Fault detection and diagnosis strategy based on a weighted and combined index in the residual subspace associated with PCA
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Chemometrics"
DOI: 10.1002/cem.2981
Abstract: Process monitoring and diagnosis are crucial for efficient and optimal operation of a chemical plant. Most multivariate statistical process monitoring strategies, such as principal component analysis, kernel principal component analysis, and dynamic principal component analysis,… read more here.
Keywords: detection; index; diagnosis; principal component ... See more keywords
Principal component regression that minimizes the sum of the squares of the relative errors: Application in multivariate calibration models
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Chemometrics"
DOI: 10.1002/cem.3341
Abstract: Relative errors are typically used in chemometrics to evaluate the performance of a multivariate predictive model. However, these models are not obtained through the criterion of minimizing relative errors, as would be expected in a… read more here.
Keywords: regression minimizes; principal component; minimizes sum; relative errors ... See more keywords
Evaluation of principal component analysis image denoising on multi‐exponential MRI relaxometry
Sign Up to like & getrecommendations! Published in 2019 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.27658
Abstract: Multi‐exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal‐to‐noise ratio (SNR). This work evaluates the use of principal‐component‐analysis (PCA) denoising to mitigate these SNR demands and improve the precision… read more here.
Keywords: multi exponential; image; principal component; relaxometry ... See more keywords
Principal component analysis of hybrid functional and vector data.
Sign Up to like & getrecommendations! Published in 2021 at "Statistics in medicine"
DOI: 10.1002/sim.9117
Abstract: We propose a practical principal component analysis (PCA) framework that provides a nonparametric means of simultaneously reducing the dimensions of and modeling functional and vector (multivariate) data. We first introduce a Hilbert space that combines… read more here.
Keywords: vector; hybrid functional; principal component; vector data ... See more keywords
A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis
Sign Up to like & getrecommendations! Published in 2018 at "Pure and Applied Geophysics"
DOI: 10.1007/s00024-018-1856-3
Abstract: For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the… read more here.
Keywords: time series; principal component; analysis; time ... See more keywords
Qualitative evaluation of ferritin in serum samples by Raman spectroscopy and principal component analysis
Sign Up to like & getrecommendations! Published in 2018 at "Lasers in Medical Science"
DOI: 10.1007/s10103-018-2576-8
Abstract: Iron molecule is of great importance in the synthesis of hemoglobin which is essential for oxygen transport. Iron levels are quantified by accurately high sensitivity tests, such as serum ferritin (SF). However, common studies to… read more here.
Keywords: serum samples; principal component; analysis; spectroscopy ... See more keywords
Random permutation principal component analysis for cancelable biometric recognition
Sign Up to like & getrecommendations! Published in 2017 at "Applied Intelligence"
DOI: 10.1007/s10489-017-1117-7
Abstract: Although biometrics is being increasingly used across the world, it also raises concerns over privacy and security of the enrolled identities. This is due to the fact that biometrics are not cancelable and if compromised… read more here.
Keywords: principal component; cancelable biometric; biometrics; random permutation ... See more keywords
Principal component analysis based on block-norm minimization
Sign Up to like & getrecommendations! Published in 2018 at "Applied Intelligence"
DOI: 10.1007/s10489-018-1382-0
Abstract: Principal Component Analysis (PCA) has attracted considerable interest for years in the studies of image recognition. So far, several state-of-the-art PCA-based robust feature extraction techniques have been proposed, such as PCA-L1 and R1-PCA. Since those… read more here.
Keywords: block; principal component; pca; block norm ... See more keywords
Research on leaf species identification based on principal component and linear discriminant analysis
Sign Up to like & getrecommendations! Published in 2017 at "Cluster Computing"
DOI: 10.1007/s10586-017-1439-6
Abstract: A method of leaf species identification based on principal component and linear discriminant analysis is proposed in this paper. The method consists of four phases. Leaf images obtained by cameras or other devices typically have… read more here.
Keywords: linear discriminant; principal component; species identification; leaf species ... See more keywords
Monitoring of a machining process using kernel principal component analysis and kernel density estimation
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Intelligent Manufacturing"
DOI: 10.1007/s10845-019-01504-w
Abstract: Tool wear is one of the consequences of a machining process. Excessive tool wear can lead to poor surface finish, and result in a defective product. It can also lead to premature tool failure, and… read more here.
Keywords: machining process; principal component; kernel principal; density ... See more keywords
MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Intelligent Manufacturing"
DOI: 10.1007/s10845-020-01721-8
Abstract: This paper proposes a moving window recursive sparse principal component analysis (MWRSPCA)-based online fault monitoring scheme, aim at providing an online fault monitoring solution for large-scale complex industrial processes (e.g., chemical industry processes) with time-varying… read more here.
Keywords: fault; sparse principal; principal component; monitoring ... See more keywords