Controlling Complex Lighting Systems

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 170)

Abstract

Designing and controlling lighting systems become more and more complex. Focusing on the control problem a rule-based control system is proposed. The system allows to control such lighting systems with high level control logic constituting so-called profiles. The profiles express lighting system behavior under certain conditions defined with rules. The light point and sensor distribution in the grid are given as graph structures. Control command sequences are inferred based on current system state, profiles, light points topology and sensor input. System’s architecture and a case study are also presented.

Keywords

Inference Engine Graph Transformation Lighting System Control Command Light Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gonzalez, A.J., Stensrud, B.S., Barrett, G.C.: Formalizing context-based reasoning: A modeling paradigm for representing tactical human behavior. Int. J. Intell. Syst. 23(7), 822–847 (2008)CrossRefGoogle Scholar
  2. 2.
    Gouyon, J.P.: Kheops users’s guide. Report of Laboratoire d’Automatique et d’Analyse des Systemes (92503) (1994)Google Scholar
  3. 3.
    Jackson, P.: Introduction to Expert Systems, 3rd edn. Addison–Wesley (1999) ISBN 0-201-87686-8Google Scholar
  4. 4.
    Kotulski, L., Strug, B.: Distributed Adaptive Design with Hierarchical Autonomous Graph Transformation Systems. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part II. LNCS, vol. 4488, pp. 880–887. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Liebowitz, J. (ed.): The Handbook of Applied Expert Systems. CRC Press, Boca Raton (1998)MATHGoogle Scholar
  6. 6.
    Ligęza, A. (ed.): Logical Foundations for Rule-Based Systems. Springer, Heidelberg (2006)MATHGoogle Scholar
  7. 7.
    Ligęza, A., Wojnicki, I., Nalepa, G.J.: Tab-Trees: A CASE Tool for the Design of Extended Tabular Systems. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 422–431. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Moore, R., Lindenfilzer, P., Hawkinson, L., Matthews, B.: Process control with the g2 real-time expert system. In: Proceedings of the 1st International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 1988, pp. 492–497. ACM, New York (1988), doi: http://doi.acm.org/10.1145/51909.51965 Google Scholar
  9. 9.
    Nalepa, G.J., Wojnicki, I.: VARDA Rule Design and Visualization Tool-Chain. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds.) KI 2008. LNCS (LNAI), vol. 5243, pp. 395–396. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Nalepa, G.J., Wojnicki, I.: Ard+ a prototyping method for decision rules. method overview, tools, and the thermostat case study. Tech. Rep. CSLTR 01/2009, AGH University of Science and Technology (2009)Google Scholar
  11. 11.
    Nalepa, G.J., Wojnicki, I.: Visual Generalized Rule Programming Model for Prolog with Hybrid Operators. In: Seipel, D., Hanus, M., Wolf, A. (eds.) INAP 2007. LNCS (LNAI), vol. 5437, pp. 178–194. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Negnevitsky, M.: Artificial Intelligence. A Guide to Intelligent Systems. Addison-Wesley, Harlow (2002) ISBN 0-201-71159-1 Google Scholar
  13. 13.
    Sędziwy, A.: Representation of objects in agent-based lighting design problem. In: DepCoS: Dependability and Complex Systems. DepCoS-RELCOMEX (2012)Google Scholar
  14. 14.
    Sędziwy, A., Kotulski, L.: Solving large-scale multipoint lighting design problem using multi-agent environment. In: Su, D., Xue, K., Zhu, S. (eds.) Key Engineering Materials. Advanced Design and Manufacture IV (2011)Google Scholar
  15. 15.
    Sędziwy, A., Kozień-Woźniak, M.: Computational methods supporting street lighting design. In: DepCoS: Dependability and Complex Systems. DepCos-RELCOMEX (2012)Google Scholar
  16. 16.
    Travé-Massuyès, L., Milne, R.: Gas-turbine condition monitoring using qualitative model-based diagnosis. IEEE Expert: Intelligent Systems and Their Applications 12, 22–31 (1997), doi: http://dx.doi.org/10.1109/64.590070 Google Scholar
  17. 17.
    Wojnicki, I.: From tabular trees to networked decision tables: an evolution of modularized knowledge-base representations. Pomiary Automatyka Kontrola 12 (2011)Google Scholar
  18. 18.
    Wojnicki, I.: Implementing general purpose applications with the rule-based approach. In: RuleML 2011: Proceedings of the 5th International Conference on Rule-based Reasoning, Programming, and Applications, pp. 360–367. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Wojnicki, I.: Separating i/o from application logic for rule-based control systems. Decision Making in Manufacturing and Services (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of AutomaticsAGH University of Science and TechnologyKrakówPoland

Personalised recommendations