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Опубліковано: 14 березня 2023

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Статті в журналах з теми "Model Based Systems Engineering Approach"

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Živčák, Jozef, Tatiana Kelemenová, Michal Kelemen, and Vladislav Maxim. "Model-based Approach to Development of Engineering Systems." Acta Mechanica Slovaca 17, no. 3 (October 31, 2013): 56–62. http://dx.doi.org/10.21496/ams.2013.033.

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Friedland, Brittany, Robert Malone, and John Herrold. "Systems Engineering a Model Based Systems Engineering Tool Suite: The Boeing Approach." INCOSE International Symposium 26, no. 1 (July 2016): 386–98. http://dx.doi.org/10.1002/j.2334-5837.2016.00167.x.

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3

Ramos, A. L., J. V. Ferreira, and J. Barcelo. "Model-Based Systems Engineering: An Emerging Approach for Modern Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, no. 1 (January 2012): 101–11. http://dx.doi.org/10.1109/tsmcc.2011.2106495.

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Zhan, Guoxiong, Bingfeng Ge, Minghao Li, and Kewei Yang. "A Data-Centric Approach for Model-Based Systems Engineering." Journal of Systems Science and Information 3, no. 6 (December 25, 2015): 549–60. http://dx.doi.org/10.1515/jssi-2015-0549.

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Анотація:
Abstract A data-centric approach is proposed to facilitate the design and analysis of challenging complex systems and address the problems of currently existing model-based systems engineering (MBSE) methodologies. More specifically, based on three core steps of current MBSE methodologies, a high-level data meta-model, depicting the semantic relationships of high-level data concepts, is first presented to guide the data modeling for systems engineering (SE). Next, with respect to the six high-level data concepts, the data elements are collected as the modeling primitives to construct static and/or executable models, which can also act as a common and consistent data dictionary for SE. Then, the mapping associations amongst core data elements are established to associate the model elements in different steps and achieve the requirement traceability matrix. Finally, the feasibility of the proposed approach is demonstrated with an illustrative example.
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5

Holt, Jon, Simon Perry, Richard Payne, Jeremy Bryans, Stefan Hallerstede, and Finn Overgaard Hansen. "A Model-Based Approach for Requirements Engineering for Systems of Systems." IEEE Systems Journal 9, no. 1 (March 2015): 252–62. http://dx.doi.org/10.1109/jsyst.2014.2312051.

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6

WANG, Haoqi. "Model-based Axiomatic Design Approach for Complex Engineering Systems." Journal of Mechanical Engineering 54, no. 7 (2018): 184. http://dx.doi.org/10.3901/jme.2018.07.184.

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McDermott, Tom, and Molly Nadolski. "Integrated Community Resilience, A Model Based Systems Engineering Approach." INCOSE International Symposium 26, no. 1 (July 2016): 205–19. http://dx.doi.org/10.1002/j.2334-5837.2016.00155.x.

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Friedland, Brittany, John Herrold, Glendora Ferguson, and Robert Malone. "Conducting a Model Based Systems Engineering Tool Trade Study Using a Systems Engineering Approach." INCOSE International Symposium 27, no. 1 (July 2017): 1087–99. http://dx.doi.org/10.1002/j.2334-5837.2017.00414.x.

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9

Neureiter, Christian, and Christoph Binder. "A Domain-Specific, Model Based Systems Engineering Approach for Cyber-Physical Systems." Systems 10, no. 2 (March 26, 2022): 42. http://dx.doi.org/10.3390/systems10020042.

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Анотація:
Model Based Systems Engineering as a scientific discipline tries to address the increasing complexity of today’s cyber-physical systems by utilizing different kinds of models. In practical application, however, this approach is often constrained to SysML-based object modeling. Even though this appears to be a suitable approach for dealing with complexity, various restrictions limit stakeholder acceptance. Considering scientific discussions in the context of modeling shows two different schools of thought. On the one hand, arguments for more formalized and rigorous concepts can be found, where on the other hand, the need for more stakeholder-oriented and easier-to-understand concepts is postulated. As both are reasonable, the question of integration arises. To address this aspect, we developed the concept of Domain Specific Systems Engineering. Our research in this field lasted for nearly a decade, and different aspects have been investigated. This paper contributes a summary of the overall approach that integrates the various aspects investigated so far. Thus, the underlying concepts are explained, and the corresponding modeling stack and tool-chain are described in more detail. Further, the practical experiences from various case studies are summarized, and identified shortcomings are discussed.
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Beery and Paulo. "Application of Model-Based Systems Engineering Concepts to Support Mission Engineering." Systems 7, no. 3 (September 4, 2019): 44. http://dx.doi.org/10.3390/systems7030044.

