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AMM:198-199
Research on the Battlefield Ferromagnetic Targets Recognition
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wucaipeng: 金币+15, ★★★★★最佳答案 2012-12-07 15:54:56

恭喜你，已检，信息如下：

Accession number:  20124715701257

  Title:  Research on the battlefield ferromagnetic targets recognition
  Authors:  Wu, Caipeng1 ; Deng, Jiahao2 ; Wu, Xiaoyi1; Yan, Lijun1; Wang, Lin1
  Author affiliation:  1 Northwest Institute of Mechanical and Electrical Engineering, Xianyang Shaanxi, China
2 National Key Laboratory of Electromechanical Engineering and Control, Beijing Institute of Technology, Beijing, China
  Corresponding author:  Wu, C. (w19820311@163.com)
  Source title:  Applied Mechanics and Materials
  Abbreviated source title:  Appl. Mech. Mater.
  Volume:  198-199
  Monograph title:  Applied Mechanics, Mechatronics Automation and System Simulation
  Issue date:  2012
  Publication year:  2012
  Pages:  1067-1072
  Language:  English
  ISSN:  16609336
  Document type:  Conference article (CA)
  Conference name:  2012 International Applied Mechanics, MechatronicsAutomation and System Simulation Meeting, AMMASS 2012
  Conference date:  June 24, 2012 - June 26, 2012
  Conference location:  Hangzhou, China
  Conference code:  93226
  Publisher:  Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670, Germany
  Abstract:  In-depth analysis of the characteristics of the battlefield on the basis of ferromagnetic targets, according to the problems of magnetic fuse detection system, such as low detection resolution and poor status of target recognition, the new type of magnetic sensors based on Giant Magneto-Impedance(GMI) effect, because of the sensitivity, power consumption, etc. natural advantages, with its fuse detection system for the magnetic core components, combined with fuzzy neural networks, can guarantee that probe nano-Tesla(nT) accurately identify the target within the scope. Then, equable magnetization rotation elliptic is used to simulate the tank and military truck, taking the magnetic moments and semi-focal length, that are Mx, My, Mz and c, as recognition characteristic quantity, and the FNN is used to recognize the tank and military truck including the categories and motion directions. The method achieved good recognition results through experimental verification, and it has great significance to improve detection range and recognition accuracy. © (2012) Trans Tech Publications, Switzerland.
  Number of references:  12
  Main heading:  Ferromagnetic materials
  Controlled terms:  Ferromagnetism  -  Fuzzy neural networks  -  Magnetic moments  -  Magnetic sensors  -  Magnetization  -  Tanks (containers)  -  Trucks
  Uncontrolled terms:  Core components  -  Detection range  -  Detection system  -  Experimental verification  -  Giant magneto impedance effect  -  GMI  -  In-depth analysis  -  Magnetization rotations  -  Military trucks  -  Motion direction  -  Recognition accuracy  -  Target recognition
  Classification code:  619.2 Tanks -  663.1 Heavy Duty Motor Vehicles -  701.2 Magnetism: Basic Concepts and Phenomena -  708.4 Magnetic Materials -  723.4 Artificial Intelligence
  DOI:  10.4028/www.scientific.net/AMM.198-199.1067
  Database:  Compendex
Compilation and indexing terms, © 2012 Elsevier Inc.