• Journal of Biosystems Engineering
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• 제23권6호
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• pp.571-582
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• 1998

The PLS(Partial Least Square) and ANN(Artificial Neural Network) were introduced to develop the soluble solids content prediction model of apples which is followed by making a subsequent selection of photosensor. For the optimal PLS model, number of factors needed for spectrum analysis were increased until the convergence of prediction residual error sum of squares. Analysis has shown that even part of the overall wavelength with no pretreatment may turn out better performing. The best PLS model was found in the 800 to 1,100nm wavelength region without pretreatment of second derivation, having $R^2$=0.9236, bias= -0.0198bx, SEP=0.2527bx for unknown samples. On the other hand, for the ANN model the second derivation led to higher performance. On partial range of 800 to 1,100nm wavelengh region, prediction model with second derivation for unknown samples reached $R^2$=0.9177, SEP=0.2903bx in contrast to $R^2$=0.7507, SEP =0.4622bx without pretreatment.

• 한국전산구조공학회논문집
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• 제20권4호
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• pp.493-499
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• 2007

본 연구에서는 미분 가능한 함수가 Taylor 전개로 표현되고 그 계수들은 주어진 함수와 미분에 대한 근사값을 제공할 수 있다는 점에 착안하여 m차 Taylor 다항식을 구성하고 이동최소제곱법을 이용하여 그 계수들을 구했다. 계산된 근사함수와 미분을 콜로케이션 개념을 바탕으로 균열 문제를 포함하는 고체문제에 대한 지배 미분방정식에 적용하여 차분식 형태의 이산화된 계방정식을 구성하였다. 본 연구의 해석기법은 격자망(grid)에 의존적이고 근사함수가 없는 유한차분법과 형상함수의 미분과 약형식의 적분산정, 필수경계조건 처리가 어려운 Galerkin법 기반의 무요소법의 단점을 효과적으로 극복한 새로운 수치기법이다.

• 한국전산구조공학회논문집
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• 제27권1호
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• pp.17-26
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• 2014

본 논문은 MLS(Moving Least Squares) 차분법을 바탕으로 동적균열전파 해석을 수행하기 위한 알고리즘을 제시한다. MLS 차분법은 절점만으로 이루어진 수치모델을 사용하며, 이동최소제곱법을 이용하여 전개한 Taylor 다항식을 기초로 미분근사식을 유도하기 때문에, 요소망의 제약에서 완벽하게 벗어난 절점해석이 가능하다. 시간항을 포함하는 동적 평형방정식은 Newmark 방법으로 시간적분 하였다. 동적하중을 받는 균열이 전파할 때, 매 시간단계마다 절점모델을 재구성하지 않고 균열선단 주변에서 국부적인 수정을 통해 해석이 가능하다. 동적균열을 묘사하기 위해 가시한계법(visibility criterion)을 적용하였고, 동적 에너지해방률을 산정하여 균열의 진전유무와 그에 상응하는 진전방향을 결정하였다. 모드 I 상태와 혼합모드 상태에서 균열이 진전하는 현상을 모사하였고, 이론해와 Element-Free Galerkin법으로 계산한 결과와의 비교를 통해 개발된 알고리즘의 정확성과 안정성을 검증하였다.

• 대한금속재료학회지
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• 제56권11호
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• pp.813-821
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• 2018

The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature.

• 품질경영학회지
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• 제39권1호
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• pp.90-97
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• 2011

The purpose of this study is to analyze various factors comprising a job satisfaction; determine possible factors that affects job satisfaction. Job satisfaction model is designed to evaluate major factors, such as job stress and strength, and to assess relationship between these factors. Partial least squares algorithm is used to develop a job satisfaction measurement model. To evaluate validity of developed model, survey data of health insurance review and assessment service is to applied.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.102.1-102
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• 2001

In this work, extensions to partial least squares (PLS) for wastewater treatment (WWT) process monitoring are discussed. Conventional data gathered by monitoring WWT systems are usually time varying, high dimensional, correlated and nonlinear, PLS has been shown to be an efficient approach in modeling and monitoring high dimensional and correlated data. To represent dynamic and nonlinear features of the data several kinds of dynamic nonlinear PLS (DNLPLS) models have been proposed. However, the complexity and ambiguity of the models make them unsuitable for WWT monitoring, Recently, dynamic fuzzy PLS (DFPLS) was proposed ...

• 한국경영과학회:학술대회논문집
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• 한국경영과학회/대한산업공학회 2003년도 춘계공동학술대회
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• pp.76-80
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• 2003

The procedure of finding operating conditions minimizing qualify loss is proposed with a real industry example. The procedure consists or major two parts - the selection or process variables critical to the response and He determination or operating conditions. The coefficients or ridge regression and the and stores or partial least squares are applied to select important process variables. Functional approach and Non-functional approach are used to find proper operating conditions of important process variables.

• Bulletin of the Korean Chemical Society
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• 제27권12호
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• pp.2101-2103
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• 2006

• Bulletin of the Korean Chemical Society
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• 제19권11호
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• pp.1189-1193
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• 1998

The determination of total paraffin, naphthene, and aromatic (PNA) contents in naphtha samples, which were directly obtained from actual refining process, has been studied using near-infrared (NIR) spectroscopy. Each of the total PNA concentrations in naphtha has been successfully analyzed using NIR spectroscopy. Partial least squares (PLS) regression method has been utilized to quantify the total PNA contents in naphtha from the NIR spectral bands. The NIR calibration results showed an excellent correlation with those of conventional gas chromatography (GC). Due to its rapidity and accuracy, NIR spectroscopy is appeared as a new analytical technique which can be substituted for the conventional GC method for the quantitative analysis of petrochemical products including naphtha.

• Bulletin of the Korean Chemical Society
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• 제20권2호
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• pp.159-162
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• 1999

Three different chemical compositions of total paraffin, total naphthene, total aromatic content in naphtha have been successfully analyzed using FT-Raman spectroscopy. Partial least squares (PLS) regression has been utilized to develop calibration models for each composition from Raman spectral bands. The PLS calibration results showed Blood correlation with those of gas chromatography (GC). Using PLS regression, the spectral information related to each composition has been successfully extracted from highly overlapped Raman spectra of naphtha.