Special Issue "Model-Based Systems Engineering: Rigorous Foundations for Digital Transformations in Science and Engineering"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Prof. Dov Dori
Website
Guest Editor
1. Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa, Israel
2. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Interests: conceptual modelling; object–process methodology; model-based systems engineering; systems and software engineering; Industry 4.0; complex systems; Internet of Things; Internet of Robotic Things; cyber-physical systems; model-based systems biology
Dr. Yaniv Mordecai
Website
Guest Editor
Engineering Systems Lab, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Interests: model-based systems engineering; cyber-physical informatics; digital systems engineering; model informatics and analytics; modelling and simulation of cyber-physical systems; command and control; enterprise architectures

Special Issue Information

Dear Colleagues,

Over the last decade, recognition is growing that models are essential as formal representations of complex systems for transforming systems engineering (SE) in a rigorous and robust meta-engineering discipline, positioning the model-based systems engineering (MBSE) paradigm as the leading SE approach. Although enterprises and organizations of all kinds and sizes have started adopting MBSE, emerging information and communication technologies, such as the Internet of Things (IoT) and the Internet of Robotic Things (IoRT), are shaping Industry 4.0, the fourth industrial revolution. This is only one of the expressions of the digital transformation we are witnessing in a variety of traditional ecosystems, from product conception, design, development, and service delivery, through supply chains and manufacturing, to customer relationship management and social networking. These emerging technologies have been challenging traditional business and operations practices; they have also begun to pose new challenges for the MBSE paradigm, which, in addition to its previous role, must now adopt and adapt to the digital transformation. Leading paradigms for complex systems modelling, analysis, and simulation, including the systems modelling language (SysML), the unified modelling language (UML), or the object process methodology (OPM), must overcome new challenges, such as the modelling of cyber-physical interactions, the digital twin revolution, continuous integration and innovation, and high edge computing workloads supported by cloud-based infrastructure. To meet these challenges, novel approaches for modelling and executable simulation of conceptual models are needed. These include rich and evolving requirements modelling; joint conceptual–computational modelling and execution; model-in-the-loop frameworks; multi-model orchestration; model-based extended (MBX) approaches that rely on the system models for tasks such as risk management, testing, and verification; product and service cataloguing; maintenance and operations; and model analytics to drive stakeholder decisions. Given these developments, enablers, such as model-based teamwork, management commitment, stakeholder engagement, model curation, and model reuse, require fresh insight.  Educating and training systems engineers that will be ready to cope with the digital systems engineering challenges, primarily Industry 4.0, need to be addressed as well. The recent COVID-19 pandemic is imposing even greater and historically unprecedented disruptions, transformations, and challenges in all avenues of human life, including healthcare, supply chains, manufacturing, commerce, energy, services, and socialization. MBSE can and should be instrumental in creating a smoother transformation to a new reality that will likely change how we live and operate in years to come.

We invite you to submit original research papers on these and related topics, some of which are listed in the keywords below.

Prof. Dov Dori
Dr. Yaniv Mordecai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Modelling
  • Model-based systems engineering (MBSE)
  • Modelling languages and constructs for Industry 4.0
  • Digital systems engineering
  • Digital transformation
  • Digital twin
  • Smart factory, smart manufacturing
  • Internet of Things (IoT)/Industrial Internet of Things (IIoT)
  • Internet of Robotic Things (IoRT)
  • Cyber-physical systems
  • Cyber-physical informatics
  • Model-based requirements
  • Model-based testing
  • MBSE training and education
  • Model-based extended approaches (MBX): MB systems thinking, MB tradespace exploration, MB risk analysis, MB compliance assurance, MB diagnosis, MB operational planning, MB lifecycle support, etc.
  • Model simulation and execution
  • Model informatics and analytics
  • Model-based application development and code generation
  • Combining conceptual modeling with other SE techniques, such as systems dynamics, design structure matrix, axiomatic design
  • Comparative studies in MBSE
  • MBSE adoption, cost-benefit analysis, and return-on-investment case studies
  • Model-based collaboration, teamwork, knowledge management, and group communication
  • Innovative applications of MBSE, such as for digital enterprise architectures, evolving systems, intelligent transportation systems, mobilization of healthcare systems, and coping with COVID 19
  • Model-Based Systems Science
  • Modeling Applied Science Research Systems

Published Papers (2 papers)

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Research

Open AccessArticle
A MBSE Application to Controllers of Autonomous Underwater Vehicles Based on Model-Driven Architecture Concepts
Appl. Sci. 2020, 10(22), 8293; https://doi.org/10.3390/app10228293 - 23 Nov 2020
Abstract
In this paper, a hybrid realization model is proposed for the controllers of autonomous underwater vehicles (AUVs). This model is based on the model-based systems engineering (MBSE) methodology, in combination with the model-driven architecture (MDA), the real-time unified modeling language (UML)/systems modeling language [...] Read more.
In this paper, a hybrid realization model is proposed for the controllers of autonomous underwater vehicles (AUVs). This model is based on the model-based systems engineering (MBSE) methodology, in combination with the model-driven architecture (MDA), the real-time unified modeling language (UML)/systems modeling language (SysML), the extended/unscented Kalman filter (EKF/UKF) algorithms, and hybrid automata, and it can be reused for designing controllers of various AUV types. The dynamic model and control structure of AUVs were combined with the specialization of MDA concepts as follows. The computation-independent model (CIM) was specified by the use-case model combined with the EKF/UKF algorithms and hybrid automata to intensively gather the control requirements. Then, the platform-independent model (PIM) was specialized using the real-time UML/SysML to design the capsule collaboration of control and its connections. The detailed PIM was subsequently converted into the platform-specific model (PSM) using open-source platforms to promptly realize the AUV controller. On the basis of the proposed hybrid model, a planar trajectory-tracking controller, which allows a miniature torpedo-shaped AUV to autonomously track the desired planar trajectory, was implemented and evaluated, and shown to have good feasibility. Full article
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Open AccessArticle
Extending Drag-and-Drop Actions-Based Model-to-Model Transformations with Natural Language Processing
Appl. Sci. 2020, 10(19), 6835; https://doi.org/10.3390/app10196835 - 29 Sep 2020
Abstract
Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to this purpose by providing a flexible, reusable, customizable, and [...] Read more.
Model-to-model (M2M) transformations are among the key components of model-driven development, enabling a certain level of automation in the process of developing models. The developed solution of using drag-and-drop actions-based M2M transformations contributes to this purpose by providing a flexible, reusable, customizable, and relatively easy-to-use transformation method and tool support. The solution uses model-based transformation specifications triggered by user-initiated drag-and-drop actions within the model deployed in a computer-aided software engineering (CASE) tool environment. The transformations are called partial M2M transformations, meaning that a specific user-defined fragment of the source model is being transformed into a specific fragment of the target model and not running the whole model-level transformation. In this paper, in particular, we present the main aspects of the developed extension to that M2M transformation method, delivering a set of natural language processing (NLP) techniques on both the conceptual and implementation level. The paper addresses relevant developments and topics in the field of natural language processing and presents a set of operators that can be used to satisfy the needs of advanced textual preprocessing in the scope of M2M transformations. Also in this paper, we describe the extensions to the previous M2M transformation metamodel necessary for enabling the solution’s NLP-related capabilities. The usability and actual benefits of the proposed extension are introduced by presenting a set of specific partial M2M transformation use cases where natural language processing provides actual solutions to previously unsolvable situations when using the previous M2M transformation development. Full article
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