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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25557
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dc.contributor.authorNiroomand, Naghmeh-
dc.contributor.authorBach, Christian-
dc.contributor.authorElser, Miriam-
dc.date.accessioned2022-09-01T12:57:56Z-
dc.date.available2022-09-01T12:57:56Z-
dc.date.issued2021-
dc.identifier.issn2169-3536de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25557-
dc.description.abstractThe overall level of emissions from the Swiss passenger cars is strongly dependent on the fleet composition. Despite technology improvements, the Swiss passenger cars fleet remains emissions intensive. To analyze the root of this problem and evaluate potential solutions, this paper applies deep learning techniques to evaluate the inter-class (namely micro, small, middle, upper middle, large and luxury class) and intra-class (namely sport utility vehicle and non-sport utility vehicle) differences in carbon dioxide (CO2) emissions. This paper takes full use of novel semi-supervised fuzzy C-means (SSFCM), random forest and AdaBoost models as well as model fusion to successfully classify passenger vehicles and enable segment-based CO2 emission evaluations.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Accessde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectAutomobilede_CH
dc.subjectCO2 emissionde_CH
dc.subjectClassification algorithmde_CH
dc.subjectClustering algorithmde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc363: Umwelt- und Sicherheitsproblemede_CH
dc.titleSegment-based CO2 emission evaluations from passenger cars based on deep learning techniquesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Management and Lawde_CH
zhaw.organisationalunitZentrum für Arbeitsmärkte, Digitalisierung und Regionalökonomie (CLDR)de_CH
dc.identifier.doi10.1109/ACCESS.2021.3135604de_CH
dc.identifier.doi10.21256/zhaw-25557-
zhaw.funding.euNode_CH
zhaw.originated.zhawNode_CH
zhaw.pages.end166327de_CH
zhaw.pages.start166314de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume9de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedW: Spitzenpublikationde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Niroomand, N., Bach, C., & Elser, M. (2021). Segment-based CO2 emission evaluations from passenger cars based on deep learning techniques. IEEE Access, 9, 166314–166327. https://doi.org/10.1109/ACCESS.2021.3135604
Niroomand, N., Bach, C. and Elser, M. (2021) ‘Segment-based CO2 emission evaluations from passenger cars based on deep learning techniques’, IEEE Access, 9, pp. 166314–166327. Available at: https://doi.org/10.1109/ACCESS.2021.3135604.
N. Niroomand, C. Bach, and M. Elser, “Segment-based CO2 emission evaluations from passenger cars based on deep learning techniques,” IEEE Access, vol. 9, pp. 166314–166327, 2021, doi: 10.1109/ACCESS.2021.3135604.
NIROOMAND, Naghmeh, Christian BACH und Miriam ELSER, 2021. Segment-based CO2 emission evaluations from passenger cars based on deep learning techniques. IEEE Access. 2021. Bd. 9, S. 166314–166327. DOI 10.1109/ACCESS.2021.3135604
Niroomand, Naghmeh, Christian Bach, and Miriam Elser. 2021. “Segment-Based CO2 Emission Evaluations from Passenger Cars Based on Deep Learning Techniques.” IEEE Access 9: 166314–27. https://doi.org/10.1109/ACCESS.2021.3135604.
Niroomand, Naghmeh, et al. “Segment-Based CO2 Emission Evaluations from Passenger Cars Based on Deep Learning Techniques.” IEEE Access, vol. 9, 2021, pp. 166314–27, https://doi.org/10.1109/ACCESS.2021.3135604.


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