Man's role in man-machine systems

Margulies, F.; Zemanek, Heinz

doi:10.1016/0005-1098(83)90031-6

Cited by 9 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the first concern that can be a stumbling block for the adaptation of digital twins just like any other automation technologies will be its acceptability by the work force. The fear of loosing jobs seemed very logical a few decades earlier [533], however even at that time contrary opinions were prevalent based on systematic studies [534]. In fact, Sheridan showed that automation just results in redistribution of workplace without much impact on employment [535].…”

Section: Socio-economic Impactsmentioning

confidence: 99%

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

2020

View full text Add to dashboard Cite

A digital twin can be defined as an adaptive model of a complex physical system. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

show abstract

Section: Socio-economic Impactsmentioning

confidence: 99%