Tae-Hyoung Park

661 citations
83 papers · 469 indexed · h-index 13
Topics
Industrial Vision Systems and Defect Detection (27 papers)Autonomous Vehicle Technology and Safety (14 papers)Image and Object Detection Techniques (13 papers)
Partner nations
South KoreaPuerto Rico

In The Last Decade

Tae-Hyoung Park

62 papers receiving 427 citations

Peers

Tae-Hyoung Park
Comparison fields: 5 of 57
  • Computer Vision and Pattern Recognition 233
  • Industrial and Manufacturing Engineering 188
  • Automotive Engineering 81
  • Media Technology 80
  • Electrical and Electronic Engineering 74
Replace Christian Frese with:
Christian Frese Germany
Sedat Dogru Portugal
Jianmin Ji China
Divyansh Garg United States
Ziyu Zhang China
Xiafu Peng China
Erke Shang China
Tae-Hyoung Park relative to Christian Frese Germany Christian Frese's profile →
Citations per field
00.5×4.8×
Christian Frese · 1×
Citations per year

Countries citing papers authored by Tae-Hyoung Park

Since Specialization
Citations

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

Fields of papers citing papers by Tae-Hyoung Park

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae-Hyoung Park. 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 Tae-Hyoung Park. The network helps show where Tae-Hyoung Park may publish in the future.

Co-authorship network of co-authors of Tae-Hyoung Park

This figure shows the co-authorship network connecting the top 25 collaborators of Tae-Hyoung Park. A scholar is included among the top collaborators of Tae-Hyoung Park 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 Tae-Hyoung Park. Tae-Hyoung Park 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
#WorkIndexed citations
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13 15
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15 30
16 43
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Estimation of Train Position Using Sensor Fusion Technique
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19 1
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A clustering algorithm for path planning of SMT inspection machines
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About Tae-Hyoung Park

Tae-Hyoung Park is a scholar working on Industrial and Manufacturing Engineering, Instrumentation and Computer Vision and Pattern Recognition, having authored 83 papers that have together received 469 indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (27 papers), Autonomous Vehicle Technology and Safety (14 papers) and Image and Object Detection Techniques (13 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (188 citations), Computer Vision and Pattern Recognition (233 citations) and Media Technology (80 citations). Tae-Hyoung Park has collaborated with scholars based in South Korea and Puerto Rico. Frequent co-authors include Young-Gyu Kim, Tae-Hyeong Kim, Jae-Seol Lee, Han‐Jin Cho, Hwa Jung Kim, Nam Soo Kim, Nam Kim, Jonghyun Ryu, Kang Soo Lee and Min-Seong Kim. Their work appears in journals such as IEEE Access, Sensors and IEEE Transactions on Intelligent Transportation Systems.

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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