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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Characterisation\, computation and classification
of conducting magnetic objects for safety and secu
rity - Paul Ledger (Keele University)
DTSTART;TZID=Europe/London:20230125T150000
DTEND;TZID=Europe/London:20230125T160000
UID:TALK195166AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/195166
DESCRIPTION:The location and identification of hidden conducti
ng magnetic threat objects using metal detection i
s an important yet challenging task. Applications
include security screening at transport hubs as we
ll as finding landmines and unexploded ordnance in
areas of former conflict\, which often involve ob
jects whose materials are both conducting and magn
etic.\nThe talk will begin with an introductory ex
ample of characterising an isolated non-conducting
magnetic object by a real symmetric rank 2 polari
zability tensor\, whose coefficients are a functio
n of the object&rsquo\;s size\, shape and permeabi
lity contrast. The explicit formulae of this econo
mical characterisation will be obtained by derivin
g an asymptotic formula for the perturbed magnetic
field due to the presence of a small object. Then
\, the asymptotic expansion of the perturbed magne
tic field for the eddy current problem\, which is
relevant for characterising conducting magnetic ob
jects for the metal problem\, will be presented. T
he explicit formulae for computing the correspondi
ng coefficients of the complex symmetric magnetic
polarizability tensor (MPT) characterisation\, whi
ch is a function of the object&rsquo\;s size\, sha
pe\, material properties and the exciting frequenc
y\, will be discussed.\nThe computation of the MPT
coefficients requires the solution of a vectorial
transmission problem. For this\, we will discuss
how a high order H(curl) conforming finite element
method (FEM) can be applied. To rapidly compute M
PT coefficients as a function of frequency (known
as the MPT spectral signature)\, we will accelerat
e the FEM computation with a proper orthogonal dec
omposition reduced order model (ROM). We will use
an a-posteriori error estimate to adaptively choos
e new snapshots to further improve the efficiency
of the ROM. In the case of conducting magnetic obj
ects\, which have very thin skin depths\, we will
use prismatic boundary layers to ensure exponentia
l convergence of the FEM solution and accurate MPT
coefficient computations.\nCombining our computat
ional approaches with scaling results will allow u
s to obtain a large dictionary of MPT spectral sig
nature characterisations of realistic threat and n
on-threat objects. The talk will also discuss how
probabilistic and non-probabilistic machine learni
ng classifiers can be applied to identify hidden o
bjects learnt from our dictionary. \;
LOCATION:Seminar Room 2\, Newton Institute
CONTACT:
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