Academic literature on the topic 'Model-based systems e'
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Journal articles on the topic "Model-based systems e"
Benila S, Benila S., and Usha Bhanu N. Benila S. "Fog Managed Data Model for IoT based Healthcare Systems." 網際網路技術學刊 23, no. 2 (March 2022): 217–26. http://dx.doi.org/10.53106/160792642022032302003.
Full textWilliamson, Ron C. "Model-based Systems Engineering for Systems of Systems." INSIGHT 12, no. 4 (December 2009): 12–14. http://dx.doi.org/10.1002/inst.200912412.
Full textJoshi, Ravindra V., and Chandrashekhar N. ".i – A Complexity Theory based Platform for Model based System Engineering." Webology 19, no. 1 (January 20, 2022): 3348–57. http://dx.doi.org/10.14704/web/v19i1/web19220.
Full textCoghill, G. M. "Towards Model-based Methods for Developing Model-based Systems." International Journal of General Systems 33, no. 5 (October 2004): 485–504. http://dx.doi.org/10.1080/0308107042000202236.
Full textNISHIMURA, Hidekazu. "Systems Engineering and Model-Based Systems Engineering." Journal of the Society of Mechanical Engineers 119, no. 1177 (2016): 646–49. http://dx.doi.org/10.1299/jsmemag.119.1177_646.
Full textDjouab, Rachida, and Moncef Bari. "An ISO 9126 Based Quality Model for the e-Learning Systems." International Journal of Information and Education Technology 6, no. 5 (2016): 370–75. http://dx.doi.org/10.7763/ijiet.2016.v6.716.
Full textCalida, Behnido Y., Raed M. Jaradat, Sawsan Abutabenjeh, and Charles B. Keating. "Governance in systems of systems: a systems-based model." International Journal of System of Systems Engineering 7, no. 4 (2016): 235. http://dx.doi.org/10.1504/ijsse.2016.080313.
Full textCalida, Behnido Y., Raed M. Jaradat, Sawsan Abutabenjeh, and Charles B. Keating. "Governance in systems of systems: a systems-based model." International Journal of System of Systems Engineering 7, no. 4 (2016): 235. http://dx.doi.org/10.1504/ijsse.2016.10001152.
Full textLeitch, R., H. Freitag, G. Tornielli, and Q. Shen. "Composing Model-Based Diagnostic Systems." Integrated Computer-Aided Engineering 2, no. 3 (July 1, 1995): 203–17. http://dx.doi.org/10.3233/ica-1995-2304.
Full textFuchs, Joachim. "Model Based Systems Engineering Editorial." INSIGHT 18, no. 2 (August 2015): 9. http://dx.doi.org/10.1002/inst.12012.
Full textDissertations / Theses on the topic "Model-based systems e"
Flanagan, Genevieve (Genevieve Elise Cregar). "Key challenges to model-based design : distinguishing model confidence from model validation." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76492.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 93-97).
Model-based design is becoming more prevalent in industry due to increasing complexities in technology while schedules shorten and budgets tighten. Model-based design is a means to substantiate good design under these circumstances. Despite this, organizations often have a lack of confidence in the use of models to make critical decisions. As a consequence they often invest heavily in expensive test activities that may not yield substantially new or better information. On the other hand, models are often used beyond the bounds within which they had been previously calibrated and validated and their predictions in the new regime may be substantially in error and this can add substantial risk to a program. This thesis seeks to identify factors that cause either of these behaviors. Eight factors emerged as the key variables to misaligned model confidence. These were found by studying three case studies to setup the problem space. This was followed by a review of the literature with emphasis on model validation and assessment processes to identify remaining gaps. These gaps include proper model validation processes, limited research from the perspective of the decision-maker, and lack of understanding of the impact of contextual variables surrounding a decision. The impact these eight factors have on model confidence and credibility was tested using a web-based experiment that included a simple model of a catapult and varying contextual details representing the factors. In total 252 respondents interacted with the model and made a binary decision on a design problem to provide a measure for model confidence. Results from the testing showed several factors proved to cause an outright change in model confidence. One factor, a representation of model uncertainty, did not result in any differences to model confidence despite support from the literature suggesting otherwise. Findings such as these were used to gain additional insights and recommendations to address the problem of misaligned model confidence. Recommendations included system-level approaches, improved quality of communication, and use of decision analysis techniques. Applying focus in these areas can help to alleviate pressures from the contextual factors involved in the decision-making process. This will allow models to be used more effectively thereby supporting model-based design efforts.
by Genevieve Flanagan.
