A Warning System for Stromboli Volcano Based on Statistical Analysis

2008 ◽  
pp. 1619-1641
Author(s):  
Giuseppe Nunnari ◽  
Giuseppe Puglisi ◽  
Alessandro Spata
2008 ◽  
Vol 165 (8) ◽  
pp. 1619-1641 ◽  
Author(s):  
Giuseppe Nunnari ◽  
Giuseppe Puglisi ◽  
Alessandro Spata

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Chenghao

<p>In this paper, information from Hunan Province lightning monitoring and warning system platform is used and 14 sample points are selected, to analyze its average annual lightning density, and establish PLS model for statistical analysis to research the complex relationship formed between lightning density and altitude, aspect and geological structures. The results show that thunderstorms path, altitude, aspect, and shade have significant effects on lightning density distribution. Soil resistivity has a certain influence on this but overall it has relatively lesser effect.</p>


2014 ◽  
Vol 1065-1069 ◽  
pp. 2397-2400
Author(s):  
Tie Lan Teng ◽  
Qi Ming Li ◽  
Jing Feng Yuan

The Early Warning System (EWS) was developed in this research aiming to forecast and monitor the Residual Value Risk (RVR) in PPP projects. RVR is a structured risk system which would happen at any time in the whole life before transfer of PPP projects, but consequently causes the Residual Value (RV) that the Government takes over cannot fulfill the specifications. To establish the EWS of RVR, the factor system was identified through questionnaire survey and evaluated based on statistical analysis. Besides the RVR could be learned by CBR, so a great amount of history PPP projects which have been already transferred should be structured into a case base. Furthermore, the conception of System vulnerability was applied to open up the link between the RVR and the RV, and the proper method was selected to determine the early-warning threshold. The main function of the EWS is monitoring the RVR of current PPP projects and warning the potential RV threat or opportunity.


1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming

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.


Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance

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.


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