@inproceedings{585d89fdb7214f97b3775478e0a5feb1,
title = "Stochastic Z{\'e}lus: Stochastic Hybrid Modeling and Verification for Nuclear I\&C System",
abstract = "The Z{\'e}lus language excels at describing hybrid systems but has limitations in handling uncertainties, such as probabilistic transitions and random failures, in nuclear Instrumentation and Control (I\&C) systems. Other languages for modeling uncertainty often choose to discretize the continuous behavior in the system, making it difficult to accurately describe the system. To address this, we propose Stochastic Z{\'e}lus, a new language integrating probabilistic automata for modeling hybrid systems in uncertain environments. We also developed a quantitative analysis framework and a prototype tool for converting Stochastic Z{\'e}lus models to NSHA (Networks of Stochastic Hybrid Automata) models supported by UPPAAL-SMC, to support quantitative analysis of stochastic Z{\'e}lus models using UPPAAL-SMC. Finally, we demonstrate the effectiveness of this approach through a case study on a Reactor Protection System (PS), proving that the method is effective in verifying the reliability of the PS in uncertain environments.",
keywords = "Formal Method, Nuclear I\&C System, Stochastic Z{\'e}lus, UPPAAL-SMC",
author = "Jiang Xiong and Letian Fang and Jing Liu and Mingxing Liu",
note = "Publisher Copyright: {\textcopyright} 2025 Knowledge Systems Institute Graduate School. All rights reserved.; 37th International Conference on Software Engineering and Knowledge Engineering, SEKE 2025 ; Conference date: 29-09-2025 Through 04-10-2025",
year = "2025",
doi = "10.18293/SEKE2025-109",
language = "英语",
series = "Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE",
publisher = "Knowledge Systems Institute Graduate School",
pages = "366--371",
booktitle = "Proceedings - SEKE 2025",
address = "美国",
}