• 대한임상검사과학회지
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• 제45권2호
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• pp.54-59
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• 2013

NonHDL cholesterol values have been suggested as a risk marker for cardiovascular disease. NonHDL cholesterol values were calculated, using a very simple measurement [nonHDL cholesterol=serum total cholesterol-HDL cholesterol]. This formula is very useful as a screening tool for identifying dyslipoproteinemias, risk assessment, and assessing the results of hypolipidemic therapy. The data from the 2009 Korean National Health and Nutrition Examination Survey were used. Analysis was done for 1,992 subjects with lipid panels (Cholesterol, HDL, LDLdirect and Triglycerides) results. We studied the relationship between nonHDL cholesterol and LDL cholesterol. As a result, nonHDL cholesterol values were plotted against the LDL direct and calculated values. The linear regression equation for nonHDL cholesterol and direct LDL cholesterol was $nonHDLchol=23.60+1.03{\times}LDLdirect$ (p<0.0001, $r^2=0.80$) in all subjects. The subjects were classified into triglyceride values. When triglycerides are below 400 mg/dL, the linear fit to LDL direct is found to be $[nonHDLchol=17.34+1.07{\times}LDLdirect]$ (p<0.0001, $r^2=0.88$) and to the Friedewald LDL calculation is $[nonHDLchol=23.10+1.02{\times}LDLcalc]$ (p<0.0001, $r^2=0.82$). For triglycerides above 400 mg/dL, the linear fit equation is $[nonHDLchol=87.57+0.92{\times}LDLdirect]$ (p<0.0001, $r^2=0.50$) and to the LDL calculated, it is $[nonHDLchol=142.70+0.50{\times}LDLcalc]$ (p<0.0001, $r^2=0.32$). This study provides examples of the utility of nonHDL cholesterol concentrations in clinical medicine.

• 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.

• Bulletin of the Korean Chemical Society
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• 제26권1호
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• pp.139-145
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• 2005

Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q$^+$), dipole moment ($\mu$), the Hildebrand solubility parameter (${\delta}_H^2$) and total charges in molecule (q$_t$) are inputs and output of ANN is log k$_2$ . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k$_2$ of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.159-159
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• 2022

Soil represents a substantial component within the global carbon cycle and small changes in the SOC stock may result in large changes of atmospheric CO₂ particularly over tens to hundreds of years. In this study, we aim to (i) evaluate the SOC stock in the topsoil 0 - 15 cm from soil physical and chemical characteristics and (ii) find the correlation of SOC and soil organic matter (SOM) for national-scale in South Korea. First of all, based on the characteristics of the soil to calculate the soil hydraulic properties, SOC stock is the SOC mass per unit area for a given depth. It depends on bulk density (BD-g/cm³), SOC content (%), the depth of topsoil (cm), and gravel content (%). Due to insufficient data on BD observation, we establish a correlation between BD and SOC content, sand content, clay content parameter. Next, we present linear and non-linear regression models of BD and the interrelationship between SOC and SOM using a linear regression model and determine the conversion factor for them, comparing with Van Bemmelen 1890's factor value for the country scale. The results obtained, helps managers come up with suitable solutions to conserve land resources.

• Journal of Biosystems Engineering
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• 제41권3호
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• pp.273-280
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• 2016

Purpose: This experiment was conducted to detect water stress in terms of the moisture content of cucumber seedlings under water stress condition using a hyperspectral image acquisition system, linear regression analysis, and partial least square regression (PLSR) to achieve a non-destructive measurement procedure. Methods: Changes in the reflectance spectrum of cucumber seedlings under water stress were measured using hyperspectral imaging techniques. A model for estimating moisture content of cucumber seedlings was constructed through a linear regression analysis that used the moisture content of cucumber seedlings and a normalized difference vegetation index (NDVI). A model using PLSR that used the moisture content of cucumber seedlings and reflectance spectrum was also created. Results: In the early stages of water stress, cucumber seedlings recovered completely when sub-irrigation was applied. However, the seedlings suffering from initial wilting did not recover when more than 42 h passed without irrigation. The reflectance spectrum of seedlings under water stress decreased gradually, but increased when irrigation was provided, except for the seedlings that had permanently wilted. From the results of the linear regression analysis using the NDVI, the model excluding wilted seedlings with less than 20% (n=97) moisture content showed a precision ($R^2$ and $R^2_{\alpha}$) of 0.573 and 0.568, respectively, and accuracy (RE) of 4.138% and 4.138%, which was higher than that for models including all seedlings (n=100). For PLS regression analysis using the reflectance spectrum, both models were found to have strong precision ($R^2$) with a rating of 0.822, but accuracy (RMSE and RE) was higher in the model excluding wilted seedlings as 5.544% and 13.65% respectively. Conclusions: The estimation model of the moisture content of cucumber seedlings showed better results in the PLSR analysis using reflectance spectrum than the linear regression analysis using NDVI.

