A novel micro-defect classification system based on attention enhancement
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DOI: 10.1007/s10845-022-02064-2
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- Peng Cheng & Hai Wang & Vladimir Stojanovic & Fei Liu & Shuping He & Kaibo Shi, 2022. "Dissipativity-based finite-time asynchronous output feedback control for wind turbine system via a hidden Markov model," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(15), pages 3177-3189, November.
- Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
- Tobias Schlosser & Michael Friedrich & Frederik Beuth & Danny Kowerko, 2022. "Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1099-1123, April.
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Keywords
Convolutional neural network; Vision attention; Intelligent manufacturing; Defect classification; Deep learning;All these keywords.
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