• Title, Summary, Keyword: Partial Least Square

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Analysis of Partial Least Square Regression on Textural Data from Back Extrusion Test for Commercial Instant Noodles (시중 즉석 조리 면의 Back Extrusion 텍스처 데이터에 대한 Partial Least Square Regression 분석)

  • Kim, Su kyoung;Lee, Seung Ju
    • Food Engineering Progress
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    • v.14 no.1
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    • pp.75-79
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    • 2010
  • Partial least square regression (PLSR) was executed on curve data of force-deformation from back extrusion test and sensory data for commercial instant noodles. Sensory attributes considered were hardness (A), springiness (B), roughness (C), adhesiveness to teeth (D), and thickness (E). Eight and two kinds of fried and non-fried instant noodles respectively were used in the tests. Changes in weighted regression coefficients were characterized as three stages: compaction, yielding, and extrusion. Correlation coefficients appeared in the order of E>D>A>B>C, root mean square error of prediction D>C>E>B>A, and relative ability of prediction D>C>E>B>A. Overall, 'D' was the best in the correlation and prediction. 'A' with poor prediction ability but high correlation was considered good when determining the order of magnitude.

A Development of Statistical Model for Pavement Response Model (도로포장 반응모형에 대한 통계모형 개발)

  • Lee, Moon Sup;Park, Hee Mun;Kim, Boo Il;Heo, Tae-Young
    • Journal of the Korea Industrial Information Systems Research
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    • v.17 no.5
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    • pp.89-96
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    • 2012
  • The Falling Weight Deflectormeter has been widely used in evaluating the structural adequacy of pavement structures. The deflections measured from the FWD are capable of estimating the stiffness of pavement layers and measuring the pavement responses in the pavement structure. The objective of paper is to develop the pavement response model using a partial least square regression technique based on the FWD deflection data. The partial least square regression method enables to solve the multicollinearity problem occurred in multiple regression model. It is also found that the pavement response model can be developed using the raw data when a partial least square regression was used.

A modified partial least squares regression for the analysis of gene expression data with survival information

  • Lee, So-Yoon;Huh, Myung-Hoe;Park, Mira
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.5
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    • pp.1151-1160
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    • 2014
  • In DNA microarray studies, the number of genes far exceeds the number of samples and the gene expression measures are highly correlated. Partial least squares regression (PLSR) is one of the popular methods for dimensional reduction and known to be useful for the classifications of microarray data by several studies. In this study, we suggest a modified version of the partial least squares regression to analyze gene expression data with survival information. The method is designed as a new gene selection method using PLSR with an iterative procedure of imputing censored survival time. Mean square error of prediction criterion is used to determine the dimension of the model. To visualize the data, plot for variables superimposed with samples are used. The method is applied to two microarray data sets, both containing survival time. The results show that the proposed method works well for interpreting gene expression microarray data.

Chlorophyll-a Forcasting using PLS Based c-Fuzzy Model Tree (PLS기반 c-퍼지 모델트리를 이용한 클로로필-a 농도 예측)

  • Lee, Dae-Jong;Park, Sang-Young;Jung, Nahm-Chung;Lee, Hye-Keun;Park, Jin-Il;Chun, Meung-Geun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.6
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    • pp.777-784
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    • 2006
  • This paper proposes a c-fuzzy model tree using partial least square method to predict the Chlorophyll-a concentration in each zone. First, cluster centers are calculated by fuzzy clustering method using all input and output attributes. And then, each internal node is produced according to fuzzy membership values between centers and input attributes. Linear models are constructed by partial least square method considering input-output pairs remained in each internal node. The expansion of internal node is determined by comparing errors calculated in parent node with ones in child node, respectively. On the other hands, prediction is performed with a linear model haying the highest fuzzy membership value between input attributes and cluster centers in leaf nodes. To show the effectiveness of the proposed method, we have applied our method to water quality data set measured at several stations. Under various experiments, our proposed method shows better performance than conventional least square based model tree method.

Exploring relationships among Korean Children's Depression, Smartphone Addiction, and School Life Satisfaction: Focusing on Partial Least Square (PLS) Path Modeling (초등학생의 우울증, 스마트폰 중독 및 학교생활 만족도의 관계에 대한 탐색: Partial Least Square(PLS) 경로모형 분석을 중심으로)

  • Joo, Jihyuk
    • Journal of Digital Convergence
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    • v.11 no.12
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    • pp.49-60
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    • 2013
  • The spread of smartphone in Korea causes several side effects and raises concerns. Especially, recently the addiction to smartphone of elementary school children has been paid attention to by their parents, teachers, and so on. After literature review, this research presented hypotheses that depression would affect children's addiction to smartphone positively and school life satisfaction (SLS) negatively, in turn their addiction to smartphone would affect SLS negatively. We employed Partial Least Square (PLS) path modeling to test the hypotheses. We found that all hypotheses were supported. The findings of this study suggest that their families and school authorities should make valid measures to lessen children's depression for preventing addicting to smartphone and, in turn, increasing SLS.

