• Journal of Food Hygiene and Safety
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• v.32 no.6
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• pp.507-512
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• 2017

There has been increasing concern regarding misuse of disinfectants and sanitizers such as ethanol, sodium hypochlorite, and hydrogen peroxide for food contact surfaces in the food industry. Examining the efficacy of the concentration of currently used disinfectants and sanitizers is urgently required in the Korean society. This study aimed to develop predictive reduction models for Escherichia coli and Staphylococcus aureus in suspension, as a function of $ClO_2$ (chlorine dioxide) and contact time using response surface methodology. E. coli ATCC 10536 and S. aureus ATCC 6538 (initial inoculum, 8-9 log CFU/mL) in tryptic soy broth were treated with different concentrations of $ClO_2$ (5, 20, and 35 ppm) for different contact times (1, 3, and 5 min) following a central composite design. The polynomial reduction models for $ClO_2$ on E. coli and S. aureus were developed under the clean condition. E. coli reduction by 35 ppm $ClO_2$ for 1, 3, and 5 min was 2.49, 2.70, and 3.65 log CFU/mL, respectively. Also, S. aureus reduction by 35 ppm $ClO_2$ for 1, 3, and 5 min was 4.59, 5.25, and 5.81 log CFU/mL, respectively. The predictive response polynomial models developed were $R=0.43231-0.056492^*X_1-0.097771^*X_2+9.24167E-003^*X_1^*X_2+3.06333E-003^*X_1{^2}$ ($R^2=0.98$) on E. coli and $R=1.10542-0.20896^*X_1-0.046062^*X_2+8.30000E-003^*X_1^*X_2+8.73300E-003^*X_1{^2}$ ($R^2=0.99$) on S. aureus, where R was the bacterial reduction (log CFU/mL), $X_1$ was the concentration and $X_2$ was the contact time. Our predictive reduction models should be validated in developing the optimal concentration and contact time of $ClO_2$ for inhibiting E. coli and S. aureus on food contact surfaces.

• 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 Korea Multimedia Society
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• v.13 no.5
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• pp.641-650
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• 2010

Color image sensors (CIS) output color images through image sensors and image signal processing. Image sensors that convert light to electrical signal are divided into CMOS image sensor and CCD image sensor according to transferring method of signal charge. In general, a CIS has RGB output signals from tri-stimulus XYZ of the scene through image signal processing. This paper presents an adaptive colorimetric analysis method to obtain chromaticity and luminance using CIS under various environments. An image sensor for the use of colorimeter is characterized based on the CIE standard colorimetric observer. We use the method of least squares to derive a colorimetric characterization matrix between camera RGB output signals and CIE XYZ tristimulus values. We first survey the camera characterization in the standard environment then derive a SGL(shutter-gain-level) function which is relationship between luminance and auto exposure (AE) characteristic of CIS, and read the status of an AWB(auto white balance) function. Then we can apply CIS to measure luminance and chromaticity from camera outputs and AE resister values without any preprocessing. Camera RGB outputs, register values, and camera photoelectric characteristic are used to analyze the colorimetric results for real scenes such as chromaticity and luminance. Experimental results show that the proposed method is valid in the measuring performance. The proposed method can apply to various fields like surveillant systems of the display or security systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.56-63
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• 2016

In this study, we introduce a design of Fuzzy RBFNNs-based digit recognition system using the incremental-PCA in order to recognize the handwritten digits. The Principal Component Analysis (PCA) is a widely-adopted dimensional reduction algorithm, but it needs high computing overhead for feature extraction in case of using high dimensional images or a large amount of training data. To alleviate such problem, the incremental-PCA is proposed for the computationally efficient processing as well as the incremental learning of high dimensional data in the feature extraction stage. The architecture of Fuzzy Radial Basis Function Neural Networks (RBFNN) consists of three functional modules such as condition, conclusion, and inference part. In the condition part, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. Also, it is used instead of gaussian function to consider the characteristic of input data. In the conclusion part, connection weights are used as the extended diverse types in polynomial expression such as constant, linear, quadratic and modified quadratic. Experimental results conducted on the benchmarking MNIST handwritten digit database demonstrate the effectiveness and efficiency of the proposed digit recognition system when compared with other studies.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.533-538
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• 2013

The damage caused by Recent frequently occurring locality torrential rains is increasing rapidly. In case of densely populated metropolitan area, casualties and property damage is a serious due to landslides and debris flows and floods. Therefore, the importance of predictions about the torrential is increasing. Precipitation characteristic of the bad weather in Korea is divided into typhoons and torrential rains. This seems to vary depending on the duration and area. Rainfall is difficult to predict because regional precipitation is large volatility and nonlinear. In this paper, Very short-term precipitation forecasting pattern model is implemented using KLAPS data used by Korea Meteorological Administration. we designed very short term precipitation forecasting pattern model using GA-based RBFNNs. the structural and parametric values such as the number of Inputs, polynomial type,number of fcm cluster, and fuzzification coefficient are optimized by GA optimization algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.477-484
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• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.

• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.187-196
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• 2021

The paper deals with comparative study for characteristics of approximation of design space according to various approximate models and sensitivity analysis using orthogonal array experiments in structure design of active type DSF which was developed for float-over installation of offshore plant. This study aims to propose the orthogonal array experiments based design methodology which is able to efficiently explore an optimum design case and to generate the accurate approximate model. Thickness sizes of main structure member were applied to the design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experiment. Best design case was also identified to improve the structure design with weight minimization. From the orthogonal array experiment results, various approximate models such as response surface model, Kriging model, Chebyshev orthogonal polynomial model, and radial basis function based neural network model were generated. The experiment results from orthogonal array method were validated by the approximate modeling results. It was found that the radial basis function based neural network model among the approximate models was able to approximate the design space of the active type DSF with the highest accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.7
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• pp.5009-5014
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• 2015

Hydraulic uplift which is caused by the action of pore water pressure can be occurred in clay underlain by granular soil during conducting narrow excavation. Estimation of hydraulic uplift is done by considering soil beam. In order to execute more precise estimation of hydraulic uplift, determination of stress distribution in soil beam is necessary. This study presents stress distribution and displacement distribution in the soil beam based on the theory of elasticity. Stress distribution developed in the soil beam by self weight was derived using stress function depicted by $5^{th}$ order of polynomial and it was seen that vertical stresses along the depth of the soil beam show parabolic distribution and those directions be downward. Regarding soil beam which has the weight of $16kN/m^3, thickness and depth are 1m respectively, maximum vertical stress was about 1.7kPa. Stress distribution by the aciton of pore water pressure was derived via superposition of the stresses corresponding to the self weight and it can be seen that vertical compressive stresses act along the depth of the soil beam when the magnitude of pore water pressure equal to 5 times of the self weight is considered. Equations for prediction of the displacements in the soil beam are also presented.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.30 no.3
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• pp.217-222
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• 1985

The yield responses of three isogenic lines in plant type of paddy rice(open-, spread- and broom-type) as affected by combined treatments of transplanting densities (47.62, 22.22, 15.15, 11.11 and 8.33 hills per sq. meter) and rates of fertilizer application (0, 0.5, 1.0 and 1.5 folds of standard rate) were studied by using of the partial differentiations by planting density(D):df(D,F)/dD, fertilizer rate(F):df(D,F)/dF, and their interaction(DXF):d$^2$ f(D,F)/dDdF from the multiple regression polynominal equations. Under the condition of wider planting, the broom-type showed most prominent and sensitive responses in yield among others. Also the action of transplanting density in the broom-type were positive both at lower and higher densities. Under the lower densities, the broom-type represented positive actions both at lower and higher rates of fertilizer application. Whereas the interactions between the density and fertilizer rate under the lower densities were rather negative. To achieve the labor-saving by lower transplanting density(11-14 hills per sq. meter), the amount of fertilizer rates were estimated as 1.3-1.5 folds much of the standard in the open-type, whereas more than 1.5 folds in the broom-type. Thus, the potentials to absorb more amounts of fertilizer may explain the compensating function of the broom-type for equivalent yields of the standards at reduced transplanting densities.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.1
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• pp.31-40
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• 2017

Absolute nodal coordinate formulation was developed in the mid-1990s, and is used in the flexible dynamic analysis. In the process of deriving the equation of motion, if the order of polynomial referring to the displacement field increases, then the degrees of freedom increase, as well as the analysis time increases. Therefore, in this study, the primary objective was to reduce the analysis time by transforming the dimensional equation of motion to a non-dimensional equation of motion. After the shape function was rearranged to be non-dimensional and the nodal coordinate was rearranged to be in length dimension, the non-dimensional mass matrix, stiffness matrix, and conservative force was derived from the non-dimensional variables. The verification and efficiency of this non-dimensional equation of motion was performed using two examples; cantilever beam which has the exact solution about static deflection and flexible pendulum.