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Combating Curse of Dimensionality in Resilient Plant Monitoring Systems: Overlapping Decomposition and Knowledge Fusion

机译:弹性工厂监控系统中的维度诅咒:重叠分解和知识融合

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Resilient plant monitoring systems (RPMS) are sensor networks that degrade gracefully under cyber-physical attacks. In the previous work, we have developed an adaptive four-layer RPMS architecture and evaluated its performance under various attack scenarios. While the steady state performance of this system has been shown to be satisfactory, the transients have not: adaptation time grows exponentially as a function of the number of states in the network. The current paper is intended to provide a method for combating this curse of dimensionality. The approach is based on the idea of overlapping plant decomposition and subsequent fusion of knowledge derived in the overlapping subnetworks. In this paper such a monitoring system is developed (five-layer architecture), analyzed, and shown to have desirable steady state and, to a certain extent, transient characteristics.
机译:弹性工厂监视系统(RPMS)是在网络物理攻击下会正常退化的传感器网络。在先前的工作中,我们已经开发了一种自适应的四层RPMS体系结构,并评估了其在各种攻击情形下的性能。尽管已证明该系统的稳态性能令人满意,但瞬变却没有:适应时间随网络中状态数量的增加呈指数增长。当前的文章旨在提供一种解决维数诅咒的方法。该方法基于重叠植物分解和重叠子网络中派生的知识的后续融合的思想。在本文中,开发了这样的监视系统(五层体系结构),进行了分析,并显示了所需的稳定状态,并在一定程度上具有瞬态特性。

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