Li Huang

Li Huang, Eun-Young Kang

Ensuring correctness of timed behaviors in cyber-physical systems (CPS) using closed-loop verification is challenging due to the hybrid dynamics in both systems and environments. Simulink and Stateflow are tools for model-based design that support a variety of mechanisms for modeling and analyzing hybrid dynamics of real-time embedded systems. In this paper, we present an SMT-based approach for formal analysis of the hybrid-dynamic timing behaviors of CPS modeled in Simulink blocks and Stateflow states (S/S). The hierarchically interconnected S/S are flattened and translated into the input language of SMT solver for formal verification. A translation algorithm is provided to facilitate the translation. Formal verification of timing constraints against the S/S models is reduced to the validity checking of the resulting SMT encodings. The applicability of our approach is demonstrated on an unmanned surface vessel case study.

Eun-Young Kang, Li Huang

Modeling and analysis of timing constraints is crucial in automotive systems. EAST-ADL is a domain specific architectural language dedicated to safety-critical automotive embedded system design. In most cases, a bounded number of violations of timing constraints in systems would not lead to system failures when the results of the violations are negligible, called Weakly-Hard (WH). We have previously specified EAST-ADL timing constraints in Clock Constraint Specification Language (CCSL) and transformed timed behaviors in CCSL into formal models amenable to model checking. Previous work is extended in this paper by including support for probabilistic analysis of timing constraints in the context of WH: Probabilistic extension of CCSL, called PrCCSL, is defined and the EAST-ADL timing constraints with stochastic properties are specified in PrCCSL. The semantics of the extended constraints in PrCCSL is translated into Proof Objective Models that can be verified using SIMULINK DESIGN VERIFIER. Furthermore, a set of mapping rules is proposed to facilitate guarantee of translation. Our approach is demonstrated on an autonomous traffic sign recognition vehicle case study.

Li Huang, Yi-feng Yang, Lei Wang

Recommender systems play an essential role in the modern business world. They recommend favorable items like books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accuracy. Exploiting the quantum to classical mapping inherent in the continuous-time quantum Monte Carlo methods, we construct a classical molecular gas model to reproduce the quantum distributions. We then utilize powerful molecular simulation techniques to propose efficient quantum Monte Carlo updates. The recommender engine approach provides a general way to speed up the quantum impurity solvers.