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Statistical Methods for Bullet Matching

机译:项目符号匹配的统计方法

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摘要

Despite being an accepted and established forensic science practice, the process of matching bullets to determine whether they were fired from the same gun barrel has come under fairly intense scrutiny in recent years. This began in earnest in 2009 with a National Academy of Sciences report questioning the scientific validity of these methods. Further criticisms were made in a 2016 report by the President's Council of Advisors on Science and Technology (PCAST). After PCAST determined that there has only been one appropriately designed study to assess the accuracy of bullet matching methods, the report concluded that "[T]he current evidence falls short of the scientific criteria for foundational validity." The report also outlines a way forward by noting "A second---and more important---direction is...to convert firearms analysis from a subjective method to an objective method." This thesis attempts to take steps towards that goal. It begins by describing an automatic algorithm for matching bullet lands, and assesses this algorithm on the James Hamby study data. These ideas are then generalized in order to increase the prediction accuracy, determine operator effects in bullet scanning, handle the case of bullet land degradation, and apply to full bullet matches. Finally, a modern web-based database and software system for bullet matching is introduced, allowing for more seamless collaboration in the research community for assessing and improving these algorithms.
机译:尽管是公认的和公认的法医科学实践,近年来,对子弹进行匹配以确定是否从同一枪管发射子弹的过程受到了严格的审查。这始于2009年,当时美国国家科学院的一份报告质疑这些方法的科学有效性。总统科学技术顾问委员会(PCAST​​)在2016年的报告中提出了进一步的批评。在PCAST​​确定只有一项经过适当设计的研究来评估项目符号匹配方法的准确性之后,该报告得出结论:“当前证据不足以证明基础有效性的科学标准。”该报告还通过指出“第二个也是更重要的方向是……将枪支分析从主观方法转变为客观方法”来概述前进的道路。本文试图为实现该目标采取步骤。首先介绍一种用于匹配子弹区的自动算法,然后根据James Hamby研究数据评估该算法。然后将这些想法归纳起来,以提高预测准确性,确定子弹扫描中的操作员效果,处理子弹降落的情况并应用于完整的子弹匹配。最后,介绍了用于子弹匹配的基于Web的现代数据库和软件系统,从而可以在研究社区中进行更无缝的协作以评估和改进这些算法。

著录项

  • 作者

    Hare, Eric Riemer.;

  • 作者单位

    Iowa State University.;

  • 授予单位 Iowa State University.;
  • 学科 Statistics.;Computer science.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 124 p.
  • 总页数 124
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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