Advertisement

Choosing Models of Appropriate Resolutions for Simulation: A MRM Approach

  • Huachao Mao
  • Gongzhuang Peng
  • Heming Zhang
Part of the Communications in Computer and Information Science book series (CCIS, volume 402)

Abstract

Multi-resolution modeling (MRM) is widely used in manufacture industry, environment science (climate, geometry, map), science (material, biology) and so on. Dozens of theories and methods are proposed to MRM. However, most of these MRMs are not designed for simulation, which leads to MRM failures in terms of information loss, consistency maintenance and resolution changes. To solve these failures, this paper introduces Connector-oriented Resolution State Chart-based System (CORES): a novel MRM approach with emphasis on choosing appropriate resolutions for simulation. In CORES, Resolution State chart, a UML state chart, is modeled to specify the resolution changes. And Connector, the connection of different resolutions, is proposed as a standby part to fulfill the four requirements on relationships between models of different resolutions. Finally, a dimension-variable linear system is modeled to demonstrate CORES approach, and the numerical results verify our approach.

Keywords

Multi-resolution modeling (MRM) Resolution State chart (ReS chart) Connector resolution control information difference 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Werther, J., Heinrich, S., Dosta, M., Hartge, E.: The ultimate goal of modeling—simulation of system and plant performance. Particuology 9, 320–329 (2011)CrossRefGoogle Scholar
  2. 2.
    Petty, M.D., Franceschini, R.W., Panagos, J.: Multi-Resolution Combat Modeling. Engineering Principles of Combat Modeling and Distributed Simulation, 607–640 (2012)Google Scholar
  3. 3.
    Horstemeyer, M.F.: Multiscale modeling: a review.: Practical aspects of computational chemistry, pp. 87–135. Springer (2010)Google Scholar
  4. 4.
    Stoter, J., Visser, T., van Oosterom, P., Quak, W., Bakker, N.: A semantic-rich multi-scale information model for topography. Int. J. Geogr. Inf. Sci. 25, 739–763 (2011)CrossRefGoogle Scholar
  5. 5.
    Dada, J.O., Mendes, P.: Multi-scale modelling and simulation in systems biology. Integrative Biology 3, 86–96 (2011)CrossRefGoogle Scholar
  6. 6.
    Davis, P.K.: An introduction to variable-resolution modeling. Naval Research Logistics (NRL) 42, 151–181 (1995)CrossRefGoogle Scholar
  7. 7.
    Powell, D.R.: Control of entity interactions in a hierarchical variable resolution simulation. Los Alamos National Lab., NM, United States (1997)Google Scholar
  8. 8.
    Davis, P.K., Bigelow, J.H.: Experiments in MRM. RAND MR-100-DARPA (1998)Google Scholar
  9. 9.
    Cubert, R.M., Goktekin, T., Fishwick, P.A.: MOOSE: architecture of an object-oriented multimodeling simulation system, 78–88 (1997)Google Scholar
  10. 10.
    Rao, D.M., Wilsey, P.A.: Multi-resolution network simulations using dynamic component substitution, 142–149 (2001)Google Scholar
  11. 11.
    Li, V.S., Ka, M., Me, V.N., Hart, P., V S Afa V R I K, J.: Simulating Details on Demand Using Variable-Resolution Modeling., Vol. 98. 39-46 (1998)Google Scholar
  12. 12.
    Natrajan, A.: Consistency maintenance in concurrent representations. University of Virginia (2000)Google Scholar
  13. 13.
    Natrajan, A., Reynolds Jr, P.F.: Concurrent Representations for Jointly-executing Models.. University of Virginia Technical Report No. CS-2001-20 (2001)Google Scholar
  14. 14.
    Chase, T., Gustavson, P., Eifert, L., RDECOM, U.A.: The Application of Base Object Models (BOMs) for Enabling Multi-Resolution Modeling (2004)Google Scholar
  15. 15.
    Drewry, D.T., Reynolds Jr., P.F., Emanuel, W.R.: Optimization as a tool for consistency maintenance in multi-resolution simulation. DTIC Document (2006)Google Scholar
  16. 16.
    Liu, B.H., Huang, K.D.: A formal description specification for multi-resolution modeling (MRM) based on DEVS formalism. In: Kim, T.G. (ed.) AIS 2004. LNCS (LNAI), vol. 3397, pp. 285–294. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Harel, D.: Statecharts: A visual formalism for complex systems. Sci. Comput. Program. 8, 231–274 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    UML state machine - Wikipedia, the free encyclopedia, http://en.wikipedia.org/wiki/UML_state_machine#Hierarchically_nested_states
  19. 19.
    Kim, T.G., Lee, C., Christensen, E.R., Zeigler, B.P.: System entity structuring and model base management. IEEE Transactions on Systems, Man and Cybernetics 20, 1013–1024 (1990)CrossRefGoogle Scholar
  20. 20.
    Kokotovic, P.V., O’Malley Jr., R.E., Sannuti, P.: Singular perturbations and order reduction in control theory — An overview. Automatica 12, 123–132 (1976)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Huachao Mao
    • 1
  • Gongzhuang Peng
    • 1
  • Heming Zhang
    • 1
  1. 1.CIMS, Department of AutomationTsinghua UniversityChina

Personalised recommendations