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Improving System Architecture Decisions by Integrating Human System Integration Extensions into Model-Based Systems Engineering

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Handbook of Model-Based Systems Engineering
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Abstract

Model-based systems engineering (MBSE) plays an increasingly important role in the development of complex systems. Currently, systems architecture models (e.g., descriptive SysML models) have focused more on depicting machine interactions with little consideration for human characteristics that are needed to make holistic architectural decisions. This chapter describes a human system integration (HSI) extension which facilitates integration of system architecture models with human task models. This integration allows tighter coupling between system architecture and analysis with a human agent. It also presents an ontology broker for tool integration. The ontology broker supports information scalability captured in the modeling ecosystem when making architectural decisions. A case study of an unmanned aerial system and an image analyst assesses whether architectural decisions resulting from tighter integration can improve the human-system performance. The results of the study show that architectural changes made and subsequent analysis of the human-system performance produce superior analysis by reducing analyst workload, eliminating bottlenecks, and achieving overall improvement in how the human analyst interacts with the system.

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References

  1. A. M. Madni, “Integrating Humans With and Within Complex Systems,” CrossTalk, vol. May/June, pp. 4–8, 2011.

    Google Scholar 

  2. Madni, A. M. (2017). Mutual Adaptation in Human-Machine Teams. ISTI-WP-02-012017.

    Google Scholar 

  3. Madni, A. M., & Freedy, A. (1986). Intelligent Interfaces for Human Control of Advanced Automation and Smart Systems in Human Productivity Enhancement. Training and Human Factors in Systems Design, 1(J. Zeidner), 318–331.

    Google Scholar 

  4. Madni, A. M. (2015). Expanding Stakeholder Participation in Upfront System Engineering Through Storytelling in Virtual Worlds. Systems Engineering (Vol. 18).

    Google Scholar 

  5. International Council on Systems Engineering, Systems Engineering Handbook, 3.2.2. San Diego, CA, 2011.

    Google Scholar 

  6. A. M. Madni, “Expanding Stakeholder Participation in Upfront System Engineering Through Storytelling in Virtual Worlds,” 2015.

    Google Scholar 

  7. A. M. Madni, “The Role of Human Factors in Expert Systems Design and Acceptance,” Hum. Factors J., vol. 30, no. 4, pp. 395–414, 1988.

    Article  Google Scholar 

  8. A. M. Madni, “HUMANE: A Designer’s Assistant for Modeling and Evaluating Function Allocation Options,” in Proceedings of Ergonomics of Advanced Manufacturing and Automated Systems Conference, 1988, pp. 291–302.

    Google Scholar 

  9. J. Axelsson, “Towards an Improved Understanding of Humans as the Components that Implement Systems Engineering,” in Proceedings 12th Symposium of the International Council on System Engineering, 2002, pp. 1–6.

    Google Scholar 

  10. A. M. Madni, “Elegant Systems Design: Creative Fusion of Simplicity and Power,” Syst. Eng., vol. 15, no. 3, pp. 347–354, 2012.

    Article  Google Scholar 

  11. R. Neches and A. M. Madni, “Towards Affordably Adaptable and Effective Systems,” Syst. Eng., vol. 16, no. 2, pp. 224–234, 2012.

    Article  Google Scholar 

  12. T. Bahill and A. M. Madni, Trade-off Decisions in System Design. Springer, 2017.

    Book  Google Scholar 

  13. A. M. Madni, Transdisciplinary Systems Engineering: Exploiting Convergence in a Hyperconnected World. Springer, 2018.

    Book  Google Scholar 

  14. Object Management Group, “Unified Modeling Language, Infrastructure v2.4.1,” no. August. 2011.

    Google Scholar 

  15. H.-P. Hoffmann, “UML 2.0-Based Systems Engineering Using a Model-Driven Development Approach,” CrossTalk J. Def. Softw. Eng., pp. 1–18, 2005.

    Google Scholar 

  16. E. Herzog and A. Pandikow, “SysML – An Assessment,” in Syntell AB, SE 100, 2005.

    Google Scholar 

  17. P. K. Balakrishnan, “Analysis of Human Factors in Specific Aspects of System Design,” in INCOSE International Symposium, 2002, pp. 1–9.

    Google Scholar 

  18. Landsburg, A. C., Avery, L., Beaton, R., Bost, J. R., Comperatore, C., Khandpur, R., … Sheridan, T. B. (2008). The Art of Successfully Applying Human Systems Integration. American Society of Naval Engineers Journal, 120(1), 77–107. https://doi.org/10.1111/j.1559-3584.2008.00113.x

    Article  Google Scholar 

  19. J. M. Narkevicius, “Human Factors and Systems Engineering – Integrating for Successful Systems Development,” in Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2008, vol. 52, no. 24, pp. 1961–1963.

