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Articles de revues sur le sujet « Complex engineering systems »

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Auteur : Grafiati
Publié le 10 mars 2023
Mis à jour le 11 mars 2023

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1

Ottino, J. M. « Engineering complex systems ». Nature 427, no 6973 (janvier 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 (1 décembre 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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4

Broggi, A., M. Hinchey et A. D. Stoyen. « Engineering complex computer systems ». Microprocessors and Microsystems 23, no 3 (octobre 1999) : 123–24. http://dx.doi.org/10.1016/s0141-9331(99)00034-4.

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Bujara, Matthias, et Sven Panke. « Engineering in complex systems ». Current Opinion in Biotechnology 21, no 5 (octobre 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 (mai 2003) : 154–56. http://dx.doi.org/10.1109/tsmcc.2003.813335.

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7

White, Brian E. « On Principles of Complex Systems Engineering-Complex Systems Made Simple ». INCOSE International Symposium 21, no 1 (juin 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 (juin 2013) : 1636. http://dx.doi.org/10.1002/j.2334-5837.2013.tb03124.x.

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

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

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Bashiri, Hassan, Amir Nazemi et Ali Mobinidehkordi. « Futures engineering in complex systems ». foresight 19, no 3 (12 juin 2017) : 306–22. http://dx.doi.org/10.1108/fs-09-2016-0042.

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Purpose This paper attempts to apply complex theory in futures studies and addresses prediction challenges when the system is complex. The purpose of the research is to design a framework to engineer the futures in complex systems where components are divers and inter-related. Relations cannot be interpreted by cause and effect concept. Design/methodology/approach First, the authors shaped a conceptual framework based on engineering, complex theory and uncertainty. To extract tacit knowledge of experts, an online questionnaire was developed. To validate the proposed framework, a workshop method was adapted with NetLogo simulation. Findings Opinion of participants in the workshop which is collected through quantitative questionnaire shows that the framework helps us in understanding and shaping scenarios. Harnessing the complexity in developing the futures was the main objective of this paper with the proposed framework which has been realized based on the experience gained from the workshop. Originality/value Iterative processes are very important to harness the complexity in systems with uncertainty. The novelty of the research is a combination of engineering achievements in terms of computation, simulation and applying tools with futures studies methods.
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12

Hsing, Tailen. « Complex Stochastic Systems and Engineering ». Technometrics 39, no 3 (août 1997) : 336. http://dx.doi.org/10.1080/00401706.1997.10485129.

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Machado, JA Tenreiro, et António M. Lopes. « Complex systems in mechanical engineering ». Advances in Mechanical Engineering 9, no 7 (juillet 2017) : 168781401771912. http://dx.doi.org/10.1177/1687814017719122.

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14

White, B. E. « Complex adaptive systems engineering (CASE) ». IEEE Aerospace and Electronic Systems Magazine 25, no 12 (décembre 2010) : 16–22. http://dx.doi.org/10.1109/maes.2010.5638784.

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15

Corrall, David. « Requirements Engineering for Complex Systems ». INSIGHT 2, no 4 (décembre 2000) : 21–23. http://dx.doi.org/10.1002/inst.20002421.

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White, Stephanie. « TRACEABILITY FOR COMPLEX SYSTEMS ENGINEERING ». INCOSE International Symposium 4, no 1 (août 1994) : 44–50. http://dx.doi.org/10.1002/j.2334-5837.1994.tb01681.x.

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Winkler, James D., Keesha Erickson, Alaksh Choudhury, Andrea L. Halweg-Edwards et Ryan T. Gill. « Complex systems in metabolic engineering ». Current Opinion in Biotechnology 36 (décembre 2015) : 107–14. http://dx.doi.org/10.1016/j.copbio.2015.08.002.

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18

Hayenga, Craig. « Complex and Complicated Systems Engineering ». INSIGHT 11, no 1 (janvier 2008) : 17–19. http://dx.doi.org/10.1002/inst.200811117.

