Document Type
Article
Publication Date
10-19-2015
Abstract
This work explores a method for classifying peaks appearing within a data-intensive time-series. We summarize a case study from a clinical trial aimed at reducing secondhand smoke exposure via the installation of air particle monitors in households. Proper orthogonal decomposition (POD) in conjunction with a k-means clustering algorithm assigns each data peak to one of two clusters. Aversive feedback from the monitors increased the proportion of short-duration, attenuated peaks from 38.8% to 96.6%. For each cluster, a distribution of parameters from a physics-based model of airborne particles is estimated. Peaks generated from these distributions are correctly identified by POD/clustering with >60% accuracy.
Recommended Citation
Berardi, V., Carretero-González, R., Klepeis, N. E., et al. (2015). Proper orthogonal decomposition methods for air particle time-series in residences: Exploring peak clustering by occupant behavior patterns. Journal of Computational Science, 11, 102-111. doi: 10.1016/j.jocs.2015.10.006
Copyright
Elsevier
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Respiratory Tract Diseases Commons
Comments
NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Computational Science. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Computational Science, volume 11, in 2015. DOI: 10.1016/j.jocs.2015.10.006
The Creative Commons license below applies only to this version of the article.