TY - GEN
T1 - Intelligent parameters identification on numerical model of EOF-based gated injection in microfluidic channels
AU - Xu, Yuanqing
AU - Deng, Yulin
AU - Geng, Lina
AU - He, Jianming
PY - 2009
Y1 - 2009
N2 - In this paper, a numerical simulation model of electro-osmosis flow (EOF) based gated injection in microfluidic channels is established. Based on which, a control purpose to quantify and locate separated sample is to be realized, in which the key problem is to define the electric field, the injection time and the separation time. In order to give the proper contol parameters, the artificial neural network (ANN) is adopted as an intelligent parameter identifier, in our design, it will give the injection time and the separation time properly if the expectation sample volume and the electric field are given. Tested by the numerical simulation model with 10 random calculation examples, the results indicate that the ANN identifier can give the corresponding control parameters correctly, and the control method on quantifying and locating the separated sample in gated injection can be successfully achieved.
AB - In this paper, a numerical simulation model of electro-osmosis flow (EOF) based gated injection in microfluidic channels is established. Based on which, a control purpose to quantify and locate separated sample is to be realized, in which the key problem is to define the electric field, the injection time and the separation time. In order to give the proper contol parameters, the artificial neural network (ANN) is adopted as an intelligent parameter identifier, in our design, it will give the injection time and the separation time properly if the expectation sample volume and the electric field are given. Tested by the numerical simulation model with 10 random calculation examples, the results indicate that the ANN identifier can give the corresponding control parameters correctly, and the control method on quantifying and locating the separated sample in gated injection can be successfully achieved.
KW - Artificial neural work
KW - Electroosmosis flow (EOF)
KW - Gated injection
KW - Microfluidic chip
KW - Numerical modeling
UR - http://www.scopus.com/inward/record.url?scp=77949505688&partnerID=8YFLogxK
U2 - 10.1109/ICICISYS.2009.5357626
DO - 10.1109/ICICISYS.2009.5357626
M3 - Conference contribution
AN - SCOPUS:77949505688
SN - 9781424447541
T3 - Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
SP - 477
EP - 481
BT - Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
T2 - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Y2 - 20 November 2009 through 22 November 2009
ER -
