Relevant bibliographies by topics / Complex engineering systems

Academic literature on the topic 'Complex engineering systems'

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Published: 10 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "Complex engineering systems"

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Ottino, J. M. "Engineering complex systems." Nature 427, no. 6973 (January 2004): 399. http://dx.doi.org/10.1038/427399a.

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Abbott, Russ. "Complex systems engineering: Putting complex systems to work." Complexity 13, no. 2 (2007): 10–11. http://dx.doi.org/10.1002/cplx.20197.

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Ahram, Tareq Z. "ENGINEERING SUSTAINABLE COMPLEX SYSTEMS." Management and Production Engineering Review 4, no. 4 (December 1, 2013): 4–14. http://dx.doi.org/10.2478/mper-2013-0032.

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Abstract Given the most competitive nature of global business environment, effective engineering innovation is a critical requirement for all levels of system lifecycle development. The society and community expectations have increased beyond environmental short term impacts to global long term sustainability approach. Sustainability and engineering competence skills are extremely important due to a general shortage of engineering talent and the need for mobility of highly trained professionals [1]. Engineering sustainable complex systems is extremely important in view of the general shortage of resources and talents. Engineers implement new technologies and processes to avoid the negative environmental, societal and economic impacts. Systems thinking help engineers and designers address sustainable development issues with a global focus using leadership and excellence. This paper introduces the Systems Engineering (SE) methodology for designing complex and more sustainable business and industrial solutions, with emphasis on engineering excellence and leadership as key drivers for business sustainability. The considerable advancements achieved in complex systems engineering indicate that the adaptation of sustainable SE to business needs can lead to highly sophisticated yet widely useable collaborative applications, which will ensure the sustainability of limited resources such as energy and clean water. The SE design approach proves critical in maintaining skills needed in future capable workforce. Two factors emerged to have the greatest impact on the competitiveness and sustainability of complex systems and these were: improving skills and performance in engineering and design, and adopting SE and human systems integration (HSI) methodology to support sustainability in systems development. Additionally, this paper provides a case study for the application of SE and HSI methodology for engineering sustainable and complex systems.
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Broggi, A., M. Hinchey, and A. D. Stoyen. "Engineering complex computer systems." Microprocessors and Microsystems 23, no. 3 (October 1999): 123–24. http://dx.doi.org/10.1016/s0141-9331(99)00034-4.

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Bujara, Matthias, and Sven Panke. "Engineering in complex systems." Current Opinion in Biotechnology 21, no. 5 (October 2010): 586–91. http://dx.doi.org/10.1016/j.copbio.2010.07.007.

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Rouse, W. B. "Engineering complex systems: implications for research in systems engineering." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 33, no. 2 (May 2003): 154–56. http://dx.doi.org/10.1109/tsmcc.2003.813335.

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White, Brian E. "On Principles of Complex Systems Engineering-Complex Systems Made Simple." INCOSE International Symposium 21, no. 1 (June 2011): 1590–844. http://dx.doi.org/10.1002/j.2334-5837.2011.tb01296.x.

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White, Brian. "On Principles of Complex Systems Engineering-Complex Systems Made Simple." INCOSE International Symposium 23, no. 1 (June 2013): 1636. http://dx.doi.org/10.1002/j.2334-5837.2013.tb03124.x.

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Sheard, Sarah A., and Ali Mostashari. "Principles of complex systems for systems engineering." Systems Engineering 12, no. 4 (September 2009): 295–311. http://dx.doi.org/10.1002/sys.20124.

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Li, Ta-Hsin, Tailen Hsing, and D. M. Titterington. "Complex Stochastic Systems and Engineering." Technometrics 39, no. 3 (August 1997): 336. http://dx.doi.org/10.2307/1271142.

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Dissertations / Theses on the topic "Complex engineering systems"

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Venkatesh, Saligrama Ramaswamy. "System-identification for complex-systems." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/10440.

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Magee, Christopher, and Weck Olivier de. "Complex System Classification." International Council On Systems Engineering (INCOSE), 2004. http://hdl.handle.net/1721.1/6753.

