Conference item
Using neural network and random forest algorithmic approaches to predicting particulate emissions from a highly boosted GDI engine
- Abstract:
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Particulate emissions from gasoline direct injection (GDI) engines continue to be a topic of substantial research interest. Forthcoming regulation both in the USA and the EU will further reduce their emission and drive innovation. Substantial research effort is spent undertaking experiments to understand, characterize, and research particle number (PN) emissions from engines and vehicles. Recent advances in computing power, data storage, and understanding of artificial intelligence algorithms...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
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(Preview, Accepted manuscript, 1.1MB, Terms of use)
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- Publisher copy:
- 10.4271/2021-24-0076
Authors
Bibliographic Details
- Publisher:
- SAE International
- Journal:
- SAE Technical Papers More from this journal
- Article number:
- 2021-24-0076
- Publication date:
- 2021-09-05
- Acceptance date:
- 2021-07-08
- Event title:
- 15th International Conference on Engines and Vehicles (ICE2021)
- Event location:
- Naples, Italy
- Event website:
- https://www.ice2021.org/
- Event start date:
- 2021-09-12
- Event end date:
- 2021-09-16
- DOI:
- EISSN:
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2688-3627
- ISSN:
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0148-7191
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1185421
- Local pid:
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pubs:1185421
- Deposit date:
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2021-07-08
Terms of use
- Copyright holder:
- SAE International
- Copyright date:
- 2021
- Rights statement:
- © 2021 SAE International. All Rights Reserved.
- Notes:
- This is the accepted manuscript version of the paper. The final version is available online from SAE International at: https://doi.org/10.4271/2021-24-0076
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