CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Online Edge Flow Imputation on Networks
Authors
Baltasar Beferull-Lozano
Elvin Isufi
Joshin Parakkulangarayil Krishnan
Rohan Thekkemarickal Money
Publication date
1 January 2022
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
Author's accepted manuscript© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.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 (i) a sparse line graph identification strategy based on a group-Lasso and (ii) 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.acceptedVersio
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Agder University Research Archive
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:uia.brage.unit.no:11250/30...
Last time updated on 26/03/2023
TU Delft Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:tudelft.nl:uuid:1a75ec15-2...
Last time updated on 04/08/2023