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Анотація:
This paper presents an approach to the utilization of model-based systems engineering (MBSE) early in the system lifecycle, which focuses on early identification of desirable system characteristics to support mission engineering (ME). The paper relies on the definition of an analysis approach and the associated mapping of architectural products. The analysis strategy focuses on integration of the results of operational simulations and system synthesis models through tradespace visualization. The architectural mapping presents the association of Systems Modeling Language (SysML) products to the analysis strategy. The coordination of these elements is presented as a demonstration of the role that MBSE concepts can play in support of ME. The approach is demonstrated through a case study analysis of a conceptual mine warfare system conducting mine countermeasure operations.
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Дисертації з теми "Model Based Systems Engineering Approach"

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Pastrana, John. "Model-Based Systems Engineering Approach to Distributed and Hybrid Simulation Systems." Doctoral diss., University of Central Florida, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6336.

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Анотація:
INCOSE defines Model-Based Systems Engineering (MBSE) as "the formalized application of modeling to support system requirements, design, analysis, verification, and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases." One very important development is the utilization of MBSE to develop distributed and hybrid (discrete-continuous) simulation modeling systems. MBSE can help to describe the systems to be modeled and help make the right decisions and partitions to tame complexity. The ability to embrace conceptual modeling and interoperability techniques during systems specification and design presents a great advantage in distributed and hybrid simulation systems development efforts. Our research is aimed at the definition of a methodological framework that uses MBSE languages, methods and tools for the development of these simulation systems. A model-based composition approach is defined at the initial steps to identify distributed systems interoperability requirements and hybrid simulation systems characteristics. Guidelines are developed to adopt simulation interoperability standards and conceptual modeling techniques using MBSE methods and tools. Domain specific system complexity and behavior can be captured with model-based approaches during the system architecture and functional design requirements definition. MBSE can allow simulation engineers to formally model different aspects of a problem ranging from architectures to corresponding behavioral analysis, to functional decompositions and user requirements (Jobe, 2008).
Ph.D.
Doctorate
Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering
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2

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.

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Анотація:
The failure of many System of Systems (SoS) enterprises can be attributed to the inappropriate application of traditional Systems Engineering (SE) processes within the SoS domain, because of the mistaken belief that a SoS can be regarded as a single large, or complex, system. SoS Engineering (SoSE) is a sub-discipline of SE; Risk Management and Modelling and Simulation (M&S) are key areas within SoSE, both of which also lie within the traditional SE domain. Risk Management of SoS requires a different approach to that currently taken for individual systems; if risk is managed for each component system then it cannot be assumed that the aggregated affect will be to mitigate risk at the SoS level. A literature review was undertaken examining three themes: (1) SoS Engineering (SoSE), (2) M&S and (3) Risk. Theme 1 of the literature provided insight into the activities comprising SoSE and its difference from traditional SE with risk management identified as a key activity. The second theme discussed the application of M&S to SoS, providing an output, which supported the identification of appropriate techniques and concluding that, the inherent complexity of a SoS required the use of M&S in order to support SoSE activities. Current risk management approaches were reviewed in theme 3 as well as the management of SoS risk. Although some specific examples of the management of SoS risk were found, no mature, general approach was identified, indicating a gap in current knowledge. However, it was noted most of these examples were underpinned by M&S approaches. It was therefore concluded a general approach SoS risk management utilising M&S methods would be of benefit. In order to fill the gap identified in current knowledge, this research proposed a new model based approach to Risk Management where risk identification was supported by a framework, which combined SoS system of interest dimensions with holistic risk types, where the resulting risks and contributing factors are captured in a causal network. Analysis of the causal network using a model technique selection tool, developed as part of this research, allowed the causal network to be simplified through the replacement of groups of elements within the network by appropriate supporting models. The Bayesian Belief Network (BBN) was identified as a suitable method to represent SoS risk. Supporting models run in Monte Carlo Simulations allowed data to be generated from which the risk BBNs could learn, thereby providing a more quantitative approach to SoS risk management. A method was developed which provided context to the BBN risk output through comparison with worst and best-case risk probabilities. The model based approach to Risk Management was applied to two very different case studies: Close Air Support mission planning and the Wheat Supply Chain, UK National Food Security risks, demonstrating its effectiveness and adaptability. The research established that the SoS SoI is essential for effective SoS risk identification and analysis of risk transfer, effective SoS modelling requires a range of techniques where suitability is determined by the problem context, the responsibility for SoS Risk Management is related to the overall SoS classification and the model based approach to SoS risk management was effective for both application case studies.
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Yacinthe, Samuel. "System Safety Development of a Performance PHEV Through a Model-Based Systems Engineering Approach." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469025640.