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The use of terms such as “Engineering Systems”, “System of systems” and others have been coming into greater use over the past decade to denote systems of importance but with implied higher complexity than for the term systems alone. This paper searches for a useful taxonomy or classification scheme for complex Systems. There are two aspects to this problem: 1) distinguishing between Engineering Systems (the term we use) and other Systems, and 2) differentiating among Engineering Systems. Engineering Systems are found to be differentiated from other complex systems by being human-designed and having both significant human complexity as well as significant technical complexity. As far as differentiating among various engineering systems, it is suggested that functional type is the most useful attribute for classification differentiation. Information, energy, value and mass acted upon by various processes are the foundation concepts underlying the technical types.
Engineering Systems Division and Mechanical Engineering, Center for Innovation in Product Development
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Case, Denise Marie. "Engineering complex systems with multigroup agents." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/19045.

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Doctor of Philosophy
Computing and Information Sciences
Scott A. DeLoach
As sensor prices drop and computing devices continue to become more compact and powerful, computing capabilities are being embedded throughout our physical environment. Connecting these devices in cyber-physical systems (CPS) enables applications with significant societal impact and economic benefit. However, engineering CPS poses modeling, architecture, and engineering challenges and, to fully realize the desired benefits, many outstanding challenges must be addressed. For the cyber parts of CPS, two decades of work in the design of autonomous agents and multiagent systems (MAS) offers design principles for distributed intelligent systems and formalizations for agent-oriented software engineering (AOSE). MAS foundations offer a natural fit for enabling distributed interacting devices. In some cases, complex control structures such as holarchies can be advantageous. These can motivate complex organizational strategies when implementing such systems with a MAS, and some designs may require agents to act in multiple groups simultaneously. Such agents must be able to manage their multiple associations and assignments in a consistent and unambiguous way. This thesis shows how designing agents as systems of intelligent subagents offers a reusable and practical approach to designing complex systems. It presents a set of flexible, reusable components developed for OBAA++, an organization-based architecture for single-group MAS, and shows how these components were used to develop the Adaptive Architecture for Systems of Intelligent Systems (AASIS) to enable multigroup agents suitable for complex, multigroup MAS. This work illustrates the reusability and flexibility of the approach by using AASIS to simulate a CPS for an intelligent power distribution system (IPDS) operating two multigroup MAS concurrently: one providing continuous voltage control and a second conducting discrete power auctions near sources of distributed generation.
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Devereaux, Jaime E. (Jaime Erin). "Obsolescence : a systems engineering and management approach for complex systems." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/59233.

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Thesis (S.M. in System Design and Management)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79-81).
Obsolescence mitigation is an increasingly important aspect of large systems development & maintenance that has often only been considered once obsolescence is imminent. For long lifecycle systems, this has become a major concern as the lifecycles of the components that are encompassed within these systems are often far shorter - up to ten times shorter - than the overall system lifecycle. Many defense systems can be characterized in this manner and therefore require obsolescence mitigation approaches to ensure the continuing ability for the system to perform and evolve. Current system-level obsolescence mitigation practices make recommendations for designing new systems to slow the onset of obsolescence and make the system more flexible when change for obsolescence is required. However, currently fielded systems were often not designed with this in mind. Other obsolescence mitigation techniques focus only on the approach to mitigating component-level obsolescence locally without examining the impact of the change on the system as a whole. This thesis combines the recommended approaches for obsolescence mitigation, the experience and lessons learned for obsolescence mitigation on a real-world case study system gained from interviews with key subject matter experts, along with systems engineering techniques for dealing with engineering change in systems to develop a robust systems engineering and management approach for obsolescence in large complex systems. The thesis provides the reader with a flow chart and a clustered DSM of the tasks along with a checklist that could be used with this obsolescence engineering and management approach.
by Jaime E. Devereaux.
S.M.in System Design and Management
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Mwanga, Alifas Yeko. "Reliability modelling of complex systems." Thesis, Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-12142006-121528.

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Holbrook, A. E. K. "Design assistance for complex engineering assemblies." Thesis, Cranfield University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303118.

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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.

