
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• pp.297-305
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• 2018

Purpose: This study aimed to analyze stress distribution and maximum von Mises stress generated in intracoronal restorations and in tooth structures of mandibular molars with various types of cavity designs and materials. Materials and Methods: Three-dimensional solid models of mandible molar such as O inlay cavity with composite and gold (OR-C, OG-C), MO inlay cavity with composite and gold (MR-C, MG-C), and minimal invasive cavity on occlusal and proximal surfaces (OR-M, MR-M) were designed. To simulate masticatory force, static axial load with total force of 200 N was applied on the tooth at 10 occlusal contact points. A finite element analysis was performed to predict stress distribution generated by occlusal loading. Results: Restorations with minimal cavity design generated significantly lower values of von Mises stress (OR-M model: 26.8 MPa; MR-M model: 72.7 MPa) compared to those with conventional cavity design (341.9 MPa to 397.2 MPa). In tooth structure, magnitudes of maximum von Mises stresses were similar among models with conventional design (372.8 - 412.9 MPa) and models with minimal cavity design (361.1 - 384.4 MPa). Conclusion: Minimal invasive models generated smaller maximum von Mises stresses within restorations. Within the enamel, similar maximum von Mises stresses were observed for models with minimal cavity design and those with conventional design.

• Korean Journal of Heritage: History & Science
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• v.43 no.2
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• pp.40-59
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• 2010

Anssolim is the unique technique which standing columns lean in a inward direction of buildings in traditional architecture, which has not been thoroughly investigated to this day. With a dearth of previous studies, the anssolim technique can only be examined through detailed three-dimensional surveys. The main halls of Korean palaces can be seen as buildings that were built with the regulations of the day in mind, making them excellent research subjects when studying the anssolim technique. The findings can be summarized as follows. 1. In the main halls that were studied, anssolim was applied most to main space (eokan) columns, then lessened for peripheral columns. 2. The largest second-floor cheoma columns were placed inward in the eokan, then became smaller as with the peripheral columns. In the case of the eokan, the columns were arranged according to the size of the anssolim. 3. The second-floor cheoma column anssolim in the middle-floor main hall were generally a third or a quarter of the size of those on the first floor. As on the first floor, the largest anssolim were applied to the eokan columns, then became gradually smaller towards the periphery columns. 4. In the palace main halls, the largest anssolim were used for the eokan columns, and became smaller with the peripheral columns. This unique structure can be seen to be a Korean technique that deviates from the Chinese "Yingzaofashi(營造法式)" techniques. Although this study is limited in that it only studies the main hall of Korean palaces, it is significant in that it shed new light on the technological implications of the anssolim technique, and can be used as important data for research into the history of technology. Although this type of data is difficult to extrapolate, it has been made as accurate as possible by minimizing the margin of error in the data for the palaces that were actually studied.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.6
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• pp.73-85
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• 2015

This study investigated the extent and magnitude of the woodland edge effects on users' thermal environments according to distance from woodland border. A series of experiments to measure air temperature, relative humidity, wind velocity, MRT and UTCI were conducted over six days between July 31 and August 5, 2015, which corresponded with extremely hot weather, at the south-facing edge of Hadongsongrim(pure Pinus densiflora stands, tree age: $100{\pm}33yr$, tree height: $12.8{\pm}2.7m$, canopy closure: 75%, N $35^{\circ}03^{\prime}34.7^{{\prime}{\prime}}$, E $127^{\circ}44^{\prime}43.3^{{\prime}{\prime}}$, elevation 7~10m) and east-facing edge of Hamyangsangrim (Quercus serrata-Carpinus tschonoskii community, tree age: 102~125yr/58~123yr, tree height: tree layer $18.6{\pm}2.3m/subtree$ layer $5.9{\pm}3.2m/shrub$ layer $0.5{\pm}0.5m$, herbaceous layer coverage ratio 60%, canopy closure: 96%, N $35^{\circ}31^{\prime}28.1^{{\prime}{\prime}}$, E $127^{\circ}43^{\prime}09.8^{{\prime}{\prime}}$, elevation 170~180m) in rural villages of Hadong and Hamyang, Korea. The minus result value of depth means woodland's outside. The depth of edge influence(DEI) on the maximum air temperature, minimum relative humidity and wind speed at maximum air temperature time during the daytime(10:00~17:00) were detected to be $12.7{\pm}4.9$, $15.8{\pm}9.8$ and $23.8{\pm}26.2m$, respectively, in the mature evergreen conifer woodland of Hadongsongrim. These were detected to be $3.7{\pm}2.2$, $4.9{\pm}4.4$ and $2.6{\pm}7.8m$, respectively, in the deciduous broadleaf woodland of Hamyansangrim. The DEI on the maximum 10 minutes average MRT, UTCI from the three-dimensional environment absorbed by the human-biometeorological reference person during the daytime(10:00~17:00) were detected to be $7.1{\pm}1.7$ and $4.3{\pm}4.6m$, respectively, in the relatively sparse woodland of Hadongsongrim. These were detected to be $5.8{\pm}4.9$ and $3.5{\pm}4.1m$, respectively, in the dense and closed woodland of Hadongsongrim. Edge effects on the thermal environments of air temperature, relative humidity, wind speed, MRT and UTCI in the sparse woodland of Hadongsongrim were less pronounced than those recorded in densed and closed woodland of Hamyansangrim. The gradient variation was less steep for maximum 10 minutes average UTCI with at least $4.3{\pm}4.6m$(Hadongsongrim) and $3.5{\pm}4.1m$(Hamyansangrim) being required to stabilize the UTCI at mature woodlands. Therefore it is suggested that the woodlands buffer widths based on the UTCI values should be 3.5~7.6 m(Hamyansangrim) and 4.3~8.9(Hadongsongrim) m on each side of mature woodlands for users' thermal comfort environments. The woodland edge structure should be multi-layered canopies and closed edge for the buffer effect of woodland edge on woodland users' thermal comfort.