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FERRETTO, DAVIDE. "Innovative Model Based Systems Engineering approach for the design of hypersonic transportation systems." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2839867.

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Kerns, Corey Michael. "Naval Ship Design and Synthesis Model Architecture Using a Model-Based Systems Engineering Approach." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/32459.

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Анотація:
The Concept and Requirements Exploration process used at Virginia Tech is based on a Multi-Objective Optimization approach that explores the design space to produce a Non-Dominated set of ship design solutions ranked objectively by Cost, Risk, and Effectiveness. Prior research and effort has also been made to leverage the validation and verification of the U.S. Navyâ s ship synthesis design tool, ASSET, into the Virginia Tech Ship Synthesis Model. This thesis applies Design Structure Matrix theory to analyze and optimize the ASSET synthesis process by reducing or removing the feedback dependencies that require the iterative convergence process. This optimized ASSET synthesis process is used as the basis to develop a new Simplified Ship Synthesis Model (SSSM) using Commercial Off-The-Shelf (COTS) software, ASSET Response Surface Models (RSMs) and simplified parametric equations to build the individual synthesis modules. The current method of calculating an Overall Measure of Effectiveness (OMOE) used at Virginia Tech is based on expert opinion and pairwise comparison. This thesis researches methods for building a Design Reference Mission (DRM) composed of multiple operational situations (OpSits) required by the shipâ s mission. The DRM is defined using a Model Based Systems Engineering (MBSE) approach and an overall Ship Design System Architecture to define and understand the relationships between various aspects of the ship design. The system architecture includes the DRM and enables the development of Operational Effectiveness Models (OEMs) as an alternative to an expert opinion-based OMOE. The system architecture also provides the means for redefining and optimizing the entire ship design process by capturing the entire process and all related data into a single repository. This thesis concludes with a preliminary assessment of the utility of these various system engineering tools to the naval ship design process.
Master of Science
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Herzig, Sebastian J. I. "A Bayesian learning approach to inconsistency identification in model-based systems engineering." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53576.

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Анотація:
Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encoded in the various models are frequently observed. The challenge is to identify such inconsistencies in an automated fashion. In this research, a probabilistic (Bayesian) approach to abductive reasoning about the existence of specific types of inconsistencies and, in the process, semantic overlaps (relationships and dependencies) in sets of heterogeneous models is presented. A prior belief about the manifestation of a particular type of inconsistency is updated with evidence, which is collected by extracting specific features from the models by means of pattern matching. Inference results are then utilized to improve future predictions by means of automated learning. The effectiveness and efficiency of the approach is evaluated through a theoretical complexity analysis of the underlying algorithms, and through application to a case study. Insights gained from the experiments conducted, as well as the results from a comparison to the state-of-the-art have demonstrated that the proposed method is a significant improvement over the status quo of inconsistency identification in MBSE.
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Raper, Adrian. "An expert systems approach to model based signal processing of shock phenomena." Thesis, University of Southampton, 1988. https://eprints.soton.ac.uk/52268/.

