Monitoring Dynamical Signals while Testing Timed Aspects of a System

Abstract. We propose to combine timed automata and linear hybrid automata model checkers for formal testing and monitoring of embedded systems with a hybrid behavior, i.e., where the correctness of the system depends on discrete as well as continuous dynamics. System level testing is considered, where requirements capture abstract behavior and often include non-determinism due to parallelism, internal counters and subtle state of physical materials. The goal is achieved by integrating the tools Uppaal [2] and PHAVe

An investigation of safe and near-optimal strategies for prevention of Covid-19 exposure using stochastic hybrid models and machine learning

In this work investigate the use of stochastic hybrid models, statistical model checking and machine learning to analyze, predict and control the rapid spreading of Covid-19. During the pandemic numerous studies using stochastic models have been produced. Most of these studies are used to predict the effect of some restrictions. In contrast, in this paper we focus on the synthesis of strategies which prevent Covid-19 spreading. The computed strategies provide valuable information which can be used by the authorities to design new and more specific restrictions. We consider two large case studies that develop in the Copenhagen area in Denmark. Our experiments show that the computed strategies significantly prevent Covid-19 spreading, and thus provide valuable information e.g. expected social distance to minimize Covid-19 spreading. On the technical side, we demonstrate the applicability of analytical methods for preventing the spreading of Covid-19 in large scenarios