Ming Hu

46 papers receiving 526 citations

Peers

Ming Hu
Comparison fields: 5 of 83
  • Computational Theory and Mathematics 159
  • Signal Processing 89
  • Information Systems 182
  • Artificial Intelligence 197
  • Management Science and Operations Research 51
Replace Sen Zhao with:
Sen Zhao China
Yongli Zhu China
Satoshi Fujita Japan
Bo Mi China
Yinan Guo China
Guodong Wang China
Yingyou Wen China
Pravir Chawdhry United Kingdom
Daxin Liu China
Seyede Fatemeh Ghoreishi United States
Ming Hu relative to Sen Zhao China Sen Zhao's profile →
Citations per field
00.5×
Sen Zhao · 1×
Citations per year

Countries citing papers authored by Ming Hu

Since Specialization
Citations

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

Fields of papers citing papers by Ming Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2001304
2 202040
3 201933
4 200828
5 20219
6 20228
7 20148
8 20218
9 20217
10 20216
11 20086
12 20136
13 20116
14 20135
15 20205
16 20205
17 20214
18 20084
19 20214
20 20094

About Ming Hu

Ming Hu is a scholar working on Mechanical Engineering, Control and Systems Engineering, Mechanics of Materials, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 47 papers that have together received 550 indexed citations. Recurring topics across this work include Robotic Mechanisms and Dynamics (7 papers), Metal and Thin Film Mechanics (4 papers), Metal Alloys Wear and Properties (4 papers), Lubricants and Their Additives (3 papers), Rough Sets and Fuzzy Logic (3 papers), Fault Detection and Control Systems (3 papers), Fatigue and fracture mechanics (3 papers) and Advanced Measurement and Detection Methods (3 papers). The work is most often cited by research in Computational Theory and Mathematics (159 citations), Signal Processing (89 citations), Information Systems (182 citations), Artificial Intelligence (197 citations) and Management Science and Operations Research (51 citations). Ming Hu has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Jerzy W. Grzymala‐Busse, Bingxu Wang, Gary C. Barber, Feng Qiu, Zhiping Wang, Weiwei Cui, Jing Yang, Yu Liu, Rui Wang and Yu Liu. Their work appears in journals such as Robotica, Journal of Materials Research and Technology, Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, Fuzzy Optimization and Decision Making and Surface Topography Metrology and Properties.

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.

Explore authors with similar magnitude of impact