• 한국가스학회지
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• 제11권3호
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• pp.13-18
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• 2007

다변량 통계 분석법(Multivariate statistical analysis method)의 대표적 방법인 다중 선형 회귀법(Multiple linear regression. MLR)을 이용하여 2성분계 혼합물의 인화점을 회귀 분석하고 예측하였다. 가연성 물질의 인화점에 대한 예측은 실제 화학 공정 설계에서 화재 및 폭발 위험성을 판단하는 중요한 부분 중의 하나이다. 본 연구에서는 순수 성분의 물성 자료만을 이용하여 2성분계 혼합물의 인화점 실험 자료에 대해 다중 선형 회귀법(MLR)을 수행하였고, 이를 이용하여 새로운 혼합물에 대한 인화점을 예측하였다. 2성분계 혼합물의 인화점에 대한 MLR의 회귀 성능과 새로운 혼합물에 대한 예측 성능을 알아보기 위해, 기존의 인화점 추정 방법인 Raoult의 법칙과 Van Laar식에 의한 추정값과 비교해 보았다.

• 한국식품과학회지
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• 제51권3호
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• pp.227-236
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• 2019

Antioxidant activity in water and ethanol extracts of dried Lycium chinense fruit, as a result of the total phenolic and tannin content, was measured using a number of chemical and biochemical assays for radical scavenging and inhibition of lipid peroxidation, with the analysis being extended by applying a bootstrapping statistical method. Previous statistical analyses mostly provided linear correlation and regression analyses between antioxidant activity and increasing concentrations of phenolics and tannins in a concentration-dependent mode. The present study showed that multiple component or multivariate analysis by applying multiple regression analysis or regression planes proved more informative than linear regression analysis of the relationship between the concentration of individual components and antioxidant activity. In this paper, we represented the multivariate analysis of antioxidant activities of both phenolic and tannin contents combined in the water and ethanol extracts, which revealed the hidden observations that were not evident from linear statistical analysis.

• Bulletin of the Korean Chemical Society
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• 제22권1호
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• pp.37-42
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• 2001

Ridge regression is compared with multiple linear regression (MLR) for determination of Research Octane Number (RON) when the baseline and signal-to-noise ratio are varied. MLR analysis of near-infrared (NIR) spectroscopic data usually encounters a collinearity problem, which adversely affects long-term prediction performance. The collinearity problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method. To evaluate the robustness of each calibration, the calibration models developed by both calibration methods were used to predict RONs of gasoline spectra in which the baseline and signal-to-noise ratio were varied. The prediction results of a ridge calibration model showed more stable prediction performance as compared to that of MLR, especially when the spectral baselines were varied. . In conclusion, ridge regression is shown to be a viable method for calibration of RON with the NIR data when only a few wavelengths are available such as hand-carry device using a few diodes.

• 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.

• 대한토목학회논문집
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• 제32권1B호
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• pp.9-20
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• 2012

최근 수위 예측을 위한 개념적 기반, 수문학적, 물리적 기반 모형 등의 단점을 극복하고자 홍수예측을 위해 자료지향형 모형 중의 하나인 다중선형회귀 모형이 널리 도입되고 있다. 본 연구의 목적은 이러한 다중선형회귀 모형의 서로 다른 회귀계수 선정 방법에 따른 홍수예측 성능을 비교 검토하고 이를 통해 적절한 다중회귀 홍수예측 모형을 구축하는 것이다. 이를 위해 입력자료의 자기상관분석을 통해 독립변수의 시간 규모를 결정한 후 최소 자승법, 가중 최소 자승법, 단계별 선택법의 각기 다른 회귀계수 산정 방법을 이용한 홍수예측 모형을 구축하고 중랑천 유역의 다양한 홍수사상에 대해 적용하였다. 구축된 모형들의 성능을 평가하기 위해 평균제곱근오차, Nash-Suttcliffe 효율계수, 평균절대오차, 수정 결정계수와 같이 4개의 통계지표들을 사용하였다. 모의결과 단계별 선택법을 이용한 다중선형회귀 홍수예측 모형이 가장 정확한 예측 결과를 보였고, 최소자승법을 이용한 홍수예측 모형이 가중 최소자승법을 이용한 홍수예측 모형보다 좀 더 나은 예측 결과를 나타냈다.

