Statistical analysis and optimal inspection planning for lifetime delayed gamma degradation processes

2021 ◽  
Author(s):  
Zan Li ◽  
Qingpei Hu ◽  
Qingyu Yang ◽  
Dan Yu
Keyword(s):  
Statistical Analysis ◽  
Inspection Planning ◽  
Degradation Processes

scholarly journals Preliminary Analysis of Material Degradation Processes in Half-Timbered Walls with Earth Infill in Spain

2021 ◽  
Vol 13 (2) ◽  
pp. 772
Author(s):  
Alicia Hueto Escobar ◽  
Camilla Mileto ◽  
Fernando Vegas López-Manzanares ◽  
Maria Diodato
Keyword(s):  
Statistical Analysis ◽  
Preliminary Analysis ◽  
Surface Erosion ◽  
Material Damage ◽  
Material Degradation ◽  
Systematic Data ◽  
Degradation Processes ◽  
Conservation Actions ◽  
The Relationship ◽  
Systematic Data Collection

This analysis of the material degradation processes most commonly found in half-timbered walls with earth infill in Spain is part of a much broader line of research aiming to analyze, catalogue and understand their constructive techniques, state of conservation, and transformation in order to propose conservation guidelines. Systematic data collection and statistical analysis of material damage for 268 half-timbered walls with earth infill have made it possible to identify the most common degradation processes. The effect of these processes on this type of technique, as well as the relationship between them and any possible conditions contributing to their frequency, were examined. Almost half of the sample studied presents some type of material degradation, most frequently surface erosion, while the presence of vegetation is the least common pathology. Any future decisions about the most suitable maintenance and conservation actions should be based on the conclusions reached here concerning the frequency and scope of these degradation phenomena.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

Intraindividual Variability in Development Within and Between Individuals

2001 ◽  
Vol 6 (3) ◽  
pp. 187-193 ◽  
Author(s):  
John R. Nesselroade
Keyword(s):  
Statistical Analysis ◽  
Research Design ◽  
Intraindividual Variability ◽  
Factor Analytic

A focus on the study of development and other kinds of changes in the whole individual has been one of the hallmarks of research by Magnusson and his colleagues. A number of different approaches emphasize this individual focus in their respective ways. This presentation focuses on intraindividual variability stemming from Cattell's P-technique factor analytic proposals, making several refinements to make it more tractable from a research design standpoint and more appropriate from a statistical analysis perspective. The associated methods make it possible to study intraindividual variability both within and between individuals. An empirical example is used to illustrate the procedure.

Review of Basic FORTRAN for Statistical Analysis.

1967 ◽  
Vol 12 (9) ◽  
pp. 467-467
Author(s):  
JOHN C. LOEHLIN
Keyword(s):  
Statistical Analysis
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