TY - GEN
T1 - Optimization Based Partitioning Selection for Improved Contaminant Detection Performance
AU - Kyriacou, Alexis
AU - Timotheou, Stelios
AU - Reppa, Vasso
AU - Boem, Francesca
AU - Panayiotou, Christos
AU - Polycarpou, Marios
AU - Parisini, Thomas
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85062169495&partnerID=8YFLogxK
U2 - 10.1109/CDC.2018.8619262
DO - 10.1109/CDC.2018.8619262
M3 - Conference contribution
AN - SCOPUS:85062169495
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5568
EP - 5573
BT - 2018 IEEE Conference on Decision and Control, CDC 2018
PB - IEEE
T2 - 57th IEEE Conference on Decision and Control, CDC 2018
Y2 - 17 December 2018 through 19 December 2018
ER -