• 문화기술의 융합
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• 제10권1호
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• pp.159-164
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• 2024

국내 석유 시장은 국제 석유 가격의 변동에 매우 민감하기 때문에 그 변동성에 대한 파악과 대처가 중요하다. 특히, 높은 소비량을 보이는 휘발유의 가격이 어떠한 요인에 인해 변화하는지 명확하게 파악하는 것이 필요하다. 국제 휘발유 가격은 휘발유 수급, 지정학적 사건, 미국 달러화 가치 변동 등 글로벌 요인에 영향을 받는다. 그러나 기존의 연구들은 휘발유의 수급에만 초점에 맞추어 진행하였다는 한계가 존재한다. 본 연구에서는 다양한 머신러닝 기반의 회귀 모델을 활용하여 거시적 경제지표와 국제 휘발유 가격 간의 인과관계를 탐색한다. 첫째, 다양한 세계 경제지표 데이터를 수집한다. 둘째, 데이터 전처리를 진행한다. 셋째, 다중선형회귀, Ridge 회귀, Lasso(Least Absolute Shrinkage and Selection Operator) 회귀 모델을 활용하여 모델링한다. 실험 결과, 테스트 데이터 셋에서 다중선형회귀 모델이 가장 높은 정확도(97.3%)를 보였다. 우리는 국제 휘발유 가격의 예측은 국내 경제 안정성과 에너지 정책 결정에 도움이 될 수 있을 것으로 기대한다.

• Computers and Concrete
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• 제26권2호
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• pp.175-183
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• 2020

Fly ash has become an important component of concrete as supplementary cementitious material with the development of concrete technology. To make use of fly ash efficiently, four types of fly ash with particle size distributions that are in conformity with four functions, namely, S.Tsivilis, Andersen, Normal and F distribution, respectively, were prepared. The four particle size distributions as functions of the strength and pore structure of concrete were thereafter constructed and investigated. The results showed that the compressive and flexural strength of concrete with the fly ash that conforming to S.Tsivilis, Normal, F distribution increased by 5-10 MPa and 1-2 MPa, respectively, compared to the reference sample at 28 d. The pore structure of the concrete was improved, in which the total porosity of concrete decreased by 2-5% at 28 d. With regarding to the fly ash with Andersen distribution, it was however not conducive to the strength development of concrete. Regression model based on the grey multiple linear regression theory was proved to be efficient to predict the strength of concrete, according to the characteristic parameters of particle size and pore structure of the fly ash.

• 산업경영시스템학회지
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• 제43권2호
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• pp.137-145
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• 2020

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

• 한국인쇄학회지
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• 제25권1호
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• pp.11-26
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• 2007

In this paper, an experiment was done where the input device used the linear multiple regression and the sRGB color space to perform a color transformation. The output device used the GOG, GOGO and sRGB for the color transformation. After the input device underwent a color transformation, a $3\;{\times}\;20\;size$ matrix was used in a linear multiple regression and the scanner's color representation of scanner was better than a digital still camera's color representation. When using the sRGB color space, the original copy and the output copy had a color difference of 11. Therefore it was more efficient to use the linear multiple regression method than using the sRGB color space. After the input device underwent a color transformation, the additivity of the LCD monitor's R, G and B signal value improved and therefore the error in the linear formula transformation decreased. From this change, the LCD monitor with the GOG model applied to the color transformation became better than LCD monitors with other models applied to the color transformation. Also, the color difference varied more than 11 from the original target in CRT and LCD monitors when a sRGB color transformation was done in restricted conditions.

• Corrosion Science and Technology
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• 제22권1호
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• pp.55-63
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• 2023

The aim of this study was to propose a multiple linear regression (MLR) equation to predict ultimate tensile strength (UTS) of 316L stainless steel with unspecified pit corrosion. Tensile specimens with pit corrosion were prepared using a potentiostatic acceleration test method. Pit corrosion was characterized by measuring ten factors using a confocal laser microscope. Data were collected from 22 tensile tests. At 85% confidence level, total pit volume, maximum pit depth, mean ratio of surface area, and mean area were significant factors showing linear relationships with UTS. The MLR equation using these three significant factors at a 85% confidence level showed considerable prediction performance for UTS. Determination coefficient (R²) was 0.903 with training and test data sets. The yield strength ratio of 316L stainless steel was found to be around 0.85. All specimens with a pit corrosion presented a yield ratio of approximately 0.85 with R² of 0.998. Therefore, pit corrosion did not affect the yield ratio.