@techreport{Nolte2013, author = {Nolte, J{\"o}rg}, title = {12. GI/ITG Fachgespr{\"a}ch Sensornetze}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus-28986}, year = {2013}, abstract = {Drahtlose Sensornetze stellen eine vielversprechende Technologie zur Beobachtung und Beeinflussung von Vorg{\"a}ngen in der realen Welt dar. Autonome Sensorknoten nehmen dabei Parameter der Umwelt durch Sensoren wahr und k{\"o}nnen diese durch Aktoren beeinflussen. Viele solcher autonomen und ressourcenbeschr{\"a}nkten Knoten kooperieren dabei mittels drahtloser Kommunikation. Die Eigenschaften dieser Knoten und Netze implizieren eine Vielzahl von neuartigen Herausforderungen, die sich in einer regen Forschungsaktivit{\"a}t widerspiegeln. Ziel dieser Reihe von Fachgespr{\"a}chen ist es, Wissenschaftlerinnen und Wissenschaftlern aus Hochschule und Industrie die M{\"o}glichkeit zu einem informellen Gedankenaustausch zu geben und die Kooperation in diesem multidisziplin{\"a}ren Forschungsbereich zu verst{\"a}rken.}, subject = {Drahtloses Sensorsystem}, language = {en} } @techreport{NoackSchmittSaretz2013, author = {Noack, Tino and Schmitt, Ingo and Saretz, Sascha}, title = {OVA-based multi-class classification for data stream anomaly detection}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus-28187}, year = {2013}, abstract = {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.}, subject = {Informatik}, language = {en} } @techreport{HerajyHeiner2013, author = {Herajy, Mostafa and Heiner, Monika}, title = {Snoopy computational steering framework - user manual version 1.0}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus-29034}, year = {2013}, abstract = {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.}, subject = {Datenstruktur; Zuverl{\"a}ssigkeit}, language = {en} }