Show simple item record

dc.contributor.authorNazlibilek, S.en_US
dc.contributor.authorEge, Y.en_US
dc.contributor.authorKalender O.en_US
dc.contributor.authorSensoy, M.G.en_US
dc.contributor.authorKaracor, D.en_US
dc.contributor.authorSazli, M.H.en_US
dc.date.accessioned2016-02-08T09:47:06Z
dc.date.available2016-02-08T09:47:06Z
dc.date.issued2012en_US
dc.identifier.issn0263-2241
dc.identifier.urihttp://hdl.handle.net/11693/21490
dc.description.abstractIn industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises. © 2012 Elsevier Ltd. All rights reserved.en_US
dc.language.isoEnglishen_US
dc.source.titleMeasurement: Journal of the International Measurement Confederationen_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.measurement.2011.12.017en_US
dc.subjectAnisotropic magnetoresistive sensor (AMR)en_US
dc.subjectMagnetic anomalyen_US
dc.subjectMagnetic materialsen_US
dc.subjectNeural networksen_US
dc.subjectRemote sensingen_US
dc.subjectAnisotropic magnetoresistive sensorsen_US
dc.subjectAnti-tank minesen_US
dc.subjectBuried minesen_US
dc.subjectEarth magnetic fieldsen_US
dc.subjectGaussian white noiseen_US
dc.subjectHigh sensitivityen_US
dc.subjectLow Poweren_US
dc.subjectLow-power consumptionen_US
dc.subjectMagnetic anomaliesen_US
dc.subjectMagnetic characteristicen_US
dc.subjectMathematical curvesen_US
dc.subjectMulti layer perceptronen_US
dc.subjectNeural network structuresen_US
dc.subjectRemote sensing systemen_US
dc.subjectSensing mechanismen_US
dc.subjectAnisotropyen_US
dc.subjectExplosivesen_US
dc.subjectFerromagnetic materialsen_US
dc.subjectGeomagnetismen_US
dc.subjectMagnetic materialsen_US
dc.subjectRadial basis function networksen_US
dc.subjectRemote sensingen_US
dc.subjectNeural networksen_US
dc.titleIdentification of materials with magnetic characteristics by neural networksen_US
dc.typeArticleen_US
dc.departmentNANOTAM - Nanotechnology Research Center
dc.citation.spage734en_US
dc.citation.epage744en_US
dc.citation.volumeNumber45en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1016/j.measurement.2011.12.017en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record