Biomedical Science and Engineering

ISSN (Print): 2373-1257

ISSN (Online): 2373-1265

Website: http://www.sciepub.com/journal/BSE

Current Issue» Volume 3, Number 1 (2015)

Article

Multi-dimensional Analysis, Information Mining and Numerical Modeling for Small Samples of Biomedical Microfluidic Droplets

1School of life science, Zhengzhou Univ. 100 Science Avenue, Zhengzhou, P.R.China

2Henan institute of pharmaceutical science, Zhengzhou University, Zhengzhou, P.R.China


Biomedical Science and Engineering. 2015, 3(1), 1-3
DOI: 10.12691/bse-3-1-1
Copyright © 2015 Science and Education Publishing

Cite this paper:
Peiyuan He, Liguo Zhang. Multi-dimensional Analysis, Information Mining and Numerical Modeling for Small Samples of Biomedical Microfluidic Droplets. Biomedical Science and Engineering. 2015; 3(1):1-3. doi: 10.12691/bse-3-1-1.

Correspondence to: Peiyuan  He, School of life science, Zhengzhou Univ. 100 Science Avenue, Zhengzhou, P.R.China. Email: hepeiyuan@live.com

Abstract

Information mining has turned to be increasingly important in the modern digital world. The mining results lead to the essential advances in not only information technology, but general natural science, such as life science, mechanics, numerical analysis, calculation technology, etc. These innovations permitted for the manipulation of biomicrofluidic droplets at micrometer-scale. Through the acquisition of raw data and the multidimensional analysis, numerical modeling can possibly be established and the further optimization can be processed. This work was focused on the multi-dimensional analysis based on small samples of the biomicrofludic droplet generation. Then numerical modeling was used to describe the fabrication of microdroplets in complex circumstances. Both linear modeling and nonlinear optimization were performed while the artificial intelligence technology was applied. Although the linear model presented the rough descriptive capacities of microdroplets variation, non-linear optimization improved its descriptive properties. Artificial neural network was established to depict the microfluidic droplets and its accuracy was validated satisfactorily.

Keywords

References

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