遥感实验室辐射探测的威胁确定
Threat determination for radiation detection from the Remote Sensing Laboratory
摘要：
搜索辐射源的能力是国土安全部门感兴趣的。希望找到任何可能在恐怖主义行为中造成伤害的合理机会的辐射源。搜索行动成功的最佳机会通常是部署尽可能多的探测系统。在这样做的时候，希望遇到的威胁源将不可避免地被埋葬在更大数量的非威胁性辐射源的遭遇中，这些辐射源通常用于许多医疗和工业用途。然后，问题就变成了有效地过滤非威胁性来源，并向循环中的人提供一个适度的潜在威胁列表。我们的方法是现场的检测系统，利用软测量算法的目的是区分潜在的威胁和非威胁对象的集合，基于各种机器学习技术。
英文摘要：
The ability to search for radiation sources is of interest to the Homeland Security community. The hope is to find any radiation sources which may pose a reasonable chance for harm in a terrorist act. The best chance of success for search operations generally comes with fielding as many detection systems as possible. In doing this, the hoped for encounter with the threat source will inevitably be buried in an even larger number of encounters with non-threatening radiation sources commonly used for many medical and industrial use. The problem then becomes effectively filtering the non-threatening sources, and presenting the human-in-the-loop with a modest list of potential threats. Our approach is to field a collection of detection systems which utilize soft-sensing algorithms for the purpose of discriminating potential threat and non-threat objects, based on a variety of machine learning techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
