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Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors / BEN WILSON

Swansea University Author: BEN WILSON

DOI (Published version): 10.23889/SUthesis.60297

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. Walk through metal detectors (WTMDs) are commonly deployed for security screening purposes in applicat...

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Published: Swansea 2022
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Ledger, Paul D. ; Giannetti, Cinzia
URI: https://cronfa.swan.ac.uk/Record/cronfa60297
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2022-06-22T13:42:27.7288191</datestamp><bib-version>v2</bib-version><id>60297</id><entry>2022-06-22</entry><title>Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors</title><swanseaauthors><author><sid>ab84329c676a14097546698eda4b6abd</sid><firstname>BEN</firstname><surname>WILSON</surname><name>BEN WILSON</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-06-22</date><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. Walk through metal detectors (WTMDs) are commonly deployed for security screening purposes in applications where these attacks are of particular con-cern such as in airports, transport hubs, government buildings and at concerts. However, there is scope to improve the identification of an object&#x2019;s shape and its material proper-ties. Using current techniques there is often the requirement for any metallic objects to be inspected or scanned separately before a patron may be determined to pose no threat, making the process slow. This can often lead to build ups of large queues of unscreened people waiting to be screened which becomes another security threat in itself. To improve the current method, there is considerable potential to use the fields applied and measured by a metal detector since, hidden within the field perturbation, is object characterisation information. The magnetic polarizability tensor (MPT) offers an economical characteri-sation of metallic objects and its spectral signature provides additional object character-isation information. The MPT spectral signature can be determined from measurements of the induced voltage over a range of frequencies for a hidden object. With classification in mind, it can also be computed in advance for different threat and non-threat objects, producing a dataset of these objects from which a machine learning (ML) classifier can be trained. There is also potential to generate this dataset synthetically, via the application of a method based on finite elements (FE). This concept of training an ML classifier trained on a synthetic dataset of MPT based characterisations is at the heart of this work.In this thesis, details for the production and use of a first of its kind synthetic dataset of realistic object characterisations are presented. To achieve this, first a review of re-cent developments of MPT object characterisations is provided, motivating the use of MPT spectral signatures. A problem specific, H(curl) based, hp-finite element discreti-sation is presented, which allows for the development of a reduced order model (ROM), using a projection based proper orthogonal decomposition (PODP), that benefits from a-posteriori error estimates. This allows for the rapid production of MPT spectral signatures the accuracy of which is guaranteed. This methodology is then implemented in Python, using the NGSolve finite element package, where other problem specific efficiencies are also included along with a series of additional outputs of interest, this software is then packaged and released as the open source MPT-Calculator. This methodology and software are then extensively tested by application to a series of illustrative examples. Using this software, MPT spectral signatures are then produced for a series of realistic threat and non-threat objects, creating the first of its kind synthetic dataset, which is also released as the open source MPT-Library dataset. Lastly, a series of ML classifiers are documented and applied to several supervised classification problems using this new syn-thetic dataset. A series of challenging numerical examples are included to demonstrate the success of the proposed methodology.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Magnetic Polarizability Tensor, Machine Leaning, Finite Element Method, Reduced Order Model</keywords><publishedDay>15</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-06-15</publishedDate><doi>10.23889/SUthesis.60297</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Ledger, Paul D. ; Giannetti, Cinzia</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><degreesponsorsfunders>EPSRC; Grant number: 2129099</degreesponsorsfunders><apcterm/><lastEdited>2022-06-22T13:42:27.7288191</lastEdited><Created>2022-06-22T13:28:03.4680855</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>BEN</firstname><surname>WILSON</surname><order>1</order></author></authors><documents><document><filename>60297__24363__d36006e5a3314ca68845cb3753009743.pdf</filename><originalFilename>Wilson_Ben_A_PhD_Thesis_Final_Redacted_Signature.pdf</originalFilename><uploaded>2022-06-22T13:35:35.9276711</uploaded><type>Output</type><contentLength>27595659</contentLength><contentType>application/pdf</contentType><version>E-Thesis &#x2013; open access</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: The author, Ben A. 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spelling 2022-06-22T13:42:27.7288191 v2 60297 2022-06-22 Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors ab84329c676a14097546698eda4b6abd BEN WILSON BEN WILSON true false 2022-06-22 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. Walk through metal detectors (WTMDs) are commonly deployed for security screening purposes in applications where these attacks are of particular con-cern such as in airports, transport hubs, government buildings and at concerts. However, there is scope to improve the identification of an object’s shape and its material proper-ties. Using current techniques there is often the requirement for any metallic objects to be inspected or scanned separately before a patron may be determined to pose no threat, making the process slow. This can often lead to build ups of large queues of unscreened people waiting to be screened which becomes another security threat in itself. To improve the current method, there is considerable potential to use the fields applied and measured by a metal detector since, hidden within the field perturbation, is object characterisation information. The magnetic polarizability tensor (MPT) offers an economical characteri-sation of metallic objects and its spectral signature provides additional object character-isation information. The MPT spectral signature can be determined from measurements of the induced voltage over a range of frequencies for a hidden