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Intelligent signal analysis methodologies for nuclear detection, identification and attribution.

机译：用于核探测，识别和归属的智能信号分析方法。

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摘要

Detection and identification of special nuclear materials can be fully performed with a radiation detector-spectrometer. Due to several physical and computational limitations, development of fast and accurate radioisotope identifier (RIID) algorithms is essential for automated radioactive source detection and characterization. The challenge is to identify individual isotope signatures embedded in spectral signature aggregation. In addition, background and isotope spectra overlap to further complicate the signal analysis. These concerns are addressed, in this thesis, through a set of intelligent methodologies recognizing signature spectra, background spectrum and, subsequently, identifying radionuclides. Initially, a method for detection and extraction of signature patterns is accomplished by means of fuzzy logic. The fuzzy logic methodology is applied on three types of radiation signal processing applications, where it exhibits high positive detection, low false alarm rate and very short execution time, while outperforming the maximum likelihood fitting approach. In addition, an innovative Pareto optimal multiobjective fitting of gamma ray spectra using evolutionary computing is presented. The methodology exhibits perfect identification while performs better than single objective fitting. Lastly, an innovative kernel based machine learning methodology was developed for estimating natural background spectrum in gamma ray spectra. The novelty of the methodology lies in the fact that it implements a data based approach and does not require any explicit physics modeling. Results show that kernel based method adequately estimates the gamma background, but algorithm's performance exhibits a strong dependence on the selected kernel.
机译：特殊的核材料的检测和鉴定可以完全通过辐射探测器光谱仪完成。由于一些物理和计算上的限制，开发快速准确的放射性同位素识别器（RIID）算法对于自动放射源检测和表征至关重要。面临的挑战是确定嵌入光谱特征集合中的单个同位素特征。此外，背景光谱和同位素光谱重叠，使信号分析更加复杂。在本文中，这些担忧是通过一套智能方法来解决的，这些方法可以识别特征光谱，背景光谱，并随后识别放射性核素。最初，借助于模糊逻辑来实现一种用于检测和提取签名图案的方法。模糊逻辑方法论适用于三种类型的辐射信号处理应用程序，在这种方法中，它表现出较高的正检测率，较低的误报率和非常短的执行时间，同时胜过最大似然拟合方法。此外，提出了使用进化计算的创新性的伽马射线谱图的帕累托最优多目标拟合。该方法具有完美的识别效果，但比单目标拟合效果更好。最后，开发了一种基于核的创新机器学习方法，用于估计伽玛射线光谱中的自然本底光谱。该方法的新颖之处在于它实现了基于数据的方法，并且不需要任何明确的物理建模。结果表明，基于核的方法可以充分估计伽玛背景，但是算法的性能表现出对所选核的强烈依赖性。

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  • 作者

    Alamaniotis, Miltiadis.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Engineering Nuclear.;Computer Science.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 166 p.
  • 总页数 166
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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