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Expert systems have been proposed as a means of storing and applying a human expert's knowledge and problem solving skills. This would be a valuable facility in the area of signal processing as analysts already rely on computers for numerical manipulation of data. This research describes the successful conception and realising of software to assist a technically competent person in directing, applying and interpreting of signal processing algorithms with particular reference to the interpretation of measured responses to shock excitation. If a structure has been excited by some driving force, a measured response contains information about parts of the structure involved in the movement. An analyst given the measurement and asked to underdtand and explain the process that caused it will interpret the signal by building a model. The model is initially an empty shell but is made specific to the data by extracting information from the signal or prior knowledge. Information in the signal is stored in two ways; the numeric data values that represent the measured variable and the patterns visible to the eye when the data values are plotted against time. The information from the numeric values is extracted using algorithms which emphasise the previously hidden information. The design of the expert system has a model of the vibration process at its heart and aims to make it specific to the data just as the analyst does. There are three sections to the program, choice of a model, defining the components of the model and finally producing a report of the analysis. The first and third sections use rule based inference but the middle section is founded on a new architecture tuned for model building. It is a blackboard control structure organised to represent a linear system model. Knowledge sources are attached to each component and are scheduled by the user. A graphical interface is provided through which the user can view any part of the model in signal or symbolic format. Software is provided that creates the signals that form a view of the model and keeps the linear system causal. There are two main areas of knowlege application that give the program unique powers. One is in characterising features in the measurement emphasised by some algorithm as parameters of one of the components. The other is in finding a comparison whereby the accuracy of the parameter, in terms of how well it lets the model mimic the measurement, is established. Both these procedures are dominated by exploiting the user's ability to find patterns in noisy signals. This is particularly true when the model can generate different views of a signal. One statistical method of assessing a parameter value is explored and that is an adaptation of the maximum likelihood function used to find the confidence of epoch locations.
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Qamar, Ahsan. "An Integrated Approach towards Model-Based Mechatronic Design." Licentiate thesis, KTH, Mekatronik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-35374.

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Анотація:
Mechatronic design is an enigma. On the one hand, mechatronic products promise enhanced functionality, and better performance at reduced cost. On the other hand, optimizing mechatronic design concepts is a major challenge to overcome during the design process. In the past, less attention has been paid to the life phases of a mechatronic product, and it was assumed that modifications in electronics and software will ensure that the product performs to expectation throughout its life time. However it has been realized that introducing design changes in mechatronics is not easy, since it is difficult to assess the consequences of a design decision, both during the design process of a new product, and during a design modification. It is also realized that there is a strong need to consider the product's life phases during the early phases of product development. Furthermore, it is rather difficult to perform a design optimization since it requires introducing changes across different domains, which is not well supported by the methods and tools available today. This thesis investigates the topic of mechatronic design and attacks some of the major challenges that have been identified regarding the design of mechatronic products. The goal is to provide support to the designers to facilitate better understanding of the consequences of their design choices as early as possible. The work also aims to provide support for assessing alternative design concepts, and for optimizing a design concept based on requirements, constraints and designer preferences at the time of design. The thesis highlights three main challenges related to mechatronic product development: the need for a common language during conceptual design; the inadequate information transfer between engineering domains; and the difficulty in assessing the properties of competing mechatronic concepts. A model-based integration approach is presented, and these key challenges are considered in relation to an integrated modeling and design infrastructure. The approach is illustrated through the design of two mechatronic systems- a two degrees-of-freedom robot, and a hospital bed propulsion system. Initial results provide evidence of good potential for information transfer across mechatronic domains. Although SysML was used for the case studies, some important questions were raised about its suitability as a common language for mechatronics. Suggestions for future work are: to utilize the developed infrastructure and incorporate a capability to model and assess consequences of competing design concepts; provide support for optimizing these concepts; and evaluate the usefulness of the developed infrastructure in a real-world design setting. These efforts should provide ample information to the designer for making adequate decisions during the design process.
QC 20110629
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9

Kerzhner, Aleksandr A. "Using logic-based approaches to explore system architectures for systems engineering." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44748.

