@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/188f86eb-e6f3-4bd8-a67a-c9c1feebdfcd> a dcat:Dataset ;
    dct:description """The environmental impact of aviation is enormous given the fact that\r
in the US alone there are nearly 6 million flights per year of commercial aircraft.\r
This situation has driven numerous policy and procedural measures to help develop\r
environmentally friendly technologies which are safe and affordable and reduce the\r
environmental impact of aviation. However, many of these technologies require significant\r
initial investment in newer aircraft fleets and modifications to existing regulations\r
which are both long and costly enterprises. We propose to use an anomaly\r
detection method based on Virtual Sensors to help detect overconsumption of fuel in\r
aircraft which relies only on the data recorded during flight of most existing commercial\r
aircraft, thus significantly reducing the cost and complexity of implementing this\r
method. The Virtual Sensors developed here are ensemble-learning regression models\r
for detecting the overconsumption of fuel based on instantaneous measurements\r
of the aircraft state. This approach requires no additional information about standard\r
operating procedures or other encoded domain knowledge. We present experimental\r
results on three data sets and compare five different Virtual Sensors algorithms. The\r
first two data sets are publicly available and consist of a simulated data set from a\r
flight simulator and a real-world turbine disk.We show the ability to detect anomalies\r
with high accuracy on these data sets. These sets contain seeded faults, meaning that\r
they have been deliberately injected into the system. The second data set is from realworld\r
fleet of 84 jet aircraft where we show the ability to detect fuel overconsumption\r
which can have a significant environmental and economic impact. To the best of our\r
knowledge, this is the first study of its kind in the aviation domain.""" ;
    dct:identifier "DASHLINK_510" ;
    dct:issued "2012-01-19"^^xsd:date ;
    dct:modified "2020-01-29"^^xsd:date ;
    dct:publisher <https://data.nasa.gov/organization/dbb294e4-8597-44f5-aaf4-c0baac12cdbc> ;
    dct:title "Greener Aviation with Virtual Sensors:  A Case Study" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "Ashok Srivastava" ;
            vcard:hasEmail <mailto:ashok.n.srivastava@gmail.com> ] ;
    dcat:distribution <https://data.nasa.gov/dataset/188f86eb-e6f3-4bd8-a67a-c9c1feebdfcd/resource/5ea69e1c-7ab3-4b5c-9ba2-5b55400074fa> ;
    dcat:keyword "ames",
        "dashlink",
        "nasa" .

<https://data.nasa.gov/dataset/188f86eb-e6f3-4bd8-a67a-c9c1feebdfcd/resource/5ea69e1c-7ab3-4b5c-9ba2-5b55400074fa> a dcat:Distribution ;
    dct:description "Greener Aviation" ;
    dct:format "PDF" ;
    dct:identifier "5ea69e1c-7ab3-4b5c-9ba2-5b55400074fa" ;
    dct:issued "2025-03-31T21:57:25.183940"^^xsd:dateTime ;
    dct:modified "2025-03-31T21:57:25.168241"^^xsd:dateTime ;
    dct:title "Srivastava DMKD 2012.pdf" ;
    dcat:accessURL <https://c3.nasa.gov/dashlink/static/media/publication/Srivastava_DMKD_2012.pdf> ;
    dcat:mediaType "application/pdf" .

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