No Cover Image

Journal article 263 views 72 downloads

Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants

Paul D. Ledger Orcid Logo, Ben Wilson, Alan Amad Orcid Logo, William R. B. Lionheart Orcid Logo

International Journal for Numerical Methods in Engineering, Volume: 122, Issue: 15, Pages: 3941 - 3984

Swansea University Authors: Ben Wilson, Alan Amad Orcid Logo

  • 60833.pdf

    PDF | Version of Record

    © 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution License

    Download (6.28MB)

Check full text

DOI (Published version): 10.1002/nme.6688

Abstract

The early detection of terrorist threat objects, such as guns and knives, through improved metal detection, has the potential to reduce the number of attacks and improve public safety and security. To achieve this, there is considerable potential to use the fields applied and measured by a metal det...

Full description

Published in: International Journal for Numerical Methods in Engineering
ISSN: 0029-5981 1097-0207
Published: Wiley 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60833
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2022-08-16T14:28:25Z
last_indexed 2023-01-13T19:21:15Z
id cronfa60833
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-08-16T15:30:29.3683999</datestamp><bib-version>v2</bib-version><id>60833</id><entry>2022-08-16</entry><title>Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants</title><swanseaauthors><author><sid>7aad8167510801e63f1b9879d307c2ca</sid><firstname>Ben</firstname><surname>Wilson</surname><name>Ben Wilson</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>fe2123481afa7460a369317354cba4ec</sid><ORCID>0000-0001-7709-5536</ORCID><firstname>Alan</firstname><surname>Amad</surname><name>Alan Amad</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-08-16</date><deptcode>REWI</deptcode><abstract>The early detection of terrorist threat objects, such as guns and knives, through improved metal detection, has the potential to reduce the number of attacks and improve public safety and security. To achieve this, there is considerable potential to use the fields applied and measured by a metal detector to discriminate between different shapes and different metals since, hidden within the field perturbation, is object characterisation information. The magnetic polarizability tensor (MPT) offers an economical characterisation of metallic objects that can be computed for different threat and non-threat objects and has an established theoretical background, which shows that the induced voltage is a function of the hidden object's MPT coefficients. In this article, we describe the additional characterisation information that measurements of the induced voltage over a range of frequencies offer compared with measurements at a single frequency. We call such object characterisations its MPT spectral signature. Then, we present a series of alternative rotational invariants for the purpose of classifying hidden objects using MPT spectral signatures. Finally, we include examples of computed MPT spectral signature characterisations of realistic threat and non-threat objects that can be used to train machine learning algorithms for classification purposes.</abstract><type>Journal Article</type><journal>International Journal for Numerical Methods in Engineering</journal><volume>122</volume><journalNumber>15</journalNumber><paginationStart>3941</paginationStart><paginationEnd>3984</paginationEnd><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0029-5981</issnPrint><issnElectronic>1097-0207</issnElectronic><keywords>finite element method; machine learning; magnetic polarizability tensor; metal detection; object classification; reduced order model; spectral; validation</keywords><publishedDay>15</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-08-15</publishedDate><doi>10.1002/nme.6688</doi><url/><notes/><college>COLLEGE NANME</college><department>Reaching Wider</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>REWI</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>Engineering and Physical Sciences Research Council. Grant Numbers: EP/R002134/2, EP/R002177/1; Royal Society. Grant Number: Royal Society Wolfson Research Merit Award</funders><projectreference/><lastEdited>2022-08-16T15:30:29.3683999</lastEdited><Created>2022-08-16T15:25:31.2907196</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Uncategorised</level></path><authors><author><firstname>Paul D.</firstname><surname>Ledger</surname><orcid>0000-0002-2587-7023</orcid><order>1</order></author><author><firstname>Ben</firstname><surname>Wilson</surname><order>2</order></author><author><firstname>Alan</firstname><surname>Amad</surname><orcid>0000-0001-7709-5536</orcid><order>3</order></author><author><firstname>William R. B.