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Анотація:
This research is focused on helping engineers design better systems by supporting their decision making. When engineers design a system, they have an almost unlimited number of possible system alternatives to consider. Modern systems are difficult to design because of a need to satisfy many different stakeholder concerns from a number of domains which requires a large amount of expert knowledge. Current systems engineering practices try to simplify the design process by providing practical approaches to managing the large amount of knowledge and information needed during the process. Although these methods make designing a system more practical, they do not support a structured decision making process, especially at early stages when designers are selecting the appropriate system architecture, and instead rely on designers using ad hoc frameworks that are often self-contradictory. In this dissertation, a framework for performing architecture exploration at early stages of the design process is presented. The goal is to support more rational and self-consistent decision making by allowing designers to explicitly represent their architecture exploration problem and then use computational tools to perform this exploration. To represent the architecture exploration problem, a modeling language is presented which explicitly models the problem as an architecture selection decision. This language is based on the principles of decision-based design and decision theory, where decisions are made by picking the alternative that results in the most preferred expected outcome. The language is designed to capture potential alternatives in a compact form, analysis knowledge used to predict the quality of a particular alternative, and evaluation criteria to differentiate and rank outcomes. This language is based on the Object Management Group's System Modeling Language (SysML). Where possible, existing SysML constructs are used; when additional constructs are needed, SysML's profile mechanism is used to extend the language. Simply modeling the selection decision explicitly is not sufficient, computational tools are also needed to explore the space of possible solutions and inform designers about the selection of the appropriate alternative. In this investigation, computational tools from the mathematical programming domain are considered for this purpose. A framework for modeling an architecture selection decision in mixed-integer linear programming (MIP) is presented. MIP solvers can then solve the MIP problem to identify promising candidate architectures at early stages of the design process. Mathematical programming is a common optimization domain, but it is rarely used in this context because of the difficulty of manually formulating an architecture selection or exploration problem as a mathematical programming optimization problem. The formulation is presented in a modular fashion; this enables the definition of a model transformation that can be applied to transform the more compact SysML representation into the mathematical programming problem, which is also presented. A modular superstructure representation is used to model the design space; in a superstructure a union of all potential architectures is represented as a set of discrete and continuous variables. Algebraic constraints are added to describe both acceptable variable combinations and system behavior to allow the solver to eliminate clearly poor alternatives and identify promising alternatives. The overall framework is demonstrated on the selection of an actuation subsystem for a hydraulic excavator. This example is chosen because of the variety of potential architecture embodiments and also a plethora of well-known configurations which can be used to verify the results.
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Andersson, Henric. "Variability and Customization of Simulator Products : A Product Line Approach in Model Based Systems Engineering." Doctoral thesis, Linköpings universitet, Maskinkonstruktion, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-73572.

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Анотація:
AIRCRAFT DEVELOPERS, like other organizations within development and manufacturing, are experiencing increasing complexity in their products and growing competition in the global market. Products are built from increasingly advanced technologies and their mechanical, electronic, and software parts grow in number and become more interconnected. Different approaches are used to manage information and knowledge of products in various stages of their lifecycle. "Reuse" and "Model Based Development" are two prominent trends for improving industrial development efficiency. The product line approach is used to reduce the time to create product variants by reusing components. The model based approach provides means to capture knowledge about a system in the early lifecycle stages for usage throughout its entire lifetime. It also enables structured data  management as a basis for analysis, automation, and team collaboration for efficient management of large systems and families of products. This work is focused on the combination of methods and techniques within; modeling and simulation-based development, and (re)use of simulation models through the product line concept. With increasing computational performance and more efficient techniques/tools for building simulation models, the number of models increases, and their usage ranges from concept evaluation to end-user training. The activities related to model verification and validation contribute to a large part of the overall cost for development and maintenance of simulation models. The studied methodology aims to reduce the number of similar models created by different teams during design, testing, and end-user support of industrial products. Results of the work include evaluation of a configurator to customize and integrate simulation models for different types of aircraft simulators that are part of a simulator product family. Furthermore, contribution comprises results where constraints in the primary product family (aircraft) govern the configuration space of the secondary product family (simulators). Evaluation of the proposed methodology was carried out in cooperation with the simulator department for the 39 Gripen fighter aircraft at Saab Aeronautics.
FLYGPLANSTILLVERKARE LIKSOM andra industrier inom utveckling och tillverkning, hanterar ökande komplexitet i sina produkter och upplever en större konkurrens på den globala marknaden. Produkter byggs från allt mer avancerad teknologi. Ingående delar av mekanik, elektronik och mjukvara växer i antal och blir allt mer integrerade. Olika metoder används för att hantera information och kunskap om produkter i olika steg av dess livscykel. ”Återanvändning” och ”Modellbaserad utveckling” är två tydliga trender för att öka effektiviteten inom industriell utveckling. Produktfamiljer används för att minska ledtider när man skapar varianter av produkter genom att återanvända färdiga komponenter. Modellbaserade metoder ger möjlighet att tidigt i livscykeln samla kunskap om ett system för att användas under hela systemets livstid. De ger också strukturerad hantering av data som grund för analys, automatisering och samarbete mellan utvecklingsteam, vilket är en förutsättning för effektiv hantering av komplexa system och produkter. Detta arbete är fokuserat på en kombination av metoder och tekniker för; utveckling som baseras på modellering och simulering, och (åter)användning av simuleringsmodeller. Med ökande beräkningsprestanda och effektivare metoder/verktyg för att bygga simuleringsmodeller så ökar antalet modeller och deras användning spänner allt från konceptvärderingen till utbildning av slutanvändare. Arbetet med verifiering och validering av simuleringsmodeller utgör en stor del av deras totala utvecklings- och underhållskostnader. De studerade metoderna syftar till att minska antalet liknande modeller som hanteras av olika team för olika syften, som till exempel; utveckling, verifiering och som stöd för slutanvändare. Resultat av arbete inkluderar utvärdering av en konfigurator för att välja, integrera och anpassa simuleringsmodeller för olika typer av flygplanssimulatorer i en simulatorproduktfamilj. Dessutom bidrar arbetet med en metodik där begränsningarna i den primära produktfamiljen (flygplan) begränsar konfigurationsutrymmet för den sekundära produktfamiljen (simulatorer). Utvärdering av den föreslagna metoden har genomförts i samarbete med simulatoravdelning för flygplan 39 Gripen på Saab Aeronautics.
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Книги з теми "Model Based Systems Engineering Approach"

