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10 results on '"Xu, Fanxin"'

2. Clinical Medical Test Decision-Making of Liver Disease Using Granular-Ball Rough Set

Author

Xu, Fanxin, Su, Zuqiang, Wang, Guoyin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hu, Mengjun, editor, Cornelis, Chris, editor, Zhang, Yan, editor, Lingras, Pawan, editor, Ślęzak, Dominik, editor, and Yao, JingTao, editor

Published
2024
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3. TE-YOLOF: Tiny and efficient YOLOF for blood cell detection

Author

Xu, Fanxin, Li, Xiangkui, Yang, Hang, Wang, Yali, and Xiang, Wei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Blood cell detection in microscopic images is an essential branch of medical image processing research. Since disease detection based on manual checking of blood cells is time-consuming and full of errors, testing of blood cells using object detectors with Deep Convolutional Neural Network can be regarded as a feasible solution. In this work, an object detector based on YOLOF has been proposed to detect blood cell objects such as red blood cells, white blood cells and platelets. This object detector is called TE-YOLOF, Tiny and Efficient YOLOF, and it is a One-Stage detector using dilated encoder to extract information from single-level feature maps. For increasing efficiency and flexibility, the EfficientNet Convolutional Neural Network is utilized as the backbone for the proposed object detector. Furthermore, the Depthwise Separable Convolution is applied to enhance the performance and minimize the parameters of the network. In addition, the Mish activation function is employed to increase the precision. Extensive experiments on the BCCD dataset prove the effectiveness of the proposed model, which is more efficient than other existing studies for blood cell detection.

Published
2021

8. Facile fabrication of Zn-doped CuAl2O4 nanofiber membranes for photocatalytic degradation of dye wastewater.

Author

Lin, Qing, Sheng, Mingnan, He, Yuxin, Xu, Fanxin, Guo, Xiaobei, Xi, Shuang, Fu, Hailuo, and Zhang, Xiaojuan

Subjects
PHOTODEGRADATION, POLYACRYLONITRILES, SEWAGE, EINSTEIN-Podolsky-Rosen experiment, X-ray diffraction, SPINEL, DYES & dyeing, SPINEL group
Abstract

Zinc-doped CuAl2O4 (Zn-CuAl2O4) nanofiber membranes with a high specific surface area, multi-pore structure, and a partial inverse spinel structure were successfully fabricated by controlling the electrospinning process and calcination atmospheres. The excessive doping of Zn2+ ions results in the formation of ZnAl2O4/CuAl2O4 fiber membrane, which has a poor UV-Vis absorption ability. The diameter of Zn-CuAl2O4 nanofibers is about 80 nm, and the specific surface area of the Zn-CuAl2O4 nanofiber membrane is 45.2 m2/g. XRD and XPS results indicate that Zn-CuAl2O4 nanofiber membranes contain spinel and inverse spinel phases. Compared with pure spinel CuAl2O4 nanofiber membranes, the UV-Vis absorption ability of the Zn-CuAl2O4 nanofiber membrane is enhanced, and transient photocurrent intensity was increased from 0.14 mA/cm2 to 0.16 mA/cm2. The free radical capture experiments and EPR spectra indicate that huge ·O2− are generated on the surface of the Zn-CuAl2O4 nanofiber membrane, and the photocatalytic degradation efficiency of MB is increased by 14%. The Zn-CuAl2O4 nanofiber membranes exhibit excellent cycling stability and have great potential for photocatalytic degradation of dye wastewater. [ABSTRACT FROM AUTHOR]

Published
2024
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