1. Optimization Based Partitioning Selection for Improved Contaminant Detection Performance
- Author
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Kyriacou, Alexis, Timotheou, Stelios, Reppa, Vasso, Boem, Francesca, Panayiotou, Christos, Polycarpou, Marios, Parisini, Thomas, IEEE, Kyriacou, A., Timotheou, S., Reppa, V., Boem, F., Panayiotou, C., Polycarpou, M., Parisini, T., Panayiotou, Christos [0000-0002-6476-9025], Polycarpou, Marios [0000-0001-6495-9171], Kyriacou, Alexis [0000-0003-4815-2989], and Reppa, Vasso [0000-0002-8599-6016]
- Subjects
Optimization ,Indoor air quality ,Computer science ,020209 energy ,11. Sustainability ,0202 electrical engineering, electronic engineering, information engineering ,Detection performance ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,02 engineering and technology ,Contamination ,Selection (genetic algorithm) ,Reliability engineering - Abstract
Indoor Air Quality monitoring is an essentialingredient of intelligent buildings. The release of variousairborne contaminants into the buildings, compromises thehealth and safety of occupants. Therefore, early contaminantdetection is of paramount importance for the timely activationof proper contingency plans in order to minimize the impact ofcontaminants on occupants health. The objective of this workis to enhance the performance of a distributed contaminantdetection methodology, in terms of the minimum detectablecontaminant release rates, by considering the joint problemof partitioning selection and observer gain design. Towardsthis direction, a detectability analysis is performed to deriveappropriate conditions for the minimum guaranteed detectablecontaminant release rate for specific partitioning configurationand observer gains.The derived detectability conditions arethen exploited to formulate and solve an optimization problemfor jointly selecting the partitioning configuration and observergains that yield the best contaminant detection performance.
- Published
- 2018