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Towards improved characterization of high-risk releases using heterogeneous indoor sensor systems

机译：使用异构室内传感器系统改进对高风险释放的表征

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摘要

The sudden release of toxic contaminants that reach indoor spaces can be hazardous to building occupants. For an acutely toxic contaminant, the speed of the emergency response strongly influences the consequences to occupants. The design of a real-time sensor system is made challenging both by the urgency and complex nature of the event, and by the imperfect sensors and models available to describe it. In this research, we use Bayesian modeling to combine information from multiple types of sensors to improve the characterization of a release. We discuss conceptual and algorithmic considerations for selecting and fusing information from disparate sensors. To explore system performance, we use both real tracer gas data from experiments in a three-story building, along with synthetic data, including information from door-position sensors. The added information from door-position sensors is found to be useful for many scenarios, but not always. We discuss the physical conditions and design factors that affect these results, such as the influence of the door positions on contaminant transport. We highlight potential benefits of multisensor data fusion, challenges in realizing those benefits, and opportunities for further improvement.
机译：突然有毒污染物释放到室内空间可能对建筑人员造成危害。对于剧毒污染物，应急响应的速度强烈影响对乘员的后果。由于事件的紧急性和复杂性，以及可用于描述事件的不完善的传感器和模型，实时传感器系统的设计都具有挑战性。在这项研究中，我们使用贝叶斯建模来组合来自多种类型传感器的信息，以改善发布的特征。我们讨论了从异构传感器中选择和融合信息的概念和算法考虑。为了探索系统性能，我们同时使用了三层楼实验中的真实示踪气体数据以及包括门位置传感器信息在内的合成数据。发现来自门位置传感器的附加信息在许多情况下很有用，但并非总是如此。我们讨论了影响这些结果的物理条件和设计因素，例如门位置对污染物传输的影响。我们重点介绍了多传感器数据融合的潜在好处，实现这些好处所面临的挑战以及进一步改进的机会。

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  • 来源
    《Building and Environment》 |2011年第2期|p.438-447|共10页
  • 作者

    Priya Sreedharan; Michael D. Sohn; William W Nazaroff; Ashok J. Gadgil;

  • 作者单位

    Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740, USA,Indoor Environment Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;

    Indoor Environment Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;

    Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740, USA,Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720-1710, USA;

    Indoor Environment Department, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA,Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720-1710, USA;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    bayesian analysis; contaminant detection; environmental systems; parameter estimation; sensor fusion;

    机译：贝叶斯分析污染物检测;环境系统;参数估计;传感器融合;

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