• 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.

• Earthquakes and Structures
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• 제24권6호
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• pp.429-437
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• 2023

This paper studied the prediction of structural damage indices to buildings after earthquake occurrence using Multiple Linear Regression (MLR) and Fuzzy Linear Regression (FLR) methods. Particularly, the structural damage degree, represented by the Maximum Inter Story Drift Ratio (MISDR), is an essential factor that ensures the safety of the building. Thus, the seismic response of a steel building was evaluated, utilizing 65 seismic accelerograms as input signals. Among the several response quantities, the focus is on the MISDR, which expresses the postseismic damage status. Using MLR and FLR methods and comparing the outputs with the corresponding evaluated by nonlinear dynamic analyses, it was concluded that the FLR method had the most accurate prediction results in contrast to the MLR method. A blind prediction applying a set of another 10 artificial accelerograms also examined the model's effectiveness. The results revealed that the use of the FLR method had the smallest average percentage error level for every set of applied accelerograms, and thus it is a suitable modeling tool in earthquake engineering.

• 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.

• Near Infrared Analysis
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• 제1권1호
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• pp.1-7
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• 2000

A convenient algorithm for optimizing wavelength selection in multiple linear regression (MLR) has been developed. MOP (MLP Optimization Program) has been developed to test all possible MLR calibration models in a given spectral range and finally find an optimal MLR model with external validation capability. MOP generates all calibration models from all possible combinations of wavelength, and simultaneously calculates SEC (Standard Error of Calibration) and SEV (Standard Error of Validation) by predicting samples in a validation data set. Finally, with determined SEC and SEV, it calculates another parameter called SAD (Sum of SEC, SEV, and Absolute Difference between SEC and SEV: sum(SEC+SEV+Abs(SEC-SEV)). SAD is an useful parameter to find an optimal calibration model without over-fitting by simultaneously evaluating SEC, SEV, and difference of error between calibration and validation. The calibration model corresponding to the smallest SAD value is chosen as an optimum because the errors in both calibration and validation are minimal as well as similar in scale. To evaluate the capability of MOP, the determination of benzene content in unleaded gasoline has been examined. MOP successfully found the optimal calibration model and showed the better calibration and independent prediction performance compared to conventional MLR calibration.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2020년도 제62차 하계학술대회논문집 28권2호
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• pp.139-142
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• 2020

NB-IoT(Narrow Band Internet of Things) is an emerging LPWAN(Low Power Wide Area Network) radio technology. NB-IoT has many advantages like low power, low cost, and high coverage. However low bandwidth and low sampling rates also lead to poor positioning accuracy. This paper proposed a solution to optimize positioning accuracy under the OTDOA(Observed Time Difference of Arrival) approach by utilizing MLR(Multiple Linear Regression) models. Through the MLR model to predict the influence degree of weather(temperature, humidity, light intensity and air pressure) on the arrival time of signal transmission to improve the measurement accuracy. The improvement of measurement accuracy can greatly improve IoT applications based on NB-IoT.

• 한국지구과학회지
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• 제28권2호
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• pp.214-226
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• 2007

강수는 다양한 대기 변수들의 영향으로 나타나기 때문에 비선형성이 매우 강하다. 따라서 역학 모형을 통해 예측된 강수의 보정은 비선형 모형인 인공 신경망 등을 통해 가능할 것이지만, 인공 신경망의 경우 초기 가중치 선택, 지역 최소화 문제, 뉴런의 수 결정 등의 문제로 인한 한계가 있다. 그러므로 본 연구에서는 가장 보편적으로 사용되는 다중 선형 회귀 모형을 이용하여 CGCM에 의해 모사된 강수를 보정하였으며, 예측성을 살펴보았다. 이를 위하여 우선 PNU/CME 접합 대순환 모형(Coupled General Circulation model, CGCM)(박혜선과 안중배, 2004)을 이용하여 1979년부터 2005년까지 매해 4월부터 8월까지 5개월간 앙상블 적분을 하였다. 적분 결과 중 한반도를 포함한 동북아시아 지역$(110^{\circ}E-145^{\circ}E,\;25^{\circ}N-55^{\circ}N)$의 여름철인 6월(리드 2), 7월(리드 3), 8월(리드 4) 및 여름철 평균인 JJA(from June to August) 기간의 PNU/CME CGCM에 의해 모사된 강수를 보정하기 위해 다중 선형 회귀(Multiple Linear Regression, MLR)를 이용하였다. PNU/CME 접합 대순환 모형의 결과 중 강수, 500 hPa 연직 속도, 200 hPa 발산장, 지상 기온 등의 예측 인자와 관측 강수와의 선형적인 관계를 이용하여 MLR 모형을 구축하였다. 그리고 교차 검증(cross- validation)을 수행하여 PNU/CME 접합 대순환 모형의 결과와 교차 검증 결과를 비교하였다. 상관계수, 적중률 (hit rate), 오보율(false alarm rate) 그리고 Heidke 기술 점수(Heidke skill score) 등을 살펴본 바, 보정하지 않은 모형의 결과에 비해 MLR 모형을 이용하여 보정한 결과의 강수에 대한 예측성이 뛰어난 것을 알 수 있었다.

