@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix org: <http://www.w3.org/ns/org#> .
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://data.nasa.gov/dataset/071c9f78-636c-4416-86d0-d25adb371dba> a dcat:Dataset ;
    dct:description """One of NASA’s key mission requirements is robust state estimation.  Sensing, using a wide range of sensors and sensor fusion approaches, plays a central role in robust state estimation, and there is a need to diagnose sensor failure as well as component failure.   Sensor validation techniques address this problem: given a vector of sensor readings, decide whether sensors have failed, therefore producing bad data.  We take in this paper a probabilistic approach, using Bayesian networks, to diagnosis and sensor validation, and investigate several relevant but slightly different Bayesian network queries. We emphasize that on-board inference can be performed on a compiled model, giving fast and predictable execution times. Our results are illustrated using an electrical power system, and we show that a Bayesian network with over 400 nodes can be compiled into an arithmetic circuit that can correctly answer queries in less than 500 microseconds on average.\r
\r
Reference:\r
\r
O. J. Mengshoel, A. Darwiche, and S. Uckun, "Sensor Validation using Bayesian Networks." In Proc. of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (iSAIRAS-08), Los Angeles, CA, 2008.\r
\r
BibTex Reference:\r
\r
@inproceedings{mengshoel08sensor,\r
  author    = {Mengshoel, O. J. and Darwiche, A. and Uckun, S.},\r
  title     = {Sensor Validation using {Bayesian} Networks},\r
  booktitle     = {Proceedings of the 9th International Symposium on Artificial Intelligence, Robotics, and Automation in Space (iSAIRAS-08)},\r
  year      = {2008}\r
}""" ;
    dct:identifier "DASHLINK_12" ;
    dct:issued "2010-09-09"^^xsd:date ;
    dct:modified "2020-01-29"^^xsd:date ;
    dct:publisher <https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> ;
    dct:title "Sensor Validation using Bayesian Networks" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "Ole Mengshoel" ;
            vcard:hasEmail <mailto:ole.j.mengshoel@nasa.gov> ] ;
    dcat:distribution <https://data.nasa.gov/dataset/071c9f78-636c-4416-86d0-d25adb371dba/resource/676d4dcb-7d68-45b8-b01d-ce11521ac562> ;
    dcat:keyword "ames",
        "dashlink",
        "nasa" .

<https://data.nasa.gov/dataset/071c9f78-636c-4416-86d0-d25adb371dba/resource/676d4dcb-7d68-45b8-b01d-ce11521ac562> a dcat:Distribution ;
    dct:description "Sensor Validation using Bayesian Networks" ;
    dct:format "PDF" ;
    dct:identifier "676d4dcb-7d68-45b8-b01d-ce11521ac562" ;
    dct:issued "2025-03-31T20:08:16.498044"^^xsd:dateTime ;
    dct:modified "2025-03-31T20:08:16.483429"^^xsd:dateTime ;
    dct:title "BNiSAIRASv15.pdf" ;
    dcat:accessURL <https://c3.nasa.gov/dashlink/static/media/publication/BNiSAIRASv15.pdf> ;
    dcat:mediaType "application/pdf" .

<https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> a org:Organization ;
    skos:prefLabel "NASA" ;
    foaf:name "NASA" .