object. With classification in mind, it can also be computed in advance for different threat and non-threat objects, producing a dataset of these objects from which a machine learning (ML) classifier can be trained. There is also potential to generate this dataset synthetically, via the application of a method based on finite elements (FE). This concept of training an ML classifier trained on a synthetic dataset of MPT based characterisations is at the heart of this work.In this thesis, details for the production and use of a first of its kind synthetic dataset of realistic object characterisations are presented. To achieve this, first a review of re-cent developments of MPT object characterisations is provided, motivating the use of MPT spectral signatures. A problem specific, H(curl) based, hp-finite element discreti-sation is presented, which allows for the development of a reduced order model (ROM), using a projection based proper orthogonal decomposition (PODP), that benefits from a-posteriori error estimates. This allows for the rapid production of MPT spectral signatures the accuracy of which is guaranteed. This methodology is then implemented in Python, using the NGSolve finite element package, where other problem specific efficiencies are also included along with a series of additional outputs of interest, this software is then packaged and released as the open source MPT-Calculator. This methodology and software are then extensively tested by application to a series of illustrative examples. Using this software, MPT spectral signatures are then produced for a series of realistic threat and non-threat objects, creating the first of its kind synthetic dataset, which is also released as the open source MPT-Library dataset. Lastly, a series of ML classifiers are documented and applied to several supervised classification problems using this new syn-thetic dataset. A series of challenging numerical examples are included to demonstrate the success of the proposed methodology. E-Thesis Swansea Magnetic Polarizability Tensor, Machine Leaning, Finite Element Method, Reduced Order Model 15 6 2022 2022-06-15 10.23889/SUthesis.60297 COLLEGE NANME COLLEGE CODE Swansea University Ledger, Paul D. ; Giannetti, Cinzia Doctoral Ph.D EPSRC; Grant number: 2129099 2022-06-22T13:42:27.7288191 2022-06-22T13:28:03.4680855 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised BEN WILSON 1 60297__24363__d36006e5a3314ca68845cb3753009743.pdf Wilson_Ben_A_PhD_Thesis_Final_Redacted_Signature.pdf 2022-06-22T13:35:35.9276711 Output 27595659 application/pdf E-Thesis – open access true Copyright: The author, Ben A. Wilson, 2022. true eng
title Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors
spellingShingle Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors
BEN WILSON
title_short Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors
title_full Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors
title_fullStr Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors
title_full_unstemmed Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors
title_sort Characterisation and Classification of Hidden Conducting Security Threats using Magnetic Polarizability Tensors
author_id_str_mv ab84329c676a14097546698eda4b6abd
author_id_fullname_str_mv ab84329c676a14097546698eda4b6abd_***_BEN WILSON
author BEN WILSON
author2 BEN WILSON
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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
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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. Walk through metal detectors (WTMDs) are commonly deployed for security screening purposes in applications where these attacks are of particular con-cern such as in airports, transport hubs, government buildings and at concerts. However, there is scope to improve the identification of an object’s shape and its material proper-ties. Using current techniques there is often the requirement for any metallic objects to be inspected or scanned separately before a patron may be determined to pose no threat, making the process slow. This can often lead to build ups of large queues of unscreened people waiting to be screened which becomes another security threat in itself. To improve the current method, there is considerable potential to use the fields applied and measured by a metal detector since, hidden within the field perturbation, is object characterisation information. The magnetic polarizability tensor (MPT) offers an economical characteri-sation of metallic objects and its spectral signature provides additional object character-isation information. The MPT spectral signature can be determined from measurements of the induced voltage over a range of frequencies for a hidden object. With classification in mind, it can also be computed in advance for different threat and non-threat objects, producing a dataset of these objects from which a machine learning (ML) classifier can be trained. There is also potential to generate this dataset synthetically, via the application of a method based on finite elements (FE). This concept of training an ML classifier trained on a synthetic dataset of MPT based characterisations is at the heart of this work.In this thesis, details for the production and use of a first of its kind synthetic dataset of realistic object characterisations are presented. To achieve this, first a review of re-cent developments of MPT object characterisations is provided, motivating the use of MPT spectral signatures. A problem specific, H(curl) based, hp-finite element discreti-sation is presented, which allows for the development of a reduced order model (ROM), using a projection based proper orthogonal decomposition (PODP), that benefits from a-posteriori error estimates. This allows for the rapid production of MPT spectral signatures the accuracy of which is guaranteed. This methodology is then implemented in Python, using the NGSolve finite element package, where other problem specific efficiencies are also included along with a series of additional outputs of interest, this software is then packaged and released as the open source MPT-Calculator. This methodology and software are then extensively tested by application to a series of illustrative examples. Using this software, MPT spectral signatures are then produced for a series of realistic threat and non-threat objects, creating the first of its kind synthetic dataset, which is also released as the open source MPT-Library dataset. Lastly, a series of ML classifiers are documented and applied to several supervised classification problems using this new syn-thetic dataset. A series of challenging numerical examples are included to demonstrate the success of the proposed methodology.
published_date 2022-06-15T04:18:17Z
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score 11.013619

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