Measuring the Magnetic Polarizability Tensor Using an Axial Multi-Coil Geometry

Özdeğer, Toykan; Davidson, Jennifer L.; Verre, Wouter van; Marsh, Liam A; Lionheart, William; Peyton, A. J.

doi:10.1109/jsen.2021.3088809

Cited by 13 publications

(9 citation statements)

References 40 publications

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“…Our suite of object characterizations has been obtained using our MPT‐Calculator software, 8 which employs a reduced order model based on proper orthogonal decomposition (POD) for efficient calculations and an a posteriori error estimate to certify its predictions with respect to full order model solutions provided by the open source finite element library NGSolve 9‐12 . Our computational MPT spectral signature simulations are in excellent agreement with measured MPT spectral signatures 13,14 and, thus, they provide realistic object characterizations.…”

Section: Introductionmentioning

confidence: 78%

“…Earrings (C 4 ) Earrings 18V (10) Pendants (C 5 ) Pendants 21V (12) Pocket items (C 6 ) Coins 8V (9) + 4V (11) Keys Rings (C 7 ) Rings 21V (13) Wrist items (C 8 ) Bracelets 21V (7) + 12V (15) Watches classifier is random forests, although, for large P (k) , the performance of random forest, gradient boost, decision trees, and SVM (particularly for SNR = 40 dB) are all very similar with 𝜅 ≈ 1 indicating a low bias and low variance. As random forest is a bagging algorithm and gradient boost is a boosting algorithm we expect them to perform well.…”

Section: Knivesmentioning

confidence: 99%

“…Earrings (C 10 ) 9 3 V (10) 18V (10) Keys (C 11 ) 4 1 V (11) 4V (11) Pendents (C 12 ) 7 3 V (12) 21V (12) Rings (C 13 ) 7 3 V (13) 21V (13) Shoe shanks (C 14 ) 3 1 V (14) 3V (14) Watches (C 15 ) 4 3 V (15) 12V (15) F I G U R E 14 Set of multiple threat and non-threat objects: Overall performance of MLP for different uniform network architectures, for P (k) = 5000 when K = 8 and SNR = 40 dB, showing 𝜅 score for different numbers of hidden layers L and numbers of neurons per layer J architectures in this range result in high 𝜅 score, while minimizing computational cost. Also, if desired, J could be further reduced without comprising accuracy.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Also shown is a vertical line, which indicates the value of

ω

that the eddy current model assumption is likely to become inaccurate for this geometry 1,30 . Finally, we include a grey window corresponding to the frequency range

5.02 \times 1 0^{4} \leq ω \leq 8.67 \times 1 0^{4}

rad/s, where measurements taken by a commercial walk through metal detector, 15 and the greater range

7.53 \times 1 0^{2} \leq ω \leq 5.99 \times 1 0^{5} rad/s

, where measurements are taken using recent MPT measurement system, 13 the latter being able to capture more information from the signature. These spectral signatures will form part of the dictionary for object classification, which will be discussed later in Section 5.2.…”

Section: Mpt Spectral Signature Invariants For Object Classificationmentioning

confidence: 99%

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Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification

Wilson

Ledger

Lionheart

2022

Numerical Meth Engineering

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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 characterization information. The magnetic polarizability tensor (MPT) offers an economical characterization of metallic objects and its spectral signature provides additional object characterization information. The MPT spectral signature can be determined from measurements of the induced voltage over a range of frequencies in a metal signature for a hidden object. With classification in mind, it can also be computed in advance for different threat and non-threat objects. In this article, we evaluate the performance of probabilistic and non-probabilistic machine learning algorithms, trained using a dictionary of computed MPT spectral signatures, to classify objects for metal detection. We discuss the importance of using appropriate features and selecting an appropriate algorithm depending on the classification problem being solved, and we present numerical results for a range of practically motivated metal detection classification problems.

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Section: Introductionmentioning

confidence: 78%

Section: Knivesmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

“…Also shown is a vertical line, which indicates the value of

ω

that the eddy current model assumption is likely to become inaccurate for this geometry 1,30 . Finally, we include a grey window corresponding to the frequency range

5.02 \times 1 0^{4} \leq ω \leq 8.67 \times 1 0^{4}

rad/s, where measurements taken by a commercial walk through metal detector, 15 and the greater range

7.53 \times 1 0^{2} \leq ω \leq 5.99 \times 1 0^{5} rad/s

Section: Mpt Spectral Signature Invariants For Object Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification

Wilson

Ledger

Lionheart

2022

Numerical Meth Engineering

Self Cite

View full text Add to dashboard Cite

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 characterization information. The magnetic polarizability tensor (MPT) offers an economical characterization of metallic objects and its spectral signature provides additional object characterization information. The MPT spectral signature can be determined from measurements of the induced voltage over a range of frequencies in a metal signature for a hidden object. With classification in mind, it can also be computed in advance for different threat and non-threat objects. In this article, we evaluate the performance of probabilistic and non-probabilistic machine learning algorithms, trained using a dictionary of computed MPT spectral signatures, to classify objects for metal detection. We discuss the importance of using appropriate features and selecting an appropriate algorithm depending on the classification problem being solved, and we present numerical results for a range of practically motivated metal detection classification problems.

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“…To overcome the issues associated with the ill-posed nature of the inverse conductivity problem and the small number of measurements that can be made in metal detection, an alternative approach, which attempts to characterise hidden conducting objects by a small number of parameters using a magnetic polarizability tensor has gained popularity. This may have considerable advantages for example in airport security scanning [82] and landmine detection [96].…”

Section: Approaches To Solving the Metal Detection Inverse Problemmentioning

confidence: 99%

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

Wilson¹

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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.

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Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest

Elgy,

Ledger

2023

Engineering with Computers

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The magnetic polarizability tensor (MPT) is an economical characterisation of a conducting magnetic object, which can assist with identifying hidden targets in metal detection. The MPT’s coefficients depend on multiple parameters of interest including the object shape, size, electrical conductivity, magnetic permeability, and the frequency of excitation. The computation of the coefficients follow from post-processing an eddy current transmission problem solved numerically using high-order finite elements. To reduce the computational cost of constructing these characterisations for multiple different parameters, we compare three methods by which the MPT can be efficiently calculated for two-dimensional parameter sets, with different levels of code invasiveness. We compare, with numerical examples, a neural network regression of MPT eigenvalues with a projection-based reduced order model (ROM) and a neural network enhanced ROM (POD–NN) for predicting MPT coefficients.

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Measuring the Magnetic Polarizability Tensor Using an Axial Multi-Coil Geometry

Cited by 13 publications

References 40 publications

Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification

Identification of metallic objects using spectral magnetic polarizability tensor signatures: Object classification

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

Reduced order model approaches for predicting the magnetic polarizability tensor for multiple parameters of interest

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