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
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)
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