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19

Beckerman, Linda P. « Application of complex systems science to systems engineering ». Systems Engineering 3, no 2 (2000) : 96–102. http://dx.doi.org/10.1002/1520-6858(2000)3:2<96 ::aid-sys4>3.0.co;2-7.

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20

Sheard, Sarah A. « 6.1.1 Principles of Complex Systems for Systems Engineering ». INCOSE International Symposium 17, no 1 (juin 2007) : 860–75. http://dx.doi.org/10.1002/j.2334-5837.2007.tb02918.x.

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21

White, B. E. « On a maturity model for complexity, complex systems, and complex systems engineering ». International Journal of Design & ; Nature and Ecodynamics 11, no 4 (1 octobre 2016) : 532–42. http://dx.doi.org/10.2495/dne-v11-n4-532-542.

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22

White, Brian E., et Mickael Bouyaud. « A Complex Adaptive Systems Engineering Methodology ». INSIGHT 24, no 2 (juillet 2021) : 25–31. http://dx.doi.org/10.1002/inst.12337.

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23

Oliver, D. W. « Engineering of complex systems with models ». IEEE Transactions on Aerospace and Electronic Systems 33, no 2 (avril 1997) : 667–85. http://dx.doi.org/10.1109/7.588386.

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24

Wilkinson, M. K., et R. J. Byers. « The engineering of complex software systems ». Computing & ; Control Engineering Journal 4, no 4 (1993) : 187. http://dx.doi.org/10.1049/cce:19930043.

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25

Lyons, Joseph B., Kolina S. Koltai, Nhut T. Ho, Walter B. Johnson, David E. Smith et R. Jay Shively. « Engineering Trust in Complex Automated Systems ». Ergonomics in Design : The Quarterly of Human Factors Applications 24, no 1 (janvier 2016) : 13–17. http://dx.doi.org/10.1177/1064804615611272.

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We studied the transparency of automated tools used during emergency operations in commercial aviation. Transparency (operationalized as increasing levels of explanation associated with an automated tool recommendation) was manipulated to evaluate how transparent interfaces influence pilot trust of an emergency landing planning aid. We conducted a low-fidelity study in which commercial pilots interacted with simulated recommendations from NASA’s Emergency Landing Planner (ELP) that varied in their associated levels of transparency. Results indicated that trust in the ELP was influenced by the level of transparency within the human–machine interface of the ELP. Design recommendations for automated systems are discussed.
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26

Haselbach, Liv M., et Michelle Maher. « Civil Engineering Education and Complex Systems ». Journal of Professional Issues in Engineering Education and Practice 134, no 2 (avril 2008) : 186–92. http://dx.doi.org/10.1061/(asce)1052-3928(2008)134:2(186).

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27

Galperin, E. M., V. A. Zayko et P. A. Gorshkalev. « Reliability Standards of Complex Engineering Systems ». IOP Conference Series : Materials Science and Engineering 262 (novembre 2017) : 012093. http://dx.doi.org/10.1088/1757-899x/262/1/012093.

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Crisp, Harry E. « Engineering of Complex, Human Centric Systems ». INSIGHT 3, no 1 (avril 2000) : 11–12. http://dx.doi.org/10.1002/inst.20003111.

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29

Panfilov, V. A., et S. P. Andreev. « ENGINEERING OF COMPLEX TECHNOLOGICAL SYSTEMS IN THE AGROINDUSTRIAL COMPLEX ». Foods and Raw materials 6, no 1 (20 juin 2018) : 23–29. http://dx.doi.org/10.21603/2308-4057-2018-1-23-29.

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30

Ottino, J. M. « Complex systems ». AIChE Journal 49, no 2 (février 2003) : 292–99. http://dx.doi.org/10.1002/aic.690490202.

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31

Brown, Theresa J., Stephen H. Conrad, Walter E. Beyeler et Robert J. Glass. « Complex adaptive systems engineering and risk reduction ». Proceedings of the Institution of Civil Engineers - Engineering Sustainability 166, no 5 (octobre 2013) : 293–300. http://dx.doi.org/10.1680/ensu.12.00036.

