Te Tang

29 papers receiving 634 citations

Peers

Te Tang
Comparison fields: 5 of 47
  • Control and Systems Engineering 355
  • Industrial and Manufacturing Engineering 83
  • Computer Vision and Pattern Recognition 132
  • Biomedical Engineering 208
  • Human-Computer Interaction 22
Replace Yu She with:
Yu She United States
HIROYUKI Nabae Japan
Andrés S. Vázquez Spain
Hitoshi Maekawa Japan
Yashraj Narang United States
Tuna Balkan Türkiye
Daniele De Gregorio Italy
Yoshihiro Nakabo Japan
Nianfeng Wang China
Van Anh Ho Japan
Te Tang relative to Yu She United States Yu She's profile →
Citations per field
00.5×1.5×1.9×
Yu She · 1×
Citations per year

Countries citing papers authored by Te Tang

Since Specialization
Citations

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

Fields of papers citing papers by Te Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201669
2 201660
3 201956
4 200956
5 201855
6 200640
7 200739
8 202239
9 201532
10 201730
11 201325
12 201618
13 201618
14 201816
15 201616
16 201815
17 201712
18 199510
19 20168
20 20076

About Te Tang

Te Tang is a scholar working on Control and Systems Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition, Aerospace Engineering and Civil and Structural Engineering, having authored 30 papers that have together received 643 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (12 papers), Robotic Mechanisms and Dynamics (9 papers), Soft Robotics and Applications (8 papers), Robotics and Sensor-Based Localization (6 papers), Phase-change materials and chalcogenides (4 papers), Concrete and Cement Materials Research (4 papers), Robotic Path Planning Algorithms (3 papers) and Teleoperation and Haptic Systems (3 papers). The work is most often cited by research in Control and Systems Engineering (355 citations), Industrial and Manufacturing Engineering (83 citations), Computer Vision and Pattern Recognition (132 citations), Biomedical Engineering (208 citations) and Human-Computer Interaction (22 citations). Te Tang has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Masayoshi Tomizuka, Hsien-Chung Lin, Yongxiang Fan, Yu Zhao, Wenjie Chen, Changhao Wang, Yandan Lin, Cai Bosong, Wenjie Chen and Peng Zhou. Their work appears in journals such as Construction and Building Materials, IEEE Robotics and Automation Letters, Electrochemical and Solid-State Letters, Applied Physics A and The International Journal of Robotics Research.

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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