Online Edge Flow Imputation on Networks

Online Edge Flow Imputation on Networks

Money, Rohan (University of Agder)
Krishnan, Joshin (Simula Metropolitan Center for Digital Engineering)
Beferull-Lozano, Baltasar (University of Agder)
Isufi, E. (TU Delft Multimedia Computing)

2022

An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via <italic>(i)</italic> a sparse line graph identification strategy based on a group-Lasso and <italic>(ii)</italic> a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation. The advantages of this first SC-based attempt for time-varying signal imputation have been demonstrated through numerical experiments using EPANET models of both synthetic and real water distribution networks.

Kalman filters
Laplace equations
Line Graph
Missing Flow Imputation
Optimization
Reactive power
Signal processing algorithms
Signal reconstruction
Simplicial Complex
Time series analysis
Topological Signal Processing

http://resolver.tudelft.nl/uuid:1a75ec15-2a37-40e3-98fa-dbdeff906d2a

https://doi.org/10.1109/LSP.2022.3221846

2023-07-01

1070-9908

IEEE Signal Processing Letters, 30, 115-119

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Rohan Money, Joshin Krishnan, Baltasar Beferull-Lozano, E. Isufi