• 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.

• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.739-748
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• 2003

The objective of this study was to estimate genetic parameters for test-day milk production and somatic cell score using field data collected by dairy herd improvement program in Korea. Random regression animal models were applied to estimate genetic variances for milk production and somatic cell score. Heritabilities for milk yields, fat percentage, protein percentage, solid-not-fat percentage, and somatic cell score from test day records of 5,796 first lactation Holstein cows were estimated by REML algorithm in single trait random regression test-day animal models. For these analyses, Legendre polynomial covariate function was applied to model the fixed effect of age-season, the additive genetic effect and the permanent environment effect as random. Homogeneous residual variance was assumed to be equal throughout lactation. Heritabilities as a function of time were calculated from the estimated curve parameters from univariate analyses. Heritability estimates for milk yields were in range of 0.13 to 0.29 throughout first lactation. Heritability estimates for fat percentage, protein percentage and solid-not-fat percentage were within 0.09 to 0.11, 0.12 to 0.19 and 0.17 to 0.23, respectively. For somatic cell score, heritabilities were within 0.02 to 0.04. Heritabilities for milk productions and somatic cell score were fluctuated by days in milk with comparing 305d milk production.

• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.2
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• pp.69-84
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• 1984

This study was carried out to investigate the time distribution of single storms and to establish the model of storm patterns in korea. Rainfall recording charts collected from 42 metheorological stations covering the Korean peninsula were analyzed. A single storm was defined as a rain period seperated from preceding and succeeding rainfall by 6 hours and more. Among the defined single storms, 1199 storms exceeding total rainfall of 80 mm were qualified for the study. Storm patterns were cklassified by quartile classification method and the relationship between cummulative percent of rainfalls and cummulative storm time was established for each quartile storm group. Time distribution models for each stations were prepared through the various analytical and inferential procedures. Obtained results are summarized as follows: 1. The percentile frequency of quartile storms for the first to the fourth quartile were 22.0%, 26.5%, 28.9% and 22.6%, respectively. The large variation of percentile frequency was show between the same quartile storms. The advanced type storm pattern was predominant in the west coastal type storm patterns predominantly when compared to the single storms with small total rainfalls. 3. The single storms with long storm durations tended to show delayed type storm patterns predominantly when compared to the single storms with short storm durations. 4. The percentile time distribution of quartile storms for 42 rin gaging stations was estimated. Large variations were observed between the percentiles of time distributions of different stations. 5. No significant differences were generally found between the time distribution of rainfalls with greater total rainfall and with less total rainfall. This fact suggests that the size of the total rainfall of single storms was not the main factor affecting the time distribution of heavy storms. 6. Also, no significant difference were found between the time distribution of rainfalls with long duration and with short duration. The fact indicates that the storm duration was no the main factor affecting the time distribution of heavy storms. 7. In Korea, among all single storms, 39.0% show 80 to 100mm of total rainfall which stands for the mode of the frequency distribution of total rainfalls. The median value of rainfalls for all single storms from the 42 stations was 108mm. The shape of the frequency distribution of total rainfalls showed right skewed features. No significant differences were shown in the shape of distribution histograms for total rainfall of quartile storms. The mode of rainfalls for the advanced type quartile storms was 80~100mm and their frequencies were 39~43% for respective quartiles. For the delayed type quartile storms, the mode was 80~100mm and their frequencies were 36!38%. 8. In Korea, 29% of all single storms show 720 to 1080 minutes of storm durations which was the highest frequency in the frequency distribution of storm durations. The median of the storm duration for all single storms form 42 stations was 1026 minutes. The shape of the frequency distribution was right skewed feature. For the advanced type storms, the higher frequency of occurrence was shown by the single storms with short durations, whereas for the delayed type quartile storms, the higher frequency was shown gy the long duration single storms. 9. The total rainfall of single storms was positively correlated to storm durations in all the stations throughout the nation. This fact was also true for most of the quartile storms. 10. The third order polynomial regression models were established for estimating the time distribution of quartile storms at different stations. The model test by relative error method resulted good agreements between estimated and observed values with the relative error of less than 0.10 in average.

• Food Science and Preservation
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• v.24 no.5
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• pp.623-630
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• 2017

Functional compounds including flavonoids, anthocyanins, polyphneols and antioxidants were extracted from blue honeysuckle (Lonicera caerulea L.) using highly efficient microwave-assisted extraction. And extraction process was modeled and optimized according to response surface methodology (RSM). The independent variables ($X_n$) were ethanol concentration ($X_1$: 0, 25, 50, 75, 100%), irradiation time ($X_2$: 1, 3, 5, 7, 9 min), and microwave power ($X_3$: 60, 120, 180, 240, 300 W). Dependent variables ($Y_n$) were total flavonoid contents ($Y_1$), total anthocyanin contents ($Y_2$), total polyphenol contents ($Y_3$) and antioxidant activity ($Y_4$). Four-dimensional response surface plots were generated based on the fitted second-order polynomial models to get optimal conditions. Estimated optimal conditions for 4 responses were ethanol concentration of 54-72%, irradiation time of 7.1-7.6 min, and microwave power of 243-251 W. Ridge analysis predicted the maximal responses of total flavonoid content, total anthocyanin content, total polyphenol content and antioxidant activity were 38.00 mg RE/g, 6.80 mg CGE/g, 14.90 mg GAE/g, 89.10%, respectively. Verification experiment was carried out at predicted optimal conditions and experimental values for total flavonoid content, total anthocyanin content, total polyphenol content and antioxidant activity were 38.10 mg RE/g, 6.72 mg CGE/g, 14.91 mg GAE/g and 89.13%, respectively. No significant difference was observed between predicted and experimental values, indicating good fitness of fitted model and successful application of RSM.

• Korean Journal of Food Science and Technology
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• v.28 no.3
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• pp.411-416
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• 1996

Supercritical fluid extraction of ${\beta}$-carotene from carrot was optimized to maximize ${\beta}$-carotene (Y) extraction yield. A central composite design involving extraction pressure ($X_1$ 200-,100 bar), temperature ($X_2,\;35-51^{\circ}C$) and time ($X_1$$ 60-200min) was used. Three independent factors ($X_1,\;X_2,\;X_3$) were chosen to determine their effects on the various responses and the function was expressed in terms of a quadratic polynomial equation,$Y={\beta}_0+{\beta}_1X_1+{\beta}_2X_2+{\beta}_3X_3+{\beta}_11X_12+{\beta}_22X_3^2+{\beta}_-12X_1X_2+{\beta}_12X_1X_2+{\beta}_13X_1X_3+{\beta}_23X_2X_3,$ which measures the linear, quadratic and interaction effects. Extraction yields of ${\beta}$-carotene were affected by pressure, time and temperature in the decreasing order, and linear effect of tenter point (${\beta}_11$) and pressure (${\beta}_1$) were significant at a level of 0.001(${\alpha}$). Based on the analysis of variance, the model fitted for ${\beta}_11$-carotene (Y) was significant at 5% confidence level and the coefficient of determination was 0.938. According to the response surface of ${\beta}$-carotene by cannoical analysis, the stationary point for quantitatively dependent variable (Y) was found to be the maximum point for extraction yield. Response area for ${\beta}$-carotene (Y) in terms of interesting region was estimated over $10,611{\mu}g$ Per 100 g raw carrot under extraction.