• Journal of Fashion Business
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• v.14 no.4
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• pp.91-101
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• 2010

This paper has the purpose of suggesting a development-method on the fashion products that would secure the competitiveness in the global markets through creating supersensitive practical products on the basis of developing originative fashion-goods being made of jacquard that is fine quality of couture. This paper has collected data through the cases of the developed nations in fashion, precedent studies and all the related literature on the subject. The results of this researching are as following: The first, it is expected that the necessity of developing new fashion products would be appealed toward the prestige group of consumers who are seeking for the fine quality and super-sensitivity in female fashion in Korea, and it is the current situation that the products made of jacquard are gradually expanding not only in the area of apparel, fashion goods and interior but also another areas. The current situation of the global market suggests the necessary strategy of survival, that is, the development of supersensitive materials and creative products which would be able to keep the high quality and lower the selling price through cost reduction. The second, the suggestion of the direction in developing the products of the female fashion made of jacquard has two points - the development of the texture that would realize a unique form and the development of the material that would be able to realize planar pattern and three dimensional pattern which are woven with thin and light materials with various solidity and delicacy through various techniques of mixing and three dimensional expression. The third, the expected ripple effect and utilization generated from the development of fashion products are as followings: As material characteristics of jacquard, It needs the specialization of various techniques and specialized production system in using jacquard, and the specialized technique and system would make it possible to produce not only the higher value-added products through expressing affluent colors and delicate design but also competitive products through cutting the process and cost, eventually, it would lead to the expansion of the jacquard market of super-sensitive prestige. Therefore, it is remarkable that various development of products toward the global market and the prestige female fashion market can suggest the vision that make the national fashion industry develop into the higher value-added knowledge industry integrating technology and culture.

• Korean Journal of Construction Engineering and Management
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• v.13 no.5
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• pp.25-34
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• 2012

It has been five years since the Post-Construction Sale System of Housing was introduced. The purpose of this study is to identify objectives and effects of the Post-Construction Sale System of Housing and analyze change of profitability at different sales time from a supplier's point of view. Apartment buildings construction projects performed in Seoul are used for the case study. The present value of sales revenues, sensitivity and the present value of expected sales prices are analyzed. According to the findings, first, profits made from a Pre-construction sales system was 5.1%~6.2% higher than those from a Post-construction sales system. Among four plans of a Pre-construction sales system (A, B, C and D plan), sales revenue from the A plan, which takes a deposit at the time of starting construction, was the greatest. Second, increase of the rate of discount and decrease of sales revenues are in direct proportion. The bigger rate of discount leads actual reduction of sales revenues. Third, for the present value of sales revenues reflecting change in basic model construction cost, a Pre-construction sales system showed a little higher than that of a Post-construction sales system by approximately 2%. It should be known that this study suggests profitability of Pre-and Post-construction sales system by clearly measuring them in the supplier's point of view and calculates sales revenues, considering change of a sale price following change of sales time.

• Journal of the Korean Society for Marine Environment & Energy
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• v.18 no.4
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• pp.304-309
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• 2015

To evaluate the degree of contamination caused by oil spill accident in the sea, the in-situ sensors which are based on the scientific method are needed in the real site. The sensors which are based on the fluorescence detection theory can provide the useful data, such as the concentration of oil. However these kinds of sensors commonly are composed of the ultraviolet (UV) light source such as UV mercury lamp, the multiple excitation/emission filters and the optical sensor which is mainly photomultiplier tube (PMT) type. Therefore, the size of the total sensing platform is large not suitable to be handled in the oil spill field and also the total price of it is extremely expensive. To overcome these drawbacks, we designed the fluorimeter for the oil spill detection which has compact size and cost effectiveness. Before the detail design process, we conducted the experiments to measure the excitation and emission spectrum of oils using five different kinds of crude oils and three different kinds of processed oils. And the fluorescence spectrometer were used to analyze the excitation and emission spectrum of oil samples. We have compared the spectrum results and drawn the each common spectrum regions of excitation and emission. In the experiments, we can see that the average gap between maximum excitation and emission peak wavelengths is near 50 nm for the every case. In the experiment which were fixed by the excitation wavelength of 365 nm and 405 nm, we can find out that the intensity of emission was weaker than that of 280 nm and 325 nm. So, if the light sources having the wavelength of 365 nm or 405 nm are used in the design process of fluorimeter, the optical sensor needs to have the sensitivity which can cover the weak light intensity. Through the results which were derived by the experiment, we can define the important factors which can be useful to select the effective wavelengths of light source, photo detector and filters.

• 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.