Tiejun Li

635 citations
45 papers · 452 · h-index 11

Impact in

Papers in

Tiejun Li

38 papers receiving 433 citations

Peers

Tiejun Li
Comparison fields: 5 of 86
  • Computer Vision and Pattern Recognition 106
  • Mechanical Engineering 156
  • Computational Mathematics 2
  • Control and Systems Engineering 75
  • Artificial Intelligence 94
Replace S. S. N. Alhady with:
S. S. N. Alhady Malaysia
Lihua You United Kingdom
Xiaolei Hou China
Chun-Chi Lai Taiwan
Guangxin Li China
Zhizhong Xing China
Zhibin Li China
G.N. Marichal Spain
Shengchun Wang China
Xin Jiang China
Tiejun Li relative to S. S. N. Alhady Malaysia S. S. N. Alhady's profile →
Citations per field
00.5×
S. S. N. Alhady · 1×
Citations per year

Countries citing papers authored by Tiejun Li

Since Specialization
Citations

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

Fields of papers citing papers by Tiejun Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 45 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202181
2 202148
3 200546
4 201735
5 202131
6 201725
7 202021
8 202119
9 201719
10 202115
11 202114
12 201910
13 201710
14 20178
15 20188
16 20097
17 20206
18 20215
19 20215
20 20204

About Tiejun Li

Tiejun Li is a scholar working on Control and Systems Engineering, Biomedical Engineering, Mechanical Engineering, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 45 papers that have together received 452 indexed citations. Recurring topics across this work include Robotic Mechanisms and Dynamics (12 papers), Robot Manipulation and Learning (7 papers), Robotic Path Planning Algorithms (5 papers), Advanced Measurement and Metrology Techniques (5 papers), Soft Robotics and Applications (5 papers), Advanced machining processes and optimization (4 papers), Robotic Locomotion and Control (3 papers) and Optical measurement and interference techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (106 citations), Mechanical Engineering (156 citations), Computational Mathematics (2 citations), Control and Systems Engineering (75 citations) and Artificial Intelligence (94 citations). Tiejun Li has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Kewen Xia, Yuqing Peng, Jiangnan Zhang, Hai‐Tao Liu, Shurui Fan, Wang Li, Yu Feng, Ming Yang, Yaoqi Wang and Hongliang Hou. Their work appears in journals such as The International Journal of Advanced Manufacturing Technology, IEEE Access, Sensors, Journal of Natural Gas Science and Engineering and Measurement.

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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