• Korean Journal of Food Science and Technology
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• v.22 no.5
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• pp.539-542
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• 1990

The effects of immersion temperature $(20,\;40\;and\;60^{\circ}C)$ and immersion times (6. 12 and 18 min) in a distilled water prior to air dehydration upon the browning reaction and pyruvic acid content of air dried onions to a 4.071 moisture content (wet basis) were analyzed by a response surface methodology (RSM). Those values were also predicted by using a second degree polynomial regression model. Immersion temperature had more influence to browning reaction and pyruvic acid content than immersion time in these experimental ranges. The processing conditions to minimize the browning reaction of dried onions at $50^{\circ}C$ of air temperature (O.D.=0.071) were $60^{\circ}C$ of immersion temperature and 18 min of immersion time compared to control (O.D.=0.168) of air dehydration at $50^{\circ}C$ Pyruvic acid contents of dried onions at $50^{\circ}C$ of air temperature were maximized $(39.85{\mu}mole/g\;onion\;solid)$ at $60^{\circ}C$ of immersion temperature and 12 min of immersion time compared to control $(24.08{\mu}mole/g\;onion\;solid)$ of air dehydration at $50^{\circ}C$.

• Korean Journal of Optics and Photonics
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• v.29 no.4
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• pp.166-172
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• 2018

In this paper, we propose a new method to predict a mirror's surface deformation due to the stress of moisture release by a coating in the environment of outer space. We measured the surface deformation of circular samples 50 mm in diameter and 1.03 mm thick, using an interferometer. The results were analyzed using Zernike fringe polynomials. The coating stress caused by moisture release was calculated to be 152.7 MPa. This value was applied to an analytic model of a 1.25 mm thickness sample mirror, confirming that the change of surface deformation could be predicted within the standard deviation of the measurement result ($78.9{\pm}5.9nm$). Using this methodology, we predicted the surface deformation of 600 mm hyperbolic mirror for the Compact Advanced Satellite, which will be launched in 2019. The result is only $2.005{\mu}m$ of focal shift, leading to 2.3% degradation of modulation transfer function (MTF) at the Nyquist frequency, which satisfies the requirement.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.361-373
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• 2002

In this paper, we propose a channel normalization method to improve the performance of CMS (cepstral mean subtraction) which is widely adopted to normalize a channel variation for speech and speaker recognition. CMS which estimates the channel effects by averaging long-term cepstrum has a weak point that the estimated channel is biased by the formants of voiced speech which include a useful speech information. The proposed Formant-broadened Cepstral Mean Subtraction (FBCMS) is based on the facts that the formants can be found easily in log spectrum which is transformed from the cepstrum by fourier transform and the formants correspond to the dominant poles of all-pole model which is usually modeled vocal tract. The FBCMS evaluates only poles to be broadened from the log spectrum without polynomial factorization and makes a formant-broadened cepstrum by broadening the bandwidths of formant poles. We can estimate the channel cepstrum effectively by averaging formant-broadened cepstral coefficients. We performed the experiments to compare FBCMS with CMS, PFCMS using 4 simulated telephone channels. In the experiment of channel estimation, we evaluated the distance cepstrum of real channel from the cepstrum of estimated channel and found that we were able to get the mean cepstrum closer to the channel cepstrum due to an softening the bias of mean cepstrum to speech. In the experiment of text-independent speaker identification, we showed the result that the proposed method was superior than the conventional CMS and comparable to the pole-filtered CMS. Consequently, we showed the proposed method was efficiently able to normalize the channel variation based on the conventional CMS.

• Korean Journal of Food Science and Technology
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• v.42 no.6
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• pp.733-738
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• 2010

The leaves of Zizyphus jujuba have been used for various purposes including medicine and nutrition. In this study, the conditions for the ethanol extraction of antioxidant from Zizyphus jujuba were optimized using response surface methodology (RSM). A Box-Behnken design containing 15 experimental runs with three replicates was employed to study the effects of solvent extraction conditions such as extraction temperature ($^{\circ}C$, $X_1$), extraction time (min, $X_2$), and ethanol concentration (%, $X_3$) on the extraction yield of antioxidants from Zizyphus jujuba. The yields of total polyphenols and total flavonoid, and electron donating activity (EDA) were considered as response variables. The second-order polynomial model gave a satisfactory description of the experimental results showing different patterns of extraction conditions with variation in the linear, quadratic, and interaction effects of the independent variables. Based on four-dimensional RSM, one of the optimized sets of conditions was 45% ethanol, $45^{\circ}C$, and an extraction time of 15 min. Under the optimal conditions, the predicted values were 177.64 mg/g dry basis, 35.99 mg/g dry basis, and 86.14% Vit.C equivalents for total polyphenols, total flavonoids, and EDA, respectively. The experimental values showed good agreements with the predicted values.

