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The work described in this report can be broadly divided into two sections. The first section considers two export features. We describe how the export for stochastic Petri nets to SBML level 1 has been added to the Petri net modelling and simulation tool Snoopy. This task was accomplished by making appropriate changes to the existing export code to generate SBML level 2. Also we demonstrate in detail, how the direct export for coloured Petri nets to both levels (i.e. 1 and 2) of SBML was realised. The next section summarises the performed comparison of different stochastic simulation tools for biochemical reaction networks. We first compare BioNetGen and SSC with each other by performing simulations on non-coloured Petri nets. Then, we compare the remaining four tools, i.e. Cain, Marcie, Snoopy and Stochkit with each other by performing simulation on coloured Petri nets.
This work builds on results by Aman Sinha [19].
This report compares some stochastic simulation tools for biochemical reaction networks. The stochastic simulation tools are selected on the basis of some selection criteria. Simulations are performed for the different stochastic simulation tools on different benchmark models. This report gives an overview of how the comparison is carried out for the chosen tools.
The tools are compared on a common evaluation protocol. The evaluation protocol comprises a set of benchmark models along with the parameters which are provided as input to the tools. The benchmark models are represented as Petri nets and fed in SBML (System Biology Markup Language) to the different tools. Experiments are performed on each tool and the results are recorded. The tools are finally compared based on the comparison criteria.
In this manual we discuss the use of Snoopy’s computational steering framework to simulate and interactively steer (stochastic, continuous, hybrid) Petri nets, e.g., biochemical network models. In a typical application scenario, a user constructs a model using a Petri net editing tool (e.g., Snoopy). Afterwards, the Petri net model is submitted to one of the running servers to quantitatively simulate it. Later, other users can adapt their steering GUIs to connect to this model. One of the connected users initialises the simulation while others could stop, pause, or restart it. When the simulator initially starts, it uses the current model settings to run the simulation. Later, other users can remotely join the running simulation and change on the fly parameters and the current marking.
Drahtlose Sensornetze stellen eine vielversprechende Technologie zur Beobachtung und Beeinflussung von Vorgängen in der realen Welt dar. Autonome Sensorknoten nehmen dabei Parameter der Umwelt durch Sensoren wahr und können diese durch Aktoren beeinflussen. Viele solcher autonomen und ressourcenbeschränkten Knoten kooperieren dabei mittels drahtloser Kommunikation. Die Eigenschaften dieser Knoten und Netze implizieren eine Vielzahl von neuartigen Herausforderungen, die sich in einer regen Forschungsaktivität widerspiegeln. Ziel dieser Reihe von Fachgesprächen ist es, Wissenschaftlerinnen und Wissenschaftlern aus Hochschule und Industrie die Möglichkeit zu einem informellen Gedankenaustausch zu geben und die Kooperation in diesem multidisziplinären Forschungsbereich zu verstärken.
Mobile cyber-physical systems (MCPSs), such as the International Space Station, are equipped with sensors which produce sensor data streams. Continuous changes like wear and tear influence the system states of a MCPS continually during runtime. Hence, monitoring is necessary to provide reliability and to avoid critical damage. Although, the monitoring process is limited by resource restrictions. Therefore, the focal point of the present paper is on time-efficient multi-class data stream anomaly detection. Our contribution is bifid. First, we use a one-versusall classification model to combine a set of heterogeneous one-class classifiers consecutively. Such a chain of one-class classifiers provides a very flexible structure while the administrative overhead is reasonably low. Second, based on the classifier chain, we introduce classifier pre-selection.