FMitF: Track II: Hybrid and Dynamical Systems Verification on the CPS-VO

FMITF：轨道 II：CPS-VO 上的混合动力系统验证

• 批准号: 1918450
• 负责人: Taylor Johnson
• 金额: $ 9.83万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918450&HistoricalAwards=false
• 关键词: FMitF; Track; II; Hybrid; Dynamical

This project aims to transition recent research results that automate portions of the verification process of Cyber-Physical Systems into broader practice, particularly with industrial and student users. Cyber-physical systems (CPS) are networked embedded computing systems coupled with physics, such as in motor vehicles, aircraft, medical devices, and the electrical grid. Due to their frequent use in safety-critical scenarios where significant losses may be incurred, such as of human life or monetarily, the engineering process for such systems devotes vast resources to the process of verification to ensure these systems meet their requirements. The approach builds upon recent successes in the hybrid systems verification competition (ARCH-COMP) hosted in the past three years, in which several dozen hybrid systems verification tools have competed across several problem and system categories. To achieve the transition of verification research results for CPS models into broader practice, this project develops a cloud-based framework to interface with and execute state-of-the-art formal verification software tools from the hybrid and dynamical systems verification community using NSF's CPS Virtual Organization (CPS-VO). The project extends the Design Studio and Tool Integration frameworks for the CPS-VO, to provide an easy-to-use interface for these verification tools. It also serves as a repository for housing executable versions of the tools and benchmarks, plus facilitating repeatability evaluations for ARCH-COMP through a distributed, cloud-based execution architecture. The core technology upon which the tool is built is the hybrid systems model source transformation and translation (HyST) software tool, which performs semantics-preserving translation between state-of-the-art hybrid systems verification tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在过渡最新的研究结果，将网络物理系统的验证过程自动化为更广泛的实践，尤其是在工业和学生用户中。网络物理系统（CPS）是网络嵌入式计算系统，以及物理，例如机动车，飞机，医疗设备和电网。由于它们频繁地在安全至关重要的情况下使用，这些情况可能会造成重大损失，例如人类的生活或货币，因此此类系统的工程过程将大量资源投入到验证过程中，以确保这些系统满足其要求。该方法基于过去三年中托管的混合系统验证竞赛（ARCH-COMP）的最新成功，其中几十个混合系统验证工具已经跨越了几个问题和系统类别。为了实现CPS模型验证研究结果的过渡到更广泛的实践，该项目开发了一个基于云的框架，可以使用NSF的CPS虚拟组织（CPS-VO）与混合和动态系统验证社区进行与最先进的正式验证软件工具进行交互并执行最先进的正式验证软件工具。该项目扩展了CPS-VO的设计工作室和工具集成框架，为这些验证工具提供了易于使用的接口。它还可以用作设置工具和基准测试的可执行版本的存储库，并通过基于云的分布式执行体系结构来促进拱门的可重复性评估。该工具的核心技术是Hybrid Systems模型源转换和翻译（HYST）软件工具，该工具在最先进的混合系统验证工具之间执行语义性能的翻译。该奖项反映了NSF的法定任务，并已通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务。

项目成果

Evaluation of Neural Network Verification Methods for Air-to-Air Collision Avoidance

• DOI: 10.2514/1.d0255
• 发表时间: 2022-10
• 期刊: Journal of Air Transportation
• 作者: Diego Manzanas Lopez;Taylor T. Johnson;Stanley Bak;Hoang-Dung Tran;Kerianne L. Hobbs

A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces

从输入输出轨迹识别和验证仿射混合自动机的框架

• DOI: 10.1145/3470455
• 发表时间: 2022
• 期刊: ACM Transactions on cyber-physical systems
• 影响因子: 2.3
• 作者: Xiaodong Yang, Omar Ali

Verification of Neural Network Compression of ACAS Xu Lookup Tables with Star Set Reachability

ACAS Xu查找表神经网络压缩星集可达性验证

• DOI: 10.2514/6.2021-0995
• 发表时间: 2021
• 期刊: AIAA Scitech 2021 Forum
• 作者: Manzanas Lopez, Diego;Johnson, Taylor;Tran, Hoang-Dung;Bak, Stanley;Chen, Xin;Hobbs, Kerianne L.
• 通讯作者: Hobbs, Kerianne L.

Partial Differential Equation Driven Dynamic Graph Networks for Predicting Stream Water Temperature

• DOI: 10.1109/icdm51629.2021.00011
• 发表时间: 2021-01-01
• 期刊: 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021)
• 作者: Bao, Tianshu;Jia, Xiaowei;Johnson, Taylor T.
• 通讯作者: Johnson, Taylor T.

ARCH-COMP21 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants

• DOI: 10.29007/kfk9
• 发表时间: 2021
• 作者: Taylor T. Johnson;Diego Manzanas Lopez;Luis Benet;M. Forets;Sebastián Guadalupe;Christian Schilling;Radoslav Ivanov;Taylor J. Carpenter;James Weimer;Insup Lee