The Architecture of Digital System Model for Systems-of-Systems Engineering

Gao, Xinghai; Zhang, Yaqi; Wang, Zhuoqi

doi:10.1109/cac48633.2019.8996449

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…In order to meet the needs of various ConOps, design elements are effectively integrated (static structure) and use cases at all levels are employed to justify the requirements and interactions (dynamic behaviors). The characteristics of MBSE to ensure that the architecture can cover the all use cases, systems and components, and drive end-to-end M&S to verify according to the attributes and behavior of the models of systems and components [21].…”

Section: Vandv In a Hybrid Virtual/real Integration Environmentmentioning

confidence: 99%

“…And at last, considering the swarm ontology technology of autonomous unmanned system as the main thread in our research, and across the conceptual model -functional and logical model -mathematical physical model, we will develop the technology of the integration environment of multi-level and multi-paradigm collaborative model and simulation, and which will become a technical evolution platform of experimental frame to support the development and evaluation of complex behaviors [21], such as swarm environment awareness and cognition, collaborative task planning and decision-making, information interaction and autonomous control, and others. We will take out a hierarchical, composable approach to the swarm development and experiment framework which is mainly composed of ConOps, capabilities, architecture, and parameters.…”

mentioning

confidence: 99%

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The Framework of Modeling and Simulation based-on Swarm Ontology for Autonomous Unmanned Systems

Gao¹,

Xiao²,

Xie³

et al. 2023

Preprint

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For the emerging autonomous swarm technology, from the perspective of systems science and systems engineering, there must be some novel elements and methods to aggregate multiple systems into a group, which distinguish with the general components with specific functions, and we expect to provide the presentation of their existence in the swarm development processes. The inspiration of our methodology origins from the integration of swarm ontology, multi-paradigm modeling, multi-agent system, cyber-physical system, etc. Therefore, we choose the model-driven technology as a framework to acquire an approach of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism is defined and formed in parallel with ConOps analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by the nonlinear causation among individual and whole behaviors. This approach highlights to use the MBSE processes and their artifacts to embed the meso mechanism to integrate the operational and the functional, and which means to connect the macro and micro aspects in formalism and so to become a whole with its expected goals, and then to verify and validate within a L-V-C simulation environment.

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Section: Vandv In a Hybrid Virtual/real Integration Environmentmentioning

confidence: 99%

mentioning

confidence: 99%

The Framework of Modeling and Simulation based-on Swarm Ontology for Autonomous Unmanned Systems

Gao¹,

Xiao²,

Xie³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to meet the needs of various ConOps, design elements are effectively integrated (static structure) and use cases at all levels are employed to justify the requirements and interactions (dynamic behaviors). The characteristics of MBSE ensure that the architecture can cover all use cases, systems, and components, and drive end-to-end M&S to verify the attributes and behavior of the models of systems and components [24].…”

Section: Vandv In a Hybrid Virtual/real Integration Environmentmentioning

confidence: 99%

“…And finally, considering the swarm ontology technology of autonomous unmanned systems as the main thread in our research, and across the conceptual ontological model-functional and logical model-mathematical physical model, we develop the technology of the integration environment of a multilevel and multiparadigm collaborative model and simulation, which will become a technical evolution platform of the experimental frame to support the development and evaluation of complex behaviors [24], such as swarm environment awareness and cognition, collaborative task planning and decision making, information interaction and autonomous control, and others. We take a hierarchical, composable approach to swarm development and the experimental framework is mainly composed of ConOps, capabilities, architecture, and parameters.…”

mentioning

confidence: 99%

A Framework of Modeling and Simulation Based on Swarm Ontology for Autonomous Unmanned Systems

Gao

Xiao²,

Xie³

et al. 2023

Applied Sciences

Self Cite

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For the emerging autonomous swarm technology, from the perspective of systems science and Systems Engineering (SE), there must be novel methodologies and elements to aggregate multiple systems into a group, which distinguish the general components with specific functions. Here, we expect to provide a presentation of their existence in swarm development processes. The inspiration for our approach originates from the integration of swarm ontology, multiparadigm modeling, multiagent systems, cyber-physical systems, etc. Therefore, we chose the model-driven architecture as a framework to provide a method of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism was defined and formed in parallel with Concept of Operations (ConOps) analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by nonlinear causation among individual and whole behaviors. Our approach highlights the use of model-based processes and their artifacts in the swarm mechanism to integrate operational and functional models, which means connecting the macro- and micro-aspects in formalism to synthesize a whole with its expected goals, and then to verify and validate within an L-V-C simulation environment.

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“…SysML (can be integrated with Matlab)trade-off and analytics using FEA, Single source of truth Kunnen et al[97] continuous data model with usage of modeling language, here SysML identification of errors and risk = identify negative influences and risk Gao et al[103] SysML based digital system model or sets of models that help integrate other discipline specific engineering models and simulations, which is initiated at the start and evolves through the system's lifecycle used or integration and to support optimization, simulation and analysis Kamburjan and Stromberg[104] formal model of a real target system that mirrors structure and behavior sufficiently for prototyping and to evaluate changes, digital twins are a variant of this prototyping and to evaluate changes and digital twins…”

mentioning

confidence: 99%

Systematic Literature Review of System Models for Technical System Development

Schmidt¹,

Zimmermann²,

Stark³

2021

Preprint

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In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

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The Architecture of Digital System Model for Systems-of-Systems Engineering

Cited by 6 publications

References 3 publications

The Framework of Modeling and Simulation based-on Swarm Ontology for Autonomous Unmanned Systems

The Framework of Modeling and Simulation based-on Swarm Ontology for Autonomous Unmanned Systems

A Framework of Modeling and Simulation Based on Swarm Ontology for Autonomous Unmanned Systems

Systematic Literature Review of System Models for Technical System Development

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