Sensor Placement for Spatial Gaussian Processes with Integral Observations

Krista Elena Longi, Chang Rajani, Tom Oskar Nikolai Sillanpää, Joni Mikko Kristian Mäkinen, Timo Rauhala, Ari Salmi, Edward Haeggström, Arto Klami

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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.
Originalspråkengelska
Titel på gästpublikationProceedings of 36th Conference on Uncertainty in Artificial Intelligence
Antal sidor10
FörlagAUAI Press / Association for Uncertainty in Artificial Intelligence
Utgivningsdatumaug 2020
StatusPublicerad - aug 2020
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangThe 36th Conference on Uncertainty in Artificial Intelligence - Virtual, Unknown
Varaktighet: 3 aug 20206 aug 2020
Konferensnummer: 36
http://auai.org/uai2020/

Publikationsserier

NamnConference on Uncertainty in Artificial Intelligence
Volym124
ISSN (tryckt)1525-3384

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap

Citera det här

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. I 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. http://www.auai.org/uai2020/proceedings/411_main_paper.pdf