1

Forrai, Alexandru. Embedded Control System Design: A Model Based Approach. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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2

Garrett, Patrick H. Computer interface engineering for real-time systems: A model-based approach. Englewood Cliffs, N.J: Prentice-Hall, 1987.

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3

Analysis and synthesis of fuzzy control systems: A model based approach. Boca Raton: CRC Presss, 2010.

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4

Hazelrigg, George A. Systems engineering: An approach to information-based design. Upper Saddle River, NJ: Prentice Hall, 1996.

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5

Ost, Alexander. Performance of Communication Systems: A Model-Based Approach with Matrix-Geometric Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

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6

Winkler, Dietmar, Stefan Biffl, and Johannes Bergsmann, eds. Software Quality. Model-Based Approaches for Advanced Software and Systems Engineering. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03602-1.

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7

Domenico, Delli Gatti, ed. Emergent macroeconomics: An agent-based approach to business fluctuations. Milan: Springer, 2008.

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Micouin, Patrice. Model-Based Systems Engineering. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118579435.

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Borky, 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.

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Pérez, José Luis Fernández. Practical model-based systems engineering. Boston: Artech House, 2019.

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Частини книг з теми "Model Based Systems Engineering Approach"

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Wehrstedt, Jan Christoph, Jennifer Brings, Birte Caesar, Marian Daun, Linda Feeken, Constantin Hildebrandt, Wolfram Klein, Vincent Malik, Boris Wirtz, and Stefanie Wolf. "Modeling and Analyzing Context-Sensitive Changes during Runtime." In Model-Based Engineering of Collaborative Embedded Systems, 125–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62136-0_6.

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AbstractFor collaborative embedded systems, it is essential to consider not only the behavior of each system and the interaction between systems, but also the interaction of systems with their often dynamic and unknown context.In this chapter, we present a solution approach based on process building blocks— describing both the modelling approach as well as the model execution approach—for engineering and operation to achieve the goal of developing systems that deal with dynamics in their open context at runtime by re-using the models from the engineering phase.
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Mesmer, Bryan, Doroth Mckinney, Michael Watson, and Azad M. Madni. "Transdisciplinary Systems Engineering Approaches." In Recent Trends and Advances in Model Based Systems Engineering, 579–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-82083-1_49.

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Obe, Patricia Aluko, Jennifer Brings, Marian Daun, Linda Feeken, Elham Mirzaei, Martin Neumann, Jochen Nickles, et al. "Goal-Based Strategy Exploration." In Model-Based Engineering of Collaborative Embedded Systems, 197–216. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62136-0_9.

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AbstractWhen collaborative embedded systems (CESs) connect to form a group, this collaborative system group (CSG) can achieve goals that are beyond the reach of individual systems. The goals such a group can achieve depend on the constituent collaborative embedded systems. Consequently, the ability of a collaborative system group to adapt itself is driven by the capabilities of its collaborative embedded systems. This tight interconnection impedes the manual handling of adaptation strategies. Therefore, this chapter introduces a goal-based approach for strategy exploration that considers the peculiarities of collaborative system groups and collaborative embedded systems. The chapter sets out the model-based approach to adaptive system (group) design, incorporating the goals of collaborative system groups and individual systems, and outlines corresponding automated validation methods. We demonstrate the applicability of our approach for a case example of collaborative transport robots.
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Saxena, Tripti, and Gabor Karsai. "MDE-Based Approach for Generalizing Design Space Exploration." In Model Driven Engineering Languages and Systems, 46–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16145-2_4.

