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Supporting Engineering Areas

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Body of Knowledge for Modeling and Simulation

Abstract

The engineering disciplines supporting, and supported by, simulation are in the scope of this chapter of the SCS M&S Body of Knowledge. The chapter provides descriptions for systems engineering, virtual and augmented reality engineering, and visualization engineering.

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References

  1. SEBoK Editorial Board (2020) The guide to the Systems Engineering Body of Knowledge (SEBoK), v. 2.2. In: Cloutier RJ (eds) The Trustees of the Stevens Institute of Technology, Hoboken, NJ. Last accessed on 29 Sept 2020. www.sebokwiki.org

  2. INCOSE (2017) Systems Engineering Vision 2020, version 2.03. International Council on Systems Engineering (INCOSE), INCOSE-TP-2004-004-02

    Google Scholar 

  3. Estefan JA (2008) Survey of model based systems engineering methodologies. International Council of Systems Engineering, San Diego, CA

    Google Scholar 

  4. Gianni D, D’Ambrogio A, Tolk A (2014) Modeling and simulation-based systems engineering handbook. CRC Press

    Google Scholar 

  5. Durak U, Ören T (2016) Towards an ontology of simulation systems engineering. In: Proceedings of the SCS spring simulation multi-conference, Pasadena, CA, USA

    Google Scholar 

  6. Loper ML (2015) Modeling and simulation in the systems engineering life cycle. Springer, Berlin

    Google Scholar 

  7. Rainey LB, Tolk A (2015) Modeling and simulation support for system of systems engineering applications. Wiley

    Google Scholar 

  8. Mittal S, Martín JLR (2013) Netcentric system of systems engineering with DEVS unified process. CRC Press

    Google Scholar 

  9. Yilmaz L, Ören T (2009) Agent-directed simulation and systems engineering. Wiley-VCH

    Book  Google Scholar 

  10. Mittal S, Diallo S, Tolk A (2018) Emergent behavior in complex systems engineering: a modeling and simulation approach. Wiley

    Google Scholar 

  11. Risco Martin JL, Mittal S, Ören T (2020) Simulation for cyber-physical systems engineering: a cloud-based context. Springer, Berlin

    Google Scholar 

  12. IEEE (2010) IEEE recommended practice for distributed simulation engineering and execution process (DSEEP), IEEE Std 1730-2010

    Google Scholar 

  13. IEEE (2010) Standard for modeling and simulation high level architecture—framework and rules, IEEE Std 1516-2010

    Google Scholar 

  14. D’Ambrogio A, Durak U (2016) Setting systems and simulation life cycle processes side by side. In: Proceedings of the 2016 IEEE international symposium on systems engineering (ISSE 2016), October 3–5, Edinburgh, UK

    Google Scholar 

  15. Atkinson C, Kuhne T (2003) Model-driven development: a metamodeling foundation. Software IEEE 20(5):36–41

    Google Scholar 

  16. OMG (2003) MDA Guide, revision 2.0 (ormsc/14-06-01)

    Google Scholar 

  17. Bocciarelli P, D’Ambrogio A, Giglio A, Paglia E (2019) Model-driven distributed simulation engineering. In: Mustafee N, Bae K-HG, Lazarova-Molnar S, Rabe M, Szabo C, Haas P, Son Y-J (eds) Proceedings of the 2019 Winter Simulation Conference (WSC 2019), December 8–11, 2019, National Harbor, USA

    Google Scholar 

  18. D’Ambrogio A, Petriu D (2011) First international workshop on model-driven approaches for simulation engineering (Mod4Sim’11). www.sel.uniroma2.it/mod4sim11

  19. Topcu O, Durak U, Oguztuzun H, Yilmaz L (2016) Distributed simulation: a model driven engineering approach. Springer

    Google Scholar 

  20. D’Ambrogio A, Bocciarelli P, Delfa J, Kisdi A (2020) Application of a model-driven approach to the development of distributed simulations: the ESA HRAF case. In: Proceedings of the 2020 Spring Simulation Conference (SpringSim 2020)

    Google Scholar 

  21. IEEE (2015) ISO/IEC/IEEE International Standard-Systems and software engineering—System life cycle processes. ISO/IEC/IEEE 15288:2015

    Google Scholar 

  22. INCOSE (2015) Systems engineering handbook: a guide for system life cycle processes and activities. In: International Council on Systems Engineering (INCOSE), INCOSE-TP-2003-002-04, Wiley

    Google Scholar 

  23. Zhao Q, Zhou B et al (2016) A brief survey on virtual reality technology. Sci Technol Rev 34(14):71–75

    Google Scholar 

  24. Yin RM, Li BH, Chai XD (2007) Image-based rendering techniques in virtual reality: a survey. J Syst Simul 19(19):4353–4357

    Google Scholar 

  25. Azuma RT (1997) A survey of augmented reality. Presence Teleoperators Virtual Environ 6(4):355–385

    Google Scholar 

  26. Dahne P, Karigiannis JN (2002, October) Archeoguide: system architecture of a mobile outdoor augmented reality system. In: IEEE Proceedings. International symposium on mixed and augmented reality, pp 263–264

    Google Scholar 

  27. Li BH, Chai XD, Zhang L, Li T, Qing DZ, Lin T, Liu Y (2018) Preliminary study of modeling and simulation technology oriented to neo-type artificial intelligent systems. J Syst Simul 30(2):349–362

