{"id":{"repo_id":"temple","oai_identifier":"oai:scholarshare.temple.edu:20.500.12613/837"},"canonical_url":"https://search.dev.ndltd.org/etd/temple/oai:scholarshare.temple.edu:20.500.12613/837","repository":{"repo_id":"temple","name":"Temple University","base_url":"https://scholarshare.temple.edu/server/oai/request"},"display":{"title":"Magnetic Signature Estimation Using Neural Networks","abstract":"Ferrous objects in earth's magnetic field cause distortion in the surrounding ambient field. This distortion is a function of the object's material properties and geometry, and is known as the magnetic signature. As a precursor to first principle modeling of the phenomenon and a proof of concept, the goal of this research is to predict offboard magnetic signatures from on-board sensor data using a neural network. This allows magnetic signature analysis in applications where direct field measurements are inaccessible. Simulated magnetic environments are generated using MATLAB's Partial Differential Equation toolbox for a 2D geometry, specifically for a rectangular shell. The resulting data sets are used to train and validate the neural network, which is configured in two layers with ten neurons. Sensor data from within the shell is used as network inputs, and the off-board field values are used as targets. The neural network is trained using the Levenberg-Marquardt algorithm and the back propagation method by comparing the estimated off-board magnetic field intensity to the true value. This research also investigates sensitivity, scalability, and implementation issues of the neural network for signature estimation in a practical environment.","abstract_html":"Ferrous objects in earth's magnetic field cause distortion in the surrounding ambient field. This distortion is a function of the object's material properties and geometry, and is known as the magnetic signature. As a precursor to first principle modeling of the phenomenon and a proof of concept, the goal of this research is to predict offboard magnetic signatures from on-board sensor data using a neural network. This allows magnetic signature analysis in applications where direct field measurements are inaccessible. Simulated magnetic environments are generated using MATLAB's Partial Differential Equation toolbox for a 2D geometry, specifically for a rectangular shell. The resulting data sets are used to train and validate the neural network, which is configured in two layers with ten neurons. Sensor data from within the shell is used as network inputs, and the off-board field values are used as targets. The neural network is trained using the Levenberg-Marquardt algorithm and the back propagation method by comparing the estimated off-board magnetic field intensity to the true value. This research also investigates sensitivity, scalability, and implementation issues of the neural network for signature estimation in a practical environment.","abstract_has_math":false,"creators":["Bosack, Matthew James"],"institution":"Temple University. Libraries","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Biswas, Saroj K."],"committee_chairs":[],"committee_members":["Ferrese, Frank","Higgins, Frank P.","Silage, Dennis"],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-27T21:22:02Z","subjects":["Engineering","Magnetic Signature","Neural Network"],"languages":["eng"],"rights":["IN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available."],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["864885450"],"render_values":[{"text":"864885450","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/20.500.12613/837","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Biswas, Saroj K."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Ferrese, Frank","Higgins, Frank P.","Silage, Dennis"]},{"key":"dc:creator","label":"Author","values":["Bosack, Matthew James"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-10-21T14:26:47Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-10-21T14:26:47Z"]},{"key":"dc:date.issued","label":"Date","values":["2012"]},{"key":"dc:publisher","label":"Institution","values":["Temple University. 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This distortion is a function of the object's material properties and geometry, and is known as the magnetic signature. As a precursor to first principle modeling of the phenomenon and a proof of concept, the goal of this research is to predict offboard magnetic signatures from on-board sensor data using a neural network. This allows magnetic signature analysis in applications where direct field measurements are inaccessible. Simulated magnetic environments are generated using MATLAB's Partial Differential Equation toolbox for a 2D geometry, specifically for a rectangular shell. The resulting data sets are used to train and validate the neural network, which is configured in two layers with ten neurons. Sensor data from within the shell is used as network inputs, and the off-board field values are used as targets. The neural network is trained using the Levenberg-Marquardt algorithm and the back propagation method by comparing the estimated off-board magnetic field intensity to the true value. This research also investigates sensitivity, scalability, and implementation issues of the neural network for signature estimation in a practical environment."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.S.E.E."]},{"key":"dc:title","label":"Title","values":["Magnetic Signature Estimation Using Neural Networks"]}]}],"canonical_facts":{"dc:contributor.advisor":["Biswas, Saroj K."],"dc:contributor.committeemember":["Ferrese, Frank","Higgins, Frank P.","Silage, Dennis"],"dc:creator":["Bosack, Matthew James"],"dc:date.accessioned":["2020-10-21T14:26:47Z"],"dc:date.available":["2020-10-21T14:26:47Z"],"dc:date.issued":["2012"],"dc:description.abstract":["Ferrous objects in earth's magnetic field cause distortion in the surrounding ambient field. This distortion is a function of the object's material properties and geometry, and is known as the magnetic signature. As a precursor to first principle modeling of the phenomenon and a proof of concept, the goal of this research is to predict offboard magnetic signatures from on-board sensor data using a neural network. This allows magnetic signature analysis in applications where direct field measurements are inaccessible. Simulated magnetic environments are generated using MATLAB's Partial Differential Equation toolbox for a 2D geometry, specifically for a rectangular shell. The resulting data sets are used to train and validate the neural network, which is configured in two layers with ten neurons. Sensor data from within the shell is used as network inputs, and the off-board field values are used as targets. 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