Atom probe tomography (APT) is a powerful method for studying the microstructure and chemistry of materials at the unit-cell level [1]. Because APT produces datasets containing up to billions of atom identities, new methods of analysis are being developed to rapidly datamine information from the APT data. Spectrum imaging (SI) methods utilizing multivariate statistical analysis (MVSA) are becom...

The aim of the present work is that of analysing the data of snow-cover measurements carried out in the northeastern Alps by the Italian National Electricity Board (ENEL) since 1966, in order to select a reduced number of measurement stations from which to evaluate the water equivalent amount. The selection was based on cluster methods for identification of homogeneous areas and afterwards on p...

A comprehensive three-dimensional (3D) microanalysis procedure using a combined scanning electron microscope (SEM)/focused ion beam (FIB) system equipped with an energy-dispersive X-ray spectrometer (EDS) has been developed. The FIB system was used first to prepare a site-specific region for X-ray microanalysis followed by the acquisition of an electron-beam generated X-ray spectral image. A sm...

The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two differe...

Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, most of the common approaches were borrowed from other fields. Each of these algorithms were developed for their own particular task, thus they look quite different at their core. It is not obvious that what these different algorithms do internally is optimal for the the tasks which they perform w...

To meet the threats of infectious diseases, many countries have established surveillance systems for the routine collection of infectious disease data. The analysis and modelling such notification data is essential in the attempt to control and prevent disease. In this thesis, statistical methodology for the analysis of multivariate time series of counts as collected in surveillance systems on ...

The goal of this paper is to compare a set of distance/similarity measures, some motivated statistically, others motivated stylistically, regarding their ability to reflect stylistic similarity between texts. To assess the ability of these distance/similarity functions to capture stylistic similarity between texts, we have tested them in the two most frequently employed multivariate statistical...

In this paper, a modified independent component analysis (ICA) and its application to process monitoring are proposed. The basic idea of this approach is to use the modified ICA to extract some dominant independent components from normal operating process data and to combine them with statistical process monitoring techniques. The proposed monitoring method is applied to fault detection and ide...