Advertisement

Remote Sensing for Environmental Monitoring. Complex Modeling

  • Victor F. MochalovEmail author
  • Andrei V. Markov
  • Olga V. Grigorieva
  • Denis V. Zhukov
  • Olga V. Brovkina
  • Ilya Y. Pimanov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)

Abstract

In this paper the concept of integrated modeling and simulation of the processes of the Complex Technical–Organizational System (CTOS) is presented. Practical directions of the remote sensing for environmental monitoring of the protected area are proposed by the authors. Methodical basis of the integrated modeling and simulation, the process of CTOS operation, and the technology of the remote sensing for environmental monitoring are considered. Results of CTOS remote sensing are shown to adapt models to a changing environment.

Keywords

Complex technical organizational system Structure dynamic control Parametric and structural adaptation of models Remote sensing for environmental monitoring Decision support systems 

References

  1. 1.
    Ohtilev, M.Yu., Sokolov, B.V., Yusupov, R.M.: Intellectual Technologies for Monitoring and Control of Structure-Dynamics of Complex Technical Objects, 410 p. Nauka, Moscow (2006) (in Russian)Google Scholar
  2. 2.
    Ivanov, D., Sokolov, B.: Adaptive Supply Chain Management. Springer, London (2010)CrossRefGoogle Scholar
  3. 3.
    Ivanov, D., Sokolov, B., Kaeschel, J.: A multi-structural framework for adaptive supply chain planning and operations with structure dynamics considerations. Eur. J. Oper. Res. 200(2), 409–420 (2010)CrossRefGoogle Scholar
  4. 4.
    Ivanov, D., Sokolov, B.: Dynamic supply chain scheduling. J. Sched. 15(2) (2012) (Elsevier, London)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ivanov, D., Sokolov, B.: Control and system-theoretic identification of the supply chain dynamics domain for planning, analysis and adaptation of performance under uncertainty. Eur. J. Oper. Res. vol. 224, Issue 2, pp. 313–323. Elsevier, London (2012)Google Scholar
  6. 6.
    Sokolov, B., Zelentsov, V., Yusupov, R., Merkuryev, Y.: Information fusion multiple-models quality definition and estimation. In: Proceedings of the International Confrence on Harbor Maritime and Multimodal Logistics M&S, Vienna, Austria, September, 19–2l, pp. 102–111 (2012)Google Scholar
  7. 7.
    Skurihin, V.I., Zabrodsky, V.A., Kopeychenko, Yu.V.: Adaptive control systems in machine-building industry. M.: Mashinostroenie (1989) (in Russian)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Victor F. Mochalov
    • 1
    Email author
  • Andrei V. Markov
    • 1
  • Olga V. Grigorieva
    • 1
  • Denis V. Zhukov
    • 1
  • Olga V. Brovkina
    • 2
  • Ilya Y. Pimanov
    • 3
  1. 1.Mozhaisky Aerospace AcademySaint PetersburgRussia
  2. 2.Global Change Research Centre Academy of Science of the Czech RepublicPragueCzech Republic
  3. 3.Saint Petersburg Institute of Informatics and Automation (SPIIRAS)Russian Academy of ScienceSaint PetersburgRussia

Personalised recommendations