    Google Scholar 

  28. Zhao Q (2011) 10 scientific problems in virtual reality. Commun ACM 54(2):116–117

    Article  Google Scholar 

  29. Van Krevelen DWF, Poelman R (2010) A survey of augmented reality technologies, applications and limitations. Int J Virtual Reality 9(2):1–20

    Article  Google Scholar 

  30. Berg LP, Vance JM (2017) Industry use of virtual reality in product design and manufacturing: a survey. Virtual Reality 21(1):1–17

    Article  Google Scholar 

  31. Ong SK, Yuan ML, Nee AYC (2008) Augmented reality applications in manufacturing: a survey. Int J Prod Res 46(10):2707–2742

    Article  MATH  Google Scholar 

  32. Rhyne T-M, Chen M (2013) Cutting-edge research in visualization. Computer 46(5):22–24

    Article  Google Scholar 

  33. Rasmussen J, Pejtersen A, Goodstein L (1994) Cognitive systems engineering. Wiely

    Google Scholar 

  34. Bennett KB, Flach JM (2011) Display and interface design: subtle science, exact art. CRC Press

    Book  Google Scholar 

  35. Sarjoughian HS, Sundaramoorthi S (2015) Superdense time trajectories for DEVS simulation models. SpringSim (TMS-DEVS), pp 249–256

    Google Scholar 

  36. Richmond B (1985) STELLA: software for bringing system dynamics to the other 98%. In: International conference of the system dynamics society. Keystone, CO, USA, pp 706–718

    Google Scholar 

  37. Walker R, Gregory C, Shah S (1982) MATRIX x: a data analysis, system identification, control design and simulation package. IEEE Control Syst Mag 30–37

    Google Scholar 

  38. Sarjoughian HS (2006) Model composability. In: Proceeding of the 2006 winter simulation conference. Institute of Electrical and Electronics Engineers, Inc., Piscataway, New Jersey, pp 149–158

    Google Scholar 

  39. Li JK, Takanori F, Kesavan S, Ross C, Mubarak M, Carothers CD, Ross RB, Ma K-L (2019) A visual analytics framework for analyzing parallel and distributed computing applications. IEEE visualization in data science. Vancouver, Canada, , pp 1–9

    Google Scholar 

  40. Few S (2006) Information dashboard design: the effective visual communication of data. O'Reilly Media, Inc.

    Google Scholar 

  41. Sarjoughian HS (2017) Restraining complexity and scale traits for component-based simulation models. In: Winter simulation conference. Institute of Electrical and Electronics Engineers, Inc., Piscataway, New Jersey, pp. 675–689

    Google Scholar 

  42. BIRT (2018) Business intelligence reporting tool. http://www.eclipse.org/birt/. Retrieved from http://www.eclipse.org/birt/

  43. Bostock M, Ogievetsky V, Heer J (2009) D3 data-driven documents. IEEE Trans Visual Comput Graph 17(12):2301–2309

    Article  Google Scholar 

  44. Zarrin B, Baumeister H (2014) Design of a domain-specific language for material flow analysis using Microsoft DSL tools: an experience paper. In: Proceedings of the 14th workshop on domain-specific modeling. Portland, Oregon, United States, pp 23–28

    Google Scholar 

  45. Object Management Group (2014) MDA Guide Version 2.0. Retrieved from https://www.omg.org/mda/

  46. Fuhrmann H, von Hanxleden R (2010) Taming graphical modeling. In: Petriu DC, Rouquette N, Haugen Ø (eds) International conference on model driven engineering languages and systems. Springer, Berlin, Heidelberg, pp 196-210

    Google Scholar 

  47. Steinberg D, Budinsky F, Merks E, Paternostro AM (2008). EMF: eclipse modeling framework, 2nd edn. Pearson Education

    Google Scholar 

  48. Zeigler BP, Sarjoughian HS (2017) Modeling and simulation of systems of systems, 2nd edn. Springer, London

    Google Scholar 

  49. Kellner M, Madachy R, Raffo D (1999) Software process simulation modeling: why? what? how? J Syst Softw 46(2–3):91–105

    Article  Google Scholar 

  50. Bell PC, Taseen AA, Kirkpatrick PF (1990) Visual interactive simulation modeling in a decision support role. Comput Oper Res 17(5):447–456

    Article  Google Scholar 

  51. Ware C (2019) Information visualization: perception for design. Morgan Kaufmann

    Google Scholar 

  52. Tufte ER (2001) The visual display of quantitative information. Graphics Press

    Google Scholar 

  53. Isaacs KE, Gimenez A, Jusufi I, Gamblin T, Bhatele A, Schulz M, Hamann B, Bremer P-T (2014) State of the art of performance visualization. In: Proceedings of Eurographics conference on visualization, pp 141–160

    Google Scholar 

  54. Rouse WB (2015) Modeling and visualization of complex systems and enterprises: modeling and visualization of complex systems and enterprise. Wiley

    Google Scholar 

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Correspondence to Andrea D’Ambrogio .

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D’Ambrogio, A., Yang, C., Sarjoughian, H.S. (2023). Supporting Engineering Areas. In: Ören, T., Zeigler, B.P., Tolk, A. (eds) Body of Knowledge for Modeling and Simulation. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-11085-6_14

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  • DOI: https://doi.org/10.1007/978-3-031-11085-6_14

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  • Print ISBN: 978-3-031-11084-9

  • Online ISBN: 978-3-031-11085-6

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