Paul Bisgambiglia

A survey of modelling and simulation software frameworks using Discrete Event System Specification

Discrete Event System Specification is an extension of the Moore machine formalism which is used for modelling and analyzing general systems. This hierarchical and modular formalism is time event based and is able to represent any continuous, discrete or combined discrete and continuous systems. Since its introduction by B.P. Zeigler at the beginning of the eighties, most general modelling formalisms able to represent dynamic systems have been subsumed by DEVS. Meanwhile, the modelling and simulation (M&S) community has introduced various software frameworks supporting DEVS-based simulation analysis capability. DEVS has been used in many application domains and this paper will present a technical survey of the major DEVS implementations and software frameworks. We introduce a set of criteria in order to highlight the main features of each software tool, then we propose a table and discussion enabling a fast comparison of the presented frameworks. 1998 ACM Subject Classification I.6 Simulation and modeling

BEHAVIORAL FAULT SIMULATION

Due to the ever-increasing complexity of VLSI circuits, the use of VHDL [Vhd87] behavioral descriptions in the fields of test generation and fault simulation becomes advised. In order to understand this evolution and the context in which it is efficient, the increasing complexity of VLSI circuits must be considered. To better cope with the complexity of existing and future systems, higher abstraction levels must be taken into account. Furthermore, the only knowledge about the device being tested may come from data sheets or signal measurements. In this case the only way to generate test patterns is behavioral testing. Today, such a task is done manually and one of the main interests in Behavioral Test Pattern Generation (BTPG) is to automate it. Another point to consider is that the test generation process is an integral part of the design process and its implementation must begin during the behavioral design phase.

Fuzzy inference models for Discrete EVent systems

For several years, we worked to improve a discrete events modeling formalism: called DEVS. Having defined a method to take into account the inaccuracies iDEVS, in this paper, we present the second part of our research work. Generally, our approach is to associate the DEVS formalism with an object class, which allows using it to new fields of study, and in our case fuzzy systems. This paper describes a new modeling methodology. It allows to modeling and to use fuzzy inference systems (FIS) with DEVS formalism in order to perform the control or the learning on systems described incompletely or with linguistic data. The advantages of this method are numerous: to extend the DEVS formalism to other application fields; to propose new DEVS models for fuzzy inference; to provide users with simple and intuitive modeling methods. Throughout this paper we describe the tools and methods which were developed to make possible the combination of these two approaches.

Fuzzy simulation for discrete events systems

The aim of our research is to develop a new method of taking into account the imperfect parameters in a discrete events modeling and simulation formalism. This article describes our approach to modeling and compares several methods for fuzzy simulations.

Enabling large scale and high definition simulation of natural systems with vector models and JDEVS

This paper describes a new methodology to enable large scale high resolution environmental simulation. Unlike the vast majority of environmental modeling techniques that split the space into cells, the use of a vector space is proposed here. A phenomena is described by its shape, decomposed in several points that can move using a displacement vector. The shape also has a dynamic structure, as each point can instantiate new point because of a change in the space properties or to obtain a better resolution model. Such vector models are generating less overhead because the phenomena is recomputed only if a part of it is entering into a different space entity with different attributes, using cellular space the model would have been recomputed for each neighboring identical cells. This technique uses the DSDEVS formalism to describe discrete event models with dynamic structure, and is implemented in the JDEVS toolkit also presented.

iDEVS: New method to study inaccurate systems

Our recent research in the fields of modeling and simulation of complex systems, led us to study fuzzy systems. A system is fuzzy, because its parameters are inaccurate, or its behavior is uncertain. We propose in this paper to describe a new modeling method based on the association of DEVS formalism and the fuzzy sets theory. The combination of these two approaches we have permit to define a new method of inaccurate modeling. Our goal is to study systems with inaccurate parameters.

An experimental frame for the simulation of forest fire spread

Wildfire is a constant risk due to its danger on both human and natural resources so modeling and simulation is an important tool to understand and forecast this phenomenon. A basic element of any simulation model is to define a way to store, compare and exchange observation and model results. Without a clear and standardized data structure, results and observations lack usability, inter-comparability and expressiveness.

Discrete events system simulation-based deFuzzification method

This article presents our approach to discrete event simulation for system with inaccurate parameters. For such systems, simulation runs according to events whose dates are known; but in fuzzy modeling, it may be that the date of the events are inaccurate. To solve this problem, we suggest using a method that converts a inaccurate value into accurate value. This method is incorporated into a modelling and simulation formalism.

Discrete event formalism to calculate acceptable safety distance

The aim of this paper is to present a dimensioning tool for fuelbreaks. It focuses on the overall approach and specifically mapping a physical model to a DEVS model, mapping a DEVS model to a DEVS service, and the client that communicates with the server. In order to assist the firefighters, we focus on a Web Service based on different software tools that can be used by firefighters to forecast fuelbreak safety zone sizes. This Web Service uses a simulation framework based on DEVS formalism, a theoretical fire spreading model developed at the University of Corsica and to display the results on a Google Map SDK. The SDK is embedded in a mobile application for touchscreen tablet. The application sends a request to our DEVS Web Service, with its geolocation, and in response receives data sets that allow to draw the safety distance.

Fuzzy modeling for discrete events systems

In this paper, we present our work on fuzzy modeling, and in particular an approach based on the integration of the uncertain theories in the formalism of multi-modeling and simulation with discrete events. The goal of this approach is to help the expert of a field to specify in a simple way the behavior of a complex system with badly defined, fuzzy, etc. parameters. This approach can be employed in multiple fields, an application to the study of the forest fires propagation is presented in order to validate the models.