Academic literature on the topic 'Complex system'

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Journal articles on the topic "Complex system"

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Chen, Juan. "Model Approximate Method of Complex System." International Journal of Modeling and Optimization 5, no. 2 (April 2015): 128–34. http://dx.doi.org/10.7763/ijmo.2015.v5.448.

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DAVE, JIGAR L. "Library as a Complex Open System." Paripex - Indian Journal Of Research 3, no. 2 (January 15, 2012): 160–61. http://dx.doi.org/10.15373/22501991/feb2014/51.

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Dudykevyc, Valeriy, Galyna Mykytyn, Taras Kret, and Andrii Rebets. "Security of Cyber-Physical Systems from Concept to Complex Information Security System." Advances in Cyber-Physical Systems 1, no. 2 (February 23, 2016): 67–75. http://dx.doi.org/10.23939/acps2016.02.067.

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Beale, Dean, Dorothy McKinney, Rudolph Oosthuizen, Gary Smith, and Michael D. Watson. "System Engineering Heuristics for Complex Systems." INCOSE International Symposium 32, no. 1 (July 2022): 293–312. http://dx.doi.org/10.1002/iis2.12932.

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Goncharenko, O. G., and A. V. Kravchuk. "BASIS OPERATUION OF ENTERPRISES PRODYCTION COMPLEX PENAL SYSTEM." SCIENTIFIC BULLETIN OF POLISSIA 2, no. 2(10) (2017): 52–56. http://dx.doi.org/10.25140/2410-9576-2017-2-2(10)-52-56.

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Velichkin, Valeriy A., and Marina V. Timoshenko. "Bank - Complex System." Journal of Automation and Information Sciences 51, no. 8 (2019): 16–30. http://dx.doi.org/10.1615/jautomatinfscien.v51.i8.20.

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Zhang, Hai-Feng, and Wen-Xu Wang. "Complex system reconstruction." Acta Physica Sinica 69, no. 8 (2020): 088906. http://dx.doi.org/10.7498/aps.69.20200001.

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Tokarev, Michael Valentinovich. "COMPLEX, MODEL, SYSTEM." European Science Review, no. 11-12 (2022): 10–13. http://dx.doi.org/10.29013/esr-22-11.12-10-13.

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Hjalmarsson, Håkan. "System Identification of Complex and Structured Systems." European Journal of Control 15, no. 3-4 (January 2009): 275–310. http://dx.doi.org/10.3166/ejc.15.275-310.

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Mirchandani, Chandru. "System Test Approach for Complex Software Systems." INSIGHT 24, no. 2 (July 2021): 32–37. http://dx.doi.org/10.1002/inst.12338.

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Dissertations / Theses on the topic "Complex system"

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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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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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Zhao, Liang. "System identification for complex financial system." Thesis, University of Sheffield, 2011. http://etheses.whiterose.ac.uk/14654/.

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The mam purpose of this thesis focuses on the investigation of major financial volatility models including the relevant mean model used in the context of volatility estimation, and the development of a systematic nonlinear identification methodology for these problems. Financial volatility is one of the key aspects in financial economics and volatility modelling involves both the mean process modelling, and the volatility process modelling. Although many volatility models have been derived to approximate the volatility process, linear mean models are almost always used and to the best of our knowledge there is no application of fitting the mean process using a nonlinear model with selected structure. Based on the fact that nonlinearity has been observed in many financial market return data sets, the Non linear AutoRegression Moving Average with eXogenous input (NARMAX) modelling methodology with the term selection algorithm Orthogonal Forward Regression (OFR) is proposed to approximate the nonlinear mean process during volatility modelling. However, the assumption of a constant variance is usually violated in financial market return data. A new Weighted OFR algorithm is therefore proposed to correct for the impact of heteroskedastic noise on the term selection of the nonlinear mean model based on the assumption that the variance process is modelled by a Generalized AutoRegressive Conditional Heteroskedastic (GARCH) model. Because the weights to use are unknown, an iterative refined procedure is developed to learn the weights and to simultaneously improve the parameter estimates of both the mean and the volatility models. New validation methods are proposed to validate the nonlinear selected mean model and the volatility model. During the validation, the assumptions associated with the mean model are tested using a correlation method and the assumptions of the volatility model are tested using a Brock-Dechert-Scheinkrnan (80S) independent and identically distributed (i.i.d.) testing method. The prediction performance of the mean and volatility models is evaluated using a hold out Cross Validation (CV)method. A departure in the prediction of the volatility for the linear mean model, when using nonlinear simulated data, is successfully identified by the new validation methods and the nonlinear selected mean model passes the test. Another application of the NARAMX model, in the very new field of modelling mortality rate, is introduced. A quadratic polynomial mortality rate model selected by the OFR algorithm is developed based on the LifeMetrics male deaths and exposures data for England & Wales from the Office of National Statistics. Comparing the long term prediction of the new model with the Cairns-Blake-Dowd (CSO) statistical mortality rate model indicates the better prediction performance of the quadratic polynomial models. A back-testing method is applied to indicate the robustness of the selected NARMAX type mortality rate models. The term selection, parameter estimation, validation methods and new identification procedures proposed in this thesis open a new gateway to apply the NARMAX modelling technique in the financial area, and for mortality rate modelling to provide a new empirical practice of the NARMAX modelling method.
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Luo, Dapeng. "SYSTEM IDENTIFICATION AND FAULT DETECTION OF COMPLEX SYSTEMS." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3583.

