most citedDigital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions

15 citations · 30 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY202115 cited

Supply Chain Digital Twin Framework Design: An Approach of Supply Chain Operations Reference Model and System of Systems

Jie Zhang, Alexandra Brintrup, Anisoara Calinescu +2

Digital twin technology has been regarded as a beneficial approach in supply chain development. Different from traditional digital twin (temporal dynamic), supply chain digital twi…

cs.CV2021

Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation

Angira Sharma, Naeemullah Khan, Muhammad Mubashar +2

In this paper we present a novel loss function, called class-agnostic segmentation (CAS) loss. With CAS loss the class descriptors are learned during training of the network. We do…

cs.CV2021

Shape-Tailored Deep Neural Networks

Naeemullah Khan, Angira Sharma, Ganesh Sundaramoorthi +1

We present Shape-Tailored Deep Neural Networks (ST-DNN). ST-DNN extend convolutional networks (CNN), which aggregate data from fixed shape (square) neighborhoods, to compute descri…

cs.LG202015 cited

Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions

Angira Sharma, Edward Kosasih, Jie Zhang +2

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation and forecasting. However,…

cs.CV2020

Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation

Angira Sharma, Naeemullah Khan, Ganesh Sundaramoorthi +1

In this paper we present a novel loss function, called class-agnostic segmentation (CAS) loss. With CAS loss the class descriptors are learned during training of the network. We do…