دورية أكاديمية
Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data
| العنوان: | Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data |
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| المؤلفون: | M. Kodama, A. Takeuchi, M. Uesugi, S. Hirai |
| المصدر: | Energy and AI, Vol 14, Iss , Pp 100305- (2023) |
| بيانات النشر: | Elsevier, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Electrical engineering. Electronics. Nuclear engineering LCC:Computer software |
| مصطلحات موضوعية: | All-solid-state lithium-ion battery, X-ray CT, Laboratory CT, Synchrotron radiation CT, Super-resolution, Machine learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765 |
| الوصف: | High-performance all-solid-state lithium-ion batteries require observation, control, and optimization of the electrode structure. X-ray computational tomography (CT) is an effective nondestructive method for observing the electrode structure in three dimensions. However, the limited availability of synchrotron radiation CT, which offers high-resolution imaging with a high signal-to-noise ratio, makes it difficult to conduct experiments and restricts the use of X-ray CT in battery development. Conversely, laboratory CT systems are widely available, but they use X-rays emitted from a metal target, resulting in lower image quality and resolution compared with synchrotron radiation CT. This study explores a method for achieving comparable resolution in laboratory CT images of all-solid-state batteries to that of synchrotron radiation CT. Our method involves using the synchrotron radiation CT images as training data for machine learning super-resolution. The results demonstrate that, by employing an appropriate machine learning algorithm and activation function, along with a sufficiently deep network, the image quality of laboratory CT becomes equivalent to that of synchrotron radiation CT. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2666-5468 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666546823000770; https://doaj.org/toc/2666-5468 |
| DOI: | 10.1016/j.egyai.2023.100305 |
| URL الوصول: | https://doaj.org/article/1a1ed06def374041b9bf4d2e3924ef1d |
| رقم الأكسشن: | edsdoj.1a1ed06def374041b9bf4d2e3924ef1d |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26665468 |
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| DOI: | 10.1016/j.egyai.2023.100305 |