Dan Wu

2.5k citations
133 papers · 1.8k · h-index 23

Impact in

Papers in

Dan Wu

120 papers receiving 1.8k citations

Peers

Dan Wu
Comparison fields: 5 of 148
  • Control and Systems Engineering 708
  • Mechanical Engineering 563
  • Industrial and Manufacturing Engineering 145
  • Biomedical Engineering 508
  • Computer Vision and Pattern Recognition 222
Replace Fei Chen with:
Fei Chen China
Qiaokang Liang China
Zool Hilmi Ismail Malaysia
Heping Chen United States
Jang-Myung Lee South Korea
Giovanni Muscato Italy
Sunan Huang Singapore
Jeremy S. Smith United Kingdom
Guoqian Jiang China
Xuewen Rong China
Dan Wu relative to Fei Chen China Fei Chen's profile →
Citations per field
00.5×1.7×
Fei Chen · 1×
Citations per year

Countries citing papers authored by Dan Wu

Since Specialization
Citations

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

Fields of papers citing papers by Dan Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 133 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017142
2 2012136
3 2009127
4 201897
5 200776
6 201863
7 202153
8 201053
9 201851
10 202048
11
高被覆率単一細胞RNA配列決定および機能的不均一性により同定された体性感覚ニューロン型【Powered by NICT】
201637
12 201936
13 202132
14 202131
15 201731
16 201429
17 201727
18 201826
19 201825
20 200525

About Dan Wu

Dan Wu is a scholar working on Biomedical Engineering, Mechanical Engineering, Control and Systems Engineering, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 133 papers that have together received 1.8k indexed citations. Recurring topics across this work include Advanced machining processes and optimization (26 papers), Advanced Surface Polishing Techniques (21 papers), Robot Manipulation and Learning (16 papers), Iterative Learning Control Systems (15 papers), Optical measurement and interference techniques (11 papers), Adaptive Control of Nonlinear Systems (9 papers), Optical Polarization and Ellipsometry (9 papers) and Advanced Machining and Optimization Techniques (9 papers). The work is most often cited by research in Control and Systems Engineering (708 citations), Mechanical Engineering (563 citations), Industrial and Manufacturing Engineering (145 citations), Biomedical Engineering (508 citations) and Computer Vision and Pattern Recognition (222 citations). Dan Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Ken Chen, Yunfei Dong, Ken Chen, Tianyu Ren, Xiong Liang, Wanneng Yang, Peng Yang, Lingfeng Duan, Guoxing Chen and Lizhong Xiong. Their work appears in journals such as The International Journal of Advanced Manufacturing Technology, International Journal of Machine Tools and Manufacture, IEEE Transactions on Instrumentation and Measurement, Multimedia Tools and Applications and Optics Communications.

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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