Optimization of Microchannels and Application of Basic Activation Functions of Deep Neural Network for Accuracy Analysis of Microfluidic Parameter Data

Feroz Ahmed, Masashi Shimizu, Jin Wang, Kenji Sakai, Toshihiko Kiwa

Research output: Contribution to journalArticlepeer-review

Abstract

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 (Formula presented.) 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.

Original languageEnglish
Article number1352
JournalMicromachines
Volume13
Issue number8
DOIs
Publication statusPublished - Aug 2022

Keywords

  • 3D simulation
  • deep learning model
  • fluid dynamics
  • Leaky ReLU
  • microfluidics
  • ReLU dense layers
  • swish activation functions

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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