Kyriacou, A;
Timotheou, S;
Reppa, V;
Boem, F;
Panayiotou, C;
Polycarpou, MM;
Parisini, T;
(2019)
Optimization Based Partitioning Selection for Improved Contaminant Detection Performance.
In:
2018 IEEE Conference on Decision and Control (CDC).
(pp. pp. 5568-5573).
IEEE
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Abstract
Indoor Air Quality monitoring is an essential ingredient of intelligent buildings. The release of various airborne contaminants into the buildings, compromises the health and safety of occupants. Therefore, early contaminant detection is of paramount importance for the timely activation of proper contingency plans in order to minimize the impact of contaminants on occupants health. The objective of this work is to enhance the performance of a distributed contaminant detection methodology, in terms of the minimum detectable contaminant release rates, by considering the joint problem of partitioning selection and observer gain design. Towards this direction, a detectability analysis is performed to derive appropriate conditions for the minimum guaranteed detectable contaminant release rate for specific partitioning configuration and observer gains. The derived detectability conditions are then exploited to formulate and solve an optimization problem for jointly selecting the partitioning configuration and observer gains that yield the best contaminant detection performance.
| Type: | Proceedings paper |
|---|---|
| Title: | Optimization Based Partitioning Selection for Improved Contaminant Detection Performance |
| Event: | 57th IEEE Conference on Decision and Control, 17-19 December 2018, Miami Beach, FL, USA |
| ISBN-13: | 978-1-5386-1395-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CDC.2018.8619262 |
| Publisher version: | https://doi.org/10.1109/CDC.2018.8619262 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Buildings , Observers , Detectors , Optimization , Uncertainty , Pollution measurement , Monitoring |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10060822 |
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