2016
DOI: 10.1016/j.entcs.2016.09.021
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Modeling Hybrid Systems in SIMTHESys

Abstract: Hybrid systems (HS) have been proven a valid formalism to study and analyze specific issues in a variety of fields. However, most of the analysis techniques for HS are based on low-level description, where single states of the systems have to be defined and enumerated by the modeler. Some high level modeling formalisms, such as Fluid Stochastic Petri Nets, have been introduced to overcome such difficulties, but simple procedures allowing the definitions of domain specific languages for HS could simplify the an… Show more

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Cited by 9 publications
(5 citation statements)
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“…Semi-finished parts need to be further worked by machines in the following stage, and then, they can be temporarily stored in buffers. 5 In the figure, we consider two segments: the first one refers to Machine1 and Machine2 served by two faulty robots, the second one is constituted by a single Machine3 and a single non-faulty robot. The main difference between robot and faulty robots is that while the former is assumed to be unbreakable and the latter may fail according to the failure rates of their components.…”
Section: A Methodology For Performability Assessmentmentioning
confidence: 99%
See 1 more Smart Citation
“…Semi-finished parts need to be further worked by machines in the following stage, and then, they can be temporarily stored in buffers. 5 In the figure, we consider two segments: the first one refers to Machine1 and Machine2 served by two faulty robots, the second one is constituted by a single Machine3 and a single non-faulty robot. The main difference between robot and faulty robots is that while the former is assumed to be unbreakable and the latter may fail according to the failure rates of their components.…”
Section: A Methodology For Performability Assessmentmentioning
confidence: 99%
“…oriented towards the automation of hardware/software codesign, as in the Metropolis, or to the modelling and analysis of discrete event systems, as in the Möbius approach [19]), to the composition of models and the joint usage of several modelling and analysis tools (e.g. oriented towards simulation as in Ptolemy II and ModHel'X, or oriented towards the analysis of non-functional properties as in the SIMTHESys multiformalism modelling framework [5] and OsMoSys [35,52]), to the composition of modelling languages, including techniques for merging metamodels [22,46] and translating models (e.g. in AToM3).…”
Section: The Multi-paradigm Modelling Domainmentioning
confidence: 99%
“…This approach also preserves, in general, tracking of numerical results back to logical elements in the model, and can provide model-wide or submodel-wide results, such as properties of parts of the system that emerge from element-related results, and may also be used to interface existing tools with new solvers, extending their applicability [10]. Multiformalism modeling approaches may support combinatorial formalisms [125], logic modeling [25], discrete state space based formalisms [6,42], continuous state space based formalisms [6], and hybrid formalisms [8] (that may use specialized solution techniques [9]). More details about multiformalism modeling concepts and principles are available for the reader in [46] and [101].…”
Section: Multiformalism Approachesmentioning
confidence: 99%
“…The continuous variable then decreases at a fluid dependent rate that mimics the behaviour presented in Section 3. Following [5], a fluid model is considered where the evolution of the continuous variable is defined by a function φ : R 2 →, where φ(x, t) = x represents a system which has an average of x jobs to be completed at time 0, will remain with an average of x jobs to be completed at time t. The fluid evolution function must satisfy the property that if the value of the continuous variable is x(t a ) at a time instant t a , then it must be that x(t b ) = φ(x(t a ), t b − t a ). This is achieved in the following way: let µ(t) denote the average number of tasks in the system at time t. For example, µ(t) could correspond to one of the curves shown in Figure 5.…”
Section: Fluid Modelmentioning
confidence: 99%