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A view on future building system modeling and simulation
Published in Jan L.M. Hensen, Roberto Lamberts, Building Performance Simulation for Design and Operation, 2019
We will make a distinction between modeling and simulation as introduced by Cellier and Kofman (2006) and illustrated in Figure 19.1. Since building energy analysis requires large amounts of input data, it is convenient for our discussion to further separate mathematical modeling into behavioral modeling and data modeling. By behavioral modeling we mean specifying the mathematical equations that model a phenomenon, which Polderman and Willems (2007) call the behavioral equations. Behavioral models may be based on physical laws, performance curves or functions that update states and outputs of a finite state machine. By data modeling, we mean representing the static data of a system without specifying the mathematical model that describes how the system responds to inputs and evolves in time.
Designing Formally-controlled Smart Home Systems for People with Disabilities
Published in Bruno Bouchard, Smart Technologies in Healthcare, 2017
Sébastien Guillet, Bruno Bouchard, Abdenour Bouzouane
Designing the aforementioned controller by constraint so that it can be obtained automatically through DCS becomes possible, but it requires the use of a formal model to specify the behavior of the underlying system under control. Behavioral modeling can be performed using various formal representations, e.g. Statecharts, Petri-nets, Communicating Sequential Processes or other ways. The toolset we use in this work, BZR and Sigali, brings us to define our system in terms of synchronous equations and Labelled Transition Systems.
A driving simulation study on drivers speed compliance with respect to variable message signs
Published in Journal of Intelligent Transportation Systems, 2022
Michal Matowicki, Ondrej Pribyl
In order to allow for better transferability of the findings from the driving simulator to other conditions, the objective of the research is to link the speed compliance to other variables related to socio-demographics characteristics or travel experiences of the experiment participants. This was explored also for example by Bains et al. (2013). The precise modeling of drivers behavior on highways proved to be a very difficult task. Collected data served as a basis for a number of numeric approaches to behavior modeling. This topic was addressed using a binary regression model for prediction of speed compliance of the drivers. As claimed by Wang et al. (2014) and Toledo (2007) drivers behavior tends to vary even within the measured sample for each individual driver. This fact was confirmed also by the presented study. For this reason, further investigation of the within- and between-person variations of speed compliance is strongly encouraged (Hoffman & Stawski, 2009).