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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.
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Wang, Cheng 1971. "Parametric uncertainty analysis for complex engineering systems." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9507.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1999.
Includes bibliographical references (p. 259-275).
With the rapid advancement of computational science, modeling and simulation have become standard methods to study the behavior of complex systems. As scientists and engineers try to capture more detail, the models become more complex. Given that there are inevitable uncertainties entering at every stage of a model's life cycle, the challenge is to identify those components that contribute most to uncertainties in the predictions. This thesis presents new methodologies for allowing direct incorporation of uncertainty into the model formulation and for identifying the relative importance of different parameters. The basis of these methods is the deterministic equivalent modeling method (DEMM), which applies polynomial chaos expansions and the probabilistic collocation approach to transform the stochastic model into a deterministic equivalent model. By transforming the model the task of determining the probability density function of the model response surface is greatly simplified. In order to advance the representation method of parametric uncertainty. a theoretical study of polynomial chaos representation of uncertain parameters has been performed and an Adomian polynomial expansion for functions of random variables has been developed. While DEMM is applied to various engineering systems to study the propagation of uncertainty in complex models, a systematic framework is introduced to quantitatively assess the effect of uncertain parameters in stochastic optimization problems for chemical product and process design. Furthermore, parametric uncertainty analysis techniques for discrete and correlated random variables have been developed such that the deterministic equivalent modeling method can be applied to a broader range of engineering problems. As a result of these developments, uncertainty analysis can now be performed 2 to 3 orders faster than conventional methods such as Monte Carlo. Examples of models in various engineering systems suggest both the accuracy and the practicality of the new framework for parametric uncertainty analysis established in this thesis.
by Cheng Wang.
Ph.D.
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Zils, Jude. "A Systems Engineering Approach to Complex Tool Realization." Digital Commons at Loyola Marymount University and Loyola Law School, 2010. https://digitalcommons.lmu.edu/etd/448.

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Tooling is defined as the work performed by a tool. In the context of industrial production tooling takes many forms from a simple drill bar to highly complex assembly jigs. In all cases the tooling exists to assist in the accurate and precise performance of work on engineering products. The engineering product therefore defines and constrains the form and function of the associated tooling. The process of defining, fabricating, and verifying tooling is often subject to individual, business, or government perspectives and processes. Relying on individual experience and inadequate processes often results in frequent rework, product design interface issues, and a lack of historical perspective and traceability on the tooling design. The Systems Engineering process, which is already valued as a necessary component of complex system definition, will be beneficial when adapted and applied to the process of defining, fabricating, and verifying tooling. The methodical processes and tools associated with Systems Engineering will embed the tooling process in the product requirement and design process and encourage increased interaction and concurrent engineering practices. A tooling process, based on System Engineering principles combined with best industry practices, that is ingrained in the product life cycle and which thoroughly documents associated technical and producibility requirements will reduce the issues currently prevalent in complex tooling realization.
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Cheng, Fook-Chun. "Object-oriented data structures in complex engineering systems." Thesis, London South Bank University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280785.

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Books on the topic "Complex engineering systems"

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Ng, Irene, Glenn Parry, Peter Wild, Duncan McFarlane, and Paul Tasker, eds. Complex Engineering Service Systems. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-189-9.

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Loureiro, Geilson, and Richard Curran, eds. Complex Systems Concurrent Engineering. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-976-7.

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Eisner, Howard. Managing Complex Systems. New York: John Wiley & Sons, Ltd., 2005.

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Zamojski, Wojciech, Jacek Mazurkiewicz, Jarosław Sugier, Tomasz Walkowiak, and Janusz Kacprzyk, eds. Dependability Engineering and Complex Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39639-2.

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M, Titterington D., and Institute of Mathematics andits Applications. Conference,, eds. Complex stochastic systems and engineering. Oxford: Oxford University Press, 1995.

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Oliver, David W. Engineering complex systems with models and objects. New York: McGraw-Hill, 1997.

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Åström, Karl. Control of Complex Systems. London: Springer London, 2001.

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Large-scale complex system and systems of systems. Hoboken, NJ: John Wiley, 2011.

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Systems architecting: Creating and building complex systems. Englewood Cliffs, N.J: Prentice Hall, 1991.

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Mittal, Saurabh, Saikou Diallo, and Andreas Tolk, eds. Emergent Behavior in Complex Systems Engineering. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119378952.