• Journal of Biosystems Engineering
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• 제23권6호
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• pp.561-570
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• 1998

The MLR(Multiple Linear Regression) models to estimate soluble solids content non-destructively were presented to make a selection of optimal photosensor utilized to measure the soluble solids content of apples. Visible and NIR absorbance in the 400 to 2498 nanometer(nm) wavelength region, soluble solids content(sugar content), hardness, and weight were measured for 400 apples(gala). Spectrophotometer with fiber optic probe was utilized for spectrum measurement and digital refractometer was used for soluble solids content. Correlation between absorbance spectrum and soluble solids content was analyzed to pick out the optimal wavelengths and to develop corresponding prediction model by means of MLR. For the coefficient of determination($R^2$) to be over 0.92, the MLR models out of the original absorbance were built based on 7 wavelengths of 992, 904, 1096, 1032, 880, 824, 1048nm, and the ones of the second derivative absorbance based on 5 wavelengths of 784, 1056, 992, 808, 872nm. The best model of the second derivative absorbance spectrum had $R^2$=0.91, bias= -0.02bx, SEP=0.28bx for unknown samples.

• Computers and Concrete
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• 제12권2호
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• pp.151-167
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• 2013

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.

• 한국연초학회지
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• 제21권1호
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• pp.36-48
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• 1999

Field test was conducted in Chungbuk province to evaluate the soil fertility using landscape and soil attributes by application of geographic information system(GIS) in 48 tobacco fields during 2 years(1996 ; 23 fields, 1997 ; 25 fields). The soil fertility factors and fertilizer effects were estimated by twenty five independent variables including 13 chemical properties and 12 GIS databases. Twenty five independent variables were classified by two groups, 15 quantitative indexes and 10 qualitative indexes and were analyzed by multiple linear regression (MLR) of SAS, REG and GLM models. The estimation model for evaluation of soil fertility and fertilizer effect was made by giving the estimate coefficient for each quantitative index and for each group of qualitative index significantly selected by MLR. Estimation for soil fertility factors and fertilizer effects by independent variables was better by MLR than single regression showing gradually improvement by adding chemical properties, quantitative indexes and qualitative indexes of GIS. Consequently, it is assumed that this approach by MLR with quantitative and qualitative indexes was available as an evaluation model of soil fertility and recommendation of optimum fertilization for tobacco field.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1625-1625
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• 2001

The calibration equations for Brix value determination of intact mango were developed using the NIR spectra in a short wavelength region from 700 to 1100 nm. Multiple linear regression (MLR) and partial least square regression (PLS) was used for the calibration. It was found that the best wavelength region for PLS calibration from 900 to 1000 nm was similar to the wavelength region selected by MLR from 906 nm to 996 nm. Both MLR and selected region PLS provided sufficiently accurate prediction equations for Brix determination of intact mango. For MLR, the prediction results were SEP = 0.45 Brix and Bias = -0.04 Brix while PLS prediction results were SEP : 0.46 Brix and Bias = -0.2 Brix. It was concluded that MLR and PLS would have similar abilities in making calibration equation for Brix determination of intact mango if the appropriate wavelengths or wavelength region were selected. The appropriate wavelength region for PLS regression could be assumed by using the wavelength region selected by MLR in place of random selection, The relationship between calibration results of MLR and PLS regression is discussed.