Skip Nav Destination
Close Modal
Update search
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
Filter
- Title
- Author
- Author Affiliations
- Full Text
- Abstract
- Keyword
- DOI
- ISBN
- ISBN-10
- ISSN
- EISSN
- Issue
- Journal Volume Number
- References
- Conference Volume Title
- Paper No
NARROW
Format
Article Type
Subject Area
Topics
Date
Availability
1-1 of 1
Keywords: deep learning prediction
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Proceedings Papers
Proc. ASME. POWER2023, ASME Power Applied R&D 2023, V001T04A008, August 6–8, 2023
Publisher: American Society of Mechanical Engineers
Paper No: POWER2023-109005
... capability of the deep-learning prediction framework. Model training was performed with acoustic signals at dilution levels of CO 2 ∼ 79, 81, 82, 84% while testing was performed with CO 2 ∼ 83 and 80%. Model optimization was considered with different epochs numbers of 20, 50, and 100. The trained model...