The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature EGT time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square PLS based on the cubic spline function or the ...

0019-1035/$ see front matter Published by Elsevier http://dx.doi.org/10.1016/j.icarus.2012.07.023 ⇑ Corresponding author at: Department of Earth S Purdue University Indianapolis, IN 46202, United Stat E-mail addresses: [email protected], njushuaili@ The goal of this study is to develop an efficient and accurate model for using visible–near infrared reflectance spectra to estimate the abundance...

Hybrid linear analysis (HLA), partial least-squares (PLS) regression, and the linear least square support vector machine (LSSVM) were used to determinate the soluble solids content (SSC) of apple by Fourier transform near-infrared (FT-NIR) spectroscopy. The performance of these three linear regression methods was compared. Results showed that HLA could be used for the analysis of complex solid ...

The multivariate calibration method, partial least square regression (PLS) was applied for thesimultaneous spectrophotometric determination of Lidocaine (LID) and Hydrocortisone acetate(HCA) in their mixtures. The parameters of chemometric technique were optimized and theproposed method was validated with synthetic samples and applied to analyze these drugs inpharmaceutical products with good a...

Analysis of images involving humans is of significant interest in computer vision because problems such as detection, modeling, recognition, and tracking are fundamental to model interactions between people and understand highlevel activities. Visual information contained in images is generally represented using feature descriptors. Many general classes of descriptors have been proposed focusin...

This work provides a novel derivation based on optimization for the partial least squares (PLS) algorithm for linear regression and the kernel partial least squares (K-PLS) algorithm for nonlinear regression. This derivation makes the PLS algorithm, popularly and successfully used for chemometrics applications, more accessible to machine learning researchers. The work introduces Direct K-PLS, a...

Empirical studies that use structural equation modeling (SEM) are widespread in information systems research. During the last few years, the component-based approach partial least squares (PLS) for testing structural models has become increasingly popular. At the same time, this approach’s limitations have become a greater concern. Some researchers even suggest using alternative approaches that...

This paper addresses the use of temperature measurements to estimate product compositions in distillation columns. A simple linear multivariate calibration procedure based on steady-state data is used, which requires minimal modeling effort. It is found that these principal-component-regression (PCR) and partial-least-squares (PLS) estimators perform well, even for multicomponent mixtures, pres...

Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new components is compared with some of the best state-of-the-art classification methods. Moreover, a boosting al...