ID | 63864 |
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著者 |
Ahmed, Feroz
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Department of Medical Bioengineering, Okayama University
Shimizu, Masashi
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Department of Medical Bioengineering, Okayama University
Wang, Jin
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Department of Medical Bioengineering, Okayama University
Sakai, Kenji
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Department of Medical Bioengineering, Okayama University
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Kiwa, Toshihiko
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Department of Medical Bioengineering, Okayama University
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抄録 | The fabrication of microflow channels with high accuracy in terms of the optimization of the proposed designs, minimization of surface roughness, and flow control of microfluidic parameters is challenging when evaluating the performance of microfluidic systems. The use of conventional input devices, such as peristaltic pumps and digital pressure pumps, to evaluate the flow control of such parameters cannot confirm a wide range of data analysis with higher accuracy because of their operational drawbacks. In this study, we optimized the circular and rectangular-shaped microflow channels of a 100 mu m microfluidic chip using a three-dimensional simulation tool, and analyzed concentration profiles of different regions of the microflow channels. Then, we applied a deep learning (DL) algorithm for the dense layers of the rectified linear unit (ReLU), Leaky ReLU, and Swish activation functions to train and test 1600 experimental and interpolation of data samples which obtained from the microfluidic chip. Moreover, using the same DL algorithm, we configured three models for each of these three functions by changing the internal middle layers of these models. As a result, we obtained a total of 9 average accuracy values of ReLU, Leaky ReLU, and Swish functions for a defined threshold value of 6 x 10(-5) using the trial-and-error method. We applied single-to-five-fold cross-validation technique of deep neural network to avoid overfitting and reduce noises from data-set to evaluate better average accuracy of data of microfluidic parameters.
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キーワード | microfluidics
fluid dynamics
3D simulation
ReLU dense layers
Leaky ReLU
swish activation functions
deep learning model
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発行日 | 2022-08-20
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出版物タイトル |
Micromachines
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巻 | 13巻
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号 | 8号
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出版者 | MDPI
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開始ページ | 1352
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ISSN | 2072-666X
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資料タイプ |
学術雑誌論文
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言語 |
英語
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OAI-PMH Set |
岡山大学
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著作権者 | © 2022 by the authors.
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論文のバージョン | publisher
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関連URL | isVersionOf https://doi.org/10.3390/mi13081352
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ライセンス | https://creativecommons.org/licenses/by/4.0/
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