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Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles.

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Title: Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles.
Authors: Fortela, Dhan Lord B.1,2, dhanlord.fortela@louisiana.edu, Fremin, Ashton C.1, Sharp, Wayne2,3, Mikolajczyk, Ashley P.1,2, Revellame, Emmanuel2,4, Holmes, William1,2, Hernandez, Rafael1,2, Zappi, Mark1,2
Source: Clean Technologies; Mar2023, Vol. 5 Issue 1, p418-435, 18p
Database: Applied Science & Technology Source
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  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 162746721
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PubType: Academic Journal
PubTypeId: academicJournal
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=162746721
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        Value: 10.3390/cleantechnol5010021
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        Text: English
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      – TitleFull: Unsupervised Machine Learning to Detect Impending Anomalies in Testing of Fuel Economy and Emissions of Light-Duty Vehicles.
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              Text: Mar2023
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