Guan-Chun Luh

1.2k total citations
33 papers, 919 citations indexed

About

Guan-Chun Luh is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Guan-Chun Luh has authored 33 papers receiving a total of 919 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Biomedical Engineering, 10 papers in Computer Vision and Pattern Recognition and 8 papers in Control and Systems Engineering. Recurrent topics in Guan-Chun Luh's work include Artificial Immune Systems Applications (12 papers), Metaheuristic Optimization Algorithms Research (7 papers) and Advanced Multi-Objective Optimization Algorithms (6 papers). Guan-Chun Luh is often cited by papers focused on Artificial Immune Systems Applications (12 papers), Metaheuristic Optimization Algorithms Research (7 papers) and Advanced Multi-Objective Optimization Algorithms (6 papers). Guan-Chun Luh collaborates with scholars based in Taiwan and United States. Guan-Chun Luh's co-authors include Chun-Yi Lin, Weiwen Liu, Weiwen Liu, Giorgio Rizzoni, Kevin M. Passino, Rey‐Chue Hwang, Shih‐Wei Lee, K. Srinivasan, Yuhan Chen and Yisheng Lee and has published in prestigious journals such as SHILAP Revista de lepidopterología, Information Sciences and Applied Soft Computing.

In The Last Decade

Guan-Chun Luh

31 papers receiving 875 citations

Peers

Guan-Chun Luh
Comparison fields: 5 of 93
  • Civil and Structural Engineering 287
  • Computational Theory and Mathematics 280
  • Artificial Intelligence 256
  • Biomedical Engineering 190
  • Control and Systems Engineering 171
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Citations per field, relative to Guan-Chun Luh
Guan-Chun Luh · 1×
Citations per year, relative to Guan-Chun Luh
Guan-Chun Luh · 1×

Countries citing papers authored by Guan-Chun Luh

Since Specialization
Citations

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

Fields of papers citing papers by Guan-Chun Luh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Guan-Chun Luh. 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 Guan-Chun Luh. The network helps show where Guan-Chun Luh may publish in the future.

Co-authorship network of co-authors of Guan-Chun Luh

This figure shows the co-authorship network connecting the top 25 collaborators of Guan-Chun Luh. A scholar is included among the top collaborators of Guan-Chun Luh 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 Guan-Chun Luh. Guan-Chun Luh 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 5
2 22
3 22
4 19
5 107
6 10
7 1
8 0
9
Dynamic Mobile RobotNavigation Using Potential Field Based Immune Network
3
10 53
11 44
12 11
13 7
14 22
15 73
16 16
17 7
18 2
19 54
20 17

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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