DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Sung Wook | ko |
dc.contributor.author | Lee, Young Gon | ko |
dc.contributor.author | Tama, Bayu Adhi | ko |
dc.contributor.author | Lee, Seungchul | ko |
dc.date.accessioned | 2023-09-13T01:01:39Z | - |
dc.date.available | 2023-09-13T01:01:39Z | - |
dc.date.created | 2023-09-13 | - |
dc.date.created | 2023-09-13 | - |
dc.date.issued | 2020-06 | - |
dc.identifier.citation | APPLIED SCIENCES-BASEL, v.10, no.11 | - |
dc.identifier.uri | http://hdl.handle.net/10203/312519 | - |
dc.description.abstract | Artificial intelligence has become the primary issue in the era of Industry 4.0, accelerating the realization of a self-driven smart factory. It is transforming various manufacturing sectors including the assembly line for a camera lens module. The recent development of bezel-less smartphones necessitates a large-scale production of the camera lens module. However, assembling the necessary parts of a module needs much room to be improved since the procedure followed by its inspection is costly and time-consuming. Consequently, the collection of labeled data is often limited. In this study, a reliable means to predict the state of an unseen camera lens module using simple semi-supervised regression is proposed. Here, an experimental study to investigate the effect of different numbers of training samples is demonstrated. The increased amount of data using simple pseudo-labeling means is shown to improve the general performance of deep neural network for the prediction of Modulation Transfer Function (MTF) by as much as 18%, 15% and 25% in terms of RMSE, MAE and R squared. The cross-validation technique is used to ensure a generalized predictive performance. Furthermore, binary classification is conducted based on a threshold value for MTF to finally demonstrate the better prediction outcome in a real-world scenario. As a result, the overall accuracy, recall, specificity and f1-score are increased by 11.3%, 9%, 1.6% and 7.6% showing that the classification of camera lens module has been improved through the suggested semi-supervised regression method. | - |
dc.language | English | - |
dc.publisher | MDPI | - |
dc.title | Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method | - |
dc.type | Article | - |
dc.identifier.wosid | 000543385900169 | - |
dc.identifier.scopusid | 2-s2.0-85086110078 | - |
dc.type.rims | ART | - |
dc.citation.volume | 10 | - |
dc.citation.issue | 11 | - |
dc.citation.publicationname | APPLIED SCIENCES-BASEL | - |
dc.identifier.doi | 10.3390/app10113832 | - |
dc.contributor.localauthor | Lee, Seungchul | - |
dc.contributor.nonIdAuthor | Kim, Sung Wook | - |
dc.contributor.nonIdAuthor | Lee, Young Gon | - |
dc.contributor.nonIdAuthor | Tama, Bayu Adhi | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | semi-supervised regression | - |
dc.subject.keywordAuthor | camera lens module | - |
dc.subject.keywordAuthor | pseudo-label | - |
dc.subject.keywordAuthor | deep neural network | - |
dc.subject.keywordAuthor | modular transfer function | - |
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