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32

Misnik, Anton Е. « Metagraphs for ontological engineering of complex systems ». Journal Of Applied Informatics 17, no 2 (31 mars 2022) : 120–32. http://dx.doi.org/10.37791/2687-0649-2022-17-2-120-132.

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The article deals with the issues of ontological engineering of complex systems. Ontological engineering includes the processes of designing and building ontologies, technologically combining object-oriented and structural analysis. Ontological engineering aims to ensure the adoption of high-quality management decisions by increasing the level of integration of the necessary information, improving search capabilities in databases and knowledge bases, providing the possibility of joint processing of knowledge based on a single semantic description of the knowledge space. This process is carried out within the framework of the proposed approach to managing complex systems. The ontology obtained as a result of engineering is subject to the requirements of convenience and flexibility, which is necessary for modeling system processes and ensuring the functioning of information and analytical processes in a complex system. The application of ordinary graphs, hypergraphs and metagraphs in ontological engineering is described. The use of metagraphs in the construction of hierarchical ontologies is substantiated. Metagraphs are considered as the basis for building an applied ontology of a complex system. A modification of the metagraph is proposed, which makes it possible to include events and data processing methods in the ontology. Such a modification integrates the process component into the ontological model of the system as an integral part of it, which makes it possible to flexibly and with less time to form process models based on the metagraph subgraphs of the general ontological model. An approach and an example of the implementation of the software-instrumental environment of ontological engineering and further construction of models of processes of a complex system are described. The technology used to implement the ontology in the PostgreSQL database management system and the database structure for storing the ontology are described
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33

Clymer, John R. « Simulation-Based Engineering Of Complex Adaptive Systems ». SIMULATION 72, no 4 (avril 1999) : 250–60. http://dx.doi.org/10.1177/003754979907200404.

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34

Franke, Milton E. « Engineering of Complex Systems for the Future ». Engineering Management Journal 13, no 2 (juin 2001) : 25–32. http://dx.doi.org/10.1080/10429247.2001.11415113.

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35

El-Rewini, H., et W. Halang. « The Engineering of Complex Distributed Computer Systems ». IEEE Concurrency 5, no 4 (octobre 1997) : 30–31. http://dx.doi.org/10.1109/mcc.1997.641624.

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36

Kesselmeier, Horst, Inga Tschiersch et Sebastian Kutscha. « The Re-Engineering of Complex Software Systems ». IFAC Proceedings Volumes 30, no 24 (septembre 1997) : 133–36. http://dx.doi.org/10.1016/s1474-6670(17)42241-5.

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Stoffels, Beate, Klaus Henning et Sebastian Kutscha. « The Re-Engineering of Complex Software Systems ». IFAC Proceedings Volumes 32, no 2 (juillet 1999) : 6490–95. http://dx.doi.org/10.1016/s1474-6670(17)57108-6.

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Schuh, G., et S. Gottschalk. « Production engineering for self-organizing complex systems ». Production Engineering 2, no 4 (5 août 2008) : 431–35. http://dx.doi.org/10.1007/s11740-008-0120-6.

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Svetinovic, Davor. « Strategic requirements engineering for complex sustainable systems ». Systems Engineering 16, no 2 (19 octobre 2012) : 165–74. http://dx.doi.org/10.1002/sys.21231.

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Banzhaf, W., et N. Pillay. « Why complex systems engineering needs biological development ». Complexity 13, no 2 (2007) : 12–21. http://dx.doi.org/10.1002/cplx.20199.

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Nikolaev, M. Y., et C. Fortin. « Systems thinking ontology of emergent properties for complex engineering systems ». Journal of Physics : Conference Series 1687 (novembre 2020) : 012005. http://dx.doi.org/10.1088/1742-6596/1687/1/012005.

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Farnell, G. P., A. J. Saddington et L. J. Lacey. « A new systems engineering structured assurance methodology for complex systems ». Reliability Engineering & ; System Safety 183 (mars 2019) : 298–310. http://dx.doi.org/10.1016/j.ress.2018.11.024.

