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      Identification of materials with magnetic characteristics by neural networks

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      Author
      Nazlibilek, S.
      Ege, Y.
      Kalender O.
      Sensoy, M.G.
      Karacor, D.
      Sazli, M.H.
      Date
      2012
      Source Title
      Measurement: Journal of the International Measurement Confederation
      Print ISSN
      0263-2241
      Volume
      45
      Issue
      4
      Pages
      734 - 744
      Language
      English
      Type
      Article
      Item Usage Stats
      141
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      121
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      Abstract
      In 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.
      Keywords
      Anisotropic magnetoresistive sensor (AMR)
      Magnetic anomaly
      Magnetic materials
      Neural networks
      Remote sensing
      Anisotropic magnetoresistive sensors
      Anti-tank mines
      Buried mines
      Earth magnetic fields
      Gaussian white noise
      High sensitivity
      Low Power
      Low-power consumption
      Magnetic anomalies
      Magnetic characteristic
      Mathematical curves
      Multi layer perceptron
      Neural network structures
      Remote sensing system
      Sensing mechanism
      Anisotropy
      Explosives
      Ferromagnetic materials
      Geomagnetism
      Magnetic materials
      Radial basis function networks
      Remote sensing
      Neural networks
      Permalink
      http://hdl.handle.net/11693/21490
      Published Version (Please cite this version)
      http://dx.doi.org/10.1016/j.measurement.2011.12.017
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      • Nanotechnology Research Center (NANOTAM) 1006
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