• 산업융합연구
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• 제16권4호
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• pp.33-46
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• 2018

개인신용평가는 은행이 대출을 승인할 때 수익성 있는 의사결정을 적절히 유도할 수 있는 효과적인 도구이다. 최근 많은 분류 알고리즘 및 모델이 개인신용평가에 사용되고 있다. 개인신용평가 기법은 대체로 통계적 방법과 비 통계적 방법으로 구분된다. 통계적 방법에는 선형회귀분석, 판별분석, 로지스틱 회귀분석, 의사결정나무 등이 포함된다. 비 통계적 방법에는 선형계획법, 신경망, 유전자 알고리즘 및 Support Vector Machines 등이 포함된다. 그러나 신용평가모형 개발을 위해 어떠한 방법이 최선인지에 관해서는 일관된 결론을 내리기는 어렵다. 본 논문에서는 중국 금융기관의 개인 신용 데이터를 사용하여 가장 대표적인 신용평가 기법인 로지스틱 회귀분석, 신경망 그리고 Support Vector Machines의 성능을 비교하고자 한다. 구체적으로, 세 가지 모형을 각각 구축하여 고객을 분류하고 분석 결과를 비교하였다. 분석결과에 따르면, Support Vector Machines이 로지스틱 회귀분석과 신경망보다 더 나은 성능을 가지는 것으로 나타났다.

• Corrosion Science and Technology
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• 제18권2호
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• pp.61-71
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• 2019

Flow-Accelerated Corrosion (FAC) is a phenomenon in which a protective coating on a metal surface is dissolved by a flow of fluid in a metal pipe, leading to continuous wall-thinning. Recently, many countries have developed computer codes to manage FAC in power plants, and the FAC prediction model in these computer codes plays an important role in predictive performance. Herein, the FAC prediction model was developed by applying a machine learning method and the conventional nonlinear regression method. The random forest, a widely used machine learning technique in predictive modeling led to easy calculation of FAC tendency for five input variables: flow rate, temperature, pH, Cr content, and dissolved oxygen concentration. However, the model showed significant errors in some input conditions, and it was difficult to obtain proper regression results without using additional data points. In contrast, nonlinear regression analysis predicted robust estimation even with relatively insufficient data by assuming an empirical equation and the model showed better predictive power when the interaction between DO and pH was considered. The comparative analysis of this study is believed to provide important insights for developing a more sophisticated FAC prediction model.

• 한국콘텐츠학회논문지
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• 제9권8호
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• pp.147-157
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• 2009

최근 객체지향 개발 방법론을 적용하는 소프트웨어 개발 프로젝트에서 개발 노력 추정 기법으로 사용사례점수(Use Case Point, UCP)에 대한 연구가 계속되고 있다. 기존의 연구는 기술적 요인과 환경적 요인을 적용한 AUCP(Adjusted Use Case Point)에 상수를 곱하여 개발 노력을 추정하는 선형모델을 제안하고 있다. 그러나 소프트웨어 규모가 증가하면 개발기간은 기하급수적으로 증가함으로서 비선형 회귀모델이 적합하다는 사실과 UCP 계산과정에서 TCF(Technical Complexity Factor)와 EF(Environmental Factor)를 적용함에 따른 FP(Function Point) 오차가 발생함으로서 AUCP로 규모를 추정하는 것은 비현실적이다. 이 논문은 사용사례점수 기반의 기존 연구의 문제점을 제시하고, 기존 연구의 문제점인 TCF와 EF를 고려하지 않고 직접 UUCP로 부터 개발 노력을 추정할 수 있는 모델(선형, 로그형, 다항식, 거듭제곱, 지수형)을 도출하고 평가한다. 그 결과, 기존의 선행 모델보다 비선형모델인 지수형 모델이 우수한 결과를 보였다. 따라서 개발될 소프트웨어 시스템의 UUCP를 계산한 후 제안된 모델을 이용하여 개발 노력을 추정함으로서 개발에 소요되는 직접비용 산정이 가능하다.

• 한국농공학회지
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• 제45권5호
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• pp.140-150
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• 2003

Observational methods such as the Asaoka's method and the hyperbolic method are widely applied on the settlement analysis using observed settlement. The most unreliable aspects in those methods is arose from the subjective discretion of initial non-linearity on linear regression. The initial non-linearity is inevitable due to the settlement behaviour itself. Therefore an objective method is essential to achieve more reliable results on settlement analysis. It was found that the initial non-linear data are statistical outliers. New automation algorithms of the hyperbolic and the Asaoka's method were developed based on outlier detection method. The methods are a successive detection of outliers and a searching method of suitable hyperbolic range for the Asaoka's and the hyperbolic method respectively. Applicability of the algorithms was verified through case studies.

• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.131-141
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• 2003

In this paper, we prove that multi-variate fuzzy polynomials are universal approximators for multi-variate fuzzy functions which are the extension principle of continuous real-valued function under $T_W-based$ fuzzy arithmetic operations for a distance measure that Buckley et al.(1999) used. We also consider a class of fuzzy polynomial regression model. A mixed non-linear programming approach is used to derive the satisfying solution.