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The proposed research is devoted to devising system identification and fault detection approaches and algorithms for a system characterized by nonlinear dynamics. Mathematical models of dynamical systems and fault models are built based on observed data from systems. In particular, we will focus on statistical subspace instrumental variable methods which allow the consideration of an appealing mathematical model in many control applications consisting of a nonlinear feedback system with nonlinearities at both inputs and outputs. Different solutions within the proposed framework are presented to solve the system identification and fault detection problems. Specifically, Augmented Subspace Instrumental Variable Identification (ASIVID) approaches are proposed to identify the closed-loop nonlinear Hammerstein systems. Then fast approaches are presented to determine the system order. Hard-over failures are detected by order determination approaches when failures manifest themselves as rank deficiencies of the dynamical systems. Geometric interpretations of subspace tracking theorems are presented in this dissertation in order to propose a fault tolerance strategy. Possible fields of application considered in this research include manufacturing systems, autonomous vehicle systems, space systems and burgeoning bio-mechanical systems.
Ph.D.
Department of Mechanical, Materials and Aerospace Engineering;
Engineering and Computer Science
Mechanical Engineering
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Topaj, Dmitri. "Synchronization transitions in complex systems." Phd thesis, [S.l.] : [s.n.], 2001. http://pub.ub.uni-potsdam.de/2002/0006/topaj.pdf.

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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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Constantinou, Marios, Michael Gehde, and Ronald Dietz. "Emitter - Material – A complex system." Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-198230.

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Der Vortrag zeigt die Komplexität der Wechselwirkungen zwischen Infrarotstrahler und Werkstoff beim Infrarotschweißen von Kunststoffen auf. Hierfür werden die Haupteinflüsse auf die Strahler-Werkstoff-Wechselwirkungen beschrieben. Diese sind das Emissionsverhalten des Infrarotstrahlers und das Absorptionsverhalten des Kunststoffs. Der Einfluss der Infrarotstrahlerart (Quarzglasstrahler, Metallfolienstrahler) und von Füllstoffen (Ruß, Glasfasern) im Kunststoff wird näher betrachtet. Zudem enthält der Vortrag eine Empfehlung für die Vorgehensweise beim Infrarotschweißen von Kunststoffen, die Vor- und Nachteile des Fügeverfahrens und einen Einblick in aktuelle Forschungsaktivitäten auf dem Gebiet des Infrarotschweißens von Kunststoffen.
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Jaishankar, Gayatri, and Matthew Tolliver. "Navigating the Complex Healthcare System." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etsu-works/8872.

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Shone, N. "Detecting misbehaviour in a complex system-of-systems environment." Thesis, Liverpool John Moores University, 2014. http://researchonline.ljmu.ac.uk/4537/.