S.M.in Engineering and Management
Kinder, Andrew M. K. "A model-based approach to System of Systems risk management." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/27553.
Full textLondon, Brian (Brian N. ). "A model-based systems engineering framework for concept development." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/70822.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 148-151).
The development of increasingly complex, innovative systems under greater constraints has been the trend over the past several decades. In order to be successful, organizations must develop products that meet customer needs more effectively than the competitors' alternatives. The development of these concepts is based on a broad set of stakeholder objectives, from which alternative designs are developed and compared. When properly performed, this process helps those involved understand the benefits and drawbacks of each option. This is crucial as firms need to effectively and quickly explore many concepts, and easily determine those most likely to succeed. It is generally accepted that a methodical design approach leads to the reduction in design flaws and cost over a product's life cycle. Several techniques have been developed to facilitate these efforts. However, the traditional tools and work products are isolated, and require diligent manual inspection. It is expected that the effectiveness of the high-level product design and development will improve dramatically through the adoption of computer based modeling and simulation. This emerging capability can mitigate the challenges and risks imposed by complex systems by enforcing rigor and precision. Model-based systems engineering (MBSE) is a methodology for designing systems using interconnected computer models. The recent proliferation of MBSE is evidence of its ability to improve the design fidelity and enhance communication among development teams. Existing descriptions of leveraging MBSE for deriving requirements and system design are prevalent. However, very few descriptions of model-based concept development have been presented. This may be due to the lack of MBSE methodologies for performing concept development. Teams that attempt a model-based approach without well defined, structured strategy are often unsuccessful. However, when MBSE is combined with a clear methodology, designs can be more efficiently generated and evaluated. While it may not be feasible to provide a "standard" methodology for concept development, a framework is envisioned that incorporates a variety of methods and techniques. This thesis proposes such a framework and presents an example based on a simulated concept development effort.
by Brian London.
S.M.in Engineering and Management
Quezada, Gomez Juan Manuel. "Model-based guidelines for automotive electronic systems software development." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100383.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 96-98).
The automobile innovation transformed the human life style ever since its introduction to the public, and for over the last one hundred years incumbent technologies have been adopted to improve its performance characteristics. Yet, we need a holistic approach to understand that automobiles shifted from being a mere assembly of mechanical parts to a multidisciplinary system that form the modern automobile. Thanks to the increased use of electronics and software in automobiles, consumers benefit from better gas mileage, more amenities and features, such as comfort, driving assistance, and entertainment. At the same time, stability and performance of automobiles as systems have been facing deterioration, and eventually vehicle owners are finding that features and functions become inoperative over time, causing frustration, loss of time and money. Reports of problems experienced by vehicle owners have stem from casual factors of system defects that model-based systems engineering can reduce or eliminate. This research presents a model-based systems engineering approach to an automobile electronic system design. The work is founded on a comprehensive OPM model and engineering guidelines for electronic control module software design. The purpose of the framework developed in this study is to support development of complex vehicle software that allows flexibility for changing features and creating new ones, and enables software developers to pinpoint systemic faults quicker and at earlier lifecycle phases, reducing rework, increasing safety, and providing for more effective resolution of such problems.
by Juan Manuel Quezada Gomez.
S.M. in Engineering and Management
Griesebner, Klaus. "Model-based Controller Development." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-34929.
Full textTorres, Edwin Ross. "Team Collaboration as a System of Systems Agent-Based Model." Thesis, The George Washington University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10743109.
