状态不可观测的信息物理融合系统运行时验证

确保信息物理融合系统（Cyber-Physical System，CPS）运行时行为正确性是至关重要的，尤其在航空航天、汽车、核电和医疗等安全攸关领域.本文针对具有随机行为且状态不可观测的CPS，提出一种基于隐马尔科夫模型的运行时安全性验证方法.首先构造状态不可观测的CPS运行时安全性验证框架，该框架通过隐马尔科夫模型表示系统，使用确定性有限自动机规约系统安全属性的否定，两者的乘积自动机作为运行时监控器，从而将CPS运行时安全性验证问题约简到监控器上的概率推断问题.然后，提出一种增量迭代安全性验证算法以及反例生成算法.实验结果表明本文算法和粒子滤波算法相比预测错误率下降了近20%，并且当系统违背安全属性时，本文的方法能给出反例.

Runtime Verification of States Unobservable Cyber-Physical System

Ensuring the correct behavior of cyber physical systems (CPS) at runtime is critical,and it is more so in safety-critical area,such as aerospace,automotive,nuclear power and medical.A method of runtime safety verification based on hidden Markov model (HMM) is proposed for the CPS with stochastic behavior and unobservable states.First,a runtime safety verification framework of the CPS is constructed where the system model is represented by HMM,and the negation of safety property is specified by deterministic finite automaton (DFA),and then the product automata of DFA and HMM is used as a runtime monitor.Thus,the problem of CPS runtime safety verification is reduced into a probabilistic inference problem on the monitor.Second,an incremental iterative safety verification algorithm and a counterexample generation algorithm are proposed.The experimental results show that the prediction error rate of the safety verification algorithm is nearly 20% lower than that of the particle filter algorithm.When the system violates the safety property,the proposed method can product a counterexample which the particle filtering method cannot do.