Dynamic Partial Least Square Path Modeling for the Front-end Product Design and Development

2012 ◽  
Vol 134 (10) ◽  
Author(s):  
Chathura Withanage ◽  
Taezoon Park ◽  
Truong Ton Hien Duc ◽  
Hae-Jin Choi
Keyword(s):  
Least Squares ◽  
Product Design ◽  
Partial Least Squares ◽  
Partial Least Square ◽  
Least Square ◽  
Path Model ◽  
Percentage Error ◽  
Product Attributes ◽  
Design And Development ◽  
Front End

The dynamic nature of today’s technology market requires new value-characteristic modeling methods; mainstream methods have limitations due to unrealistic assumptions, such as static customer preferences and no multicollinearity among product attributes. In particular, products with longer cycle times can suffer because the static model ignores changes in the market during the concept-to-customer lead time. This study proposes a dynamic, partial least squares path model for customer driven product design and development in order to reduce model uncertainty by formulating preference models to reflect market dynamics. The proposed dynamic model adopted partial least squares regression to handle the limited observations plagued by multicollinearity among product attributes. The main advantage of the proposed model is its ability to evaluate design alternatives during the front-end concept screening phase, using the overall product-value metric, customer-revealed value. A case study analyzing the US car market data for sedans from 1990 to 2010 showed the potential for the proposed method to be effective, with a 3.40 mean absolute percentage error.

Research on Relevance Vector Machine Model Based on Kernel Partial Least Square in Properties of Engineering Materials

2014 ◽  
Vol 952 ◽  
pp. 311-314
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Feature Extraction ◽  
Least Squares ◽  
Partial Least Squares ◽  
Relevance Vector Machine ◽  
Partial Least Square ◽  
Least Square ◽  
Machine Model ◽  
Prediction Of Properties ◽  
Kernel Partial Least Squares ◽  
Engineering Materials

To improve the prediction of properties of engineering materials, a Relevance Vector Machine (RVM) regression algorithm based on Kernel Partial Least Squares (KPLS) is proposed. In the algorithm, firstly execute the feature extraction from the original samples using KPLS, and then use obtained feature to realize RVM regression. The simulation shows that the hybrid regression algorithm can effectively reduce the difficulty on RVM modeling and has a wide application in prediction of properties of engineering materials.

A Consensus Partial Least Squares Regression for Analysis of Near-Infrared Spectroscopy

2013 ◽  
Vol 765-767 ◽  
pp. 528-531
Author(s):  
Dan Peng ◽  
Qing Chen Nie
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Squares Regression ◽  
Prediction Ability ◽  
Successful Model ◽  
Consensus Strategy

To improve the prediction performance of partial least square regression algorithm (PLS), the consensus strategy was applied to develop the multivariate regression model using near-infrared (NIR) spectra and named as C-PLS. Coupled with the consensus strategy, this algorithm can take the advantage of reducing dependence on single model to obtain prediction precision and stability by randomly changing the calibration set. Through an optimization of the parameters involved in the model including criterion threshold and number of sub-models, a successful model was achieved by effectively combining many sub-models with different accuracy and diversity together. To validate the C-PLS algorithm, it was applied to measure the original extract concentration of beer using NIR spectra. The experimental results showed that the prediction ability and robustness of model obtained in subsequent partial least squares calibration using consensus strategy was superior to that obtained using conventional PLS algorithm, and the root mean square error of prediction can improve by up to 45.2%, indicating that it is an efficient tool for NIR spectra regression.

Information Granule Reduction and Cluster Based Partial Least Squares Applied in EEG Signals

2014 ◽  
Vol 496-500 ◽  
pp. 2256-2259
Author(s):  
Zhen Dong Mu ◽  
Jian Feng Hu ◽  
Jing Hai Yin
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Clustering Algorithm ◽  
Main Idea ◽  
Electrical Signal ◽  
Partial Least Square ◽  
Least Square ◽  
Complex Signal ◽  
Eeg Signals ◽  
Information Granule

EEG is a complex signal source, feature extraction and classification algorithm was studied for the brain electrical signal is also a key point in the research of brain waves, information granule clustering algorithm is one of the main idea, at the same time, the partial least square method is an effective method of dimension reduction, this paper, the use of information granule and partial least squares analysis of visual evoked potential EEG signals, the results show that this method can effectively extract the characteristics.

scholarly journals Fault identification for chiller sensor based on partial least square method

2021 ◽  
Vol 233 ◽  
pp. 03057
Author(s):  
Bang Wu ◽  
Yunpeng Hu ◽  
Chuanhui Zhou ◽  
Guaiguai Chen ◽  
Guannan Li
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Fault Identification ◽  
Least Squares Regression

Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

A Production Estimation Algorithm in Commingled Oil Wells Based Kernel Partial Least Square

2014 ◽  
Vol 1051 ◽  
pp. 1023-1027
Author(s):  
Xiao Min Yang ◽  
Bin Yu Yan ◽  
Zong Rui Yang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Oil Recovery ◽  
Petroleum Industry ◽  
Single Layer ◽  
Kernel Functions ◽  
Partial Least Square ◽  
Least Square ◽  
Oil Well ◽  
Maximum Absolute Error

Commingling is employed in the petroleum industry to enhance oil recovery and reduce costs. It is of great importance to monitor the production of each oil well oilfields. Nowadays, more and more oilfields use chromatographic fingerprint to estimate single-zone production allocation. In order to insure the efficiency and affectivity of the commingled oil well exploiting, the productivity contribution of every single layer must be acquainted. Kernel partial least squares (KPLS) is a promising regression method for tackling nonlinear systems because it can efficiently compute regression coefficients in high-dimensional feature spaces by means of nonlinear kernel functions. Unlike other nonlinear partial least squares (PLS) techniques KPLS does not entail any nonlinear optimization procedures and has a complexity similar to that of linear PLS. Using the technology of crude oil chromatography fingerprint, an algorithm for predicting productivity contribution based on KPLS is proposed. The validity of the method is proved by laboratory artificial experiments. The maximum absolute error of predicted and real proportion is less than 10%. The model can also be applied to other wells which are similar to those used in the experiment. The experiment results show the prediction model is feasible.

