• Journal of the Korean Wood Science and Technology
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• 제49권3호
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• pp.213-225
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• 2021

본 논문에서는 열수 탄화(hydrothermal carbonization)에 의해 제조된 리그닌 하이드로차의 탄화 특성을 조사하였고, 근적외선 분광법과 부분 최소 제곱(partial least squares) 회귀를 이용하여 탄화 거동을 예측하기 위한 모델을 수립하였다. 온도 200℃에서 열수 탄화된 리그닌의 탄소 함량은 무처리 시료 보다 약 3 wt% 높았으며 가열 시간이 증가할수록 탄소 함량도 서서히 증가하는 경향이 나타났다. 열수 탄화는 리그닌을 더욱 탄소 집약적으로 변화시키고 마이크로 파티클을 제거하여 더욱 균질한 특성을 부여하였다. 근적외선 분광법과 부분 최소 제곱 회귀를 이용한 판별 및 예측 모델은 수열 탄화의 적용 여부를 완벽히 구분했으며 높은 정확도로 열수 탄화 리그닌의 탄소 함량을 예측하였다. 본 연구로부터 근적외선 분광법과 결합된 부분 최소 제곱 회귀 모델을 이용하여 열수 탄화에 의해 제조된 리그닌 하이드로차의 탄화 특성을 빠르고 비파괴적으로 예측할 수 있다는 것이 확인되었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2002년도 학술발표회 논문집(I)
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• pp.541-546
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• 2002

• 상하수도학회지
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• 제12권3호
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• pp.99-106
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• 1998

This paper presents a procedure for determining the design rainfall depth and the design rainfall intensity at Incheon city area in Korea. In this study the eight probability distributions are considered to estimate the probable rainfall depths for 11 different durations. The Kolmogorov - Smirnov test and the Chi-square test are adopted to test each distribution. The probable rainfall intensity formulas are then determined by i) the least squares (LS) method, ii) the least median squares (LMS) method, iii) the reweighted least squares method based on the LMS (RLS), and iv) the constrained regression (CR) model. The Talbot, the Sherman, the Japanese, and the Unified type are considered to determine the best type for the Incheon station. The root mean squared (RMS) errors are computed to test the formulas derived by four methods. It is found that the Unified type is the most reliable and that all methods presented herein are acceptable for determining the coefficients of rainfall intensity formulas from an engineering point of view.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.873-878
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• 2003

LS-SVM(Least-Squares Support Vector Machine) has been used as a promising method for regression as well as classification. Suykens et al.(2000) used only the magnitude of residuals to obtain SVs(Support Vectors). Suykens' method behaves well for homogeneous model. But in a heteroscedastic model, the method shows a poor behavior. The present paper proposes a new method to get SVs. The proposed method uses the variance of noise as well as the magnitude of residuals to obtain support vectors. Through the simulation study we justified excellence of our proposed method.

• Asian Pacific Journal of Cancer Prevention
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• 제15권7호
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• pp.3145-3149
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• 2014

Gene expression profiling facilitates the understanding of biological characteristics of gliomas. Previous studies mainly used regression/variance analysis without considering various background biological and environmental factors. The aim of this study was to investigate gene expression differences between grade III and IV gliomas through partial least squares (PLS) based analysis. The expression data set was from the Gene Expression Omnibus database. PLS based analysis was performed with the R statistical software. A total of 1,378 differentially expressed genes were identified. Survival analysis identified four pathways, including Prion diseases, colorectal cancer, CAMs, and PI3K-Akt signaling, which may be related with the prognosis of the patients. Network analysis identified two hub genes, ELAVL1 and FN1, which have been reported to be related with glioma previously. Our results provide new understanding of glioma pathogenesis and prognosis with the hope to offer theoretical support for future therapeutic studies.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.303-312
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• 1995

We consider the following type of general semi-parametric non-linear regression model : $y_i = f_i(\theta) + \epsilon_i, i=1, \cdots, n$ where ${f_i(\cdot)}$ represents the set of non-linear functions of the unknown parameter vector $\theta' = (\theta_1, \cdots, \theta_p)$ and ${\epsilon_i}$ represents the set of measurement errors with unknown distribution. Under suitable finite-sample, small-dispersion asymptotic framework, we derive a general lower bound for the asymptotic mean squared error (AMSE) matrix of the Gauss-consistent estimator of $\theta$. We then prove the fundamental result that the general non-linear least squares estimator (NLSE) is an optimal estimator within the class of all regular Gauss-consistent estimators irrespective of the type of the distribution of the measurement errors.

• Bulletin of the Korean Chemical Society
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• 제27권6호
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• pp.863-868
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• 2006

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of sulfite and sulfide is described. This method is based on the difference between the rate of the reaction of sulfide and sulfite with Malachite Green in pH 7.0 buffer solution and at 25 ${^{\circ}C}$. The absorption kinetic profiles of the solutions were monitored by measuring the decrease in the absorbance of Malachite Green at 617 nm in the time range 10-180 s after initiation of the reactions with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 24 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 0.030-1.5 and 0.030-1.2 $\mu$g m$L ^{-1}$ for sulfite and sulfide, respectively. The proposed method was successfully applied to simultaneous determination of sulfite and sulfide in water samples and whole human blood.

• Environmental Engineering Research
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• 제22권2호
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• pp.175-185
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• 2017

As the energy and environmental problems are increasingly severe, researches about carbon dioxide emissions has aroused widespread concern. The accurate prediction of carbon dioxide emissions is essential for carbon emissions controlling. In this paper, we analyze the relationship between carbon dioxide emissions and influencing factors in a comprehensive way through correlation analysis and regression analysis, achieving the effective screening of key factors from 16 preliminary selected factors including GDP, total population, total energy consumption, power generation, steel production coal consumption, private owned automobile quantity, etc. Then fruit fly algorithm is used to optimize the parameters of least squares support vector machine. And the optimized model is used for prediction, overcoming the blindness of parameter selection in least squares support vector machine and maximizing the training speed and global searching ability accordingly. The results show that the prediction accuracy of carbon dioxide emissions is improved effectively. Besides, we conclude economic and environmental policy implications on the basis of analysis and calculation.

• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.39-46
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• 2005

We propose an equivariant and robust estimator in multivariate regression model based on the least quartile difference (LQD) estimator in univariate regression. We call this estimator as the multivariate least quartile difference (MLQD) estimator. The MLQD estimator considers correlations among response variables and it can be shown that the proposed estimator has the appropriate equivariance properties defined in multivariate regressions. The MLQD estimator has high breakdown point as does the univariate LQD estimator. We develop an algorithm for MLQD estimate. Simulations are performed to compare the efficiencies of MLQD estimate with coordinatewise LQD estimate and the multivariate least trimmed squares estimate.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.507-518
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• 1995

The ordinary least squares based estiamte $S^2$ of the disturbance variance is considered in the linear regression model when the disturbances follow the first-order moving-average process. It is shown that $S^2$ is weakly consistent estimate for the disturbance varaince without any restriction on the regressor matrix X. Also, simple exact bounds on the relative bias of $S^2$ are given in finite sample sizes.