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dc.contributor.authorKube, Ralph
dc.contributor.authorBianchi, Filippo Maria
dc.contributor.authorLaBombard, Brian
dc.contributor.authorBrunner, Dan
dc.date.accessioned2019-03-22T12:53:18Z
dc.date.available2019-03-22T12:53:18Z
dc.date.issued2019-01-16
dc.description.abstractUnderstanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow us to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution, we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. In this way, we avoid defining complicated threshold rules to identify outliers, which require strong assumptions and introduce biases in the analysis. By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat fluxes are between approximately 5% and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.en_US
dc.description.sponsorshipU.S. Department of Energy, Office of Science, Office of Fusion Energy Sciencesen_US
dc.descriptionThis article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in the <i>Review of Scientific Instruments, 90</i>, 013505, and may be found at <a href=https://doi.org/10.1063/1.5049519>https://doi.org/10.1063/1.5049519</a>.en_US
dc.identifier.citationKube, R., Bianchi, F.M., Brunner, D. & LaBombard, B. (2019). Outlier classification using autoencoders: Application for fluctuation driven flows in fusion plasmas. <i>Review of Scientific Instruments, 90</i>, 013505. https://doi.org/10.1063/1.5049519en_US
dc.identifier.cristinIDFRIDAID 1660953
dc.identifier.doi10.1063/1.5049519
dc.identifier.issn0034-6748
dc.identifier.issn1089-7623
dc.identifier.urihttps://hdl.handle.net/10037/15048
dc.language.isoengen_US
dc.publisherAmerican Institute of Physicsen_US
dc.relation.journalReview of Scientific Instruments
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430::Space and plasma physics: 437en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Rom- og plasmafysikk: 437en_US
dc.subjectPlasmasen_US
dc.subjectTurbulence measurementen_US
dc.subjectStatistical analysisen_US
dc.subjectPlasma diagnosticsen_US
dc.subjectArtificial neural networksen_US
dc.subjectPlasma blobsen_US
dc.titleOutlier classification using autoencoders: Application for fluctuation driven flows in fusion plasmasen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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