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《China Mechanical Engineering(中国机械工程)》 2006-01
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Support Vector Machine and Chaos-genetic Optimization Based Ultrasonic Flaw Identification

Liu Qingkun Que Peiwen Guo Huawei Song Shoupeng Shanghai Jiao Tong University, 200030  
Automatic identification of flaws is very important for ultrasonic nondestructive testing and evaluation of pipelines. A novel automatic identification system of flaws was presented. Wavelet packet decomposition (WPD) was applied to feature extraction of ultrasonic signals, chaos-genetic optimization to eliminate redundant and irrelevant features and support vector machine to perform the identification task.To validate this system, some experiments are performed and the results show the proposed system has very high identification performance for pipeline flaws and outperforms other discussed methods.
【Fund】: 国家863高技术研究发展计划资助项目(2001AA602021)
【CateGory Index】: TB559
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1 Fan Wenbing1,Wang Quanquan1,Lei Tianyou2,Xing Junyang1(1.School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China;2.Office of Scientific Research,Zhenzhou University,Zhengzhou 450001,China);A Method of Malfunction Images Recognition and Classification Based on Relief[J];Ordnance Industry Automation;2010-10
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