Principal component analysis used for structural and elementary datas coming from iron corrosion products layer

We argued that if human spatial memory of multifloored environments (here a cylindrical one) is preferentially exploited by floors regardless of the learning mode, then

the evaluation of Equations 2.3, 2.4, and 2.5 with the Jacobian matrix being calculated with only the major species alone does not guarantee that the most meaningful or

Following the same framework and considering dynamic responses as random variables as in [12], this paper is focused on the development of a damage detection indicator that combines

Nonlinear principal component analysis (NLPCA) is applied on detrended monthly Sea Surface Temperature Anomaly (SSTA) data from the tropical Atlantic Ocean (30°W-20°E, 26°S-22°..

We show how slight variants of the regularization term of [1] can be used successfully to yield a structured and sparse formulation of principal component analysis for which we

I It can be chosen such that the components with variance λ α greater than the mean variance (by variable) are kept. In normalized PCA, the mean variance is 1. This is the Kaiser

Principal Component Analysis for Fault Detection and Structure Health Monitoring.. Nicolas Stoffels, Vincent Sircoulomb, Guillaume Hermand,

Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion strategies..