Abstract
This paper presents contaminant source localization and characterization in a sensor-rich multi-story building with a large-scale domain. Bayesian framework infers the posterior distribution of source location and characteristics from the sensor network with the model uncertainty and inaccurate prior knowledge. A Markov Chain Monte Carlo method with a Metropolis-Hastings algorithm provides samples extracted from the posterior distribution. A computationally efficient Gaussian process emulator allows Markove Chain Monte Carlo sampling to use a physics-based model with tractable computational cost and time. The posterior distribution obtained by the proposed method through hypothetical contaminant release in a four-story building with total 156 subzones and sensors approaches true values of parameters of interest closely and shows the efficacy for parameter inference in a large-scale domain.
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Acknowledgement
This work was supported in part by Microsoft Research Asia Accelerating Urban Informatics with Azure Program, and in part by the KI Project via KI for Design of Complex Systems.
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Seok, JH., Lee, SJ., Choi, HL. (2015). A Gaussian Process-Enabled MCMC Approach for Contaminant Source Characterization in a Sensor-Rich Multi-Story Building. In: Ravela, S., Sandu, A. (eds) Dynamic Data-Driven Environmental Systems Science. DyDESS 2014. Lecture Notes in Computer Science(), vol 8964. Springer, Cham. https://doi.org/10.1007/978-3-319-25138-7_17
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DOI: https://doi.org/10.1007/978-3-319-25138-7_17
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