摘要：An intelligent security systems engineering approach is used to analyze fire and explosive critical incidents, a growing concern in urban communities. A feed-forward back-propagation neural network models the damages arising from these critical incidents. The overall goal is to promote fire safety and sustainable security. The intelligent security systems engineering prediction model uses a fully connected multilayer neural network, and considers a number of factors related to the fire or explosive incident including the type of property affected, the time of day, and the ignition source. The network was trained on a large number of critical incident records reported in Toronto, Canada between 2000 and 2006. Our intelligent security systems engineering approach can help emergency responders by improving cr^tical incident analysis, sustainable security, and fire risk management.

摘要：目的:建筑物的材质信息是灾害评估和城市调查等领域的重要信息。本文旨在利用高光谱遥感影像提取地面建筑物的表面材质信息(包括材质类型和主要组成成份),并对提取方法进行对比,给出应用建议。创新点:对建筑物材料进行光谱测试,并对其高光谱响应规律进行分析,找出有诊断意义的光谱位置;基于实验和验证得出应用方法的适应性,以提高信息提取精度。方法:1.设计建筑物材质信息提取流程(图1),并对高光谱数据进行基础处理;2.对建筑物材料进行光谱测试(波长范围为350~2500 nm,图3),并完成各类建筑物的诊断性光谱分析;3.利用光谱角度法(公式(1))和光谱信息散度法(公式(2))进行材质信息提取(图5和6);4.综合分析两种方法的应用过程与控制参数和准确率的关系。结论:1.两种方法皆可提取建筑物材质信息,但在应用过程中需要进行参数的适应性调整,这是提高准确率的关键;2.在建筑物材质信息提取方面,光谱角度法的提取准确率略高于光谱散度法。