• Title/Summary/Keyword: PLS Regression

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Determination of Human Skin Moisture in the Near-Infrared Region from 1100 to 2200 nm by Portable NIR System (1100∼2200 nm 파장 영역의 휴대용 근적외선 분광분석기를 이용한 사람피부의 수분측정)

  • 안지원;서은정;우영아;김효진
    • YAKHAK HOEJI
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    • v.47 no.3
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    • pp.148-153
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    • 2003
  • Skin moisture is an important factor in skin health. Measurement of moisture content can provide diagnostic information on the condition of skin. In this study, a portable near-infrared (NIR) system was newly integrated with a photo diode array detector that has no moving parts, and this system has been successfully applied for the evaluation of human skin moisture. Diffuse reflectance spectra were collected and transformed to absorbance using 1 nm step size over the wavelength range of 1100 nm to 2200 nm. Partial least squares regression (PLSR) was applied to develop a calibration model. For practical use for the evaluation of human skin moisture, the PLS model for human skin moisture was developed in vivo using the portable NIR system on the basis of the relative water content values of stratum corneum from the conventional capacitance method. The PLS model showed a good correlation. The calibration with the use of PLS model predicted human moisture with a standard error of prediction (SEP) of 3.5 at 1120∼1730 nm range. This study showed the possibility of skin moisture measurement using portable NIR system.

A Statistical Approach to Screening Product Design Variables for Modeling Product Usability (사용편의성에 영향을 미치는 제품 설계 변수의 통계적 선별 방법)

  • Kim, Jong-Seo;Han, Seong-Ho
    • Journal of the Ergonomics Society of Korea
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    • v.19 no.3
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    • pp.23-37
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    • 2000
  • Usability is one of the most important factors that affect customers' decision to purchase a product. Several studies have been conducted to model the relationship between the product design variables and the product usability. Since there could be hundreds of design variables to be considered in the model, a variable screening method is required. Traditional variable screening methods are based on expert opinions (Expert screening) in most Kansei engineering studies. Suggested in this study are statistical methods for screening important design variables by using the principal component regression(PCR), cluster analysis, and partial least squares(PLS) method. Product variables with high effect (PCR screening and PLS screening) or representative variables (Cluster screening) can be used to model the usability. Proposed variable screening methods are used to model the usability for 36 audio/visual products. The three analysis methods (PCR, Cluster, and PLS) show better model performance than the Expert screening in terms of $R^2$, the number of variables in the model, and PRESS. It is expected that these methods can be used for screening the product design variables efficiently.

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Estimation of VOCs Affecting a Used Car Air Conditioning Smell via PLSR (부분최소자승법을 이용한 중고차 에어컨냄새 원인물질 추정)

  • You, Hanmin;Lee, Taehee;Sung, Kiwoo
    • Transactions of the Korean Society of Automotive Engineers
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    • v.21 no.6
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    • pp.175-182
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    • 2013
  • Lately, customers think highly of the emotional satisfaction and as a result, issues on odor are matters of concern. The cases are odor of interior material and air-conditioner of vehicles. In particualar, with respect to the odor of air-conditioner, customers strongly claimed defects with provocative comments : "It smells like something rotten," "It smells like a foot odor," "It stinks like a rag." Generally, it is known that mold of evaporator core in the air-conditioning system decays and this produce VOCs which causes the odor to occur. In this study, partial least squares regression model is applied to predict the strength of the odor and select of important VOCs which affect car air conditioning smell. The PLS method is basically a particular multilinear regression algorithm which can handle correlated inputs and limited data. The number of latent variable is determined by the point which is stabilized mean absolute deviations of VOCs data. Also multiple linear regression is carried out to confirm the validity of PLS method.

Variable Selection in PLS Regression with Penalty Function (벌점함수를 이용한 부분최소제곱 회귀모형에서의 변수선택)

  • Park, Chong-Sun;Moon, Guy-Jong
    • Communications for Statistical Applications and Methods
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    • v.15 no.4
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    • pp.633-642
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    • 2008
  • Variable selection algorithm for partial least square regression using penalty function is proposed. We use the fact that usual partial least square regression problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Then simulated annealing algorithm can be used in searching for optimal solutions of above maximization problem with penalty functions added. The HARD penalty function would be suggested as the best in several aspects. Illustrations with real and simulated examples are provided.

