Feng Yan

538 citations
31 papers · 370 · h-index 10

Impact in

Papers in

Feng Yan

30 papers receiving 354 citations

Peers

Feng Yan
Comparison fields: 5 of 61
  • Computational Mathematics 41
  • Control and Systems Engineering 134
  • Applied Mathematics 38
  • Computational Theory and Mathematics 51
  • Artificial Intelligence 88
Replace Ratikanta Behera with:
Ratikanta Behera India
Dimitrios Gerontitis Greece
Kim Batselier Hong Kong
D. de Falco Italy
Neil White United States
Kiryung Lee United States
Dong Xia Hong Kong
Lingchen Kong China
Gang Wu China
Feng Yan relative to Ratikanta Behera India Ratikanta Behera's profile →
Citations per field
00.5×5.7×
Ratikanta Behera · 1×
Citations per year

Countries citing papers authored by Feng Yan

Since Specialization
Citations

This map shows the geographic impact of Feng Yan'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 Feng Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feng Yan more than expected).

Fields of papers citing papers by Feng Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Feng Yan. 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 Feng Yan. The network helps show where Feng Yan may publish in the future.

Co-authors

The 25 scholars most cited alongside Feng Yan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Feng Yan Line = papers co-authored together Feng Yan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201362
2 201255
3
Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units
200953
4 200842
5 200325
6
Sparse Gaussian Process Regression via L1 Penalization
201016
7 201815
8 200912
9 202010
10 201110
11
An Automatic Corona-discharge Detection System for Railways Based on Solar-blind Ultraviolet Detection
20178
12 20197
13 20097
14 20097
15 20105
16 20104
17 20094
18 20084
19 20184
20 20203

About Feng Yan

Feng Yan is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering, Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications, having authored 31 papers that have together received 370 indexed citations. Recurring topics across this work include HVDC Systems and Fault Protection (5 papers), Power Systems Fault Detection (5 papers), Smart Grid and Power Systems (5 papers), Islanding Detection in Power Systems (3 papers), Power Systems and Technologies (3 papers), Gaussian Processes and Bayesian Inference (3 papers), High-Voltage Power Transmission Systems (3 papers) and Matrix Theory and Algorithms (3 papers). The work is most often cited by research in Computational Mathematics (41 citations), Control and Systems Engineering (134 citations), Applied Mathematics (38 citations), Computational Theory and Mathematics (51 citations) and Artificial Intelligence (88 citations). Feng Yan has collaborated with scholars based in China and United States. Frequent co-authors include Yuan Qi, Zenglin Xu, Minghui Wang, Ningyi Xu, Musheng Wei, Lisong Xu, Pengzhi Li, Peiyue Li, Xueliang Wang and Peng Li. Their work appears in journals such as IEEE Access, IEEE Transactions on Pattern Analysis and Machine Intelligence, The Journal of Engineering, Chinese Optics Letters and Mechanical Systems and Signal Processing.

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.

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