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Modern systems are becoming increasingly complex, integrated and distributed, in order to meet the escalating demands for functionality. This has given rise to concepts such as system-of-systems (SoS), which organise a myriad of independent component systems into a collaborative super-system, capable of achieving unmatchable levels of functionality. Despite its advantages, SoS is still an infantile concept with many outstanding security concerns, including the lack of effective behavioural monitoring. This can be largely attributed to its distributed, decentralised and heterogeneous nature, which poses many significant challenges. The uncertainty and dynamics of both the SoS’s structure and function poses further challenges to overcome. Due to the unconventional nature of a SoS, existing behavioural monitoring solutions are often inadequate as they are unable to overcome these challenges. This monitoring deficiency can result in the occurrence of misbehaviour, which is one of the most serious yet underestimated security threats facing SoSs and their components. This thesis presents a novel misbehaviour detection framework specifically developed for operation in a SoS environment. By combining the use of uniquely calculated behavioural threshold profiles and periodic threshold adaptation, the framework is able to cope with monitoring the dynamic behaviour and suddenly occurring changes that affect threshold reliability. The framework improves SoS contribution and monitoring efficiency by controlling monitoring observations using statecharts, which react to the level of behavioural threat perceived by the system. The accuracy of behavioural analysis is improved by using a novel algorithm to quantify detected behavioural abnormalities, in terms of their level of irregularity. The framework utilises collaborative behavioural monitoring to increase the accuracy of the behavioural analysis, and to combat the threat posed by training based attacks to the threshold adaptation process. The validity of the collaborative behavioural monitoring is assured by using the novel behavioural similarity assessment algorithm, which selects the most behaviourally appropriate SoS components to collaborate with. The proposed framework and its subsequent techniques are evaluated via numerous experiments. These examine both the limitations and relative merits when compared to monitoring solutions and techniques from similar research areas. The results of these conclude that the framework is able to offer misbehaviour monitoring in a SoS environment, with increased efficiency and reduced false positive rates, false negative rates, resource usage and run-time requirements.
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Aragon, Lasumar (Mark) R. "Technology insertion considerations for complex system of systems development." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://edocs.nps.edu/npspubs/scholarly/theses/2008/Sept/08Sep%5FAragon.pdf.

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Thesis (M.S. in Systems Engineering)--Naval Postgraduate School, September 2008.
Thesis Advisor(s): Paulo, Eugene. "September 2008." Description based on title screen as viewed on October 31, 2008. Includes bibliographical references (p. 63-64). Also available in print.
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Books on the topic "Complex system"

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Keating, Charles B., Polinpapilinho F. Katina, Charles W. Chesterman, and James C. Pyne, eds. Complex System Governance. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93852-9.

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Myers, Albert. Complex System Reliability. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-414-2.

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Golès, E. Complex Systems. Dordrecht: Springer Netherlands, 2001.

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

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Jamil, Tariq. Complex Binary Number System. India: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-0854-9.

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P, Murthy D. N., and Kobbacy Khairy A. H, eds. Complex system maintenance handbook. London: Springer, 2008.

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Hooker, C. A. Philosophy of complex systems. Oxford, UK: North Holland, 2011.

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Modeling complex systems. 2nd ed. New York: Springer, 2010.

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Modeling complex systems. New York: Springer, 2004.

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T, Ivancevic Tijana, and SpringerLink (Online service), eds. Complex Dynamics: Advanced System Dynamics in Complex Variables. Dordrecht: Springer, 2007.

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Book chapters on the topic "Complex system"

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Alfieri, Roberta, and Luciano Milanesi. "Complex System." In Encyclopedia of Systems Biology, 456. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1073.

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Jaradat, Raed M., Charles B. Keating, Fatine Elakramine, Safae El Amrani, and Larif Oumaima. "Systems Thinking for Complex System Governance." In Complex System Governance, 411–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93852-9_14.

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Castelle, Kaitlynn, Joseph M. Bradley, and Charles W. Chesterman. "Systems Theory for Complex System Governance." In Complex System Governance, 97–118. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93852-9_4.

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Calida, Behnido Y. "System Governance." In Complex System Governance, 69–96. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93852-9_3.

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Keating, Charles B. "Complex System Governance." In Complex System Governance, 151–86. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93852-9_6.

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Shultz, Thomas R., Scott E. Fahlman, Susan Craw, Periklis Andritsos, Panayiotis Tsaparas, Ricardo Silva, Chris Drummond, et al. "Complex Adaptive System." In Encyclopedia of Machine Learning, 194. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_147.

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Mobus, George E., and Michael C. Kalton. "Behavior: System Dynamics." In Understanding Complex Systems, 213–61. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1920-8_6.

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Fidanboy, Mahsa. "System." In Organizations and Complex Adaptive Systems, 1–17. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003182764-1.