• Journal of the Korea institute for structural maintenance and inspection
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• v.19 no.5
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• pp.92-103
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• 2015

Box-Wilson experimental design method, known as central composite design, is the design of any information-gathering exercises where variation is present. This method was devised to gather as much data as possible in spite of the low design cost. This method was employed to model the effect of mixing factors on several performances of 60 MPa high strength self compacting concrete and to numerically calculate the optimal mix proportion. The nonlinear relations between factors and responses of HSSCC were approximated in the form of second order polynomial equation. In order to characterize five performances like compressive strength, passing ability, segregation resistance, manufacturing cost and density depending on five factors like water-binder ratio, cement content, fine aggregate percentage, fly ash content and superplasticizer content, the experiments were made at the total 52 experimental points composed of 32 factorial points, 10 axial points and 10 center points. The study results showed that Box-Wilson experimental design was really effective in designing the experiments and analyzing the relation between factor and response.

• Korean Chemical Engineering Research
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• v.57 no.1
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• pp.78-84
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• 2019

Recently, bio-butanol is being promoted as environmentally friendly sustainable energy. However, some problems are still obstacle for commercialization of bio-butanol: the development of cheap biomass and enhancement of fermentation ratio and preparation of economical separation process for fermented products. In the conventional ABE biobutanol fermentation process, organic acids with acetone, butanol, and ethanol are produced. Therefore, it is necessary to study phase equilibrium data and mixture properties for the design and operation of separation process. However, there is lack of design data for organic acids except acetic acid contained system. In this study, therefore, binary solid-liquid equilibria (SLE) and mixture properties: the excess molar volumes ($V^E$), molar refraction deviation (${\Delta}R$) and deviation of viscosity (${\Delta}v$) at 298.15 for $C_3-C_6$ organic acid were reported. The experimental SLE data were correlated with the NRTL and UNIQUAC activity coefficient model with less than 0.5 K of root mean square deviation (RMSD). In addition, $V^E$, ${\Delta}R$ and ${\Delta}v$ for the same binary systems were satisfactorily fitted using the Redlich-Kister polynomial with less than ca. 0.004 standard deviation.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.685-690
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• 2023

It was analyzed that the volume of deep excavation works adjacent to existing underground structures is increasing according to the population growth and density of cities. Currently, many underground structures and tracks are damaged by external factors, and the cause is analyzed based on the measurement results in the tunnel, and measurements are being made for post-processing, not for prevention. The purpose of this study is to analyze the effect on the deformation of the structure due to the excavation work adjacent to the urban railway track in use. In addition, the safety of structures is evaluated through machine learning techniques for displacement of structures before damage and destruction of underground structures and tracks due to external factors. As a result of the analysis, it was analyzed that the model suitable for predicting the structure management standard value time in the analyzed dataset was a polynomial regression machine. Since it may be limited to the data applied in this study, future research is needed to increase the diversity of structural conditions and the amount of data.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.141-148
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• 2023

Accurate overlay metrology is essential to achieve high yields of semiconductor products. Overlay metrology performance is greatly affected by overlay target design and measurement method. Therefore, in order to improve the performance of the overlay target, measurement methods applicable to various targets are required. In this study, we propose a new algorithm that can measure image-based overlay. The proposed measurement algorithm can estimate the sub-pixel position by using a motion vector. The motion vector may estimate the position of the sub-pixel unit by applying a quadratic equation model through polynomial expansion using pixels in the selected region. The measurement method using the motion vector can calculate the stacking error in all directions at once, unlike the existing correlation coefficient-based measurement method that calculates the stacking error on the X-axis and the Y-axis, respectively. Therefore, more accurate overlay measurement is possible by reflecting the relationship between the X-axis and the Y-axis. However, since the amount of computation is increased compared to the existing correlation coefficient-based algorithm, more computation time may be required. The purpose of this study is not to present an algorithm improved over the existing method, but to suggest a direction for a new measurement method. Through the experimental results, it was confirmed that measurement results similar to those of the existing method could be obtained.

• Food Science and Preservation
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• v.19 no.5
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• pp.633-638
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• 2012

The aim of this study was to investigate the growth of aerobic bacteria in fresh-cut salad during short-term temperature abuse ($4{\sim}30^{\circ}C$temperature for 1, 2, and 3 h) for 72 h and to develop predictive models for the growth of total viable cells (TVC) based on Predictive food microbiology (PFM). The tool that was used, Pathogen Modeling program (PMP 7.0), predicts the growth of Aeromonas hydrophila (broth Culture, aerobic) at pH 5.6, NaCl 2.5%, and sodium nitrite 150 ppm for 72 h. Linear models through linear regression analysis; DMFit program were created based on the results obtained at 5, 10, 20, and $30^{\circ}C$ for 72 h ($r^2$ >0.9). Secondary models for the growth rate and lag time, as a function of storage temperature, were developed using the polynomial model. The initial contamination level of fresh-cut salad was 5.6 log CFU/mL of TVC during 72 h storage, and the growth rate of TVC was shown to be 0.020~1.083 CFU/mL/h ($r^2$ >0.9). Also, the growth tendency of TVC was similar to that of PMP (grow rate: 0.017~0.235 CFU/mL/h; $r^2=0.994{\sim}1.000$). The predicted shelf life with PMP was 24.1~626.5 h, and the estimated shelf life of the fresh-cut salads with short-term temperature abuse was 15.6~31.1 h. The predicted shelf life was more than two times the observed one. This result indicates a 'fail safe' model. It can be taken to a ludicrous extreme by adopting a model that always predicts that a pathogenic microorganism will grow even under conditions so strict as to be actually impossible.

• 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.