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62 results on '"CHEN Tian-Lu"'

54. Fractional Fourier transform of beams in the use of the Wigner distribution function method

Author

Wu Ping, Lyu Bai-Da, and Chen Tian-Lu

Subjects
Beam diameter, Distribution (number theory), Plane (geometry), Mathematical analysis, General Physics and Astronomy, Fractional Fourier transform, symbols.namesake, Fourier transform, symbols, Physics::Accelerator Physics, Wigner distribution function, Intensity (heat transfer), Beam (structure), Mathematics
Abstract

By using the Wigner distribution function(WDF)method,the fractional Fourier transform (FRFT) of beams is studied.HermiteGaussian (HG) beams are taken as an example; closedform expressions for the intensity distribution and beam widths of HG beams at the FRFT's plane are derived and changes in the intensity dist ribution of HG beams with the order of the FRFT are illustrated with numerical examples.It is shown that a symmetrical intensity distribution with equal beam width in the x and y directions can be achieved by a suitable choice of the transform order p.

Published
2005

58. Performance of the muon detector A under TIBET III array.

Author

LIU Cheng, BI Xiao-Jun, CHEN Tian-Lu, CHEN Wen-Yi, CUI Shu-Wang, DANZENGLUOBU, DING Lin-Kai, DING Xiao-Hong, FENG Cun-Feng, FENG Zhao-Yang, FENG Zhen-Yong, GOU Quan-Bu, GUO Hong-Wei, GUO Yi-Qing, HE Hui-Hai, HOU Zheng-Tao, HU Hai-Bing, HU Hong-Bo, HUANG Jing, and LI Wan-Jie

Published
2013
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59. Identification of Magnetic Flux Leak Inspection Signals for Pipeline Detection Using an Improved BP Neural Netwok Algorithm.

Author

JIN Tao, QUE Pei-wen, CHEN Tian-lu, and LI Liang

Abstract

Recognizing the appearance parameters of defects is the main object in the signal processing of offshore oil pipeline inspection. Based on the feature extraction of magnetic flux leakage (MFL) inspection data, a modified back-propagation (BP) neural network system were presented to recognize pipeline defect and corrosion. The process of teaching and testing of BP neural networks to characterize the defect signals was analyzed. The adjusting method of network output weight and rate of learning based on Levenberg-Marquardt algorithm was also discussed. In order to obtain the perfect structure parameters of network and research each parameter's effect on the performance of network, a large number of simulation tests were conducted. The MFL data processing experiment proves that the defect recognition neural network system has high convergence speed and good ability of approaching defect features. [ABSTRACT FROM AUTHOR]

Published
2005

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