Modeling and Comparison for Auto-association using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR) in Online Monitoring Techniques (상시감시기술에서 SVR과 PLSR을 이용한 Auto-association 모델링 및 성능비교)

  • Kim, Seong-Jun;Seo, In-Yong
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.4
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    • pp.483-488
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    • 2010
  • An online monitoring based upon sensor system is essential to assure both efficient operation and safety in the power plant. Of great importance is modeling for auto-association (AA) in online monitoring technique. The objective of auto-associative models lies in predicting true values of plant operation parameters from sensor signals transmitted. This paper presents two AA models using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). The presented models are useful, in particular, when there are many parameters to monitor in the power plant. Illustrative examples are given by using a real-world plant dataset. AA performances of SVR and PLSR are finally summarized in terms of accuracy and sensitivity. According to our results, SVR shows much higher accuracy and, however, its sensitivity is relatively degraded.

A Study on Measurement of Blood Pressure by Partial Least Square Method (부분최소자승법을 이용한 혈압 측정에 관한 연구)

  • Kim, Yong-Joo;Nam, Eun-Hye;Choi, Chang-Hyun;Kim, Jong-Deok
    • Journal of Biosystems Engineering
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    • v.33 no.6
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    • pp.438-445
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    • 2008
  • The purpose of this study was to develop a measurement model based on PLS (Partial least square) method for blood pressures. Measurement system for blood pressure signals consisted of pressure sensor, va interface and embedded module. A mercury sphygmomanometer was connected with the measurement system through 3-way stopcock and used as reference of blood pressures. The blood pressure signals of 20 subjects were measured and tests were repeated 5 times per each subject. Total of 100 data were divided into a calibration set and a prediction set. The PLS models were developed to determine the systolic and the diastolic blood pressures. The PLS models were evaluated by the standard methods of the British Hypertension Society (BHS) protocol and the American Association for the Advancement of Medical Instrumentation (AAMI). The results of the PLS models were compared with those of MAA (maximum amplitude algorithm). The measured blood pressures with PLS method were highly correlated to those with a mercury sphygmomanometer in the systolic ($R^2=0.85$) and the diastolic blood pressure ($R^2=0.84$). The results showed that the PLS models were the effective tools for blood pressure measurements with high accuracy, and satisfied the standards of the BHS protocol and the AAMI.

The Development of a Fault Diagnosis Model based on the Parameter Estimations of Partial Least Square Models (부분최소제곱법 모델의 파라미터 추정을 이용한 화학공정의 이상진단 모델 개발)

  • Lee, Kwang Oh;Lee, Chang Jun
    • Journal of the Korean Society of Safety
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    • v.34 no.4
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    • pp.59-67
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    • 2019
  • Since it is really hard to construct process models based on prior process knowledges, various statistical approaches have been employed to build fault diagnosis models. However, the crucial drawback of these approaches is that the solutions may vary according to the fault magnitude, even if the same fault occurs. In this study, the parameter monitoring approach is suggested. When a fault occurs in a chemical process, this leads to trigger the change of a process model and the monitoring parameters of process models is able to provide the efficient fault diagnosis model. A few important variables are selected and their predictive models are constructed by partial least square (PLS) method. The Euclidean norms of parameters of PLS models are estimated and a fault diagnosis can be performed as comparing with parameters of PLS models based on normal operational conditions. To improve the monitoring performance, cumulative summation (CUSUM) control chart is employed and the changes of model parameters are recorded to identify the type of an unknown fault. To verify the efficacy of the proposed model, Tennessee Eastman (TE) process is tested and this model can be easily applied to other complex processes.

FCM for the Multi-Scale Problems (고속 최소자승 점별계산법을 이용한 멀티 스케일 문제의 해석)

  • 김도완;김용식
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • pp.599-603
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    • 2002
  • We propose a new meshfree method to be called the fast moving least square reproducing kernel collocation method(FCM). This methodology is composed of the fast moving least square reproducing kernel(FMLSRK) approximation and the point collocation scheme. Using point collocation makes the meshfree method really come true. In this paper, FCM Is shown to be a good method at least to calculate the numerical solutions governed by second order elliptic partial differential equations with geometric singularity or geometric multi-scales. To treat such problems, we use the concept of variable dilation parameter.

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A Method for Screening Product Design Variables for Building A Usability Model : Genetic Algorithm Approach (사용편의성 모델수립을 위한 제품 설계 변수의 선별방법 : 유전자 알고리즘 접근방법)

  • Yang, Hui-Cheol;Han, Seong-Ho
    • Journal of the Ergonomics Society of Korea
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    • v.20 no.1
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    • pp.45-62
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    • 2001
  • This study suggests a genetic algorithm-based partial least squares (GA-based PLS) method to select the design variables for building a usability model. The GA-based PLS uses a genetic algorithm to minimize the root-mean-squared error of a partial least square regression model. A multiple linear regression method is applied to build a usability model that contains the variables seleded by the GA-based PLS. The performance of the usability model turned out to be generally better than that of the previous usability models using other variable selection methods such as expert rating, principal component analysis, cluster analysis, and partial least squares. Furthermore, the model performance was drastically improved by supplementing the category type variables selected by the GA-based PLS in the usability model. It is recommended that the GA-based PLS be applied to the variable selection for developing a usability model.

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