Full textThere is a current need to study and understand the behaviors and characteristics of systems of systems. Studying a single system is relatively straightforward when compared to studying a system of systems. A system of systems has unique characteristics that distinguish it from a single system. The additional complexity in a system of systems leads to complicated models and advanced computer simulations. Although modeling and simulation are popular methods for researching a single system, there have been fewer attempts at modeling and simulating systems of systems. Agent-based modeling is an effective approach for researching systems of systems, but validation of agent-based models is difficult, especially if data are not available. Finally, communicating an agent-based model is more difficult than communicating an analytical model because analytical models use familiar mathematical notation. The purpose of this research is to increase the knowledge of system of systems engineering by developing, executing, and analyzing an agent-based model of team collaboration in a real-world, operational system of systems. This research has several goals. The first goal is to address a current need to increase the understanding of the behaviors and characteristics of systems of systems. More specifically, this research aims to model and explain how collaboration and integration in a real-world system of systems affect the achievability of the overall goal of the system of systems. There is an emphasis on the operations and integration of heterogeneous component systems of the collaborative system of systems. This includes understanding the behaviors, characteristics, and interactions among the component systems. The second goal is to develop and thoroughly document a new, repeatable agent-based model of the real-world system of systems. The final goal is to develop a useful tool for understanding and predicting the achievability of the overall goal of the system of systems. Specifically, this research explores team collaboration in a National Basketball Association offensive lineup. This lineup possesses the necessary characteristics to categorize it as a system of systems. Players are the individual, heterogeneous component systems that belong to and operate in the system of systems. This research introduces a new agent-based model and simulation to understand how the individual component systems affect the achievability of the system of systems goal. The NetLogo modeling platform provides an effective environment for executing the model. Data for initialization and validation come from the National Basketball Association. Results show that the overall goal of scoring is an emergent behavior of the collaborative system of systems. Top performing combinations of lineups and collaboration levels emerge. The heterogeneity and interactions of the component systems affect the achievability of the overall goal in different ways. Specific combinations of the collaboration levels and integration of individual component systems determine the scoring output. Observing the component systems individually offers no explanation for the achievability of the overall goal. Instead, it is necessary to view the component systems as a whole. Finally, the verified and validated agent-based model of the offensive lineup contributes to system of systems research, and it is an effective tool for understanding and exploring offensive lineups in the National Basketball Association.
Ramos, Ana Luísa Ferreira Andrade. "Model-based systems engineering: a system for traffic & environment." Doctoral thesis, Universidade de Aveiro, 2011. http://hdl.handle.net/10773/7273.
Full textThe contemporary world is crowded of large, interdisciplinary, complex systems made of other systems, personnel, hardware, software, information, processes, and facilities. The Systems Engineering (SE) field proposes an integrated holistic approach to tackle these socio-technical systems that is crucial to take proper account of their multifaceted nature and numerous interrelationships, providing the means to enable their successful realization. Model-Based Systems Engineering (MBSE) is an emerging paradigm in the SE field and can be described as the formalized application of modelling principles, methods, languages, and tools to the entire lifecycle of those systems, enhancing communications and knowledge capture, shared understanding, improved design precision and integrity, better development traceability, and reduced development risks. This thesis is devoted to the application of the novel MBSE paradigm to the Urban Traffic & Environment domain. The proposed system, the GUILTE (Guiding Urban Intelligent Traffic & Environment), deals with a present-day real challenging problem “at the agenda” of world leaders, national governors, local authorities, research agencies, academia, and general public. The main purposes of the system are to provide an integrated development framework for the municipalities, and to support the (short-time and real-time) operations of the urban traffic through Intelligent Transportation Systems, highlighting two fundamental aspects: the evaluation of the related environmental impacts (in particular, the air pollution and the noise), and the dissemination of information to the citizens, endorsing their involvement and participation. These objectives are related with the high-level complex challenge of developing sustainable urban transportation networks. The development process of the GUILTE system is supported by a new methodology, the LITHE (Agile Systems Modelling Engineering), which aims to lightening the complexity and burdensome of the existing methodologies by emphasizing agile principles such as continuous communication, feedback, stakeholders involvement, short iterations and rapid response. These principles are accomplished through a universal and intuitive SE process, the SIMILAR process model (which was redefined at the light of the modern international standards), a lean MBSE method, and a coherent System Model developed through the benchmark graphical modeling languages SysML and OPDs/OPL. The main contributions of the work are, in their essence, models and can be settled as: a revised process model for the SE field, an agile methodology for MBSE development environments, a graphical tool to support the proposed methodology, and a System Model for the GUILTE system. The comprehensive literature reviews provided for the main scientific field of this research (SE/MBSE) and for the application domain (Traffic & Environment) can also be seen as a relevant contribution.
