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1. 成都理工大学 地质灾害防治与地质环境保护国家重点实验室,成都,610059
2. 成都理工大学 地学核技术四川省重点实验室,成都,610059
3. 西南科技大学 核废物与环境安全国防重点学科实验室,绵阳,621010
[ "通讯作者: 庹先国,E-mail: 13982021384@163.com E-mail:372710383@qq.com" ]
纸质出版日期:2014-12-04
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李 磊, 庹先国, 刘明哲, 等. 基于TI-DWT-MSSNF的谱光滑与评价[J]. 核技术, 2014,37(12):120203
LI Lei, TUO Xianguo, LIU Mingzhe, et al. Smoothing and evaluation of spectrum based on TI-DWT-MSSNF[J]. Nuclear techniques, 2014, 37(12): 120203
李 磊, 庹先国, 刘明哲, 等. 基于TI-DWT-MSSNF的谱光滑与评价[J]. 核技术, 2014,37(12):120203 DOI: 10.11889/j.0253-3219.2014.hjs.37.120203.
LI Lei, TUO Xianguo, LIU Mingzhe, et al. Smoothing and evaluation of spectrum based on TI-DWT-MSSNF[J]. Nuclear techniques, 2014, 37(12): 120203 DOI: 10.11889/j.0253-3219.2014.hjs.37.120203.
提出了基于平移不变离散小波变换(Translation Invariant Discrete Wavelet Transform
TI-DWT)小波模极大值空间选择性滤波(Translation Invariant Discrete Wavelet Transform Wavelet Modulus Maxima Spatial Selectivity Filter
TI-DWT-MSSNF)的能谱平滑算法,并构建了光滑效果评价指标。分别使用?谱仪和?谱仪获取了239Pu、241Am的?能谱和137Cs的?能谱,将5点3次多项式最小二乘法、传统的小波模极大值法和TI-DWT-MSSNF分别用于?和?谱平滑处理。结果表明:相比较5点3次多项式最小二乘法和传统的小波模极大值法,TI-DWT-MSSNF消除统计涨落更加彻底,特征信息保留更好,峰形畸变更小,是一种更优的方法。
Background: Nuclear decay
electronic noise
statistic fluctuations
etc.
exist inherently. Therefore
the measured spectrum always has statistic fluctuation. Purpose: In order to reduce the statistical fluctuation and electronics noise in detector
translation invariant discrete wavelet transform (TI-DWT) wavelet modulus maxima spatial selectivity filter (TI-DWT-MSSNF) smoothing algorithm was put forward to preprocess data for the de-convolution of spectrum. Methods: The ?-spectrum was acquired by using ORTEC-8 channel ? spectrometer to measure the source numbered AMPU1103 (239Pu and 241Am) under vacuum conditions of ?0.03MPa. The ?-spectrum was obtained by using ? spectrometer and 137Cs source. Cubical smoothing algorithm with five-point approximation (“5-3”)
the traditional method of wavelet modulus maxima (WTMM) and TI-DWT-MSSNF were applied to smooth ? and ? spectra. Results: The study showed that TI-DWT-MSSNF method could eliminate statistical fluctuation more thoroughly
retain feature information better compared with “5-3” and WTMM. The D(r) values of TI-DWT-MSSNF were greater and ?2 values of TI-DWT-MSSNF were more close to 1 compared with those of “5-3” and WTMM. Conclusion: Comprehensive research indicates that it is feasible to reduce the statistical fluctuations of spectrum using TI-DWT-MSSNF. And TI-DWT-MSSNF outperforms both the “5-3” and WTMM. Abstract
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