Predicting Site Quality by Partial Least Squares Regression Using Site and Soil Attributes in Quercus mongolica Stands (신갈나무 임분의 입지 및 토양 속성을 이용한 부분최소제곱 회귀의 지위추정 모형)

  • Choonsig Kim;Gyeongwon Baek;Sang Hoon Chung;Jaehong Hwang;Sang Tae Lee
    • Journal of Korean Society of Forest Science
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    • v.112 no.1
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    • pp.23-31
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    • 2023
  • Predicting forest productivity is essential to evaluate sustainable forest management or to enhance forest ecosystem services. Ordinary least squares (OLS) and partial least squares (PLS) regression models were used to develop predictive models for forest productivity (site index) from the site characteristics and soil profile, along with soil physical and chemical properties, of 112 Quercus mongolica stands. The adjusted coefficients of determination (adjusted R2) in the regression models were higher for the site characteristics and soil profile of B horizon (R2=0.32) and of A horizon (R2=0.29) than for the soil physical and chemical properties of B horizon (R2=0.21) and A horizon (R2=0.09). The PLS models (R2=0.20-0.32) were better predictors of site index than the OLS models (R2=0.09-0.31). These results suggest that the regression models for Q. mongolica can be applied to predict the forest productivity, but new variables may need to be developed to enhance the explanatory power of regression models.

Estimation of product compositions for multicomponent distillation columns

  • Shin, Joonho;Lee, Moonyong;Park, Sunwon
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10a
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    • pp.295-298
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    • 1996
  • In distillation column control, secondary measurements such as temperatures and flows are widely used in order to infer product composition. This paper addresses the design of static estimators using the secondary measurements for estimating the product compositions of the multicomponent distillation columns. Based on the unified framework for the estimator problems, the relationships among several typical static estimators are discussed including the effect of the measured inputs. Design guidelines for the composition estimator using PLS regression are also presented. The estimator based on the guidelines is robust to sensor noise and has a good predictive power.

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Development of Prediction Model for Moisture and Protein Content of Single Kernel Rice using Spectroscopy (분광분석법을 이용한 단립 쌀의 함수율 및 단백질 함량 예측모델 개발)

  • 김재민;최창현;민봉기;김종훈
    • Journal of Biosystems Engineering
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    • v.23 no.1
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    • pp.49-56
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    • 1998
  • The objectives of this study were to develop models to predict the contents of moisture and protein of single kernel of brown rice based on visible/NIR (near-infrared) spectroscopic technique. The reflectance spectra of rice were obtained in the range of the wavelength 400 to 2,500 nm with 2 nm intervals. Multiple linear regression(MLR) and partial least squares (PLS) were used to develop the models. The MLR model using the first derivative spectra(10 nm of gap) with Standard Normal Variate and Detrending (SNV and Drt.) preprocessing showed the best results to predict moisture content of the sin린e kernel brown rice. To predict the protein content of a single kernel of brown ricer the PLS model used the raw spectra with multiplicative scatter correction(MSC) preprocessing over the wavelength of 1,100~1,500 nm.

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Use of partial least squares analysis in concrete technology

  • Tutmez, Bulent
    • Computers and Concrete
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    • v.13 no.2
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    • pp.173-185
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    • 2014
  • Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.

Identifying Regional Characteristics Faxtors Affecting the Number of Tuberculosis Death - The Comparative Analysis between Urban and Rural areas - (결핵 사망자수에 영향을 미치는 지역특성 요인 규명 - 도시 및 비도시지역 비교분석 -)

  • Yoon, Sanghoon;Park, Keunoh
    • Journal of the Society of Disaster Information
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    • v.16 no.3
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    • pp.513-525
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    • 2020
  • Purpose: The purpose of this study is to analyze the characteristics of local factors affecting number of tuberculosis death by urban and rural areas. Method: The Partial Least Square(PLS) Regression analysis was used to solve the problem of multicollinearity and number of samples. Result: As a result of analysis, The number of tuberculosis deaths in urban and rural areas is about three times as large. As a result of analysis about Regional Characteristics Factor, In general, children, elderly people, and economically vulnerable populations are more likely to be exposed to tuberculosis. In differential results, it shows that environmental factors such as ultrafine dust and sulfur dioxide have a significant impact on the number of tuberculosis deaths in urban areas and social factors such as depression experience rate in rural areas. Conclusion: The Tuberculosis prevention and management policies that reflect the characteristics of urban and rural areas are needed in the future.

A Study on the Decision-making Factors of Living-in Idea into Unsold Apartment of Metropolitan Area (수도권 미분양아파트 구매의사결정 영향요인 분석)

  • Tak, Jung-Ho;Rho, Jeong-Hyun
    • The Journal of the Korea Contents Association
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    • v.17 no.4
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    • pp.247-255
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    • 2017
  • The study figured out the preference factors which should be considered for investor on decision making of unsold apartment and analyzed by comparing the difference according to the type. Then, it investigated the preference factors through the previous studies to analyze the influence factor of decision making and demonstrated the effects through the PLS (Partial Least Squares) regression. In addition, it separated the target type to tenants and construction firms and carried out the survey for comparing the preference factors of investor type. The result of analysis found out that tenants emphasis on preference factors such as the internal factor (1.141), conditional relaxation (1.114), environment factor (1.107), social factor (1.048), external factor (1.030), educational environment factor (1.010) and etc. Then, construction firms emphasis on preference factors such as the social factor (1.401), environment factor (1.251), conditional relaxation (1.133) and etc. In addition, common preferences factors are the conditional relaxation, social factor, environment factor.