Sensor Placement for Spatial Gaussian Processes with Integral Observations

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Longi , K E , Rajani , C , Sillanpää , T O N , Mäkinen , J M K , Rauhala , T , Salmi , A , Haeggström , E & Klami , A 2020 , Sensor Placement for Spatial Gaussian Processes with Integral Observations . in Proceedings of 36th Conference on Uncertainty in Artificial Intelligence . Conference on Uncertainty in Artificial Intelligence , vol. 124 , AUAI Press / Association for Uncertainty in Artificial Intelligence , The 36th Conference on Uncertainty in Artificial Intelligence , Unknown , 03/08/2020 . < http://www.auai.org/uai2020/proceedings/411_main_paper.pdf >

Title: Sensor Placement for Spatial Gaussian Processes with Integral Observations
Author: Longi, Krista Elena; Rajani, Chang; Sillanpää, Tom Oskar Nikolai; Mäkinen, Joni Mikko Kristian; Rauhala, Timo; Salmi, Ari; Haeggström, Edward; Klami, Arto
Contributor: University of Helsinki, Department of Computer Science
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Physics
University of Helsinki, Materials Physics
University of Helsinki, Department of Physics
University of Helsinki, Department of Physics
University of Helsinki, Department of Computer Science
Publisher: AUAI Press / Association for Uncertainty in Artificial Intelligence
Date: 2020-08
Number of pages: 10
Belongs to series: Proceedings of 36th Conference on Uncertainty in Artificial Intelligence
Belongs to series: Conference on Uncertainty in Artificial Intelligence
URI: http://hdl.handle.net/10138/324500
Abstract: Gaussian processes (GP) are a natural tool for estimating unknown functions, typically based on a collection of point-wise observations. Interestingly, the GP formalism can be used also with observations that are integrals of the unknown function along some known trajectories, which makes GPs a promising technique for inverse problems in a wide range of physical sensing problems. However, in many real world applications collecting data is laborious and time consuming. We provide tools for optimizing sensor locations for GPs using integral observations, extending both model-based and geometric strategies for GP sensor placement. We demonstrate the techniques in ultrasonic detection of fouling in closed pipes.
Subject: 113 Computer and information sciences
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