National Cheng Kung University

About: National Cheng Kung University is a education organization based out in Tainan City, Taiwan. It is known for research contribution in the topics: Population & Thin film. The organization has 49723 authors who have published 69799 publications receiving 1437420 citations. The organization is also known as: NCKU.

The hydrodynamic focusing effect inside rectangular microchannels

Abstract: This paper presents a theoretical and experimental investigation into the hydrodynamic focusing effect in rectangular microchannels. Two theoretical models for two-dimensional hydrodynamic focusing are proposed. The first model predicts the width of the focused stream in symmetric hydrodynamic focusing in microchannels of various aspect ratios. The second model predicts the location and the width of the focused stream in asymmetric hydrodynamic focusing in microchannels with a low or high aspect ratio. In both models, the theoretical results are shown to be in good agreement with the experimental data. Hence, the models provide a useful means of performing a theoretical analysis of flow control in microfluidic devices using hydrodynamic focusing effects. The ability of the proposed models to control the focused stream within a micro flow cytometer is verified in a series of experimental trials performed using polystyrene microparticles with a diameter of 20 µm. The experimental data show that the width of the focused stream can be reduced to the same order of magnitude as that of the particle size. Furthermore, it is shown that the microparticles can be successfully hydrodynamically focused and switched to the desired outlet port of the cytometer. Hence, the models presented in this study provide sufficient control to support cell/particle counting and sorting applications.

Robust radial basis function neural networks

Abstract: Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which has shown a great promise in this sort of problems because of its faster learning capacity. A traditional RBF network takes Gaussian functions as its basis functions and adopts the least-squares criterion as the objective function, However, it still suffers from two major problems. First, it is difficult to use Gaussian functions to approximate constant values. If a function has nearly constant values in some intervals, the RBF network will be found inefficient in approximating these values. Second, when the training patterns incur a large error, the network will interpolate these training patterns incorrectly. In order to cope with these problems, an RBF network is proposed in this paper which is based on sequences of sigmoidal functions and a robust objective function. The former replaces the Gaussian functions as the basis function of the network so that constant-valued functions can be approximated accurately by an RBF network, while the latter is used to restrain the influence of large errors. Compared with traditional RBF networks, the proposed network demonstrates the following advantages: (1) better capability of approximation to underlying functions; (2) faster learning speed; (3) better size of network; (4) high robustness to outliers.