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Osmundson, John S., Russell Gottfried, Chee Yang Kum, Lau Hui Boon, Lim Wei Lian, Poh Seng Wee Patrick et Tan Choo Thye. « Process modeling : A systems engineering tool for analyzing complex systems ». Systems Engineering 7, no 4 (2004) : 320–37. http://dx.doi.org/10.1002/sys.20012.

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44

Hodge, Richard J., Stephen Craig, Joseph M. Bradley et Charles B. Keating. « Systems Engineering and Complex Systems Governance – Lessons for Better Integration ». INCOSE International Symposium 29, no 1 (juillet 2019) : 421–33. http://dx.doi.org/10.1002/j.2334-5837.2019.00612.x.

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45

Herrera, Manuel, Marco Pérez-Hernández, Ajith Kumar Parlikad et Joaquín Izquierdo. « Multi-Agent Systems and Complex Networks : Review and Applications in Systems Engineering ». Processes 8, no 3 (8 mars 2020) : 312. http://dx.doi.org/10.3390/pr8030312.

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.
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Mukhopad, Yurii, Aleksandr Mukhopad et Daba Punsyk-Namzhilov. « Control automata of complex engineering real time systems ». Science Bulletin of the Novosibirsk State Technical University, no 1 (20 mars 2017) : 53–62. http://dx.doi.org/10.17212/1814-1196-2017-1-53-62.

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47

Baciu, Constantin, Gabriel Dragos Vasilescu et Tiberiu Attila Csaszar. « RESEARCH IN THE ENGINEERING OF COMPLEX SYSTEMS SAFETY ». Environmental Engineering and Management Journal 8, no 1 (2009) : 55–58. http://dx.doi.org/10.30638/eemj.2009.020.

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48

Evstifeev, Andrew, Margarita Zaeva, Svetlana Krasnikova et Victor Shuvalov. « Multi-Criteria Equipment Control in Complex Engineering Systems ». Asian Journal of Applied Sciences 8, no 1 (15 décembre 2014) : 86–91. http://dx.doi.org/10.3923/ajaps.2015.86.91.

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49

Voskoboynikov, Yuri, et Vasilisa Boeva. « Non-parametric identification algorithms for complex engineering systems ». Science Bulletin of the Novosibirsk State Technical University, no 4 (18 décembre 2020) : 47–64. http://dx.doi.org/10.17212/1814-1196-2020-4-47-64.

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In a practice, it often happens that complex engineering systems consist of several interconnected different-type simpler subsystems. An adequate model formulation for every subsystem is impractical due to the complexity of physical processes proceeding in the subsystem. In such cases, a non-detailed black-box model is commonly used. For stationary linear systems (or subsystems), the connection between an input and an output of the black-box is defined by the Volterra integral equation of the first kind with an undetermined difference kernel also known as an impulse response in the automatic control theory. It is necessary to evaluate the unknown impulse response to use the black-box model .This statement is a non-parametric identification problem. For complex systems, the problem needs to be solved both for a whole system and for every isolated subsystem that makes identification substantially complex. Formally, impulse response evaluation is a solution of the integral equation of the first kind for its kernel over registered noise-contaminated discrete input and output values. This problem is ill-posed because of possible solution instability regarding measurement noises in initial data. To find a unique stable solution regularizing algorithms are used, but specific input and output signals in impulse response identification experiments do not allow applying computational methods of these algorithms (system of linear equations or discrete Fourier transformation). In this paper, the authors propose two specific-considering identification algorithms for complex engineering systems. In these algorithms, smoothing cubic splines are used for stable calculation of first derivatives of identified system signals. The results of the complex “Heater-Blower-Room” system identification prove the efficiency of algorithms proposed.
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Delic, Kemal A. « Science and Engineering of Large-Scale Complex Systems ». Ubiquity 2005, March (mars 2005) : 2. http://dx.doi.org/10.1145/1066348.1066324.

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