• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.434-449
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• 2019

In this study, we develop a new cross-shore sediment module which takes the effect of infra-gravity waves of bound mode, and boundary layer streaming on the sediment transport into account besides the well-known asymmetry and under-tow. In doing so, the effect of individual random waves occurring during the unit observation period of 1 hr on sediment transport is also fully taken into account. To demonstrate how the individual random waves would affect the sediment transport, we numerically simulate the non-linear shoaling process of random wavers over the beach of uniform slope. Numerical results show that with the consistent frequency Boussinesq Eq. the application of which is lately extended to surf zone, we could simulate the saw-tooth profile observed without exception over the surf zone, infra-gravity waves of bound mode, and boundary-layer streaming accurately enough. It is also shown that when yearly highest random waves are modeled by the equivalent nonlinear uniform waves, the maximum cross-shore transport rate well exceeds the one where the randomness is fully taken into account as much as three times. Besides, in order to optimize the free parameter K involved in the long-shore sediment module, we carry out the numerical simulation to trace the yearly shoreline change of Mang-Bang beach from 2017.4.26 to 2018.4.20 as well, and proceeds to optimize the K by comparing the traced shoreline change with the measured one. Numerical results show that the optimized K for Mang-Bang beach would be 0.17. With K = 0.17, via yearly grand circulation process comprising severe erosion by consecutively occurring yearly highest waves at the end of October, and gradual recovery over the winter and spring by swell, the advance of shore-line at the northern and southern ends of Mang-Bang beach by 18 m, and the retreat of shore-line by 2.4 m at the middle of Mang-Bang beach can be successfully duplicated in the numerical simulation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.8
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• pp.759-769
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• 2013

Electronic equipment has been applied to virtually every area associated with commercial, industrial, and military applications. Specifically, electronics have been incorporated into avionics components installed in aircraft. This equipment is exposed to dynamic loads such as vibration, shock, and acceleration. Especially, avionics components installed in a helicopter are subjected to simultaneous sine and random base excitations. These are denoted as sine on random vibrations according to MIL-STD-810F, Method 514.5. In the past, isolators have been applied to avionics components to reduce vibration and shock. However, an isolator applied to an avionics component installed in a helicopter can amplify the vibration magnitude, and damage the chassis, circuit card assembly, and the isolator itself via resonance at low-frequency sinusoidal vibrations. The objective of this study is to investigate the dynamic characteristics of an avionics component installed in a helicopter and the structural dynamic modification of its tray plate without an isolator using both a finite element analysis and experiments. The structure is optimized by dynamic loads that are selected by comparing the vibration, shock, and acceleration loads using vibration and shock response spectra. A finite element model(FEM) was constructed using a simplified geometry and valid element types that reflect the dynamic characteristics. The FEM was verified by an experimental modal analysis. Design parameters were extracted and selected to modify the structural dynamics using topology optimization, and design of experiments(DOE). A prototype of a modified model was constructed and its feasibility was evaluated using an FEM and a performance test.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.88-89
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• 2013

A variety of influenza A viruses from animal hosts are continuously prevalent throughout the world which cause human epidemics resulting millions of human infections and enormous industrial and economic damages. Thus, early diagnosis of such pathogen is of paramount importance for biomedical examination and public healthcare screening. To approach this issue, here we propose a fully integrated Rotary genetic analysis system, called Rotary Genetic Analyzer, for on-site detection of influenza A viruses with high speed. The Rotary Genetic Analyzer is made up of four parts including a disposable microchip, a servo motor for precise and high rate spinning of the chip, thermal blocks for temperature control, and a miniaturized optical fluorescence detector as shown Fig. 1. A thermal block made from duralumin is integrated with a film heater at the bottom and a resistance temperature detector (RTD) in the middle. For the efficient performance of RT-PCR, three thermal blocks are placed on the Rotary stage and the temperature of each block is corresponded to the thermal cycling, namely $95^{\circ}C$ (denature), $58^{\circ}C$ (annealing), and $72^{\circ}C$ (extension). Rotary RT-PCR was performed to amplify the target gene which was monitored by an optical fluorescent detector above the extension block. A disposable microdevice (10 cm diameter) consists of a solid-phase extraction based sample pretreatment unit, bead chamber, and 4 ${\mu}L$ of the PCR chamber as shown Fig. 2. The microchip is fabricated using a patterned polycarbonate (PC) sheet with 1 mm thickness and a PC film with 130 ${\mu}m$ thickness, which layers are thermally bonded at $138^{\circ}C$ using acetone vapour. Silicatreated microglass beads with 150~212 ${\mu}L$ diameter are introduced into the sample pretreatment chambers and held in place by weir structure for construction of solid-phase extraction system. Fig. 3 shows strobed images of sequential loading of three samples. Three samples were loaded into the reservoir simultaneously (Fig. 3A), then the influenza A H3N2 viral RNA sample was loaded at 5000 RPM for 10 sec (Fig. 3B). Washing buffer was followed at 5000 RPM for 5 min (Fig. 3C), and angular frequency was decreased to 100 RPM for siphon priming of PCR cocktail to the channel as shown in Figure 3D. Finally the PCR cocktail was loaded to the bead chamber at 2000 RPM for 10 sec, and then RPM was increased up to 5000 RPM for 1 min to obtain the as much as PCR cocktail containing the RNA template (Fig. 3E). In this system, the wastes from RNA samples and washing buffer were transported to the waste chamber, which is fully filled to the chamber with precise optimization. Then, the PCR cocktail was able to transport to the PCR chamber. Fig. 3F shows the final image of the sample pretreatment. PCR cocktail containing RNA template is successfully isolated from waste. To detect the influenza A H3N2 virus, the purified RNA with PCR cocktail in the PCR chamber was amplified by using performed the RNA capture on the proposed microdevice. The fluorescence images were described in Figure 4A at the 0, 40 cycles. The fluorescence signal (40 cycle) was drastically increased confirming the influenza A H3N2 virus. The real-time profiles were successfully obtained using the optical fluorescence detector as shown in Figure 4B. The Rotary PCR and off-chip PCR were compared with same amount of influenza A H3N2 virus. The Ct value of Rotary PCR was smaller than the off-chip PCR without contamination. The whole process of the sample pretreatment and RT-PCR could be accomplished in 30 min on the fully integrated Rotary Genetic Analyzer system. We have demonstrated a fully integrated and portable Rotary Genetic Analyzer for detection of the gene expression of influenza A virus, which has 'Sample-in-answer-out' capability including sample pretreatment, rotary amplification, and optical detection. Target gene amplification was real-time monitored using the integrated Rotary Genetic Analyzer system.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.