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keepstrong
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wucaipeng: ½ð±Ò+15, ¡ï¡ï¡ï¡ï¡ï×î¼Ñ´ð°¸ 2012-12-07 15:54:56
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¹§Ï²Ä㣬ÒѼ죬ÐÅÏ¢ÈçÏ£º 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. |

2Â¥2012-12-07 13:19:16
wucaipeng
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3Â¥2012-12-07 15:53:36
yang97718
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4Â¥2012-12-07 20:30:36
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5Â¥2012-12-10 00:18:36
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6Â¥2012-12-12 00:45:27














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