• 한국멀티미디어학회논문지
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• 제20권8호
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• 한국방재학회 논문집
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• 제9권5호
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• pp.125-130
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• 2009

한강의 홍수예경보에 자주 사용되고 있는 저류함수법은 강우-유출관계의 비선형성을 고려한 적용성이 뛰어난 모형이지만, 우리나라의 지형특성을 고려한 매개변수 산정식이 존재하지 않아 실무에서 유역별, 사상별 매개변수 추정에 많은 노력과 시간을 투자하고 있는 실정이다. 그러므로 본 연구에서는 다중회귀분석을 이용하여 한강유역의 저류함수법 매개변수를 계산하기 위한 공식을 유도하여 저류함수법의 적용성을 높이고자 하였다. 상관분석을 통하여 다중회귀분석의 독립변수로는 유역의 유역면적, 하천경사, 유로연장이 사용되도록 결정되었으며, 다중공선성을 가지고 있는 독립변수들을 제거하고, 독립변수의 수를 달리하면서 한강유역 내 30개 소유역에 대해 일반화된 매개변수 산정식을 유도하였다. 제안된 회귀식은 모형의 개발에 사용되지 않은 한강유역 내 다른 지점인 문막수위표의 강우에 적용하여 그 적용성을 검증하였다. 제안된 회귀식을 한강공식이라고 명하고, 이는 한강유역 내에 홍수예경보나 유출계산에 저류함수법 적용 시 유용한 자료로 활용하고자 하였다.

• 산업경영시스템학회지
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• 제15권26호
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• pp.59-66
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• 1992

This Paper, first, tries to optimize the output specifications with uncertain characteristics. And then aims to solve the problem not only by making use of transformed multiple regression equation which can yield objective function of output characteristics but also by formulating developed multiple fuzzy goal programming using fuzzy set theory which can treat uncertainty easily, and the efficiency of these techniques, will be also demonstrated through a case study.

• 정보처리학회논문지D
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• 제11D권7호
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• pp.1483-1490
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• 2004

소프트웨어분야에서 성공적인 프로젝트를 완수하기 위해서는 프로젝트를 완수하는데 필요한 개발노력이 정확히 추정되어야 한다. 그러나 이러한 개발노력은 소프트웨어의 크기나 여러 가지 운영환경의 영향으로 인해 프로젝트에 따라서 총 개발 노력의 규모는 차이가 있다. 일반적으로 기존의 연구는 개발노력을 추정하기 위하여 소프트웨어 규모인 기능점수(FP ; Function Point)를 이용하였다. 본 연구를 위해서 1990년대에 개발된 789개의 소프트웨어 개발 프로젝트들에 관련된 데이터를 이용하였다. 실험을 통해서 개발노력에 영향을 미치는 변수를 조사하였다. 또한 변수사이에 선형적인 관계를 조사하기 위하여 다중회귀분석을 실시하였다. 이 경우 전체의 데이터를 이용하는 것이 아니라 프로젝트 인도비율(PDR ; Project Delivery Rate : Hours/FP)을 다단계로 나누어서 각 단계별로 개발노력에 영향을 미치는 변인을 찾아내고 가장 이상적인 회귀식으로 도출하였다.

• Asian-Australasian Journal of Animal Sciences
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• 제33권10호
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• pp.1633-1641
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• 2020

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R² values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R² values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

• 성인간호학회지
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• 제27권1호
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• pp.52-62
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• 2015

Purpose: The purpose of this study was to examine the relationships between frailty, nutritional status, positive thinking, family function, and health conservation and to identify the factors influencing health conservation of the elderly at home. Methods: The research design was a descriptive survey using a convenience sampling. Data were collected from 142 elders using self-reported questionnaires. Data were analyzed using the SPSS/WIN 20.0 program for descriptive statistics, Pearson's correlation coefficients, and multiple linear regression. Results: The average health conservation score was 98.85. There were significant correlations between frailty, nutritional status, positive thinking, family function and health conservation. As a result of the multiple linear regression analysis, positive thinking, perceived health status, spouse and frailty accounted for 69% of the variance in health conservation of the elderly at home. Conclusion: These influencing factors on health conservation can be taken into account in the development of nursing intervention programs for improving health conservation of the elderly at home.