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Samih, Hamza, Hélène Le Guen, Ralf Bogusch, Mathieu Acher, and Benoit Baudry. "An Approach to Derive Usage Models Variants for Model-Based Testing." In Advanced Information Systems Engineering, 80–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44857-1_6.

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Al-Herz, Ahmed, and Moataz Ahmed. "Model-Based Web Components Testing: A Prioritization Approach." In Software Engineering and Computer Systems, 25–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22203-0_3.

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Fitzgerald, John, Jeremy Bryans, and Richard Payne. "A Formal Model-Based Approach to Engineering Systems-of-Systems." In IFIP Advances in Information and Communication Technology, 53–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32775-9_6.

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da Cruz, António Miguel Rosado, and João Pascoal Faria. "A Metamodel-Based Approach for Automatic User Interface Generation." In Model Driven Engineering Languages and Systems, 256–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16145-2_18.

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Gerostathopoulos, Ilias, and Alexander auf der Straße. "Online Experiment-Driven Learning and Adaptation." In Model-Based Engineering of Collaborative Embedded Systems, 295–303. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62136-0_15.

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AbstractThis chapter presents an approach for the online optimization of collaborative embedded systems (CESs) and collaborative system groups (CSGs). Such systems have to adapt and optimize their behavior at runtime to increase their utilities and respond to runtime situations. We propose to model such systems as black boxes of their essential input parameters and outputs, and search efficiently in the space of input parameters for values that optimize (maximize or minimize) the system’s outputs. Our optimization approach consists of three phases and combines online (Bayesian) optimization with statistical guarantees stemming from the use of statistical methods such as factorial ANOVA, binomial testing, and t-tests in different phases. We have applied our approach in a smart cars testbed with the goal of optimizing the routing of cars by tuning the configuration of their parametric router at runtime.
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Yang, Pengfei, Jinzhi Lu, Lei Feng, Shouxuan Wu, Guoxin Wang, and Dimitris Kiritsis. "A Knowledge Management Approach Supporting Model-Based Systems Engineering." In Advances in Intelligent Systems and Computing, 581–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72651-5_55.

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Тези доповідей конференцій з теми "Model Based Systems Engineering Approach"

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Soyler, 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.

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Fosse, E., and C. L. Delp. "Systems engineering interfaces: A model based approach." In 2013 IEEE Aerospace Conference. IEEE, 2013. http://dx.doi.org/10.1109/aero.2013.6497322.

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Al-Refai, Mohammed, Walter Cazzola, and Sudipto Ghosh. "A Fuzzy Logic Based Approach for Model-Based Regression Test Selection." In 2017 ACM/IEEE 20th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.17.

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Quamara, Megha, Gabriel Pedroza, and Brahim Hamid. "Multi-layered Model-based Design Approach towards System Safety and Security Co-engineering." In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2021. http://dx.doi.org/10.1109/models-c53483.2021.00048.

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"An Approach based on SysML and SystemC to Simulate Complex Systems." In International Conference on Model-Driven Engineering and Software Development. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004809205550560.

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Iqbal, Muhammad Zohaib, Hassan Sartaj, Muhammad Uzair Khan, Fitash Ul Haq, and Ifrah Qaisar. "A Model-Based Testing Approach for Cockpit Display Systems of Avionics." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019. http://dx.doi.org/10.1109/models.2019.00-14.

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Iftikhar, Sidra, Muhammad Zohaib Iqbal, Muhammad Uzair Khan, and Wardah Mahmood. "An automated model based testing approach for platform games." In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338274.

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Prehofer, Christian, Andreas Wagner, and Yucheng Jin. "A model-based approach for multi-device user interactions." In MODELS '16: ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2976767.2976776.

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"SysML-Sec - A Model Driven Approach for Designing Safe and Secure Systems." In Special Session on Security and Privacy in Model Based Engineering. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005402006550664.

