BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Characterisation\, computation and classification of conducting ma
 gnetic objects for safety and security - Paul Ledger (Keele University)
DTSTART:20230125T150000Z
DTEND:20230125T160000Z
UID:TALK195166@talks.cam.ac.uk
DESCRIPTION:The location and identification of hidden conducting magnetic 
 threat objects using metal detection is an important yet challenging task.
  Applications include security screening at transport hubs as well as find
 ing landmines and unexploded ordnance in areas of former conflict\, which 
 often involve objects whose materials are both conducting and magnetic.\nT
 he talk will begin with an introductory example of characterising an isola
 ted non-conducting magnetic object by a real symmetric rank 2 polarizabili
 ty tensor\, whose coefficients are a function of the object&rsquo\;s size\
 , shape and permeability contrast. The explicit formulae of this economica
 l characterisation will be obtained by deriving an asymptotic formula for 
 the perturbed magnetic field due to the presence of a small object. Then\,
  the asymptotic expansion of the perturbed magnetic field for the eddy cur
 rent problem\, which is relevant for characterising conducting magnetic ob
 jects for the metal problem\, will be presented. The explicit formulae for
  computing the corresponding coefficients of the complex symmetric magneti
 c polarizability tensor (MPT) characterisation\, which is a function of th
 e object&rsquo\;s size\, shape\, material properties and the exciting freq
 uency\, will be discussed.\nThe computation of the MPT coefficients requir
 es the solution of a vectorial transmission problem. For this\, we will di
 scuss how a high order H(curl) conforming finite element method (FEM) can 
 be applied. To rapidly compute MPT coefficients as a function of frequency
  (known as the MPT spectral signature)\, we will accelerate the FEM comput
 ation with a proper orthogonal decomposition reduced order model (ROM). We
  will use an a-posteriori error estimate to adaptively choose new snapshot
 s to further improve the efficiency of the ROM. In the case of conducting 
 magnetic objects\, which have very thin skin depths\, we will use prismati
 c boundary layers to ensure exponential convergence of the FEM solution an
 d accurate MPT coefficient computations.\nCombining our computational appr
 oaches with scaling results will allow us to obtain a large dictionary of 
 MPT spectral signature characterisations of realistic threat and non-threa
 t objects. The talk will also discuss how probabilistic and non-probabilis
 tic machine learning classifiers can be applied to identify hidden objects
  learnt from our dictionary.&nbsp\;
LOCATION:Seminar Room 2\, Newton Institute
END:VEVENT
END:VCALENDAR