Jin Qi

1.7k citations
89 papers · 1.3k · h-index 18

Impact in

Papers in

Jin Qi

83 papers receiving 1.3k citations

Peers

Jin Qi
Comparison fields: 5 of 105
  • Management Science and Operations Research 364
  • Control and Systems Engineering 340
  • Industrial and Manufacturing Engineering 80
  • Management of Technology and Innovation 69
  • Artificial Intelligence 180
Replace Jie Hu with:
Jie Hu China
Yong Tao Yu Japan
John Riker Haymaker United States
Shanhe Lou China
A.M.M. Sharif Ullah Japan
Xin Han China
Ji Zhou China
Zhaohui Ren China
Rong Mo China
Jin Qi relative to Jie Hu China Jie Hu's profile →
Citations per field
00.5×1.5×
Jie Hu · 1×
Citations per year

Countries citing papers authored by Jin Qi

Since Specialization
Citations

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

Fields of papers citing papers by Jin Qi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 89 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015234
2 2019171
3 202080
4 201457
5 202248
6 202042
7 201939
8 201434
9 200933
10 201432
11 202131
12 201929
13 201726
14 202026
15 202226
16 201125
17 201521
18 202518
19 201015
20 201715

About Jin Qi

Jin Qi is a scholar working on Control and Systems Engineering, Management Science and Operations Research, Artificial Intelligence, Cellular and Molecular Neuroscience and Management of Technology and Innovation, having authored 89 papers that have together received 1.3k indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (14 papers), Robot Manipulation and Learning (12 papers), Design Education and Practice (12 papers), Neuroscience and Neural Engineering (8 papers), AI-based Problem Solving and Planning (8 papers), Robotic Mechanisms and Dynamics (6 papers), Product Development and Customization (5 papers) and Reinforcement Learning in Robotics (4 papers). The work is most often cited by research in Management Science and Operations Research (364 citations), Control and Systems Engineering (340 citations), Industrial and Manufacturing Engineering (80 citations), Management of Technology and Innovation (69 citations) and Artificial Intelligence (180 citations). Jin Qi has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jie Hu, Yinghong Peng, Guo‐Niu Zhu, Yi Fang, Wenhai Liu, Weiming Wang, Jin Ma, Zhenfei Zhan, Jie Hu and Haiqing Huang. Their work appears in journals such as Advanced Engineering Informatics, Applied Sciences, Expert Systems with Applications, Engineering Applications of Artificial Intelligence and Applied Soft Computing.

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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