Wu Jun

493 citations
28 papers · 343 · h-index 10

Impact in

Papers in

Wu Jun

24 papers receiving 297 citations

Peers

Wu Jun
Comparison fields: 5 of 67
  • Artificial Intelligence 237
  • Signal Processing 22
  • Control and Systems Engineering 40
  • Medical Laboratory Technology 2
  • Research and Theory 1
Replace Sanjay Kumar Sonbhadra with:
Sanjay Kumar Sonbhadra India
Matthew Molineaux United States
Nusrat Jahan Prottasha Bangladesh
Gavin Pearson United States
Erin J. Hastings United States
Zehong Hu China
N.J. Rao India
Julia Badger United States
Yuanfeng Song Hong Kong
Wu Jun relative to Sanjay Kumar Sonbhadra India Sanjay Kumar Sonbhadra's profile →
Citations per field
00.5×1.5×2.4×
Sanjay Kumar Sonbhadra · 1×
Citations per year

Countries citing papers authored by Wu Jun

Since Specialization
Citations

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

Fields of papers citing papers by Wu Jun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Wu Jun, 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 Wu Jun Line = papers co-authored together Wu Jun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199884
2 200070
3 199934
4 200022
5
Faculty and student perceptions of academic incivility in the People's Republic of China.
201218
6 202015
7 200214
8
Descriptive study of student incivility in the People's Republic of China.
201014
9 202313
10 202312
11 20079
12 20218
13 20017
14 20096
15 20214
16 20224
17 20112
18
Exploration and Practice of the Practice Teaching Reform on Engineering Disciplines
20111
19 20241
20 20241

About Wu Jun

Wu Jun is a scholar working on Artificial Intelligence, Control and Systems Engineering, Mechanical Engineering, Sociology and Political Science and Electrical and Electronic Engineering, having authored 28 papers that have together received 343 indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (7 papers), Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers), Speech Recognition and Synthesis (4 papers), Gear and Bearing Dynamics Analysis (3 papers), Neural Networks and Applications (2 papers), Workplace Violence and Bullying (2 papers) and Fault Detection and Control Systems (2 papers). The work is most often cited by research in Artificial Intelligence (237 citations), Signal Processing (22 citations), Control and Systems Engineering (40 citations), Medical Laboratory Technology (2 citations) and Research and Theory (1 citation). Wu Jun has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Sanjeev Khudanpur, Eric Brill, Liping Wang, Cynthia M. Clark, Liping Wang, Decheng Wang, Hongjun Zhao, Jian Wu, Zhengwen Xu and Qiang Li. Their work appears in journals such as IEEE Transactions on Intelligent Transportation Systems, Applied Sciences, Computer Speech & Language, Journal of Energy Storage and BMC Infectious Diseases.

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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