O mundo contemporâneo é caracterizado por sistemas de grande dimensão e de natureza marcadamente complexa, sócio-técnica e interdisciplinar. A Engenharia de Sistemas (ES) propõe uma abordagem holística e integrada para desenvolver tais sistemas, tendo em consideração a sua natureza multifacetada e as numerosas inter-relações que advêm de uma quantidade significativa de diferentes pontos de vista, competências, responsabilidades e interesses. A Engenharia de Sistemas Baseada em Modelos (ESBM) é um paradigma emergente na área da ES e pode ser descrito como a aplicação formal de princípios, métodos, linguagens e ferramentas de modelação ao ciclo de vida dos sistemas descritos. Espera-se que, na próxima década, a ESBM desempenhe um papel fundamental na prática da moderna Engenharia de Sistemas. Esta tese é dedicada à aplicação da ESBM a um desafio real que constitui uma preocupação do mundo actual, estando “na agenda” dos líderes mundiais, governantes nacionais, autoridades locais, agências de investigação, universidades e público em geral. O domínio de aplicação, o Tráfego & Ambiente, caracteriza-se por uma considerável complexidade e interdisciplinaridade, sendo representativo das áreas de interesse para a ES. Propõe-se um sistema (GUILTE) que visa dotar os municípios de um quadro de desenvolvimento integrado para adopção de Sistemas de Transporte Inteligentes e apoiar as suas operações de tráfego urbano, destacando dois aspectos fundamentais: a avaliação dos impactos ambientais associados (em especial, a poluição atmosférica e o ruído) e a divulgação de informação aos cidadãos, motivando o seu envolvimento e participação. Estes objectivos relacionam-se com o desafio mais abrangente de desenvolver redes de transporte urbano sustentáveis. O processo de desenvolvimento do sistema apoia-se numa nova metodologia (LITHE), mais ágil, que enfatiza os princípios de comunicação contínua, feedback, participação e envolvimento dos stakeholders, iterações curtas e resposta rápida. Estes princípios são concretizados através de um processo de ES universal e intuitivo (redefinido à luz dos padrões internacionais), de um método simples e de linguagens gráficas de modelação de referência (SysML e OPDs/OPL). As principais contribuições deste trabalho são, na sua essência, modelos: um modelo revisto para o processo da ES, uma metodologia ágil para ambientes de desenvolvimento baseados em modelos, uma ferramenta gráfica para suportar a metodologia proposta e o modelo de um sistema para as operações de tráfego & ambiente num contexto urbano. Contribui-se ainda com uma cuidada revisão bibliográfica para a principal área de investigação (ES/ESBM) e para o domínio de aplicação (Tráfego & Ambiente).
Ghosheh, Emad. "A novel model for improving the maintainability of web-based systems." Thesis, University of Westminster, 2010. https://westminsterresearch.westminster.ac.uk/item/905xy/a-novel-model-for-improving-the-maintainability-of-web-based-systems.
Full textWilmer, Greg. "OPM model-based integration of multiple data repositories." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100389.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (page 90).
Data integration is at the heart of a significant portion of current information system implementations. As companies continue to move towards a diverse, growing set of Commercial Off the Shelf (COTS) applications to fulfill their information technology needs, the need to integrate data between them continues to increase. In addition, these diverse application portfolios are becoming more geographically dispersed as more software is provided using the Software as a Service (SaaS) model, and companies continue the pattern of moving their internal data centers to cloud-based computing. As the growth of data integration activities continues, several prominent data integration patterns have emerged, and commercial software packages have been created that covers each of the patterns below: 1. Bulk and/or batch data extraction and delivery (ETL, ELT, etc.); 2. Messaging / Message-oriented data movement; 3. Granular, low-latency data capture and propagation (data synchronization). As the data integration landscape within an organization, and between organizations, becomes larger and more complex, opportunities exist to streamline aspects of the data integrating process not covered by current toolsets including: 1. Extensibility by third parties. Many COTS integration toolsets today are difficult if not impossible to extend by third parties; 2. Capabilities to handle different types of structured data from relational to hierarchical to graph models; 3. Enhanced modeling capabilities through use of data visualization and modeling techniques and tools; 4. Capabilities for automated unit testing of integrations; 5. A unified toolset that covers all three patterns, allowing an enterprise to implement the pattern that best suites business needs for the specific scenario; 6. A Web-based toolset that allows configuration, management and deployment via Web-based technologies allowing geographical indifference for application deployment and integration. While discussing these challenges with a large Fortune 500 client, they expressed the need for an enhanced data integration toolset that would allow them to accomplish such tasks. Given this request, the Object Process Methodology (OPM) and the Opcat toolset were used to begin design of a data integration toolset that could fulfill these needs. As part of this design process, lessons learned covering both the use of OPM in software design projects as well as enhancement requests for the Opcat toolset were documented.
