Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Control theory & Microstructure. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

A miniaturized liquid core waveguide-capillary electrophoresis system with flow injection sample introduction and fluorometric detection using light-emitting diodes.

Abstract: A novel miniaturized capillary electrophoresis (CE) system is described where a Teflon AF-coated silica capillary serves both as the separation channel and as a transversely illuminated liquid core waveguide. This device uniquely uses flow injection (FI)-based split-flow sample introduction through a falling-drop interface. An H-channel structure fixed on a microscope glass slide utilizes a horizontal separation capillary with tubular sidearms on each end that serve as inlet and outlet flow-through electrode reservoirs. The inlet reservoir also functions as a falling-drop interface for coupling to the FI system. A blue LED is used as excitation source. A large-core optical fiber takes the emitted fluorescence to an inexpensive PMT with two layers of green plastic used for optical filtering. No focusing arrangement is needed. Continuous FI introduction of a series of 30-μL samples containing a mixture of of fluorescein isothiocyanate (FITC)-labeled amino acids allowed a throughput rate up to 144 samples/h,...

Data-Core-Based Fuzzy Min–Max Neural Network for Pattern Classification

Abstract: A fuzzy min-max neural network based on data core (DCFMN) is proposed for pattern classification. A new membership function for classifying the neuron of DCFMN is defined in which the noise, the geometric center of the hyperbox, and the data core are considered. Instead of using the contraction process of the FMNN described by Simpson, a kind of overlapped neuron with new membership function based on the data core is proposed and added to neural network to represent the overlapping area of hyperboxes belonging to different classes. Furthermore, some algorithms of online learning and classification are presented according to the structure of DCFMN. DCFMN has strong robustness and high accuracy in classification taking onto account the effect of data core and noise. The performance of DCFMN is checked by some benchmark datasets and compared with some traditional fuzzy neural networks, such as the fuzzy min-max neural network (FMNN), the general FMNN, and the FMNN with compensatory neuron. Finally the pattern classification of a pipeline is evaluated using DCFMN and other classifiers. All the results indicate that the performance of DCFMN is excellent.