Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/29423
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Huang, Q | - |
dc.contributor.author | Chen, Y | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Chen, X | - |
dc.contributor.author | Nandi, A | - |
dc.contributor.author | Li, M | - |
dc.contributor.author | Jin, Y | - |
dc.date.accessioned | 2024-07-26T14:05:20Z | - |
dc.date.available | 2024-07-26T14:05:20Z | - |
dc.date.issued | 2024-04-29 | - |
dc.identifier | ORCiD: Asoke Nandi https://orcid.org/0000-0001-6248-2875 | - |
dc.identifier | ORCiD: Maozhen Li https://orcid.org/0000-0002-0820-5487 | - |
dc.identifier.citation | Huang, Q. et al. (2024) 'Research On Discrimination Method Of Carbon Deposit Degree Of Automobile Engine Based On Deep Learning', Computing and Informatics, 43 (1), pp. 126 - 148. doi: 10.31577/cai_2024_1_126. | en_US |
dc.identifier.issn | 1335-9150 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29423 | - |
dc.description.abstract | The detection of carbon deposit degree is of great significance to the maintenance of automobile engine. Due to issues with poor feature aggregation, inter-class similarity, and intra-class variance in carbon deposit data with a small number of samples, model-based discriminative approaches cannot be widely implemented in the market. In order to overcome this technical barrier, the article examines the impact of DCNNs (Deep Convolutional Neural Networks) level on the recognition effect of the degree of carbon deposit, introduces a dropout structure and data enhancement strategy to lower the risk of overfitting brought on by the small dataset, and suggests a recognition method based on the kernel of dual-dimensional multiscale-multifrequency information features to enhance the differentiation characteristic. After experimental testing, the accuracy of this method is 86.9 %, the F1-score is 87.2 %, and the inference speed is 190 FPS, which can meet the practical requirements and provide basic support for the large-scale promotion of the model discrimination. | en_US |
dc.description.sponsorship | This work was supported by the Shanxi Province Key Research and Development Program Projects (No. 202302020101008) and the Research Project Supported by Shanxi Scholarship Council of China (No. 2022-145) and the Graduate Science and Technology Project Supported by North University of China (No. 2022180506). | en_US |
dc.format.extent | 126 - 148 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Bratislava Institute of Informatics, Slovak Academy of Sciences | en_US |
dc.rights | Copyright © 2024 The Authors / Slovak Academy of Sciences. This is an open access article. | - |
dc.subject | determination of carbon deposit degree | en_US |
dc.subject | small datasets | en_US |
dc.subject | fine-grained images | en_US |
dc.subject | feature enhancement | en_US |
dc.subject | deep learning | en_US |
dc.title | Research On Discrimination Method Of Carbon Deposit Degree Of Automobile Engine Based On Deep Learning | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.31577/cai_2024_1_126 | - |
dc.relation.isPartOf | Computing and Informatics | - |
pubs.issue | 1 | - |
pubs.publication-status | Published | - |
pubs.volume | 43 | - |
dc.identifier.eissn | 2585-8807 | - |
dc.rights.holder | The Authors / Slovak Academy of Sciences | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2024 The Authors / Slovak Academy of Sciences. This is an open access article. | 2.5 MB | Adobe PDF | View/Open |
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.