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Hu, Zhenchao, Jinzhi Lu, Jinwei Chen, Xiaochen Zheng, Dimitrios Kyritsis, and Huisheng Zhang. "A Complexity Analysis Approach for Model-based System Engineering." In 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE). IEEE, 2020. http://dx.doi.org/10.1109/sose50414.2020.9130478.

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Звіти організацій з теми "Model Based Systems Engineering Approach"

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Tkachuk, Viktoriia V., Vadym P. Shchokin, and Vitaliy V. Tron. The Model of Use of Mobile Information and Communication Technologies in Learning Computer Sciences to Future Professionals in Engineering Pedagogy. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2668.

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Research goal: the research is aimed at developing a model of use of mobile ICT in learning Computer Sciences to future professionals in Engineering Pedagogy. Object of research is the model of use of mobile ICT in learning Computer Sciences to future professionals in Engineering Pedagogy. Results of the research: the developed model of use of mobile ICT as tools of learning Computer Sciences to future professionals in Engineering Pedagogy is based on the competency-based, person-centered and systemic approaches considering principles of vocational education, general didactic principles, principles of Computer Science learning, and principles of mobile learning. It also takes into account current conditions and trends of mobile ICT development. The model comprises four blocks: the purpose-oriented block, the content-technological block, the diagnostic block and the result-oriented block. According to the model, the learning content of Computer Sciences consists of 5 main units: 1) Fundamentals of Computer Science; 2) Architecture of Modern Computers; 3) Fundamentals of Algorithmization and Programming; 4) Software of Computing Systems; 5) Computer Technologies in the Professional Activity of Engineer-pedagogues.
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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.

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Blackburn, 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.

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Hamill, Daniel D., Jeremy J. Giovando, Chandler S. Engel, Travis A. Dahl, and Michael D. Bartles. Application of a Radiation-Derived Temperature Index Model to the Willow Creek Watershed in Idaho, USA. U.S. Army Engineer Research and Development Center, August 2021. http://dx.doi.org/10.21079/11681/41360.

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The ability to simulate snow accumulation and melting processes is fundamental to developing real-time hydrological models in watersheds with a snowmelt-dominated flow regime. A primary source of uncertainty with this model development approach is the subjectivity related to which historical periods to use and how to combine parameters from multiple calibration events. The Hydrologic Engineering Center, Hydrological Modeling System, has recently implemented a hybrid temperature index (TI) snow module that has not been extensively tested. This study evaluates a radiatative temperature index (RTI) model’s performance relative to the traditional air TI model. The TI model for Willow Creek performed reasonably well in both the calibration and validation years. The results of the RTI calibration and validation simulations resulted in additional questions related to how best to parameterize this snow model. An RTI parameter sensitivity analysis indicates that the choice of calibration years will have a substantial impact on the parameters and thus the streamflow results. Based on the analysis completed in this study, further refinement and verification of the RTI model calculations are required before an objective comparison with the TI model can be completed.
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Lines, K., and H. Krishnamurthy. Model-based systems engineering at NPL: an initial investigation. National Physical Laboratory, February 2023. http://dx.doi.org/10.47120/npl.ms42.

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Noonan, 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.

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Carroll, 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.

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Hansson, 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.

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Chamberlain, C. A., and K. Lochhead. Data modeling as applied to surveying and mapping data. Natural Resources Canada/CMSS/Information Management, 1988. http://dx.doi.org/10.4095/331263.

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The Geodetic Survey Division of the Canada Centre for Surveying is replacing the National Geodetic Data Base (NGDB) with the National Geodetic Information System (NGIS). For the NGIS to be successful, it was recognized that a sound, well engineered data mode was essential. The methodology chosen to design the data mode! was Nijssen's Information Analysis Methodology (NIAM), a binary modeling technique that is supported by a Computer Aided Software Engineering (CASE) tool, PC-IAST. An NGIS prototype has also been developed using Digital Equipment of Canada's Relational Database (Rdb) management system and COGNOS Corporations POWERHOUSE 4th generation language. This paper addresses the need for, and the advantages of using a strong engineering approach to data modeling and describes the use of the NIAM methodology in NGIS development. The paper identifies the relationship between the data mode!, data structures, the design and development of a database and the use of automated tools for systems development. In conclusion, critical success factors for the continuation of the N.G.I.S. developments are identified and the benefits that will accrue are enumerated.
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White, Corina L. Development of Systems Engineering Competency Career Development Model: An Analytical Approach using Blooms Taxonomy. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada608033.

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