Ran Jin

1.7k citations
94 papers · 1.3k · h-index 19

Impact in

Papers in

Ran Jin

84 papers receiving 1.2k citations

Peers

Ran Jin
Comparison fields: 5 of 112
  • Industrial and Manufacturing Engineering 311
  • Condensed Matter Physics 297
  • Statistics, Probability and Uncertainty 166
  • Automotive Engineering 169
  • Statistics and Probability 69
Replace Diganta Das with:
Diganta Das United States
Xu Andy Sun United States
Xiaoguang Wei China
Faisal Khan Pakistan
Shuai Zhao China
Pan Wang China
Jung-Wook Park South Korea
José Celaya United States
Zhenkun Wang China
Wenjin Zhang China
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Citations per field
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Citations per year

Countries citing papers authored by Ran Jin

Since Specialization
Citations

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

Fields of papers citing papers by Ran Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 94 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1993167
2 201782
3 201661
4 201549
5 201843
6 201639
7 201733
8 201132
9 201128
10 201128
11 201526
12 201224
13 202023
14 199422
15 202121
16 201321
17 201419
18 201919
19 201919
20 201618

About Ran Jin

Ran Jin is a scholar working on Industrial and Manufacturing Engineering, Statistics, Probability and Uncertainty, Artificial Intelligence, Condensed Matter Physics and Computer Vision and Pattern Recognition, having authored 94 papers that have together received 1.3k indexed citations. Recurring topics across this work include Advanced Statistical Process Monitoring (19 papers), Manufacturing Process and Optimization (18 papers), Industrial Vision Systems and Defect Detection (17 papers), Physics of Superconductivity and Magnetism (14 papers), Advanced Condensed Matter Physics (11 papers), Fault Detection and Control Systems (8 papers), Additive Manufacturing and 3D Printing Technologies (8 papers) and Digital Transformation in Industry (7 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (311 citations), Condensed Matter Physics (297 citations), Statistics, Probability and Uncertainty (166 citations), Automotive Engineering (169 citations) and Statistics and Probability (69 citations). Ran Jin has collaborated with scholars based in United States, China and Switzerland. Frequent co-authors include H. R. Ott, A. Schilling, Xinwei Deng, Hongyue Sun, Xiaoyu Chen, Jian Guo, Jianjun Shi, Yifu Li, Hang Z. Yu and Kaibo Wang. Their work appears in journals such as IISE Transactions, Physica C Superconductivity, Journal of Manufacturing Science and Engineering, Physical review. B, Condensed matter and IEEE Transactions on Industrial Informatics.

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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