Guoxian Dai

980 total citations
34 papers, 710 citations indexed

About

Guoxian Dai is a scholar working on Mechanics of Materials, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Guoxian Dai has authored 34 papers receiving a total of 710 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Mechanics of Materials, 12 papers in Computer Vision and Pattern Recognition and 11 papers in Computational Mechanics. Recurrent topics in Guoxian Dai's work include Muon and positron interactions and applications (13 papers), 3D Shape Modeling and Analysis (11 papers) and Image Processing and 3D Reconstruction (7 papers). Guoxian Dai is often cited by papers focused on Muon and positron interactions and applications (13 papers), 3D Shape Modeling and Analysis (11 papers) and Image Processing and 3D Reconstruction (7 papers). Guoxian Dai collaborates with scholars based in United States, China and United Arab Emirates. Guoxian Dai's co-authors include Jin Xie, Fan Zhu, Yi Fang, Yi Fang, Edward K. Wong, Y. C. Jean, Meng Wang, Tiantian Xu, Y. C. Jean and Yi Fang and has published in prestigious journals such as Physical review. B, Condensed matter, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing.

In The Last Decade

Guoxian Dai

33 papers receiving 689 citations

Peers

Guoxian Dai
Comparison fields: 5 of 70
  • Computer Vision and Pattern Recognition 402
  • Computational Mechanics 342
  • Geology 147
  • Mechanics of Materials 146
  • Materials Chemistry 88
Replace Yuan-Chen Guo with:
Yuan-Chen Guo China
Lai Kang China
Jaebin Lee South Korea
Patrick Laug France
Sijie Shao China
Yuan Liu China
Yuan-Chen Guo China View profile →
Citations per field, relative to Guoxian Dai
Guoxian Dai · 1×
Citations per year, relative to Guoxian Dai
Guoxian Dai · 1×

Countries citing papers authored by Guoxian Dai

Since Specialization
Citations

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

Fields of papers citing papers by Guoxian Dai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guoxian Dai

This figure shows the co-authorship network connecting the top 25 collaborators of Guoxian Dai. A scholar is included among the top collaborators of Guoxian Dai 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 Guoxian Dai. Guoxian Dai 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
# Work Indexed citations
1 41
2 14
3 17
4 7
5 99
6 3
7 134
8 7
9 3
10 2
11 1
12 1
13 10
14 18
15 16
16 4
17 9
18 17
19 9
20 22

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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