• The Research Journal of the Costume Culture
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• v.18 no.2
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• pp.261-276
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• 2010

The analysis of an art trend in the principle dimension starts by observing the object of work in the perspective of formative composition and recognizing it as a universal system. It can be said that it is consistent with an interpretation method for a form theory of formal history by Heinrich W$\ddot{o}$lfflin, a leading form critic in art criticism. Hence, the purpose of this study was to find out what are the formative principles in Renaissance Classicism as a design principle to be applicable to modern fashion by reviewing the formative characteristics of Renaissance Classicism Architecture with which W$\ddot{o}$lfflin directly dealt. As for the theoretical literature review, I used W$\ddot{o}$lfflin's theoretical framework and looked at the Renaissance Classicism Architecture that he studied and examined the possibility of utilizing his theory as a layout principle and the characteristics. As for analysis of design cases, I applied the aforementioned architecture layout principle to modern fashion and conducted case study analysis to delve into distinctive layout principles found in fashion. The study showed that the Renaissance Classicism Architectural Style is marked by linearity, planarity, closing and multiple unity: linearity was expressed in the observation form in fixed frontal view and an emphasis on a tangible silhouette homeogenous and definite line structures; planarity was achieved in the form of paralleled layers of frontal view element, planarity style, and identical and proportional repetition of various sizes.; closing signified the pursuit of complete and clear regularity, and architecture developed in a constructive phase through organizational inevitability and absolute invariability.; multiple unity was expressed in self-completedness and independent parallel of discrete forms and harmony of emphasized individual elements in a totality. Applying these layout characteristics of the Renaissance Classicism Architectural style and to see their individual expressive features, I found out that in adopting layout principles of the Renaissance Classicism Architecture to modern fashion, it turned out to be an emphasis of individual silhouettes, a flattened space, completed objects, organic harmony among independent parts: the emphasis of individual silhouettes was expressed in individual definitiveness of formative lines of clothes in accordance with body joints and an emphasis on formative lines of clothes; the flattened space was marked by single layer structure, planarity of elements of clothes, and listing arrangement by appropriate proportion.; the completedness of the objects was expressed by the stationary state where overall image is fixed, the construction of homogeneous and complete space, and absolute inevitability of internal layout in proportion; lastly, organic harmony of independent parts was stressed in independent completedness of each detail, and organic harmony of the whole. The expressive features would lead to a unique expression style of linear emphasis, proportion, constructive forms, and two-dimensional arrangement. The meaning of this study is follows: The characteristics of art school of thought are given shape by appling & analysing the architectural layout principles of historical art school of thought to modern fashion in the view point of formal construction dimension. The applied possibility of historical art school of thought as the source of inspiration about the fashion design is extended.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.5
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• pp.21-30
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• 2008

The fuel cell generation convert fuel source, and gas directly to electricity in an electro-chemical process. Unlike traditional and conventional turbine engines, the process of fuel cell generation do not burn the fuel and run pistons or shafts, and it has not revolutionary machine, so have fewer efficiency losses, low emissions and no noisy moving parts. A high power density allows fuel cells to be relatively compact source of electric power, beneficial in application with space constraints. In this system, the fuel cell itself is nearly small-sized by other components of the system such as the fuel reformer and power inverter. So, the fuel cell energy's stationary fuel cells produce reliable electrical power for commercial and industrial companies as well as utilities. In this paper, a fuel cell system has been modeled using PSCAD/EMTDC to analyze its electric signals and characteristics. Also the power quality of the fuel cell system has been evaluated and the problems which can be occurred during its operation have been studied by modeling it more detailed. Particularly, we have placed great importance on its power quality and signal characteristics when it is connected with a power grid.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.7-14
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• 2019

In South Korea, six large outbreaks of highly pathogenic avian influenza (HPAI) have occurred since the first confirmation in 2003 from chickens. For the past 15 years, HPAI outbreaks have become an annual phenomenon throughout the country and has extended to wider regions, across rural and urban environments. An understanding of the spatial epidemiology of HPAI occurrence is essential in assessing and managing the risk of the infection; however, local spatial variations of relationship between HPAI incidences in Korea and related risk factors have rarely been derived. This study examined whether spatial heterogeneity exists in this relationship, using a geographically weighted Poisson regression (GWPR) model. The outcome variable was the number of HPAI-positive farms at 252 Si-Gun-Gu (administrative boundaries in Korea) level notified to government authority during the period from January 2014 to April 2016. This response variable was regressed to a set of sociodemographic and topographic predictors, including the number of wild birds infected with HPAI virus, the number of wintering birds and their species migrated into Korea, the movement frequency of vehicles carrying animals, the volume of manure treated per day, the number of livestock farms, and mean elevation. Both global and local modeling techniques were employed to fit the model. From 2014 to 2016, a total of 403 HPAI-positive farms were reported with high incidence especially in western coastal regions, ranging from 0 to 74. The results of this study show that local model (adjusted R-square = 0.801, AIC = 954.5) has great advantages over corresponding global model (adjusted R-square = 0.408, AIC = 2323.1) in terms of model fitting and performance. The relationship between HPAI incidence in Korea and seven predictors under consideration were significantly spatially non-stationary, contrary to assumptions in the global model. The comparison between global Poisson and GWPR results indicated that a place-specific spatial analysis not only fit the data better, but also provided insights into understanding the non-stationarity of the associations between the HPAI and associated determinants. We demonstrated that an empirically derived GWPR model has the potential to serve as a useful tool for assessing spatially varying characteristics of HPAI incidences for a given local area and predicting the risk area of HPAI occurrence. Considering the prominent burden of HPAI this study provides more insights into spatial targeting of enhanced surveillance and control strategies in high-risk regions against HPAI outbreaks.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Journal of Life Science
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• v.33 no.11
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• pp.915-922
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• 2023

Lactic acid bacteria (LAB) are widespread in a variety of environments including fermented dairy products, gastroinstetinal and urogenital tracts of human and animals, plant, soil and water. Leuconostoc mesenteroides DB3 was detected by the strongest antibacterial activities among 24 Leuconostoc strains isolated from Camellia japonica flowers. Acid tolerance of L. mesenteroides DB3 existed up to pH 2.5, but the resistance did not show at pH 2.0, which relatively excellent acid resistance existed. Bile acid tolerance was very stable within the test range to 1.2%. L. mesenteroides DB3 exhibited the optimal growth at 30℃, and showed a slight slow growth when compared with L. mesenteroides KCTC3505, which reached a stationary phase at 18 hr. The pH was changed along with the growth curve, but was maintained above pH 3.98. L. mesenteroides DB3 had higher initial antibacterial activities when compared to L. mesenteroides KCTC3505, but it showed similar activities with the standard strain after the latter part of the logarithmic growth phase. Although lactic acid production in L. mesenteroides DB3 was induced by lower amount in the initial part to the standard strain, it was exhibited by similar amounts after the late logarithmic growth phase. Muicin adhesion of L. mesenteroides DB-3 maintained superior to L. mesenteroides KCTC3505. Both strains showed excellent emulsification ability for kerosene. In summary, we evaluate that L. mesenteroides DB-3 has a high potential for application as probiotics owing to its excellent antibacterial activity, acid resistance, bile acid resistance, and muicin adhesion.