摘要
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α谱仪和γ谱仪获取了239Pu、241Am的α能谱和137Cs的γ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α和γ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 a-spectrum was acquired by using ORTEC-8 channel α spectrometer to measure the source numbered AMPU1103 （239pu and 241Am） under vacuum conditions of-0.03 MPa. The γ-spectrtun was obtained by using γ 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 α and γ 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 Z2 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.
出处
《核技术》
CAS
CSCD
北大核心
2014年第12期16-22,共7页
Nuclear Techniques
基金
国家国家杰出青年科学基金项目(No.41025015)
国家自然科学基金项目(No.41274130)
四川省青年科技创新研究团队项目(No.2011JTD0013)
四川省科技支撑计划(No.2013FZ0022)资助
关键词
平移不变离散小波变换
模极大值
光滑效果评价
Translation invariant discrete wavelet transform （TI-DWT）, Modulus maxima, Smoothing evaluation
