Min Tan

3.8k total citations · 3 hit papers
89 papers, 3.0k citations indexed

About

Min Tan is a scholar working on Computer Vision and Pattern Recognition, Computer Networks and Communications and Molecular Biology. According to data from OpenAlex, Min Tan has authored 89 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Computer Vision and Pattern Recognition, 15 papers in Computer Networks and Communications and 12 papers in Molecular Biology. Recurrent topics in Min Tan's work include Advanced Image and Video Retrieval Techniques (16 papers), Distributed Control Multi-Agent Systems (14 papers) and RNA and protein synthesis mechanisms (12 papers). Min Tan is often cited by papers focused on Advanced Image and Video Retrieval Techniques (16 papers), Distributed Control Multi-Agent Systems (14 papers) and RNA and protein synthesis mechanisms (12 papers). Min Tan collaborates with scholars based in China, United States and France. Min Tan's co-authors include Zeng‐Guang Hou, Long Cheng, Long Cheng, Jun Yu, Yong Rui, Dacheng Tao, Hongyuan Zhang, Huiyang Liu, Wenjun Zhang and Yingzi Lin and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Min Tan

80 papers receiving 2.9k citations

Hit Papers

Decentralized Robust Adaptive Control for the Multiagent ... 2009 2026 2014 2020 2009 2019 2010 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Min Tan China 24 1.5k 926 767 318 298 89 3.0k
Cheng‐Lin Liu China 26 1.6k 1.1× 807 0.9× 532 0.7× 300 0.9× 55 0.2× 178 2.8k
Masayuki Fujita Japan 27 923 0.6× 1.6k 1.7× 505 0.7× 168 0.5× 307 1.0× 319 3.3k
Chaoyang Chen China 22 778 0.5× 682 0.7× 298 0.4× 323 1.0× 101 0.3× 153 2.2k
Chun Yin China 30 679 0.5× 1.4k 1.5× 244 0.3× 479 1.5× 58 0.2× 127 3.1k
ءMohammad Haeri Iran 35 1.3k 0.9× 2.3k 2.5× 326 0.4× 428 1.3× 109 0.4× 241 5.0k
Moshe Kam United States 22 683 0.5× 388 0.4× 391 0.5× 671 2.1× 55 0.2× 187 2.1k
Alexey S. Matveev Russia 30 1.4k 0.9× 1.9k 2.1× 971 1.3× 327 1.0× 181 0.6× 179 3.4k
Xiangke Wang China 28 1.1k 0.8× 995 1.1× 780 1.0× 251 0.8× 30 0.1× 191 2.4k
Shuai Song China 36 1.4k 1.0× 1.9k 2.1× 232 0.3× 753 2.4× 75 0.3× 131 3.6k
Dongyu Li China 28 1.0k 0.7× 1.3k 1.4× 222 0.3× 311 1.0× 36 0.1× 139 2.3k

Countries citing papers authored by Min Tan

Since Specialization
Citations

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

Fields of papers citing papers by Min Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Min Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Min Tan. A scholar is included among the top collaborators of Min Tan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Min Tan. Min Tan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Tan, Min, et al.. (2025). Mitochondria-targeted bifunctional probes for monitoring SO2 and viscosity in diverse environments. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy. 338. 126181–126181. 1 indexed citations
3.
Tan, Min, et al.. (2025). OA-Stereo: Self-Supervised Opti-Acoustic Stereo for Robust 3D Perception of Underwater Vehicles. IEEE Robotics and Automation Letters. 10(11). 11339–11346.
4.
Zhao, Yaqin, et al.. (2025). Path optimization model of manipulator based on d-h parameter method and genetic algorithm. Journal of Physics Conference Series. 2964(1). 12018–12018. 1 indexed citations
5.
Tan, Min, Yang Yang, Zhe Chen, et al.. (2025). Identification of Candidate Genes Related to the Husk Papillae in Foxtail Millet (Setaria italica (L.) P. Beauv). Plants. 14(16). 2535–2535.
6.
Xing, Shiyu, et al.. (2024). Maximum Allowable TCF Calibration Error for Robotic Pose Servoing. IEEE Robotics and Automation Letters. 10(2). 1744–1751. 1 indexed citations
7.
Miao, Yonghao, Kun Wang, Min Tan, et al.. (2024). CT image segmentation of foxtail millet seeds based on semantic segmentation model VGG16-UNet. Plant Methods. 20(1). 169–169. 3 indexed citations
8.
Zhu, Bin, Alfredo J. Hernandez, Min Tan, et al.. (2015). Synthesis of 2′-Fluoro RNA by Syn5 RNA polymerase. Nucleic Acids Research. 43(14). e94–e94. 29 indexed citations
9.
Yan, Wei, Qing Ye, Min Tan, et al.. (2015). Modulation of Aminoacylation and Editing Properties of Leucyl-tRNA Synthetase by a Conserved Structural Module. Journal of Biological Chemistry. 290(19). 12256–12267. 6 indexed citations
10.
Yan, Wei, Min Tan, Gilbert Eriani, & En‐Duo Wang. (2013). Leucine-specific domain modulates the aminoacylation and proofreading functional cycle of bacterial leucyl-tRNA synthetase. Nucleic Acids Research. 41(9). 4988–4998. 9 indexed citations
11.
Zhou, Xiao-Long, et al.. (2011). Role of tRNA amino acid-accepting end in aminoacylation and its quality control. Nucleic Acids Research. 39(20). 8857–8868. 37 indexed citations
12.
Chen, Xin, Jingjing Ma, Min Tan, et al.. (2010). Modular pathways for editing non-cognate amino acids by human cytoplasmic leucyl-tRNA synthetase. Nucleic Acids Research. 39(1). 235–247. 44 indexed citations
13.
Tan, Min, et al.. (2010). Studying base pair open–close kinetics of tRNALeu by TROSY‐based proton exchange NMR spectroscopy. FEBS Letters. 584(21). 4449–4452. 2 indexed citations
14.
Cheng, Long, Zeng‐Guang Hou, Min Tan, Yingzi Lin, & Wenjun Zhang. (2010). Neural-Network-Based Adaptive Leader-Following Control for Multiagent Systems With Uncertainties. IEEE Transactions on Neural Networks. 21(8). 1351–1358. 295 indexed citations breakdown →
15.
Hou, Zeng‐Guang, Long Cheng, & Min Tan. (2009). Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 39(3). 636–647. 531 indexed citations breakdown →
16.
Zhu, Bin, Peng Yao, Min Tan, Gilbert Eriani, & En‐Duo Wang. (2008). tRNA-independent Pretransfer Editing by Class I Leucyl-tRNA Synthetase. Journal of Biological Chemistry. 284(6). 3418–3424. 49 indexed citations
17.
Tan, Min. (2007). Automated Robotic Welding Based on Teaching and Visual Correction. Robot. 2 indexed citations
18.
Tan, Min. (2007). Approaching methods for camera characteristics in uncalibrated visual control system for robots. Kongzhi yu juece. 1 indexed citations
19.
Li, Yuan, De Xu, & Min Tan. (2006). A image processing and features extraction method for structured light image of welding seam. Transactions of the China Welding Institution. 1 indexed citations
20.
Li, Youfu, et al.. (2006). New Pose-Detection Method for Self-Calibrated Cameras Based on Parallel Lines and Its Application in Visual Control System. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 36(5). 1104–1117. 5 indexed citations

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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