By achieving their purposes through interactions with the physical world, Cyber-Physical Systems (CPS) pose new challenges in terms of dependability. Indeed, the evolution of the physical systems they control with transducers can be affected by surrounding physical processes over which they have no control and which may potentially hamper the achievement of their purposes. While it is illusory to hope for a comprehensive model of the physical environment at design time to anticipate and remove faults that may occur once these systems are deployed, it becomes necessary to evaluate their degree of effectiveness in vivo. In this paper, the degree of effectiveness is formally defined and generalized in the context of the measure theory. The measure is developed in the context of the Transferable Belief Model (TBM), an elaboration on the Dempster-Shafer Theory (DST) of evidence so as to handle epistemic and aleatory uncertainties respectively pertaining the users' expectations and the natural variability of the physical environment. The TBM is used in conjunction with the Input/Output Hidden Markov Modeling framework (we denote by Ev-IOHMM) to specify the expected evolution of the physical system controlled by the CPS and the tolerances towards uncertainties. The measure of effectiveness is then obtained from the forward algorithm, leveraging the conflict entailed by the successive combinations of the beliefs obtained from observations of the physical system and the beliefs corresponding to its expected evolution. The proposed approach is applied to autonomous vehicles and show how the degree of effectiveness can be used for bench-marking their controller relative to the highway code speed limitations and passengers' well-being constraints, both modeled through an Ev-IOHMM.

中文翻译：

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网络物理系统的有效性评估

通过与物理世界的交互来实现其目的，网络物理系统 (CPS) 在可靠性方面提出了新的挑战。事实上，他们用换能器控制的物理系统的演变可能会受到他们无法控制的周围物理过程的影响，这可能会阻碍他们实现目标。虽然希望在设计时建立物理环境的综合模型来预测和消除这些系统部署后可能发生的故障是虚幻的，但有必要评估它们在体内的有效性程度。在本文中，有效性程度在测度理论的背景下被正式定义和概括。该措施是在可转移信念模型（TBM）的背景下开发的，详细阐述了 Dempster-Shafer 理论 (DST) 的证据，以处理分别与用户期望和物理环境的自然可变性相关的认知和随机不确定性。TBM 与输入/输出隐马尔可夫建模框架（我们用 Ev-IOHMM 表示）结合使用，以指定由 CPS 控制的物理系统的预期演变以及对不确定性的容忍度。然后从前向算法中获得有效性的度量，利用从物理系统的观察中获得的信念和与其预期进化相对应的信念的连续组合所带来的冲突。