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Augmented Reality (AR) devices offer novel capabilities that can be exploited in AR systems to positively impact human-machine interactions in a variety of future-work and education contexts. This paper presents a systems model for a no-code AR systems framework that can be used to create AR applications that present just-in-time informatics to assist and guide users in the completion of complex task sequences while ensuring operator and environment safety. The salient structural and behavioral aspects of the system, and key use cases are modeled using the Systems Modeling Language (SysML). Representative examples of the model are presented using use case, block definition, internal block, activity, and state-machine diagrams. These models offer new insights into how AR capabilities can be integrated with a variety of engineered systems. In the future such SysML models can steer the design of new tools and an ontology to strengthen connections to domain knowledge.
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Text-to-Model Transformation: Natural Language-Based Model Generation Framework
System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model generation framework leveraging Python and the spaCy NLP library to process text input and generate class/block definition diagrams using PlantUML for visual representation. The intent of this framework is to aid in reducing the manual effort in creating SysML v1.6 diagrams—class/block definition diagrams in this case. We evaluate the effectiveness of the framework using precision and recall measures. The contribution of this paper to the systems modeling domain is two-fold. First, a review and analysis of natural language processing techniques for the automated generation of SysML diagrams are provided. Second, a framework to automatically extract textual relationships tailored for generating a class diagram/block diagram that contains the classes/blocks, their relationships, methods, and attributes is presented.
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- Award ID(s):
- 2412813
- PAR ID:
- 10561380
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Systems
- Volume:
- 12
- Issue:
- 9
- ISSN:
- 2079-8954
- Page Range / eLocation ID:
- 369
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/systems12090369