by Greg Wilmer.
S.M. in Engineering and Management
Wright, Lynda. "Model-Based Systems Engineering: Status and Challenges." Digital Commons at Loyola Marymount University and Loyola Law School, 2014. https://digitalcommons.lmu.edu/etd/438.
Full textBooks on the topic "Model-based systems e"
Micouin, Patrice. Model-Based Systems Engineering. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118579435.
Full textBorky, John M., and Thomas H. Bradley. Effective Model-Based Systems Engineering. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-95669-5.
Full textModel-based testing for embedded systems. Boca Raton, FL: CRC Press, 2011.
Find full textNicolescu, G. Model-based design for embedded systems. Boca Raton, FL: CRC Press, 2010.
Find full textMadni, Azad M., Norman Augustine, and Michael Sievers, eds. Handbook of Model-Based Systems Engineering. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-27486-3.
Full textLam, Hak-Keung. Polynomial Fuzzy Model-Based Control Systems. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-34094-4.
Full textChamberlain, Roger, Martin Edin Grimheden, and Walid Taha, eds. Cyber Physical Systems. Model-Based Design. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41131-2.
Full textPohl, Klaus, Harald Hönninger, Reinhold Achatz, and Manfred Broy, eds. Model-Based Engineering of Embedded Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34614-9.
Full textBroy, Manfred, Bengt Jonsson, Joost-Pieter Katoen, Martin Leucker, and Alexander Pretschner, eds. Model-Based Testing of Reactive Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/b137241.
Full textChamberlain, Roger, Walid Taha, and Martin Törngren, eds. Cyber Physical Systems. Model-Based Design. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23703-5.
Full textBook chapters on the topic "Model-based systems e"
Micouin, Patrice. "Technological Systems." In Model-Based Systems Engineering, 25–40. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118579435.ch2.
Full textMicouin, Patrice. "Knowledge Systems." In Model-Based Systems Engineering, 41–58. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118579435.ch3.
Full textWillems, Jan C. "Linear Systems in Discrete Time." In Model-Based Control:, 3–12. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0895-7_1.
Full textMicouin, Patrice. "General Systems Theory." In Model-Based Systems Engineering, 1–24. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118579435.ch1.
Full textGiere, Ronald N. "Models as Parts of Distributed Cognitive Systems." In Model-Based Reasoning, 227–41. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0605-8_13.
Full textMicouin, Patrice. "Semiotic Systems and Models." In Model-Based Systems Engineering, 59–75. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118579435.ch4.
Full textRobert, Thomas, and Jérôme Hugues. "Model-Based Analysis." In Embedded Systems, 251–64. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118569535.ch12.
Full textBoniol, Frederic, Philippe Dhaussy, Luka Le Roux, and Jean-Charles Roger. "Model-Based Analysis." In Embedded Systems, 157–83. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118569535.ch8.
Full textKahrs, Olaf, Marc Brendel, Claas Michalik, and Wolfgang Marquardt. "Incremental Identification of Hybrid Models of Dynamic Process Systems." In Model-Based Control:, 185–202. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0895-7_11.
Full textScherer, Carsten W. "Robust Controller Synthesis is Convex for Systems without Control Channel Uncertainties." In Model-Based Control:, 13–30. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0895-7_2.
Full textConference papers on the topic "Model-based systems e"
Donghun Yoon. "A generic conceptual model and actual systems of IC-Card system for model-based systems engineering." In 2009 International Conference on Model-Based Systems Engineering (MBSE). IEEE, 2009. http://dx.doi.org/10.1109/mbse.2009.5031722.