scholarly journals PENGARUH MOTIVASI BELAJAR, KEAKTIFAN MAHASISWA DAN KINERJA DOSEN TERHADAP PRESTASI BELAJAR MAHASISWA: PENDEKATAN PARTIAL LEAST SQUARES-SEM ANALISYS

Edusentris ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 128
Author(s):  
Darul Wiyono
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Partial Least Square ◽  
Least Square

Prestasi belajar yang tinggi selalu menjadi harapan semua pihak. Bagi pihak perguruan tinggi prestasi belajar mahasiswanya merupakan salah satu indikator efektif proses belajar mengajar, yang sekaligus dapat digunakan untuk meningkatkan citra perguruan tinggi tersebut. Banyak faktor yang mempengaruhi prestasi belajar mahasiswa, hal ini sangat penting untuk ditinjau lebih dalam demi meningkatkan prestasi mahasiswa. Tujuan dalam penelitian ini adalah untuk menganalisis pengaruh motivasi belajar, keaktifan mahasiswa, dan kinerja dosen terhadap prestasi belajar mahasiswa. Penelitian dipaparkan secara deskriptif dan verifikatif dengan menggunakan metode penelitian explanatory survey. Hipotesis penelitian ini akan dianalisis menggunakan model persamaan struktural (Structural Equation Model, SEM) dengan metode alternatif Partial Least Square (PLS). Hasil studi menunjukkan bahwa motivasi belajar, keaktifan mahasiswa dan kinerja dosen mempengaruhi dalam peningkatan prestasi belajar mahasiswa. Hal ini menegaskan prestasi belajar mahasiswa tidak terlepas dari motivasi belajar, keaktifan mahasiswa, dan kinerja dosen baik secara parsial maupun bersama-sama.

Development of Robust Partial Least Squares Regression Model for Spectroscopic Determination of Diclofenac Sodium in Environmental Samples

2020 ◽  
Vol 16 (3) ◽  
pp. 241-249
Author(s):  
Biswanath Mahanty ◽  
Angel P. John
Keyword(s):  
Variable Selection ◽  
Least Squares ◽  
Spectral Data ◽  
Partial Least Squares ◽  
Environmental Samples ◽  
Partial Least Square ◽  
Least Square ◽  
Analytical Sensitivity ◽  
Pls Regression ◽  
Spiked Soil

Background: Diclofenac (DCF) is an important widely used non-steroidal antiinflammatory drug. Disposal of expired formulation, excretion from administered dose, the poor performance of sewage treatment process, contributes to its frequent detection in environment. Analysis of DCF in environmental sample requires time consuming pretreatment, extraction steps. Though, UV absorption analysis of DCF is simple but spectral interference of soil organic matter is a problem. The aim of this paper is to establish appropriate partial least square chemometric model for DCF quantitation through variable selection, and validation of analytical method through multivariate figure of merit analysis. Methods: Spectral data of DCF spiked soil solution is recorded and variants of partial least squares (PLS) regression viz., backward-interval PLS (biPLS), synergy-interval PLS (siPLS) and genetic algorithm (GA) based PLS models (GA-PLS) are developed from autoscaled and 2nd order differential spectrum. Prediction fidelity of the selected models was evaluated from a blind-folded semi-synthetic spectral data. The method was validated through figures of merit estimates, such as selectivity, analytical sensitivity, limits of detection and quantitation. Results: The siPLS model developed offered the minimum root mean square error of crossvalidation (RMSECV) of 0.1896 mg/l and 0.1910 mg/l for autoscaled data (9 variables) and derivative spectra (12 variables), respectively. Refinement of the derivative spectrum with GA offered a simplified model (RMSECV:0.1712, 10 variable). Conclusion: The GA based variable selection for PLS regression analysis offers robust analytical tool for DCF in environmental samples. Further research is warranted to model variable interference in spectral data unknown to analyst in priori.

scholarly journals Simultaneous Detemination of Atorvastatin Calcium and Amlodipine Besylate by Spectrophotometry and Multivariate Calibration Methods in Pharmaceutical Formulations

2011 ◽  
Vol 8 (4) ◽  
pp. 1670-1679 ◽  
Author(s):  
Amir H. M. Sarrafi ◽  
Elahe Konoz ◽  
Maryam Ghiyasvand
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Multivariate Calibration ◽  
Mean Squared Error ◽  
Sample Pretreatment ◽  
Partial Least Square ◽  
Least Square ◽  
Full Spectrum ◽  
Calibration Methods

Resolution of binary mixture of atorvastatin (ATV) and amlodipine (AML) with minimum sample pretreatment and without analyte separation has been successfully achieved using a rapid method based on partial least square analysis of UV–spectral data. Multivariate calibration modeling procedures, traditional partial least squares (PLS-2), interval partial least squares (iPLS) and synergy partial least squares (siPLS), were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. The simultaneous determination of both analytes was possible by PLS processing of sample absorbance between 220-425 nm. The correlation coefficients (R) and root mean squared error of cross validation (RMSECV) for ATV and AML in synthetic mixture were 0.9991, 0.9958 and 0.4538, 0.2411 in best siPLS models respectively. The optimized method has been used for determination of ATV and AML in amostatin commercial tablets. The proposed method are simple, fast, inexpensive and do not need any separation or preparation methods.

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