Jin Wan

868 citations
43 papers · 622 · h-index 14

Impact in

Papers in

Jin Wan

37 papers receiving 605 citations

Peers

Jin Wan
Comparison fields: 5 of 122
  • Surfaces, Coatings and Films 58
  • Research and Theory 6
  • Mechanics of Materials 135
  • Media Technology 46
  • Computer Vision and Pattern Recognition 106
Replace Xingchen Dong with:
Xingchen Dong China
Hu Luo China
Björn Kruse Sweden
Hongkai Li China
Joel F. Destino United States
Lílian P. Dávila United States
Younsoo Kim South Korea
Dae-Chul Kim South Korea
Bo‐Yen Lin Taiwan
Jin Wan relative to Xingchen Dong China Xingchen Dong's profile →
Citations per field
00.5×7.3×
Xingchen Dong · 1×
Citations per year

Countries citing papers authored by Jin Wan

Since Specialization
Citations

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

Fields of papers citing papers by Jin Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002172
2 201351
3 201843
4 201737
5 202036
6 201535
7 201228
8 201224
9 201123
10 201417
11 201517
12 202317
13 202013
14 201813
15 202110
16 202110
17 201810
18 20129
19 20159
20 20228

About Jin Wan

Jin Wan is a scholar working on Computer Vision and Pattern Recognition, Mechanics of Materials, Molecular Biology, Atomic and Molecular Physics, and Optics and Artificial Intelligence, having authored 43 papers that have together received 622 indexed citations. Recurring topics across this work include Image Enhancement Techniques (9 papers), Advanced Vision and Imaging (8 papers), Advanced Image Processing Techniques (7 papers), Adhesion, Friction, and Surface Interactions (7 papers), Force Microscopy Techniques and Applications (5 papers), Anomaly Detection Techniques and Applications (5 papers), Computer Graphics and Visualization Techniques (4 papers) and Advanced Image Fusion Techniques (3 papers). The work is most often cited by research in Surfaces, Coatings and Films (58 citations), Research and Theory (6 citations), Mechanics of Materials (135 citations), Media Technology (46 citations) and Computer Vision and Pattern Recognition (106 citations). Jin Wan has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Feng Gao, Dongyuan Zhao, Jianwei Zhao, Pengyuan Yang, Yu Tian, Ming Zhou, Hui Yin, Noshir S. Pesika, Yanting Liu and Yonggang Meng. Their work appears in journals such as Applied Intelligence, IEEE Transactions on Intelligent Vehicles, PLoS ONE, Electronics and IEEE Transactions on Broadcasting.

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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