Ahmed Feroz, Shimizu Masashi, Wang Jin, Sakai Kenji, Kiwa Toshihiko
Graduate School of Interdisciplinary Science and Engineering in Health Systems, Department of Medical Bioengineering, Okayama University, 3-1-1, Tsushima-naka, Kita-ku, Okayama 700-8530, Japan.
Micromachines (Basel). 2022 Aug 20;13(8):1352. doi: 10.3390/mi13081352.
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 μm 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 6×10-5 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.
在评估微流控系统性能时，要在优化设计方案、最小化表面粗糙度以及控制微流控参数的流量方面高精度地制造微流道具有挑战性。使用传统的输入设备，如蠕动泵和数字压力泵来评估此类参数的流量控制，由于其操作缺陷，无法更准确地确认广泛的数据分析。在本研究中，我们使用三维模拟工具优化了100μm微流控芯片的圆形和矩形微流道，并分析了微流道不同区域的浓度分布。然后，我们将深度学习（DL）算法应用于整流线性单元（ReLU）、泄漏ReLU和Swish激活函数的密集层，以训练和测试从微流控芯片获得的1600个实验数据样本和插值数据样本。此外，使用相同的DL算法，我们通过改变这些模型的内部中间层，为这三个函数中的每一个配置了三个模型。结果，我们使用试错法，针对定义为6×10⁻⁵的阈值，总共获得了ReLU、泄漏ReLU和Swish函数的9个平均准确率值。我们应用了深度神经网络的单到五重交叉验证技术，以避免过拟合并减少数据集中的噪声，从而更好地评估微流控参数数据的平均准确率。
