Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A roller bearing fault diagnosis method based on EMD energy entropy and ANN
2005469 citationsYu Yang, Junsheng Cheng et al.profile →
Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings
2003411 citationsDejie Yu, Junsheng Cheng et al.profile →
An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis
2019269 citationsJunsheng Cheng, Yu Yang et al.profile →
Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis
2018239 citationsHaiyang Pan, Yu Yang et al.profile →
Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder
2019230 citationsHaidong Shao, Junsheng Cheng et al.profile →
Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples
2019212 citationsHaidong Shao, Junsheng Cheng et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of Yu Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yu Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu Yang more than expected).
This network shows the impact of papers produced by Yu Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yu Yang. The network helps show where Yu Yang may publish in the future.
Co-authorship network of co-authors of Yu Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Yu Yang.
A scholar is included among the top collaborators of Yu Yang based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Yu Yang. Yu Yang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Qian, Hong, Yi-Qi Hu, & Yu Yang. (2016). Derivative-free optimization of high-dimensional non-convex functions by sequential random embeddings. International Joint Conference on Artificial Intelligence. 1946–1952.17 indexed citations
13.
Yang, Yu, et al.. (2013). A Review of Relation Extraction. Shuju fenxi yu zhishi faxian. 29(11). 30–39.121 indexed citations
14.
Yang, Yu. (2013). Roller bearing fault diagnosis based on local mean decomposition and morphological fractal dimension. Zhendong yu chongji.5 indexed citations
15.
Yang, Yu, Yu-Feng Li, & Zhi‐Hua Zhou. (2011). Diversity regularized machine. International Joint Conference on Artificial Intelligence. 1603–1608.35 indexed citations
16.
Yang, Yu. (2010). Roller bearing fault diagnosis method based on LMD and neural network. Zhendong yu chongji.17 indexed citations
Yang, Yu. (2009). Numerical research of three dimensional flow-path in a ram-rotor. Journal of Aerospace Power.3 indexed citations
19.
Yang, Yu. (2007). Application of correlation dimension and EMD-based AR model in the fault diagnosis. Systems engineering and electronics.2 indexed citations
20.
Yang, Yu. (2003). Application of Emprical Mode Decomposition (EMD) in Roller Bearing Fault Diagnosis. Journal of Hunan University.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.