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Mallick, Rajib B. "System of Systems." In System Dynamics for Complex Problems in Pavement Engineering, 111–17. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003345596-11.

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Holubec, Pavel. "Conceptualizing the Urban System as a System of Flows." In Understanding Complex Systems, 79–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30178-5_6.

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Conference papers on the topic "Complex system"

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Guillerm, R., H. Demmou, and N. Sadou. "Safety evaluation of complex system." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482461.

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Voss, Mark S., and Xin Feng. "Emergent system identification using particle swarm optimization." In Complex Adaptive Structures, edited by William B. Spillman, Jr. SPIE, 2001. http://dx.doi.org/10.1117/12.446767.

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Maleki-dizaji, Saeedeh, H. O. Nyongesa, and J. Siddiqqi. "Adaptive multi-agent system for information retrieval." In Complex Adaptive Structures, edited by William B. Spillman, Jr. SPIE, 2001. http://dx.doi.org/10.1117/12.446766.

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Boxer, Philip J., and Suzanne Garcia. "Enterprise architecture for complex system-of-systems contexts." In 2009 3rd Annual IEEE Systems Conference. IEEE, 2009. http://dx.doi.org/10.1109/systems.2009.4815807.

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Ali, Nauman bin, Kai Petersen, and Mika Mäntylä. "Testing highly complex system of systems." In the ACM-IEEE international symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2372251.2372290.

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Muranaka, T., Michio Tokuyama, Irwin Oppenheim, and Hideya Nishiyama. "A Molecular Dynamics Simulation of a Supercooled System with 3,200 Model Polymers." In COMPLEX SYSTEMS: 5th International Workshop on Complex Systems. AIP, 2008. http://dx.doi.org/10.1063/1.2897796.

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Nakagawa, Natsuko, Kaoru Ohno, Michio Tokuyama, Irwin Oppenheim, and Hideya Nishiyama. "Monte Carlo Simulation for Ternary System of Water∕Oil∕ABA Triblock Copolymers." In COMPLEX SYSTEMS: 5th International Workshop on Complex Systems. AIP, 2008. http://dx.doi.org/10.1063/1.2897844.

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Crisman, Mark V. "Integration of the immune system: a complex adaptive supersystem." In Complex Adaptive Structures, edited by William B. Spillman, Jr. SPIE, 2001. http://dx.doi.org/10.1117/12.446761.

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Meissner, Ken E. "Adaptive optical system for improved activation of PDT photosensitizers." In Complex Adaptive Structures. SPIE, 2001. http://dx.doi.org/10.1117/12.446776.

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Yamamoto, Tetsunori, Keigo Nishikawa, Ayumu Sugiyama, Acep Purqon, Taku Mizukami, Hideto Shimahara, Hidemi Nagao, et al. "Theoretical Study of Free Energy in Docking Stability of Azurin(II)-Cytochrome c[sub 551](II) Complex System." In COMPLEX SYSTEMS: 5th International Workshop on Complex Systems. AIP, 2008. http://dx.doi.org/10.1063/1.2897903.

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Reports on the topic "Complex system"

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Raychev, Nikolay. System architecture for maintenance of complex distributed systems. Web of Open Science, July 2020. http://dx.doi.org/10.37686/ser.v1i2.60.

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Kowalchuck, Michael, Siddhartha Singh, and Kathleen M. Carley. CORES - Complex Organizational Reasoning System. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada460017.

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Smart, J. Allison Cory. Dependency visualization for complex system understanding. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/80354.

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Blanchette, Jr, Crosson Stephen, Boehm Steven, and Barry. Evaluating the Software Design of a Complex System of Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada515880.

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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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Werner, Brad. Hierarchical Models of the Nearshore Complex System. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada435474.

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Petrie, Samuel, and Paul A. Peters. Implementation considerations in a Complex Adaptive System. Carleton University, September 2018. http://dx.doi.org/10.22215/sdhlab/2018.9.

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Carreau, Bernard. Transforming the Interagency System for Complex Operations. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada463738.

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Van Eeckhout, E., D. Roberts, R. Oakes, A. Shieh, W. Hardie, and P. Pope. Environmental management policy analysis using complex system simulation. Office of Scientific and Technical Information (OSTI), April 1999. http://dx.doi.org/10.2172/334233.

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