Semiautomated Development of Textual Requirements: Combined NLP and Multidomain Semantic Modeling Approach
Sachraa G. Borjigin, Mark Austin, Edward J. Zontek-Carney
A framework for the semiautomated development of textual requirements for the building construction industry, a domain in which quality of textual requirements has a direct bearing on the avoidance of unnecessary project losses and failures.
Journal of Management in Engineering (ASCE)
Architecting for Digital Twins and MCE with AI/ML, Part II
Mark Blackburn, Mark Austin
The final report of research conducted with Stevens Institute of Technology in its Systems Engineering Research Center UARC. The authors investigated digital twin design architectures that support Artificial Intelligence (AI) and Machine Learning (ML) formalisms working side-by-side as a team. The unique aspects of this research are the use semantic technologies that can leverage AI and ML providing complementary and supportive roles in the collection, formalizing representations and processing of data, identification and correlation of events, in evolving spatial contexts and automated decision making throughout the system lifecycle. The research developed graph embedding procedures with ML tasks, which together can enhance digital twin design and decision making to factor in evolving temporal and spatial information, such as those encountered in urban settings.
Final Technical Report SERC-2021-TR-007
Teaching Machines to Understand Urban Networks: A Graph Autoencoder Approach
Maria Coelho, Mark Austin, Shivam Mishra, Mark Blackburn
Due to remarkable advances in computer, communications and sensing technologies over the past three decades,large-scale urban systems are now far more heterogeneous and automated than their predecessors. They may, in fact, be connected to other types of systems in completely new ways. These characteristics make the tasks of system design, analysis and integration of multi-disciplinary concerns much more difficult than in the past. We believe these challenges can be addressed by teaching machines to understand urban networks. This paper explores opportunities for using a recently developed graph autoencoding approach to encode the structure and associated network attributes as low-dimensional vectors. We exercise the proposed approach on a problem involving identification of leaks in urban water distribution systems.
IARIA International Journal on Advances in Networks and Services
Teaching Machines to Understand Urban Networks
Maria Coelho, Mark Austin
Explores opportunities for using recently developed graph embedding procedures to encode the structure and associated network attributes as low-dimensional vectors. The AI/ML concept is demonstrated on a problem involving identification of leaks in an urban water distribution system.
15th International Conference on Systems (ICONS 2020)
Architecting Smart City Digital Twins: Combined Semantic Model and Machine Learning Approach
Mark Austin, Parastoo Delgoshaei, Maria Coelho, Mohammad Heidarinejad
Explores the approaches and challenges of architecting and operating smart city digital twins, and proposes a path that supports semantic knowledge representation and reasoning, as well as machine learning formalisms, to provide complementary and supportive roles in collecting and processing data, identifying events, and automating decision making.
Journal of Management in Engineering