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Book chapters on the topic "Complex engineering systems"

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Mobus, George E., and Michael C. Kalton. "Systems Engineering." In Understanding Complex Systems, 699–731. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1920-8_14.

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Norman, Douglas O., and Michael L. Kuras. "Engineering Complex Systems." In Understanding Complex Systems, 206–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-32834-3_10.

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Moser, Hubert Anton. "Systems Engineering and Learning." In Understanding Complex Systems, 11–57. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03895-7_2.

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Hitchins, Derek. "Natural Systems Engineering." In Complex Systems Design & Management, 315–34. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02812-5_23.

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Baeten, Jos C. M., Joanna M. van de Mortel-Fronczak, and Jacobus E. Rooda. "Integration of Supervisory Control Synthesis in Model-Based Systems Engineering." In Complex Systems, 39–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28860-4_2.

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Robinett, Rush D., and David G. Wilson. "Case Study #4: Fundamental Power Engineering." In Understanding Complex Systems, 207–23. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-823-2_9.

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Kaluza, Pablo, Hiroshi Kori, and Alexander S. Mikhailov. "Evolutionary Engineering of Complex Functional Networks." In Understanding Complex Systems, 351–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75261-5_17.

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Balslev, Henrik. "The Systems Engineering Concept." In Complex Systems Design & Management, 233. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04209-7_19.

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Jézéquel, Jean-Marc, David Méndez-Acuña, Thomas Degueule, Benoit Combemale, and Olivier Barais. "When Systems Engineering Meets Software Language Engineering." In Complex Systems Design & Management, 1–13. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11617-4_1.

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Luzeaux, Dominique. "Engineering Large-Scale Complex Systems." In Large scale Complex Systems and Systems of Systems Engineering: Case Studies, 1–84. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118601495.ch1.

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Conference papers on the topic "Complex engineering systems"

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White, B. E. "Complex Adaptive Systems Engineering (CASE)." In 2009 3rd Annual IEEE Systems Conference. IEEE, 2009. http://dx.doi.org/10.1109/systems.2009.4815774.

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Cloutier, Robert, and Regina Griego. "Applying Object Oriented Systems Engineering to Complex Systems." In 2008 2nd Annual IEEE Systems Conference. IEEE, 2008. http://dx.doi.org/10.1109/systems.2008.4519058.

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"Complex Engineering Systems and Systems Engineering." In 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2018. http://dx.doi.org/10.1109/etfa.2018.8502549.

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Furness, Zach, and Valerie Gawron. "Enabling engineering of complex systems through simulation-based experimentation." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482451.

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DeRosa, Joseph K., Anne-Marie Grisogono, Alex J. Ryan, and Douglas O. Norman. "A Research Agenda for the Engineering of Complex Systems." In 2008 2nd Annual IEEE Systems Conference. IEEE, 2008. http://dx.doi.org/10.1109/systems.2008.4518982.

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"WIP Complex Engineering Systems and Systems Engineering." In 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2018. http://dx.doi.org/10.1109/etfa.2018.8502617.

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Rhodes, Donna H., and Adam M. Ross. "Five aspects of engineering complex systems emerging constructs and methods." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482431.

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Vakili, Golnaz, and Siavash Khorsandi. "Engineering a peer to peer architecture: A complex adaptive system approach." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482487.

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White, Brian E. "On Interpreting Scale (or View) and Emergence in Complex Systems Engineering." In 2007 1st Annual IEEE Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/systems.2007.374660.

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DeRosa, Joseph K., and L. Keith McCaughin. "Combined Systems Engineering and Management in the Evolution of Complex Adaptive Systems." In 2007 1st Annual IEEE Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/systems.2007.374653.

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Reports on the topic "Complex engineering systems"

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Detry, Richard Joseph, John Michael Linebarger, Patrick D. Finley, S. Louise Maffitt, Robert John, Jr Glass, Walter Eugene Beyeler, and Arlo Leroy Ames. Complex Adaptive Systems of Systems (CASOS) engineering environment. Office of Scientific and Technical Information (OSTI), February 2012. http://dx.doi.org/10.2172/1038222.

