Paper Info
Title
Optimization Based Partitioning Selection For Improved Contaminant Detection Performance
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.
Year
DOI
Venue
2018
10.1109/CDC.2018.8619262
2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC)
Field
DocType
ISSN
Mathematical optimization,Computer science,Contingency plan,Observer (quantum physics),Indoor air quality,Optimization problem,Contamination
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Alexis Kyriacou111.03
Stelios Timotheou270735.80
Vasso Reppa3517.18
Francesca Boem48312.10
Christos G. Panayiotou547258.98
Marios Polycarpou62020206.96
T Parisini7935113.17