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**Harvard**

Ognissanti, D. (2014) *Permeability prediction using Support vector machines*. Göteborg : Chalmers University of Technology

** BibTeX **

@mastersthesis{

Ognissanti2014,

author={Ognissanti, Damiano},

title={Permeability prediction using Support vector machines},

abstract={This thesis explores the possibility of calculating the permeability of materials
with regression based of classification software instead of using famous
physics formulae, like the Kozeny-Carman equation. The reason for this is
that regarding permeability, no universal and exact formula has been discovered
to date; the existing formulae depend on the constitution of the
materials. The chosen model is based on classification from support vector
machines. This classification algorithm was chosen because support vector
machines have a history of showing accuracy comparable to those of other
methods in recognizing various data and because they have a rigorous mathematical
base. The thesis consists of a theoretical part and an applied one,
where the first describe the basis on which the results rely and the second
explains how certain parameters are calculated and used for the classification
algorithm to perform well. It is shown that the classification algorithm
surpasses the famous Kozeny-Carman equation in terms of accuracy of the
calculations for fibre structures. It is also shown that it suffices to extract
parameters from two dimensional images of the three dimensional structures
to gain equal precision as if the whole three dimensional structure is taken
into account. This raises hope that microscopy images can be used to calculate
the permeability of materials. Finally it is shown that the content
of the training set is more important than its size for the support vector
machine to perform well.},

publisher={Institutionen för matematiska vetenskaper, Chalmers tekniska högskola},

place={Göteborg},

year={2014},

note={47},

}

** RefWorks **

RT Generic

SR Electronic

ID 199243

A1 Ognissanti, Damiano

T1 Permeability prediction using Support vector machines

YR 2014

AB This thesis explores the possibility of calculating the permeability of materials
with regression based of classification software instead of using famous
physics formulae, like the Kozeny-Carman equation. The reason for this is
that regarding permeability, no universal and exact formula has been discovered
to date; the existing formulae depend on the constitution of the
materials. The chosen model is based on classification from support vector
machines. This classification algorithm was chosen because support vector
machines have a history of showing accuracy comparable to those of other
methods in recognizing various data and because they have a rigorous mathematical
base. The thesis consists of a theoretical part and an applied one,
where the first describe the basis on which the results rely and the second
explains how certain parameters are calculated and used for the classification
algorithm to perform well. It is shown that the classification algorithm
surpasses the famous Kozeny-Carman equation in terms of accuracy of the
calculations for fibre structures. It is also shown that it suffices to extract
parameters from two dimensional images of the three dimensional structures
to gain equal precision as if the whole three dimensional structure is taken
into account. This raises hope that microscopy images can be used to calculate
the permeability of materials. Finally it is shown that the content
of the training set is more important than its size for the support vector
machine to perform well.

PB Institutionen för matematiska vetenskaper, Chalmers tekniska högskola,

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/199243/199243.pdf

OL 30