• Korean Journal of Environment and Ecology
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• v.26 no.2
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• pp.273-282
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• 2012

This study has estimated Gyeongju National Park's natural environment and cultural relic value in the same way and then been performed to compare the size of the value. Representative method to measure environmental property is contingent valuation methods, CVM. The variables and estimated models adopted for the calculation were same and the respondents were asked by distinguishing between the amount which they would pay to preserve the natural environment and that which they were willing to pay to preserve the cultural relics. As the result, WTP(Willing to pay), the amount that they were willing to pay to preserve the natural environment of Gyeongju National Park was 17,838 won per person and that to preserve the cultural relics appeared to be 316,248 won per person. Based on this, it was estimated that the value of the natural environment with which Gyeongju National Park provided annual visitors was 47 billion won and that the annual value of the cultural relics was 845.7 billion. If the natural environment and the cultural relics value elements are united, it can be estimated that the natural environment and cultural relic value got at the time of people's first visit to Gyeongju National Park is 334,086 won and that the annual value is 893.4 billion won. In this study, the value of the cultural relics has been estimated 18 times higher than that of the natural environment. This reason was that visitors judged that a total of 66 cultural properties including 11 national treasures, 23 treasures, 13 historic places, one historic sites and scenic spot and 18 local cultural properties, etc. which were distributed in Gyeongju National Park were worth far more than the natural environment. Based on the result of this study, the operating management plan of Gyeongju National Park should include a differentiated operation strategy through consultation with relevant experts by taking into account characteristics of the physical components.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Science of Art and Design
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• v.7
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• pp.64-104
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• 2005

Visual media transmits image and information reproduced in large quantities, such as a photography, film, television, video, advertisement, or computer image. Correspondence to the students' reception and recognition of culture in the future. arrangements for the field of studies of visual culture. 'Visual Culture' implies cultural phenomena of visual images via visual media, which includes not only the categories of traditional arts like a painting, sculpture, print, or design, but the performance arts including a fashion show or parade of carnival, and the mass and electronic media like a photography, film, television, video, advertisement, cartoon, animation, or computer image. In the world of visual media, Image' functions as an essential medium of communication. Therefore, people call the culture of today fra of Image Culture', which has been converted from an alphabet convergence era to an image convergence one. Image, via visual media, has become a dominant means for communication in large part of human life, so we can designate an Image' as a typical aspect of visual culture today. Image, as an essential medium of communication, plays an important role in contemporary society. The one way is the conversion of analogue image like an actual picture, photograph, or film into digital one through the digitalization of digital camera or scanner as 'an analogue/digital commutator'. The other is a way of process with a computer drawing, or modeling of objects. It is appropriate to the production of pictorial and surreal images. Digital images, produced by the other, can be divided into the form of Pixel' and form of Vector'. Vector is a line linking the point of departure to the point of end, which organizes informations. Computer stores each line's standard location and correlative locations to one another Digital image shows for more 'Perfectness' than any other visual media. Digital image has been evolving in the diverse aspects, such as a production of geometrical or organic image compositing, interactive art, multimedia art, or web art, which has been applied a computer as an extended trot of painting. Someone often interprets digitalized copy with endless reproduction of original even as an extension of a print. Visual af is no longer a simple activity of representation by a painter or sculptor, but now is intimately associated with a matter of application of media. There is some problem in images via visual media. First, the image via media doesn't reflect a reality as it is, but reflects an artificial manipulated world, that is, a virtual reality. Second, the introduction of digital effect and the development of image processing technology have enhanced a spectacle of destructive and violent scenes. Third, a child intends to recognize the interactive images of computer game and virtual reality as a reality, or truth. Education needs not only to point out an ill effect of mass media and prevent the younger generation from being damaged by it, but also to offer a knowledge and know-how to cope actively with social, cultural circumstances. Visual media education is one of these essential methods for the contemporary and future human being in the overflowing of image informations. The fosterage of 'Visual Literacy' can be considered as a very purpose of visual media education. This is a way to lead an individual to the discerning, active consumer and producer of visual media in life as far as possible. The elements of 'Visual Literacy' can be divided into a faculty of recognition related to the visual media, a faculty of critical reception, a faculty of appropriate application, a faculty of active work and a faculty of creative modeling, which are promoted at the same time by the education of 'visual literacy'. In conclusion, the education of 'Visual Literacy' guides students to comprehend and discriminate the visual image media carefully, or receive them critically, apply them properly, or produce them creatively and voluntarily. Moreover, it leads to an artistic activity by means of new media. This education can be approached and enhanced by the connection and integration with real life. Visual arts and education of them play an important role in the digital era depended on visual communications via image information. Visual me야a of day functions as an essential element both in daily life and in arts. Students can soundly understand visual phenomena of today by means of visual media, and apply it as an expression tool of life culture as well. A new recognition and valuation visual image and media education is required to cultivate the capability of active, upright dealing with the changes of history of civilization. 1) Visual media education helps to cultivate a sensibility for images, which reacts to and deals with the circumstances. 2) It helps students to comprehend the contemporary arts and culture via new media. 3) It supplies a chance of students' experiencing a visual modeling by means of new media. 4) There are educational opportunities of images with temporality and spaciality, and therefore a discerning person becomes to increase. 5) The modeling activity via new media leads students to be continuously interested in the school and production of plastic arts. 6) It raises the ability of visual communications dealing with image information society. 7) An education of digital image is significant in respect of cultivation of man of talent for the future society of image information as well. To correspond to the changing and developing social, cultural circumstances, and the form and recognition of students' reception of them, visual arts education must arrange the field of studying on a new visual culture. Besides, a program needs to be developed, which is in more systematic and active level in relation to visual media education. Educational contents should be extended to the media for visual images, that is, photography, film, television, video, computer graphic, animation, music video, computer game and multimedia. Every media must be separately approached, because they maintain the modes and peculiarities of their own according to the conveyance form of message. The concrete and systematic method of teaching and the quality of education must be researched and developed, centering around the development of a course of study. Teacher's foundational capability of teaching should be cultivated for the visual media education. In this case, it must be paid attention to the fact that a technological level of media is considered as a secondary. Because school education doesn't intend to train expert and skillful producers, but intends to lay stress on the essential aesthetic one with visual media under the social and cultural context, in respect of a consumer including a man of culture.