Full textDori, Dov, Richard Martin, and Alex Blekhman. "Model-based meta-standardization." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482321.
Full textTellioglu, Hilda. "Practicing modelling in manufacturing." In 2009 International Conference on Model-Based Systems Engineering. IEEE, 2009. http://dx.doi.org/10.1109/mbse.2009.5031723.
Full textYaroker, Yevgeny, Valeriya Perelman, and Dov Dori. "OPM model based simulation environment for systems engineering conceptualization phase." In 2009 International Conference on Model-Based Systems Engineering (MBSE). IEEE, 2009. http://dx.doi.org/10.1109/mbse.2009.5031715.
Full textGrobshtein, Yariv, and Dov Dori. "Creating SysML views from an OPM model." In 2009 International Conference on Model-Based Systems Engineering (MBSE). IEEE, 2009. http://dx.doi.org/10.1109/mbse.2009.5031718.
Full textCardei, Ionut, Mihai Fonoage, and Ravi Shankar. "Model Based Requirements Specification and Validation for Component Architectures." In 2008 2nd Annual IEEE Systems Conference. IEEE, 2008. http://dx.doi.org/10.1109/systems.2008.4519001.
Full textSoyler, Asli, and Serge Sala-Diakanda. "A model-based systems engineering approach to capturing disaster management systems." In 2010 4th Annual IEEE Systems Conference. IEEE, 2010. http://dx.doi.org/10.1109/systems.2010.5482340.
Full textRhodes, Donna H., and Adam M. Ross. "Anticipatory capacity: Leveraging model-based approaches to design systems for dynamic futures." In 2009 International Conference on Model-Based Systems Engineering (MBSE). IEEE, 2009. http://dx.doi.org/10.1109/mbse.2009.5031719.
Full textFoustok, Mohamad. "Experiences in Large-Scale, Component Based, Model-Driven Software Development." In 2007 1st Annual IEEE Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/systems.2007.374657.
Full textMaraee, Azzam, and Mira Balaban. "Efficient recognition of finite satisfiability in UML class diagrams: Strengthening by propagation of disjoint constraints." In 2009 International Conference on Model-Based Systems Engineering (MBSE). IEEE, 2009. http://dx.doi.org/10.1109/mbse.2009.5031714.
Full textReports on the topic "Model-based systems e"
Blackburn, Mark. Introducing Model Based Systems Engineering Transforming System Engineering through Model-Based Systems Engineering. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada605264.
Full textBlackburn, Mark, Rob Cloutier, Gary Witus, and Eirik Hole. Introducing Model-Based System Engineering Transforming System Engineering through Model-Based Systems Engineering. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada603095.
Full textHamscher, Walter C. Model-Based Troubleshooting of Digital Systems. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada201041.
Full textMarzouk, Youssef M., Chi Feng, and Xun Huan. Model-Based Optimal Experimental Design for Complex Physical Systems. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ada627240.
Full textNoonan, Nicholas James. Product Lifecycle Management Architecture: A Model Based Systems Engineering Analysis. Office of Scientific and Technical Information (OSTI), July 2015. http://dx.doi.org/10.2172/1191879.
Full textDabrowski, Christopher, Kevin L. Mills, and Stephen Quirolgico. A model-based analysis of first-generation service discovery systems. Gaithersburg, MD: National Institute of Standards and Technology, 2005. http://dx.doi.org/10.6028/nist.sp.500-260.
Full textCarroll, Edward Ralph, and Robert Joseph Malins. Systematic Literature Review: How is Model-Based Systems Engineering Justified?. Office of Scientific and Technical Information (OSTI), March 2016. http://dx.doi.org/10.2172/1561164.
Full textHansson, Joergen, Peter H. Feiler, and John Morley. Building Secure Systems using Model-Based Engineering and Architectural Models. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada632581.
Full textDeLoach, Scott A., and Robby. Organization-based Model-driven Development of High-assurance Multiagent Systems. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada586694.
Full textMaiden, Wendy M. DualTrust: A Trust Management Model for Swarm-Based Autonomic Computing Systems. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/1021296.
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