• Journal of the Korea Organic Resources Recycling Association
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• v.18 no.3
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• pp.77-86
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• 2010

In this study, the model of the indirect wind suction waste sorting machine for characteristics of the screening of waste was studied using computational fluid dynamics and the drag coefficient for the model and the suction wind speed were obtained. The wind separator are developing by installing a cyclone air outlet to the suction blower impeller waste is selective in a way that does not pass the features and characteristics of the inlet pipe of the pressure loss and separation efficiency can have a significant impact on. Using Wind separator for selection of waste in the waste prior research on the aerodynamic properties are essential. For plastic cases, it is reasonable to take the drag coefficient between 0.8 and 1.0, and for cans, compression depending on whether the cans, the drag coefficient is in the range from 0.2 to 0.7. The separation efficiency of waste as change suction speed was the highest efficiency when the suction speed was 25~26 m/s. Shape of the inlet, depending on how the transfer pipe of the duct pressure loss occurs because the inlet velocity changes through the appropriate design standards to allow for continued research is needed.

• 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 Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Journal of the Korean earth science society
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• v.28 no.7
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• pp.857-870
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• 2007

The structure and evolution of a thunderstorm outflow in two dimensions with no environmental wind are investigated using a cloud-resolving model with explicit liquid-ice phase microphysical processes (ARPS: Advanced Regional Prediction System). The turbulence structure of the outflow is explicitly resolved with a high-resolution grid size of 50m. The simulated single-cell storm and its associated Kelvin-Helmholtz (KH) billows are found to have the lift stages of development maturity, and decay. The secondary pulsation and splitting of convective cells resulted from interactions between cloud dynamics and microphysics are observed. The cooled downdrafts caused by the evaporation of rain and hail in the relatively dry lower atmosphere result in thunderstorm cold-air outflow. The outflow head propagates with almost constant speed. The KH billows formed by the KH instability cause turbulence mixing from the top of the outflow and control the structure of the outflow. Ihe KH billows are initiated at the outflow head, and pow and decay as moving rearward relative to the gust front. The numerical simulation results of the ratio of the horizontal wavelength of the fastest growing perturbation to the critical shear-layer depth and the ratio of the horizontal wavelength of the billow to its maximum amplitude are matched well with the results of other studies.

• Journal of Korea Water Resources Association
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• v.37 no.3
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• pp.219-231
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• 2004

The use of air diffuser system to ameliorate the reservoir by breaking stratification is now widespread. This study focuses on the hydrodynamic behavior of bubble plumes, which is the major mechanism of destratification and their combined effect of adjacent plumes on destratification efficiency. By introducing 2-phase Computational Fluid Dynamics(CFD) technique, we could suggest the optimal diffuser spacing having optimal destratification efficiency by simply analyzing the complex destratification procedures varying with the seasonal stratification intensity and bubble flow rate. Lab experiments were also carried out to verify CFD model in thermally stratified fresh water which quite differs from former researches using salts. This study showed that the mixing efficiency strongly depends on the spacing of neighboring plumes. When diffuser spacing is lower than 1.5 times the depth, the combined effect is stronger; as Plume Number(PN) is increased, the efficiency is strongly affected by spacing. If the distance is shorter than the depth of water, the efficiency increases linearly in proportion to PN. Otherwise, the efficiency increases non-linearly. These findings suggest that the combined effect should be more quantitatively taken into consideration for design and operation of air-diffuser destratification system, and recommend that the optimal destratification efficiency will be when plume number is 1000 and the spacing between neighboring diffusers is 1.5 times the depth.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.85-93
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• 2019

Computational Science and Engineering is a convergence study that understands and solves complex problems such as science, engineering, and social phenomena through modeling using computing resources. Computational science and engineering combines algorithms, computational and informatics, and infrastructure. The importance of computational science is increasing with the improvement of computer performance and the development of large data processing technology. In Korea, Korea Institute of Science and Technology Information (KISTI) has been developing national computational science engineering software and utilization technology by combining basic science and computing technology through EDISON project. The EDISON project builds an open EDISON platform and integrates and services information systems in seven areas of computational science and engineering (computational thermal fluids, nanophysics, computational chemistry, structural dynamics, computational design, and computational medicine). Using this, we have established a web-based curriculum to lay the groundwork for fostering scientific talent and commercializing computational science and engineering software. The purpose of this study is to derive the quality characteristic factors of computational science platform and to empirically examine the effect on user satisfaction. This paper examines how the quality characteristics of information systems, the computational science engineering platform, affect the user satisfaction by modifying the research questions according to the propensity of the computational science platform by referring to the success factors of DeLone and McLean's information system. Based on the results of this study, we will suggest strategic implications for platform improvement by searching the priority of quality characteristics of computational science platform.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.7
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• pp.961-967
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• 2019

This research presents an efficient method based on computational fluid dynamics (CFD) for estimating the resistance performance of a ship with a large settlement amount and a dynamic trim. The settlement of the inviscid flow analysis and the results of dynamic trim were used to set a large attitude for the ship prior to performing a viscous flow analysis; a viscous flow analysis was subsequently performed by Dynamic Fluid Body Interaction (DFBI). This method is termed as method I, in which a simple grating system can be used without employing the overset mesh technique by setting many attitudes before interpretation. Thus, method I is advantageous in reducing calculation time and improving calculation accuracy. The viscous flow analysis was performed using a commercial CFD code STAR-CCM+. Compared with the final convergence result, the first viscous flow analysis result of method I exhibited a variation of less than 1 % of resistance. The result was obtained by changing the gratings each time an attitude is changed at each calculation stage, based on the DFBI method provided to STAR-CCM+ using a simple grating system, which is not a superposed grating. This method is termed as method II. Compared with method II of resistance, method I exhibited a dif erence of 0.03-0.6 % for linear velocity. The results of method I were confirmed to be qualitatively and quantitatively appropriate through comparison with several trillion simulations.

• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.78-86
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• 2019

This paper describes the stiffness optimization of the torsion spring hinge of the large SAR antenna considering the deployment performance. A large SAR antenna is folded in a launch environment and then unfolded when performing a mission in orbit. Under these conditions, it is very important to find the proper stiffness of the torsion spring hinge so that the antenna panels can be deployed with minimal impact in a given time. If the torsion spring stiffness is high, a large impact load at the time of full deployment damages the structure. If it is weak, it cannot guarantee full deployment due to the deployment resistance. A multi-body dynamics analysis model was developed to solve this problem using RecurDyn and the development performance were predicted in terms of: development time, latching force, and torque margin through deployment analysis. In order to find the optimum torsion spring stiffness, the deployment performance was approximated by the response surface method (RSM) and the optimal design was performed to derive the appropriate stiffness value of the rotating springs.

• Bulletin of the Korean Chemical Society
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• v.7 no.5
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• pp.367-371
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• 1986

Dynamic and equilibrium properties of n-alkane chains immersed in solvent molecules have been investigated by a molecular dynamics method. The n-alkane chain is assumed to be a chain of elements (CH$_2$) interconnected by bonds having a fixed bond length and bond angle, but each bond of the chain is allowed to execute hindered internal rotation. We studied the effect of the number of the chain elements (N$_c$ = 10, 15 and 20) on the equilibrium properties of the system, e.g., the pair correlation functions between a chain element and solvent molecules, g$_{cs}$(r), and between the chain elements, g$_{cc}$(r), and the configurational properties such as the mean-square end-to-end distance < R$^2$ >, the mean-square radius of gyration < S$^2$ >, and the eigenvalues of the moment-of-inertia tensor < S$_i^2$ > / < S$^2$ > (i = 1, 2 and 3). We also studied the dynamic properties of the system, e.g., the autocorrelation function C(A;t) where A = R$^2$(t), = S$^2$(t), or = ${\vec{V}}(t)({\vec{V}}$ = velocity of the center of mass), and the diffusion coefficient D. The g$_{cs}$(r)'s are almost equal irrespective of the change of Nc while g$_{cc}$(r) becomes larger as N$_c$ increases; The MD computed configurational properties < R$^2$2 > and < S$^2$ > were found to be a little different from the values calculated from the statistical equations of < R$^2$ > and < S$^2$ >, it may be due to the fact that our model for the MD simulations includes a long-range volume effect. From the < S$_i^2$ > / < S$^2$ >, it is found that the chain molecule has a nearly spherical shape irrespective of the variation of N$_c$. For the dynamic properties we found that the C(R$^2$;t) and C(S$^2$;t) of lower N$_c$ decay faster than those of higher N$_c$, while the C($\vec V$;t) of the center of mass in the chain is weakly dependent on the N$_c$. The center of mass diffusion coefficient D$_c$ decreases as N$_c$ increases while the end point diffusion coefficient D$_e$ is nearly equal irrespective of the change of N$_c$.

• Korean Journal of Environmental Biology
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• v.26 no.3
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• pp.252-263
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• 2008

The purpose of this study was to determine trophic state, based on nutrients (TN, TP), transparency (SD), and chlorophyll-${\alpha}$ (Chl) and identify their empirical relations of TN-Chl, TP-Chl and Chl-SD depending on the dataset used along with dynamics of conductivity and suspended solids. Analysis of trophic states showed that more than half of 36 reservoirs were judged as eutrophic-hypertrophic conditions depending on the trophic variables. Seasonal values of TP varied by nearly 500% and showed greater in August than any other months. In contrast, TN varied within less than 90% and all monthly mean values of TN were never fall less than 1.2 mg L$^{-1}$ indicating low seasonal variations and high ambient concentrations (eutrophic-hypertrophic state). Analysis of empirical relations in the trophic variables showed that transparency had greater functional relations with Chl (R$^2$=0.31, p<0.001) than TP (R$^2$=0.15, p<0.001) and TN (R$^2$=0.20, p<0.001). Ratios of TN : TP in the ambient water indicated that most reservoirs showed a potential phosphorous limitation on the algal growth. Thus, algal biomass, based on Chl values, was more regulated by phosphorous than nitrogen. Analysis of linear regression model, based on log-transformed annual mean values, showed that only 30% in the variation of Chl was explained by TP (R$^2$=0.295, p=0.001, n=36) and 15% by TN (R$^2$=0.151, p=0.019, n=36). However, linear regression model, based on individual system, showed that Chl-TP model had strong positive relations (R$^2$=0.62, p=0.002, n=12), whereas the model had no any relations (p=0.892, n=12). Overall, our data suggested that averaging effect in the empirical model developments may influence the significance in the statistical analysis.