TU Ilmenau

Dr. Niclas Feldkamp

Raum
Werner-Bischoff-Bau
Raum F1110

niclas.feldkamp@tu-ilmenau.de 

+49 (0) 3677 69-4044

 

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Research Gate

ACM Digital Library

Forschungsschwerpunkte

  • Data Farming
  • Innovative Methoden zur Simulationsdatenanalyse, Data Mining, Visual Analytics
  • Nutzen von KI Methoden im Kontext der Simulation
 

Lehre

  • Data Science für industrielle Anwendungen
  • Simulation 1, Simulation 2
  • Methoden und Werkzeuge der Digitalen Fabrik
  • Industrie 4.0

Mitgliedschaften

  • ACM Special Interest Group for Simulation and Modeling (ACM PADS)
  • Arbeitsgemeinschaft Simulation (ASIM) der Gesellschaft für Informatik (GI)
     

Gutachtertätigkeit

  • Information Sciences
  • Journal of Simulation
  • Winter Simulation Conference
  • Hawaii International Conference on System Sciences (HICSS)
  • ASIM Fachtagung Simulation in Produktion und Logistik
 

Publikationsliste

Anzahl der Treffer: 35
Erstellt: Thu, 28 Mar 2024 23:14:27 +0100 in 0.0389 sec


Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Visual analytics of manufacturing simulation data. - In: Proceedings of the 2015 Winter Simulation Conference, ISBN 978-1-4673-9743-8, (2015), S. 779-790

Discrete event simulation is an accepted technology for investigating the dynamic behavior of complex manufacturing systems. Visualizations created within simulation studies often focus on the animation of the dynamic processes of a single simulation run, supplemented with graphs of certain performance indicators obtained from replications of a simulation run or a few manually conducted simulation experiments. This paper suggests a much broader visually aided analysis of simulation input and output data and their relations than it is commonly applied today. Inspired from the idea of visual analytics, we suggest the application of data farming approaches for obtaining datasets of a much broader spectrum of combinations of input and output data. These datasets are then processed by data mining methods and visually analyzed by the simulation experts. This process can uncover causal relationships in the model behavior that were previously not known, leading to a better understanding of the systems behavior.



http://dx.doi.org/10.1109/WSC.2015.7408215
Bergmann, Sören; Feldkamp, Niclas; Hinze, Ulrich; Straßburger, Steffen
Emulation of control strategies through machine learning in manufacturing simulations :
Abbildung von Steuerungslogiken durch maschinelles Lernen für die Simulation von Produktionssystemen. - In: Simulation in production and logistics 2015, (2015), S. 481-490

In the context of discrete-event simulation of production and logistics systems, modelling an exact representation of the real system is needed for obtaining sound and reliable results. The automatic generation of simulation models is an approach for saving time and effort for creating models and, therefore, it is a recurring issue in modelling methodology research. In automatic model generation, the approximation of dynamic behaviour is a challenging problem. This is for example the case when the dispatching and scheduling of production jobs needs to be adequately emulated, but the underlying rules are unknown. In previous work, we presented an approach for approximating dynamic behaviour through artificial neural networks. In this paper, we propose an improved approach and investigate its suitability again with artificial neuronal networks as well as with other data mining and supervised machine learning methods.



Feldkamp, Niclas; Bergmann, Sören; Bergmann, Sören *1979-*; Straßburger, Steffen;
Knowledge discovery in manufacturing simulations. - In: SIGSIM PADS'15, ISBN 978-1-4503-3565-2, (2015), S. 3-12

Discrete event simulation studies in a manufacturing context are a powerful instrument when modeling and evaluating processes of various industries. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually varying parameters through educated assumptions and according to a prior defined goal. Moreover, simulation experts try to reduce complexity and number of simulation runs by excluding parameters that they consider as not influential regarding the simulation project scope. On the other hand, today's world of big data technology enables us to handle huge amounts of data. We therefore investigate the potential benefits of designing large scale experiments with a much broader coverage of possible system behavior. In this paper, we propose an approach for applying data mining methods on simulation data in combination with suitable visualization methods in order to uncover relationships in model behavior to discover knowledge that otherwise would have remained hidden. For a prototypical demonstration we used a clustering algorithm to divide large amounts of simulation output datasets into groups of similar performance values and depict those groups through visualizations to conduct a visual investigation process of the simulation data.



Feldkamp, Niclas; Straßburger, Steffen;
Automatic generation of route networks for microscopic traffic simulations. - In: Winter Simulation Conference (WSC), 2014, ISBN 978-1-4799-7487-0, (2014), S. 2848-2859

Microscopic traffic simulation is a well-accepted simulation approach for simulation problems where the effects of individual driver behavior and/or vehicle interactions need to be taken into account at a fairly detailed level. Such problems include the optimization of traffic light controls patterns or the design of lane layouts at intersections. Such simulation models typically require very detailed and accurate models of the underlying road networks. The manual creation of such networks constitutes a high effort, limiting the simulated area in practical applications to the absolutely necessary. With the increased availability of satellite based geographical data we investigate, if and how such data can be automatically transformed into route networks with adequate level of detail for microscopic traffic simulation models. We further outline the design of data structures for an extensible simulation framework for microscopic traffic simulation which is capable of including different types of publically available data sources.



http://dx.doi.org/10.1109/WSC.2014.7020126
Feldkamp, Niclas;
Automatische Generierung von Routennetzen für die mikroskopische Verkehrssimulation. - 111 S. : Ilmenau, Techn. Univ., Masterarbeit, 2014

Im Rahmen der Modellerstellung für mikroskopische Verkehrssimulationen ist die exakte Abbildung der geographischen und topologischen Eigenschaften des Routennetzwerks mit Aufwand und Kosten verbunden. Ein möglicher Ansatz zur Reduzierung dieses Aufwands liegt in der automatischen Modellgenerierung durch die Nutzung digitaler Karten. Diese Arbeit untersucht am Beispiel von OpenStreetMap, inwiefern offene, frei verfügbare Datenquellen für die automatische Routennetzgenerierung herangezogen werden können. Hierzu wurden Methoden und Algorithmen entwickelt, um OpenStreetMap-Daten in geeignete Datenstrukturen für ein Routennetz zu überführen. Weiter wurden diese Datenstrukturen in ein ganzheitliches und erweiterbares Framework für die mikroskopische Verkehrssimulation eingebettet. Dieses umfasst die Konfiguration der Simulation, Datenstrukturen für dynamische Simulationsentitäten sowie die Anbindung an einen Simulator. Zudem wurde mit Hilfe des konzeptionellen Ansatzes ein Prototyp entwickelt, anhand dessen gezeigt werden kann, welche Potenziale und Einschränkungen die Nutzung von OpenStreetMap-Daten hinsichtlich der Routennetzgenerierung bietet.