</firstname><surname>Lionheart</surname><orcid>0000-0003-0971-4678</orcid><order>4</order></author></authors><documents><document><filename>60833__24937__6417fb6c6d8d419084357333182481dc.pdf</filename><originalFilename>60833.pdf</originalFilename><uploaded>2022-08-16T15:28:59.8036867</uploaded><type>Output</type><contentLength>6580953</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution License</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2022-08-16T15:30:29.3683999 v2 60833 2022-08-16 Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants 7aad8167510801e63f1b9879d307c2ca Ben Wilson Ben Wilson true false fe2123481afa7460a369317354cba4ec 0000-0001-7709-5536 Alan Amad Alan Amad true false 2022-08-16 REWI The early detection of terrorist threat objects, such as guns and knives, through improved metal detection, has the potential to reduce the number of attacks and improve public safety and security. To achieve this, there is considerable potential to use the fields applied and measured by a metal detector to discriminate between different shapes and different metals since, hidden within the field perturbation, is object characterisation information. The magnetic polarizability tensor (MPT) offers an economical characterisation of metallic objects that can be computed for different threat and non-threat objects and has an established theoretical background, which shows that the induced voltage is a function of the hidden object's MPT coefficients. In this article, we describe the additional characterisation information that measurements of the induced voltage over a range of frequencies offer compared with measurements at a single frequency. We call such object characterisations its MPT spectral signature. Then, we present a series of alternative rotational invariants for the purpose of classifying hidden objects using MPT spectral signatures. Finally, we include examples of computed MPT spectral signature characterisations of realistic threat and non-threat objects that can be used to train machine learning algorithms for classification purposes. Journal Article International Journal for Numerical Methods in Engineering 122 15 3941 3984 Wiley 0029-5981 1097-0207 finite element method; machine learning; magnetic polarizability tensor; metal detection; object classification; reduced order model; spectral; validation 15 8 2021 2021-08-15 10.1002/nme.6688 COLLEGE NANME Reaching Wider COLLEGE CODE REWI Swansea University Engineering and Physical Sciences Research Council. Grant Numbers: EP/R002134/2, EP/R002177/1; Royal Society. Grant Number: Royal Society Wolfson Research Merit Award 2022-08-16T15:30:29.3683999 2022-08-16T15:25:31.2907196 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Paul D. Ledger 0000-0002-2587-7023 1 Ben Wilson 2 Alan Amad 0000-0001-7709-5536 3 William R. B. Lionheart 0000-0003-0971-4678 4 60833__24937__6417fb6c6d8d419084357333182481dc.pdf 60833.pdf 2022-08-16T15:28:59.8036867 Output 6580953 application/pdf Version of Record true © 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution License true eng http://creativecommons.org/licenses/by/4.0/
title Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
spellingShingle Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
Ben Wilson
Alan Amad
title_short Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
title_full Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
title_fullStr Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
title_full_unstemmed Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
title_sort Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object characterisation and invariants
author_id_str_mv 7aad8167510801e63f1b9879d307c2ca
fe2123481afa7460a369317354cba4ec
author_id_fullname_str_mv 7aad8167510801e63f1b9879d307c2ca_***_Ben Wilson
fe2123481afa7460a369317354cba4ec_***_Alan Amad
author Ben Wilson
Alan Amad
author2 Paul D. Ledger
Ben Wilson
Alan Amad
William R. B. Lionheart
format Journal article
container_title International Journal for Numerical Methods in Engineering
container_volume 122
container_issue 15
container_start_page 3941
publishDate 2021
institution Swansea University
issn 0029-5981
1097-0207
doi_str_mv 10.1002/nme.6688
publisher Wiley
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description The early detection of terrorist threat objects, such as guns and knives, through improved metal detection, has the potential to reduce the number of attacks and improve public safety and security. To achieve this, there is considerable potential to use the fields applied and measured by a metal detector to discriminate between different shapes and different metals since, hidden within the field perturbation, is object characterisation information. The magnetic polarizability tensor (MPT) offers an economical characterisation of metallic objects that can be computed for different threat and non-threat objects and has an established theoretical background, which shows that the induced voltage is a function of the hidden object's MPT coefficients. In this article, we describe the additional characterisation information that measurements of the induced voltage over a range of frequencies offer compared with measurements at a single frequency. We call such object characterisations its MPT spectral signature. Then, we present a series of alternative rotational invariants for the purpose of classifying hidden objects using MPT spectral signatures. Finally, we include examples of computed MPT spectral signature characterisations of realistic threat and non-threat objects that can be used to train machine learning algorithms for classification purposes.
published_date 2021-08-15T04:19:16Z
_version_ 1763754279574700032
score 11.013371

Similar Items