YANG Yanli
Professor and Doctor
Tel.:+86-22-83955164
Fax:+86-22-83955164
E-mail: yangyanli@tiangong.edu.cn
Introduction
He received the Ph.D. degree from Beijing Institute of Technology in 2010. He is currently a Professor in School of Electronics and Information Engineering at Tiangong University. He has authored more thantwenty publications in international journals.He has authorized ten national invention patents. As the project leader, he presided four national and ministerial level projects. His research interestsinclude signal processing and artificial neural network.
Research areas
Intelligent computing, Intelligent signal processing, Machine learning
Main courses taught
《Information Theory》,《Control Theory》
Main publications
[1] Recognition of bird nests on transmission lines based on YOLOv5 and DETR using small samples, Energy Reports, 2023.
[2] Insulator detection using small samples based on YOLOv5 in natural background, Multimedia Tools and Applications, 2023.
[3] A fault identification method based on an ensemble deep neural network and a correlation coefficient, Soft Computing, 2022.
[4] Quantitative analysis on generalization ability of deep feedforward neural network, Journal of Intelligent and Fuzzy System, 2021.
[5] Modulated signal detection method for fault diagnosis.IET Science Measurement and Technology, 2020
[6] Insulator self-shattering detection: A deep convolutionalneural network approach, Multimedia Tools and Applications, 2019.
[7] Insulator identification and self-shattering detection based on mask region with convolutional neural network, Journal of Electronic Imaging, 2019.
[8]Rolling-element bearing fault data automatic clustering based on wavelet and deep neural network, Shock and Vibration, 2018.
[9] Empirical mode decomposition as a time-varying multirate signal processing system. Mechanical Systems and Signal Processing, 2016.
[10]Analysis on frequency resolution of EMD based on B-spline interpolation. AEU-International Journal of Electronics and Communications, 2016.
[11] On-line conveyor belts inspection based on machine vision. Optik, 2014.
[12] An analytical expression of empirical mode decomposition based on B-spline interpolation, Circuits, Systems, and Signal Processing, 2013.