    Google Scholar 

  20. Vanderperren, Y., & Dehaene, W. (2006). From UML/SysML to Matlab/Simulink: Current State and Future Perspectives. In Proceedings of the Design Automation & Test in Europe Conference. Ieee. https://doi.org/10.1109/DATE.2006.244002

  21. M. Bajaj, D. Zwemer, R. Peak, A. Phung, A. G. Scott, and M. W. Wilson, “SLIM: Collaborative Model-Based Systems Engineering Workspace for Next-Generation Complex Systems,” in IEEE Aerospace Conference, 2011, pp. 1–15.

    Google Scholar 

  22. Mayer, R. J., Crump, J. W., Fernandes, R., Keen, A., & Painter, M. K. (1995). Information Integration for Concurrent Engineering (IICE) Compendium of Methods Report. Wright-Patterson Air Force Base, Ohio. Retrieved from http://www.idef.com/pdf/idef3_fn.pdf

    Book  Google Scholar 

  23. H. A. H. Handley and R. J. Smillie, “Architecture Framework Human View: The NATO Approach,” J. Syst. Eng., vol. 11, no. 2, pp. 156–164, 2008.

    Article  Google Scholar 

  24. K. Baker, A. Stewart, C. Pogue, and R. Ramotar, “Human Views: Extensions to the Department of Defense Architecture Framework,” 2008.

    Google Scholar 

  25. Watson, Michael & Rusnock, Christina & Miller, Michael & Colombi, John. (2017). Informing System Design Using Human Performance Modeling. Systems Engineering. 20. https://doi.org/10.1002/sys.21388.

  26. Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A Model for Types and Levels of Human Interaction with Automation. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans : A Publication of the IEEE Systems, Man, and Cybernetics Society, 30(3), 286–97. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/11760769

  27. Dolan, N., & Narkevicius, J. M. (2005). Systems Engineering, Acquisition and Personnel Integration (SEAPRINT): Achieving the Promise of Human Systems Integration. In Meeting Proceedings RTO-MP-HFM-124 (pp. 1–6). Neuilly-sur-Seine, France.

    Google Scholar 

  28. Ijtsma, Martijn & Pritchett, Amy & Ma, Lanssie & Feigh, Karen. (2017). Modeling Human-Robot Interaction to Inform Function Allocation in Manned Spaceflight Operations.

    Google Scholar 

  29. A. M. Madni and M. Sievers, “Systems Integration: Key Perspectives, Experiences, and Challenge,” Syst. Eng., vol. 16, no. 4, pp. 1–23, 2013.

    Google Scholar 

  30. N. G. Leveson, “Intent Specifications: An Approach to Building Human-Centered Specifications,” IEEE Trans. Softw. Eng., vol. 26, no. 1, pp. 15–35, 2000.

    Article  Google Scholar 

  31. N. L. Miller, J. J. Crowson Jr, and J. M. Narkevicius, “Human Characteristics and Measures in Systems Design,” in Handbook of Human Systems Integration, H. R. Booher, Ed. John Wiley & Sons, Inc, 2003, pp. 699–742.

    Google Scholar 

  32. McKenney, Martin & McKenney, Martin & Handley, Holly. (2020). Using the Design Science Research Method (DSRM) to develop a Skills Gaps Analysis Model. IEEE Engineering Management Review. PP. 1-1. https://doi.org/10.1109/EMR.2020.3011704.

  33. S. C. Spangelo et al., “Applying Model Based Systems Engineering (MBSE) to a Standard CubeSat,” in 2012 IEEE Aerospace Conference, 2012, pp. 1–20.

    Google Scholar 

  34. S. C. Spangelo, “Model Based Systems Engineering (MBSE) Applied to Radio Aurora Explorer (RAX) CubeSat Mission Operational Scenarios,” in IEEE Aerospace Conference, 2013, pp. 1–18.

    Google Scholar 

  35. B. P. Hunn and O. H. Heuckeroth, “A Shadow Unmanned Aerial Vehicle (UAV) Improved Performance Research Integration Tool (IMPRINT) Model Supporting Future Combat Systems,” Aberdeen Proving Ground, MD, 2006.

    Google Scholar 

  36. B. P. Hunn, K. M. Schweitzer, J. A. Cahir, and M. M. Finch, “IMPRINT Analysis of an Unmanned Air System Geospatial Information Process,” Aberdeen Proving Ground, MD, 2008.

    Google Scholar 

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Correspondence to D. W. Orellana .

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Orellana, D.W. (2022). Improving System Architecture Decisions by Integrating Human System Integration Extensions into Model-Based Systems Engineering. In: Madni, A.M., Augustine, N., Sievers, M. (eds) Handbook of Model-Based Systems Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-27486-3_27-1

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  • DOI: https://doi.org/10.1007/978-3-030-27486-3_27-1

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  • Print ISBN: 978-3-030-27486-3

  • Online ISBN: 978-3-030-27486-3

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