Statistical Analysis Of SEM-EDX Spectrum Images Of A Metal-Ceramic Braze

1999 ◽  
Vol 5 (S2) ◽  
pp. 806-807
Author(s):  
P.G. Kotula ◽  
I.M. Anderson ◽  
J.J. Stephens
Keyword(s):  
Statistical Analysis ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Polycrystalline Alumina ◽  
Polished Cross Section ◽  
Simple Mapping ◽  
Characterization Of Materials

Spectrum imaging, where a complete spectrum is acquired at each pixel in an image, is potentially a very powerful technique for the characterization of materials. The primary challenge to overcome is to extract all of the relevant information from what are typically large data sets that cannot be readily visualized in their entirety. For energy-dispersive X-ray (EDX) spectrum images, simple mapping has typically been done. In this work, the application of multivariate statistical analysis (MSA) to the characterization of a metal/alumina braze by SEM-EDX spectrum images is described.The braze characterized in this work joins polycrystalline alumina and a copper-silver eutectic alloy containing some titanium (an ‘active’ metal). The specimen geometry consisted of a sandwich of two pieces of alumina, two braze layers and a Kovar (primarily Fe, Ni and Co) filler layer in the middle. The entire assembly was heat-treated to bond the interfaces and then a polished cross-section was prepared.

scholarly journals Multivariate Statistical Analysis of Spectrum Lines and Images

1997 ◽  
Vol 3 (S2) ◽  
pp. 931-932 ◽  
Author(s):  
Ian M. Anderson ◽  
Jim Bentley
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Quantitative Imaging ◽  
Boundary Segregation ◽  
Large Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Recent Developments ◽  
Data Files

Recent developments in instrumentation and computing power have greatly improved the potential for quantitative imaging and analysis. For example, products are now commercially available that allow the practical acquisition of spectrum images, where an EELS or EDS spectrum can be acquired from a sequence of positions on the specimen. However, such data files typically contain megabytes of information and may be difficult to manipulate and analyze conveniently or systematically. A number of techniques are being explored for the purpose of analyzing these large data sets. Multivariate statistical analysis (MSA) provides a method for analyzing the raw data set as a whole. The basis of the MSA method has been outlined by Trebbia and Bonnet.MSA has a number of strengths relative to other methods of analysis. First, it is broadly applicable to any series of spectra or images. Applications include characterization of grain boundary segregation (position-), of channeling-enhanced microanalysis (orientation-), or of beam damage (time-variation of spectra).

Automated Analysis of SEM X-Ray Spectral Images: A Powerful New Microanalysis Tool

2003 ◽  
Vol 9 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Paul G. Kotula ◽  
Michael R. Keenan ◽  
Joseph R. Michael
Keyword(s):  
Image Data ◽  
Large Data ◽  
Automated Analysis ◽  
Phase Identification ◽  
Data Sets ◽  
Multivariate Statistical ◽  
X Ray ◽  
Analysis Technique ◽  
Statistical Analysis Technique ◽  
Multi Phase

Spectral imaging in the scanning electron microscope (SEM) equipped with an energy-dispersive X-ray (EDX) analyzer has the potential to be a powerful tool for chemical phase identification, but the large data sets have, in the past, proved too large to efficiently analyze. In the present work, we describe the application of a new automated, unbiased, multivariate statistical analysis technique to very large X-ray spectral image data sets. The method, based in part on principal components analysis, returns physically accurate (all positive) component spectra and images in a few minutes on a standard personal computer. The efficacy of the technique for microanalysis is illustrated by the analysis of complex multi-phase materials, particulates, a diffusion couple, and a single-pixel-detection problem.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

scholarly journals Identification of the dominant hydrogeochemical processes and characterization of potential contaminants in groundwater in Qingyuan, China, by multivariate statistical analysis

RSC Advances ◽  
2018 ◽  
Vol 8 (58) ◽  
pp. 33243-33255 ◽  
Author(s):  
Zuobing Liang ◽  
Jianyao Chen ◽  
Tao Jiang ◽  
Kun Li ◽  
Lei Gao ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Water Source ◽  
Hydrogeochemical Processes ◽  
Karst Aquifers ◽  
Multivariate Statistical ◽  
Drinking And Irrigation ◽  
Karst Areas

In karst areas, groundwater is an important water source for drinking and irrigation purposes; however, karst aquifers are vulnerable and recovery from damage is difficult.

Searching for Pulsating Stars Using Clustering Algorithms

2017 ◽  
Vol 14 (S339) ◽  
pp. 310-313
Author(s):  
R. Kgoadi ◽  
I. Whittingham ◽  
C. Engelbrecht
Keyword(s):  
Clustering Algorithms ◽  
Large Data ◽  
Relevant Information ◽  
Variable Stars ◽  
Data Sets ◽  
Specific Class ◽  
Pulsating Stars ◽  
Expectation Maximisation ◽  
Input Variables ◽  
Physical Features

AbstractClustering algorithms constitute a multi-disciplinary analytical tool commonly used to summarise large data sets. Astronomical classifications are based on similarity, where celestial objects are assigned to a specific class according to specific physical features. The aim of this project is to obtain relevant information from high-dimensional data (at least three input variables in a data-frame) derived from stellar light-curves using a number of clustering algorithms such as K-means and Expectation Maximisation. In addition to identifying the best performing algorithm, we also identify a subset of features that best define stellar groups. Three methodologies are applied to a sample of Kepler time series in the temperature range 6500–19,000 K. In that spectral range, at least four classes of variable stars are expected to be found: δ Scuti, γ Doradus, Slowly Pulsating B (SPB), and (the still equivocal) Maia stars.

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