Pin-Jar Yuan

468 total citations
13 papers, 349 citations indexed

About

Pin-Jar Yuan is a scholar working on Aerospace Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Pin-Jar Yuan has authored 13 papers receiving a total of 349 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Aerospace Engineering, 4 papers in Artificial Intelligence and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Pin-Jar Yuan's work include Guidance and Control Systems (11 papers), Inertial Sensor and Navigation (8 papers) and Military Defense Systems Analysis (4 papers). Pin-Jar Yuan is often cited by papers focused on Guidance and Control Systems (11 papers), Inertial Sensor and Navigation (8 papers) and Military Defense Systems Analysis (4 papers). Pin-Jar Yuan collaborates with scholars based in Taiwan. Pin-Jar Yuan's co-authors include Jeng-Shing Chern and has published in prestigious journals such as IEEE Transactions on Aerospace and Electronic Systems, Journal of Guidance Control and Dynamics and AIAA Guidance, Navigation, and Control Conference and Exhibit.

In The Last Decade

Pin-Jar Yuan

13 papers receiving 325 citations

Peers

Pin-Jar Yuan
Comparison fields: 5 of 19
  • Aerospace Engineering 342
  • Statistical and Nonlinear Physics 92
  • Control and Systems Engineering 88
  • Computer Vision and Pattern Recognition 59
  • Artificial Intelligence 49
Haim Weiss Israel
Nathan Harl United States
Fumiaki Imado Japan
Bong-Gyun Park South Korea
Peter H. Zipfel United States
V.S. Patsko Russia
Qunli Xia China
Róbert Fónod Netherlands
N. Rajan United States
Sheng Sun China
Haim Weiss Israel View profile →
Citations per field, relative to Pin-Jar Yuan
Pin-Jar Yuan · 1×
Citations per year, relative to Pin-Jar Yuan
Pin-Jar Yuan · 1×

Countries citing papers authored by Pin-Jar Yuan

Since Specialization
Citations

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

Fields of papers citing papers by Pin-Jar Yuan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Pin-Jar Yuan. 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 Pin-Jar Yuan. The network helps show where Pin-Jar Yuan may publish in the future.

Co-authorship network of co-authors of Pin-Jar Yuan

This figure shows the co-authorship network connecting the top 25 collaborators of Pin-Jar Yuan. A scholar is included among the top collaborators of Pin-Jar Yuan 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 Pin-Jar Yuan. Pin-Jar Yuan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

13 of 13 papers shown
# Work Indexed citations
1 4
2 8
3 7
4 1
5 11
6 2
7 31
8 25
9 23
10 11
11 72
12 32
13 122

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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2026