scholarly journals Towards Model-Driven Digital Twin Engineering: Current Opportunities and Future Challenges

2020 ◽  
pp. 43-54
Author(s):  
Francis Bordeleau ◽  
Benoit Combemale ◽  
Romina Eramo ◽  
Mark van den Brand ◽  
Manuel Wimmer
Keyword(s):  
Digital Twin ◽  
Model Driven ◽  
Future Challenges

Multisource Model-Driven Digital Twin System of Robotic Assembly

2020 ◽  
pp. 1-10
Author(s):  
Xin Li ◽  
Bin He ◽  
Yanmin Zhou ◽  
Gang Li
Keyword(s):  
Robotic Assembly ◽  
Digital Twin ◽  
Model Driven ◽  
Twin System

Model-Driven Adaptive Enterprise

2020 ◽  
pp. 1-14
Author(s):  
Vinay Kulkarni ◽  
Sreedhar Reddy ◽  
Tony Clark
Keyword(s):  
Artificial Intelligence ◽  
Information Systems ◽  
Control Theory ◽  
A Priori ◽  
Systems Design ◽  
Dynamic Environments ◽  
Information Systems Design ◽  
Systems Of Systems ◽  
Digital Twin ◽  
Model Driven

Modern enterprises are large complex systems operating in dynamic environments and are therefore required to respond quickly to a variety of change drivers. Moreover, they are systems of systems wherein understanding is only available in localized contexts and is partial and uncertain. Given that the overall system behaviour is hard to know a-priori and that conventional techniques for systemwide analysis either lack rigour or are defeated by the scale of the problem, the current practice often exclusively relies on human expertise for adaptation. This chapter outlines the concept of model-driven adaptive enterprise that leverages principles from modeling, artificial intelligence, control theory, and information systems design leading to a knowledge-guided simulation-aided data-driven model-based evidence-backed approach to impart adaptability to enterprises. At the heart of a model-driven adaptive enterprise lies a digital twin (i.e., a simulatable digital replica of the enterprise). The authors discuss how the digital twin can be used to analyze, control, adapt, transform, and design enterprises.

Model-driven digital twin construction

2020 ◽  
Author(s):  
Jörg Christian Kirchhof ◽  
Judith Michael ◽  
Bernhard Rumpe ◽  
Simon Varga ◽  
Andreas Wortmann
Keyword(s):  
Digital Twin ◽  
Model Driven

scholarly journals Interactive Smart Space for Single-Person Households Using Electroencephalogram through Fusion of Digital Twin and Artificial Intelligence

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7771
Author(s):  
Seung Yeul Ji
Keyword(s):  
Artificial Intelligence ◽  
Emotional State ◽  
Smart Space ◽  
Digital Twin ◽  
Model Driven ◽  
Implementation Model ◽  
Core Technology ◽  
Single Person ◽  
Eeg Data ◽  
Spatial Dimensions

The core technology for building a smart space includes the capability to analyse the space for users using various sensors. The purpose of this study was to propose a personalised interactive smart space implementation model driven by the fusion of digital twin (DT) and artificial intelligence (AI) based on electroencephalogram (EEG) data. This study utilised a handheld EEG sensor to identify a user’s emotion information and focused on the connection with the space. A smart space for single-person households that responds to EEG-based biometric information was designed for an interactive space that can improve the current emotional state of the space user. The technical characteristics of DT and AI were analysed to control spatial changes according to the user’s emotional state and to address safety-related issues. Furthermore, a fusion mechanism for DT and AI was developed for intelligent motor control to change the dimensions of the space in order to improve the EEG state of the user. In addition, using an AI model that converts EEG data into emotional state information, the user’s emotional state was analysed, and key issues related to the spatial dimensions and change of space that induce psychological stability were investigated.

scholarly journals A Model-Driven Digital Twin Framework Development for Sulfur Dioxide Conversion Units Simulation

2021 ◽  
Vol 6 (2) ◽  
pp. 122-131
Author(s):  
Amine Mounaam ◽  
Ridouane Oulhiq ◽  
Ahmed Souissi ◽  
Mohamed Salouhi ◽  
Khalid Benjelloun ◽  
...  
Keyword(s):  
Sulfur Dioxide ◽  
Digital Twin ◽  
Model Driven ◽  
Framework Development

scholarly journals Evolving models in Model-Driven Engineering: State-of-the-art and future challenges

2016 ◽  
Vol 111 ◽  
pp. 272-280 ◽  
Author(s):  
Richard F. Paige ◽  
Nicholas Matragkas ◽  
Louis M. Rose
Keyword(s):  
State Of The Art ◽  
Model Driven Engineering ◽  
Model Driven ◽  
Future Challenges ◽  
Engineering State ◽  
Evolving Models
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