Sensor network optimization using Bayesian networks, decision graphs, and value of information
Sensor network optimization using Bayesian networks, decision graphs, and value of information
复制标题
使用贝叶斯网络、决策图和信息价值进行传感器网络优化
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
M. Pozzi
中科院分区：
文献类型：
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作者：
C. Malings;M. Pozzi
贝叶斯网络（BN）和决策图提供了一个有用的框架，建模土木工程基础设施的不确定性行为受到各种风险，以及管理代理人采取的风险缓解行动的潜在结果。这些图还可以通过最大化感测工作的信息价值来指导基础设施的最佳感测和检查。本文提出了一个通用的框架，使用BN的基础设施系统建模和评估传感器放置在这个模型中的指标。一个例子的应用程序的信息度量的价值在引导最佳的传感系统中的基础设施资产在旧金山弗朗西斯科湾地区受到地震风险。参数研究还调查了BN系统模型的各种参数的信息度量的值的灵敏度。
Bayesian Networks (BNs) and decision graphs provide a useful framework for modeling the uncertain behavior of civil engineering infrastructures subjected to various risks, as well as the potential outcomes of risk mitigation actions undertaken by managing agents. These graphs can also guide optimal sensing and inspection of infrastructure by maximizing the value of information of sensing efforts. This paper presents a general framework for modeling infrastructure systems using BNs and for evaluating sensor placement metrics within this model. An example application of the use of the value of information metric in guiding optimal sensing in a system of infrastructure assets in the San Francisco Bay area subjected to seismic risk is then presented. A parametric study also investigates the sensitivity of the value of information metric to various parameters of the BN system model.