• 아동학회지
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• 제15권2호
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• pp.153-168
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• 1994

The purpose of this study was to examine mother's child rearing practices by mothers of their first-born child as a function of child's sex, temperament, and social class and of mother's marital satisfaction. The subjects were 158 mothers of 3-year-old first-born children in Seoul. Block (1984)'s CRPR, Buss and Plomin (1975)'s EAS, and Roach et al.'s MSS (1981) were used to measure maternal child rearing practices, children's temperament, and mother's marital satisfaction, respectively. The statistical methods for data analysis included t-test, ANOVA, Duncan's multiple range test, multiple regression. The major findings showed there were no sex of child differences in child rearing practices. Social class differences were found in "encouragement of independence", "enjoyment of child" and "openness to experience" in maternal child rearing practices. There were differences in maternal child rearing practices by child's temperament and mother's marital satisfaction. In a multiple regression analysis, it was found that the most significant predictor of maternal child rearing practices was mother's marital satisfaction.

• Child Health Nursing Research
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• 제14권1호
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• pp.61-70
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• 2008

Purpose: The purpose of this study was to investigate the relationship between high school students' self-differentiation, family function and their level of stress. Method: A questionnaire which consisted of questions on general characteristics of the high school students, and 36 questions on self-differentiation, 17 questions on family function, and 37 questions on level of stress was used to collect the data. Participants were 201 second grade high school students from Bucheon City. Descriptive statistics, T-test, ANOVA, correlation and multiple regression were used with SPSS 10.0 to analyze the data. Results: The mean scores for self-differentiation, family function, and levels of stress were 3.27, 3.39, and 2.61 respectively. The relationship between self-differentiation and level of stress revealed a significant negative correlation. The relationship between self-differentiation and family function showed a significant positive correlation. The relationship between family function and stress level showed a significant negative correlation. Conclusion: The results of the study show that variation in level of stress was related to family regression, recognition/emotional function, family projection, role recognition and emotional support and emotional cutoff which together explained 40.9% of the variance in level of stress.

• 한국산업융합학회 논문집
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• 제26권5호
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• pp.733-741
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• 2023

The purpose his study was a descriptive research study to identify the influencing factors of mentoring for nursing students, and was conducted with 120 nursing students. The collected data were subjected to real number and percentage, mean and standard deviation, t-test, ANOVA, Scheffe test, Pearson's correlation, and multiple regression analysis using SPSS/WIN 25.0. As a result of the study, mentoring was found to have a significant positive correlation with organizational socialization, core nursing competency, and clinical performance competency, and the explanatory power of the regression model was 64.1%. Since mentoring is an effective teaching method, based on this study, we propose a study to develop a structured mentoring program including organizational socialization, core nursing competency, and clinical performance competency to test the effectiveness. In addition, proposes a study to identify the relationship with various variables by dividing mentoring into sub-competencies of career development function, psychological stability function, and role model function.

• 한국안전학회지
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• 제30권1호
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• pp.8-13
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• 2015

The experimental design methodology was applied in the drop tube furnace (DTF) to predict the various combustion properties according to the operating conditions and to assess the coal plant safety. Response surface method (RSM) was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of DTF. The dependent variables such as burnout ratios (BOR) of coal and $CO/CO_2$ ratios were mathematically described as a function of three independent variables (coal particle size, carrier gas flow rate, wall temperature) being modeled by the use of the central composite design (CCD), and evaluated using a second-order polynomial multiple regression model. The prediction of BOR showed a high coefficient of determination (R2) value, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the simulation data. However, $CO/CO_2$ ratio had a big difference between calculated values and predicted values using conventional RSM, which might be mainly due to the dependent variable increses or decrease very steeply, and hence the second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, $CO/CO_2$ ratio was taken as common logarithms and worked again with RSM. The application of logarithms in the transformation of dependent variables showed that the accuracy was highly enhanced and predicted the simulation data well.