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Bayesian based design of real-time s...

Sreedharan, Priya.

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Bayesian based design of real-time sensor systems for high-risk indoor contaminants.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bayesian based design of real-time sensor systems for high-risk indoor contaminants./
Author:	Sreedharan, Priya.
Description:	350 p.
Notes:	Advisers: William W. Nazaroff; Van P. Carey.
Contained By:	Dissertation Abstracts International69-03B.
Subject:	Engineering, Environmental. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3306352
ISBN:	9780549529583

Bayesian based design of real-time sensor systems for high-risk indoor contaminants.
Sreedharan, Priya.

Bayesian based design of real-time sensor systems for high-risk indoor contaminants. - 350 p.

Advisers: William W. Nazaroff; Van P. Carey.

Thesis (Ph.D.)--University of California, Berkeley, 2007.

The sudden release of toxic contaminants that reach indoor spaces can be hazardous to building occupants. To respond effectively, the contaminant release must be quickly detected and characterized to determine unobserved parameters, such as release location and strength. Characterizing the release requires solving an inverse problem. Designing a robust real-time sensor system that solves the inverse problem is challenging because the fate and transport of contaminants is complex, sensor information is limited and imperfect, and real-time estimation is computationally constrained.

ISBN: 9780549529583Subjects--Topical Terms:

783782
Engineering, Environmental.

Bayesian based design of real-time sensor systems for high-risk indoor contaminants.
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020 $a 9780549529583
035 $a (UMI)AAI3306352
035 $a AAI3306352
040 $a UMI $c UMI
100 1 $a Sreedharan, Priya. $3 1285922
245 1 0 $a Bayesian based design of real-time sensor systems for high-risk indoor contaminants.
300 $a 350 p.
500 $a Advisers: William W. Nazaroff; Van P. Carey.
500 $a Source: Dissertation Abstracts International, Volume: 69-03, Section: B, page: 1867.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2007.
520 $a The sudden release of toxic contaminants that reach indoor spaces can be hazardous to building occupants. To respond effectively, the contaminant release must be quickly detected and characterized to determine unobserved parameters, such as release location and strength. Characterizing the release requires solving an inverse problem. Designing a robust real-time sensor system that solves the inverse problem is challenging because the fate and transport of contaminants is complex, sensor information is limited and imperfect, and real-time estimation is computationally constrained.
520 $a This dissertation uses a system-level approach, based on a Bayes Monte Carlo framework, to develop sensor-system design concepts and methods. I describe three investigations that explore complex relationships among sensors, network architecture, interpretation algorithms, and system performance. The investigations use data obtained from tracer gas experiments conducted in a real building.
520 $a The influence of individual sensor characteristics on the sensor-system performance for binary-type contaminant sensors is analyzed. Performance tradeoffs among sensor accuracy, threshold level and response time are identified; these attributes could not be inferred without a system-level analysis. For example, more accurate but slower sensors are found to outperform less accurate but faster sensors.
520 $a Secondly, I investigate how the sensor-system performance can be understood in terms of contaminant transport processes and the model representation that is used to solve the inverse problem. The determination of release location and mass are shown to be related to and constrained by transport and mixing time scales. These time scales explain performance differences among different sensor networks. For example, the effect of longer sensor response times is comparably less for releases with longer mixing time scales.
520 $a The third investigation explores how information fusion from heterogeneous sensors may improve the sensor-system performance and offset the need for more contaminant sensors. Physics- and algorithm-based frameworks are presented for selecting and fusing information from noncontaminant sensors. The frameworks are demonstrated with door-position sensors, which are found to be more useful in natural airflow conditions, but which cannot compensate for poor placement of contaminant sensors.
520 $a The concepts and empirical findings have the potential to help in the design of sensor systems for more complex building systems. The research has broader relevance to additional environmental monitoring problems, fault detection and diagnostics, and system design.
590 $a School code: 0028.
650 4 $a Engineering, Environmental. $3 783782
650 4 $a Engineering, Mechanical. $3 783786
650 4 $a Engineering, System Science. $3 1018128
690 $a 0548
690 $a 0775
690 $a 0790
710 2 $a University of California, Berkeley. $3 687832
773 0 $t Dissertation Abstracts International $g 69-03B.
790 $a 0028
790 1 0 $a Carey, Van P., $e advisor
790 1 0 $a Nazaroff, William W., $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3306352