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Linebarger, John Michael, S. Louise Maffitt, Robert John, Jr Glass, Walter Eugene Beyeler, and Arlo Leroy Ames. Complex Adaptive System of Systems (CASoS) Engineering Applications. Version 1.0. Office of Scientific and Technical Information (OSTI), October 2011. http://dx.doi.org/10.2172/1038214.

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Moore, Thomas W., Tu-Thach Quach, Richard Joseph Detry, Stephen Hamilton Conrad, Andjelka Kelic, Shirley J. Starks, Walter Eugene Beyeler, et al. Phoenix : Complex Adaptive System of Systems (CASoS) engineering version 1.0. Office of Scientific and Technical Information (OSTI), August 2011. http://dx.doi.org/10.2172/1038215.

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Brodsky, Nancy S., Patrick D. Finley, Walter Eugene Beyeler, John Michael Linebarger, Thomas W. Moore, Robert John, Jr Glass, S. Louise Maffitt, Michael David Mitchell, and Arlo Leroy Ames. Complex Adaptive Systems of Systems (CASoS) engineering and foundations for global design. Office of Scientific and Technical Information (OSTI), January 2012. http://dx.doi.org/10.2172/1035333.

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Doyle, John. Bioinspired Concepts: Unified Theory for Complex Biological and Engineering Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada484230.

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Doyle, John C. Bioinspired Concepts: Unified Theory for Complex Biological and Engineering Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada434182.

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Nagahi, Morteza, Raed Jaradat, Simon Goerger, Michael Hamilton, Randy Buchanan, Sawsan Abutabenjeh, and Junfeng Ma. The impact of practitioners’ personality traits on their level of systems-thinking skills preferences. Engineer Research and Development Center (U.S.), October 2022. http://dx.doi.org/10.21079/11681/45791.

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In this study, we used a structural equation modeling method to investigate the relationship between systems engineers and engineering managers’ Systems-Thinking (ST) skills preferences and their Personality Traits (PTs) in the domain of complex system problems. As organizations operate in more and more turbulent and complex environments, it has become increasingly important to assess the ST skills preferences and PTs of engineers. The current literature lacks studies related to the impact of systems engineers and engineering managers’ PTs on their ST skills preferences, and this study aims to address this gap. A total of 99 engineering managers and 104 systems engineers provided the data to test four hypotheses posed in this study. The results show that the PTs of systems engineers and engineering managers have a positive impact on their level of ST skills preferences and that the education level, the current occupation type, and the managerial experience of the systems engineers and engineering managers moderate the main relationship in the study.
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Deiotte, Ray, Jr Garrett, and Robert K. A Novel Approach to Mission-Level Engineering of Complex Systems of Systems: Addressing Integration and Interoperability Shortfalls by Interrogating the Interstitials. Fort Belvoir, VA: Defense Technical Information Center, December 2013. http://dx.doi.org/10.21236/ada595201.

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Hossain, Niamat Ullah Ibne, Raed Jaradat, Michael Hamilton, Charles Keating, and Simon Goerger. A historical perspective on development of systems engineering discipline : a review and analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40259.

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Since its inception, Systems Engineering (SE) has developed as a distinctive discipline, and there has been significant progress in this field in the past two decades. Compared to other engineering disciplines, SE is not affirmed by a set of underlying fundamental propositions, instead it has emerged as a set of best practices to deal with intricacies stemming from the stochastic nature of engineering complex systems and addressing their problems. Since the existing methodologies and paradigms (dominant pat- terns of thought and concepts) of SE are very diverse and somewhat fragmented. This appears to create some confusion regarding the design, deployment, operation, and application of SE. The purpose of this paper is 1) to delineate the development of SE from 1926-2017 based on insights derived from a histogram analysis, 2) to discuss the different paradigms and school of thoughts related to SE, 3) to derive a set of fundamental attributes of SE using advanced coding techniques and analysis, and 4) to present a newly developed instrument that could assess the performance of systems engineers. More than Two hundred and fifty different sources have been reviewed in this research in order to demonstrate the development trajectory of the SE discipline based on the frequency of publication.
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Kevrekidis, Ioannis G. Equation-free and variable free modeling for complex/multiscale systems. Coarse-grained computation in science and engineering using fine-grained models. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1347549.

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