• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.790-798
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• 2008

Counter-rotating axial flow fan(CRF) consists of two counter-rotating rotors without stator blades. CRF shows the complex flow characteristics of the three-dimensional, viscous, and unsteady flow fields. For the understanding of the entire core flow in CRF, it is necessary to investigate the three-dimensional unsteady flow field between the rotors. This information is also essential to improve the aerodynamic characteristics and to reduce the aerodynamic noise level and vibration characteristics of the CRF. In this paper, experimental study on the three-dimensional unsteady flow of the CRF is performed at the design point(operating point). Flow fields in the CRF are measured at the cross-sectional planes of the upstream and downstream of each rotor using the $45^{\circ}$ inclined hot-wire. The phase-locked averaged hot-wire technique utilizes the inclined hot-wire, which rotates successively with 120 degree increments about its own axis. Three-dimensional unsteady flow characteristics such as tip vortex, secondary flow and tip leakage flow in the CRF are shown in the form of the axial, radial and tangential velocity vector plot and velocity contour. The phase-locked averaged velocity profiles of the CRF are analyzed by means of the stationary unsteady measurement technique. At the mean radius of the front rotor inlet and the outlet, the phase-locked averaged velocity profiles show more the periodical flow characteristics than those of the hub region. At the tip region of the CRF, the axial velocity is decreased due to the boundary layer effect of the fan casing and the tip vortex flow. The radial and the tangential velocity profiles show the most unstable and unsteady flow characteristics compared with other position of rotors. But, the phase-locked averaged velocity profiles of the downstream of the rear rotor show the aperiodic flow pattern due to the mixture of the front rotor wake period and the rear rotor rotational period.

• Wind and Structures
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• v.38 no.1
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• pp.59-74
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• 2024

Large Eddy Simulation (LES) is used to explore the influence of vibration frequency and amplitude on the aerodynamic performance of a rectangular cylinder with an aspect ratio of B/D=5 (B: breadth; D: depth of cylinder) at a Reynolds number of 22,000 near resonance frequency. In smooth flow conditions, the research employs a sequence of three-dimensional simulations under forced vibration with diverse frequency ratios f_e / f_o = 0.8-1.2 (f_e : oscillation frequency; f_o : Strouhal frequency when the rectangular cylinder is stationary ) and oscillation amplitudes A_h/D = 0.05 - 0.3. The individual influences of f_e / f_o and A_h/D on the characteristics of integrated and distributed aerodynamic forces are the focal points of discussion. For the integrated aerodynamic force, particular emphasis is placed on the analysis of the dependence of velocity-proportional component C₁ and displacement-proportional component C₂ of unsteady aerodynamic force on amplitude and frequency ratio. Near the resonance frequency, the dependencies of C₁ and C₂ on amplitude are stronger than that of frequency ratio. For the distributed aerodynamic force, the increase in frequency and amplitude promotes the position of the main vortex core and reattachment to the leading edge in the streamwise direction. In the spanwise direction, vibration enhances the spanwise correlation of aerodynamic force to weaken the three-dimensional effect of the flow field, and a lower frequency ratio and larger amplitude amplify this effect.

• Journal of Broadcast Engineering
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• v.10 no.4 s.29
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• pp.474-487
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• 2005

T-DMB (Terrestrial-Digital Multimedia Broadcasting) adopted MPEG-4 BIFS (Binary Format for Scene) Core2D scene description profile and graphics profile as the standard of video associated data service. By using BIFS, we can support to overlay objects, i.e. text, stationary image, circle, polygon, etc., on the main display of receiving end according to the properties designated in broadcasting side and to make clickable buttons and website links on desired objects. Therefore, a variety of interactive data services can be served by BIFS. In this paper, we implement real-time video associated data service system far T-DMB. Our developing system places emphasis on real-time data service by user operation and on inter-working and stability with our previously developed video encoder. Our system consists of BIFS Real-time System, Automatic Stream Control System and Receiving Monitoring System. Basic functions of our system are designed to reflect T-DMB programs and characteristics of program production environment as a top priority. Our developed system was used in BIFS trial service via KBS T-DMB, it is supposed to be used in T-DMB main service after improvement process such as intensifying system stability.

• Journal of Microbiology and Biotechnology
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• v.14 no.6
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• pp.1217-1226
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• 2004

A psychrotrophic bacterial strain, Pseudomonas fluorescens BM07, synthesized unsaturated fatty acids (UFA) from fructose in response to lowering of growth temperature, and incorporated them into both polyhydroxyalkanoic acid (PHA) and membrane lipid. The blocking of PHA synthesis by adding 5 mM 2-bromooctanoic acid to the growth medium, containing 70 mM fructose, was found to be a useful means to profile the composition of membrane lipid by gas chromatography. As the growth temperature changed from 35 to $50^{\circ}C$, the total content of two UFA, 3-hydroxy-cis-5­dodecenoic acid ($C_{12:1}$) and 3-hydroxy-cis-7-tetradecenoic acid ($C_{14:1}$), in PHA increased from 31 to 44 $mol\%$. The growth at lower temperatures also led to an increase in the level of two major UFA, palmitoleic acid (C16:1 cis9) and cis-vaccenic acid (C18:1 cis11), in membrane lipid. A fraction of these membrane-lipid UFA was converted to their corresponding cyclopropane fatty acids (CFA). The CFA conversion was a function of culture time, exhibiting biphasic increase before and after entering the stationary phase. However, pH changes in growth media had no effect on the CFA conversion, which is contrary to the case of E. coli reported. The cells grown at $30^{\circ}C$ responded to a cold shock (lowering the medium temperature down to $10^{\circ}C$) by increasing the level of C16:1 cis9 and C 18: I cis II up to that of $10^{\circ}C$-grown control cells and concomitantly decreasing the relative level of cis-9,10­methylenehexadecanoic acid (the CFA converted from C16:1 cis9) from 14 to 8 $mol\%$, whereas the 10-grown cells exhibited little change in the lipid composition when exposed to a warmer environment of $30^{\circ}C$ for 12 h. Based on this one- way response, we suggest that this psychrotrophic strain responds more efficiently and sensitively to a cold shock than to a hot shock. It is also suggested that BM07 strain is a good producer of two unsaturated 3-hydroxyacids, $C_{12:1}\;and\;C_{141:1}$.

• 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.