LIU Yi-xin, WEI Biao, XU Yang, ZHUO Ren-hong, WEN De-zhi, DING Da-jie, MAO Ben-jiang. Metrological Feature Extraction Method for Scattering γ Spectrum in Minitype Reference Radiation[J]. Atomic Energy Science and Technology, 2016, 50(12): 2256-2262. DOI: 10.7538/yzk.2016.50.12.2256
Citation: LIU Yi-xin, WEI Biao, XU Yang, ZHUO Ren-hong, WEN De-zhi, DING Da-jie, MAO Ben-jiang. Metrological Feature Extraction Method for Scattering γ Spectrum in Minitype Reference Radiation[J]. Atomic Energy Science and Technology, 2016, 50(12): 2256-2262. DOI: 10.7538/yzk.2016.50.12.2256

Metrological Feature Extraction Method for Scattering γ Spectrum in Minitype Reference Radiation

More Information
  • In the determination of the gamma air kerma conventional true value (CAK) in a minitype reference radiation (MRR), the scattering γ spectrum is affected by the radioactive statistical fluctuation and other electronic noise. Moreover, the excess data of the spectrum after dispersing also make it unable to characterize the MRR accurately and decrease the construction efficiency and prediction precision of the prediction model of CAK. The wavelet analysis and principal component analysis (PCA) were employed to de-noise the scattering γ spectrum and extract the metrological feature components. When the metrological feature components were used to replace the spectrum for CAK prediction model construction, the construction efficiency and prediction precision were improved significantly.
  • [1]
    ISO 4037-1: 1996X and gamma reference radiation for calibration dosimeters and dose rate meters and for determining their response as a function of photo energy, Part 1: Radiation characteristics and production methods[S]. Geneva: ISO, 1996.
    [2]
    ISO 4037-2: 1997X and gamma reference radiation for calibration dosimeters and dose rate meters and for determining their response as a function of photo energy, Part 2: Dosimetry for radiation protection over the energy ranges 8 keV to 1.3 MeV and 4 MeV to 9 MeV[S]. Geneva: ISO, 1997.
    [3]
    李宗扬. 计量技术基础[M]. 北京:原子能出版社,2002.
    [4]
    李丽娟. 最小二乘支持向量机建模及预测控制算法研究[D]. 杭州:浙江大学信息科学与工程学院,2008.
    [5]
    阎威武,朱宏栋,邵惠鹤. 基于最小二乘支持向量机的软测量建模[J]. 系统仿真学报,2003,15(10):1494-1496.YAN Weiwu, ZHU Hongdong, SHAO Huihe. Soft sensor modeling based on support vector machines[J]. Journal of System Simulation, 2003, 15(10): 1494-1496(in Chinese).
    [6]
    GONZALO C, JAROSLAV K. Air kerma rate estimation by means of in-situ gamma spectrometry: A Bayesian approach[J]. Applied Radiation and Isotopes, 2010, 68: 804-806.
    [7]
    陈斌,黄传旭,陆道礼. 多尺度分解和主成分法在近红外光谱分析中的应用[J]. 江苏大学学报:自然科学版,2004,25(2):105-108.CHEN Bin, HUANG Chuanxu, LU Daoli. Use of multi-resolution decomposition and principal components analysis in information abstraction from NIR spectrum[J]. Journal of Jiangsu University: Natural Science Edition, 2004, 25(2): 105-108(in Chinese).
    [8]
    ATTIX F H. Introduction to radiological physics and radiation dosimetry[M]. New York: Wiley, 1987.
    [9]
    董长虹. Matlab小波分析工具箱原理与应用[M]. 北京:国防工业出版社,2004.
    [10]
    GUO Xiaoling, CHENG Jian. Application of wavelet-based active power filter in accelerator magnet power supply[J]. Chinese Physics C, 2014, 38: 117007.
    [11]
    史东生,弟宇鸣,周春林. 小波变换域傅里叶变换在γ能谱降噪处理中的比较研究[J]. 核电子学与探测技术,2006,26(6):134-137.SHI Dongsheng, DI Yuming, ZHOU Chunlin. Comparative study on γ energy spectrum denoise by Fourier and wavelet transforms[J]. Nuclear Electronics & Detection Technology, 2006, 26(6): 134-137(in Chinese).
    [12]
    陈强,黄生享,王韦. 小波去噪效果评价的另一指标[J]. 测绘信息与工程,2008,33(5):13-14.CHEN Qiang, HUANG Shengxiang, WANG Wei. An evaluation indicator of wavelet denoising[J]. Journal of Geomatics, 2008, 33(5): 13-14(in Chinese).
    [13]
    RICHARD A J, DEAN W W. Applied multivariate statistical analysis[M]. New York: Pearson Education, 2007.
    [14]
    王桂增,叶昊. 主元分析与偏最小二乘法[M]. 北京:清华大学出版社,2012.
    [15]
    CRISTIANINI N, SHAWE T J. An introduction to support vector machines and other kernel based learning methods[M]. Cambridge: Cambridge University Press, 2000.
    [16]
    刘京礼. 鲁棒最小二乘支持向量机研究与应用[D]. 合肥:中国科学技术大学管理学院,2010.
    [17]
    DUYGU Ç, ESIN D. A new intelligent hepatitis diagnosis system: PCA-LSSVM[J]. Expert Systems with Application, 2011, 38: 10705-10708.

Catalog

    Article views (176) PDF downloads (1167) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return