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https://doi.org/10.21256/zhaw-25557| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Segment-based CO2 emission evaluations from passenger cars based on deep learning techniques |
| Autor/-in: | Niroomand, Naghmeh Bach, Christian Elser, Miriam |
| et. al: | No |
| DOI: | 10.1109/ACCESS.2021.3135604 10.21256/zhaw-25557 |
| Erschienen in: | IEEE Access |
| Band(Heft): | 9 |
| Seite(n): | 166314 |
| Seiten bis: | 166327 |
| Erscheinungsdatum: | 2021 |
| Verlag / Hrsg. Institution: | IEEE |
| ISSN: | 2169-3536 |
| Sprache: | Englisch |
| Schlagwörter: | Automobile; CO2 emission; Classification algorithm; Clustering algorithm |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 363: Umwelt- und Sicherheitsprobleme |
| Zusammenfassung: | The 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/25557 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Management and Law |
| Organisationseinheit: | Zentrum für Arbeitsmärkte, Digitalisierung und Regionalökonomie (CLDR) |
| Enthalten in den Sammlungen: | Publikationen School of Management and Law |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Niroomand-etal_Segment-based-CO2-emission-evaluations.pdf | 1.47 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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