• Preventive Nutrition and Food Science
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• 제13권2호
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• pp.104-111
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• 2008

As a way to account for the variability of the primary model parameters in the secondary modeling of microbial growth, three different regression approaches were compared in determining the confidence interval of the temperature-dependent primary model parameters and the estimated microbial growth during storage: bootstrapped regression with all the individual primary model parameter values; bootstrapped regression with average values at each temperature; and simple regression with regression lines of 2.5% and 97.5% percentile values. Temperature dependences of converted parameters (log $q_o$, ${\mu}_{max}^{1/2}$, log $N_{max}$) of hypothetical initial physiological state, maximum specific growth rate, and maximum cell density in Baranyi's model were subjected to the regression by quadratic, linear, and linear function, respectively. With an advantage of extracting the primary model parameters instantaneously at any temperature by using mathematical functions, regression lines of 2.5% and 97.5% percentile values were capable of accounting for variation in experimental data of microbial growth under constant and fluctuating temperature conditions.

• 소성∙가공
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• 제26권6호
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• pp.341-347
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• 2017

Flexibly-reconfigurable roll forming (FRRF) is a novel sheet metal forming technology conducive to produce multi-curvature surfaces by controlling strain distribution along longitudinal direction. Reconfigurable rollers could be arranged to implement a kind of punch die set. By utilizing these reconfigurable rollers, desired curved surface can be formed. In FRRF process, three-dimensional surface is formed from two-dimensional curve. Thus, it is difficult to predict the forming result. In this study, a regression analysis was suggested to construct a predictive model for a longitudinal curvature of FRRF process. To facilitate investigation, input parameters affecting the longitudinal curvature of FRRF were determined as maximum compression value, curvature radius in the transverse direction, and initial blank width. Three-factor three-level full factorial experimental design was utilized and 27 experiments using FRRF apparatus were performed to obtain sample data of the regression model. Regression analysis was carried out using experimental results as sample data. The model used for regression analysis was a quadratic nonlinear regression model. Determination factor and root mean square root error were calculated to confirm the conformity of this model. Through goodness of fit test, this regression predictive model was verified.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.337-339
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• 2004

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs). Proceeding the layer, this model creates the optimal network architecture through the selection and the elimination of nodes by itself. So, there is characteristic of flexibility. We use a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. GAs is applied to improve the performance with optimal input variables and number of input variables and order. To evaluate the performance of the GAs-based FPNNs, the models are experimented with the use of Medical Imaging System(MIS) data.

• 한국정보처리학회논문지
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• 제7권6호
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• pp.1867-1873
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• 2000

함수근사는 과학과 고학부야에서 공범위하게 응용된다. 시포트 벡터 기계(support vector machine, SVM)는 원래 분류를 위해 계안되어져 문자인식, 얼굴인식 등의 응용분야에서 좋은 결과를 보여주고 있다. 최근 SVM이론 함수근사로 확장되어 많이 활용되려 하고 있다. 그러나 함수근사를 위한 SVM 알고리즘은 QP(quadratic proramming)문제와 관련되어있어 계산에 시간이 걸리며 QP를 위한 패키지가 있어야 한다. 본 논문에서는 함수근사를 위해 커널-애더트론 알고리즘을 이용한 SVM을 제안하고 QP를 이용한 SVM과 성능을 비교하고자 한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.270-272
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• 2006

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.425-426
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• 2006

Overdriving schemes are used to improve the response time of liquid crystal display. Typically they are implemented by using LUTs (look-up table) within an image processor. However, the size of LUT is limited by the physical memory size and system cost. In this paper, we present an improved method for LUT implementation using linear interpolation and piecewise least-square polynomial regression. Using the proposed method, the performance of LUT can be improved and memory size of that can be reduced.

• Journal of the Korean Data and Information Science Society
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• 제28권2호
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• pp.435-442
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• 2017

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}$ (p ${\geq}$ 3) under the quadratic loss from multi-variate normal population. We find a James-Stein type estimator which shrinks towards the projection vectors when the underlying distribution is that of a variance mixture of normals. In this case, the norm ${\parallel}{\theta}-K{\theta}{\parallel}$ is known where K is a projection vector with rank(K) = q. The class of this type estimator is quite general to include the class of the estimators proposed by Merchand and Giri (1993). We can derive the class and obtain the optimal type estimator. Also, this research can be applied to the simple and multiple regression model in the case of rank(K) ${\geq}2$.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• 통합자연과학논문집
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• 제9권2호
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• pp.144-151
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• 2016

The purpose of this study is to investigate the emotion effects on academic achievement for university students. The results are as follows. Resulting on the each emotions difference by the statistical variables, anxiety scores by gender showed a significant difference in the p<.01 level(F=7.685). The males anxiety(2.478, standard deviation: 0.180) had significantly lower scores than females(3.076, standard deviation: 0.168). But fear, anger, activity, and sociability scores were not significantly different respectively between male and female students. To see the emotions effect of academic achievement, the analysis method of the linear regression line was used. As the result, anxiety, fear, anger, activity, and sociability did not significantly influence academic achievement. And so unlike previous methods, the analysis method of the quadratic regression curve was used. As the result, anxiety, fear, anger, activity, and sociability showed did significantly influence academic achievement respectively within 5% of statistical significance level, to more than F=3.06. Therefore, the values on academic achievement of the each anxiety, fear, anger, activity, and sociability showed a quadratic regression curve. That is, [Academic achievement]=$-0.9685{\times}[Anxiety]^2+5.1342{\times}[Anxiety]+8.2679$,[Academic achievement]=$-1.0638{\times}[Fear]^2+5.5694{\times}[Fear]+7.5635$,[Academic achievement]=$-1.3497{\times}[Anger]^2+9.1284{\times}[Anger]+0.6720$,[Academic achievement]=$-1.0589{\times}[Activity]^2+7.4386{\times}[Activity]+1.8272$,[Academic achievement]=$-1.6830{\times}[Sociability]^2+11.2325{\times}[Sociability]-3.8258$. Therefore, we were able to determine the following conclusions. First, we were able to predict the degree of academic achievement by the each emotions scale. Second, when the each emotion scores of students was a moderate, the academic achievement was most excellent. So, in order for the students to become higher academic achievement, the maintenance of medium degree of the each emotions scores is required.

• 한국약용작물학회지
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• 제3권2호
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• pp.77-80
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• 1995

한국산 광릉쥐오줌풀에 대한 광합성 및 호흡특성과 아울러 광합성의 일중(日中)변화와 이와 관련된 특성 및 생육상황에 미치는 온도의 영향을 조사하였던 그 결과는 다음과 같다. 1. 온도와 쥐오줌풀 잎의 광합성간에는 고도로 유의한 2차곡선회귀가 인정되었으며, 이 회귀식에 의해 산출한 최대광합성을 위한 은도는 $17.7^{\circ}C$이었다. 2. 쥐오줌풀 잎의 기공수는 잎의 표면에서 약 $25/mm^2$, 이면에서 $85mm^2$이었으며 기공의 크기는 $21{\sim}30/{\mu}m$로 나타났다. 3. 온도와 쥐오줌풀의 엽폭 및 근중간에는 각각 고도로 유의한 2차곡선회귀가 인정되었으며 이 회귀식에 의해 산출한 근생장(根生長)의 최적온도는 약 $20.3^{\circ}C$이었다.