Kun Yu

1.8k citations
15 papers · 1.2k indexed · 1 hit paper · h-index 8
Topics
Power Systems and Renewable Energy (2 papers)Face and Expression Recognition (2 papers)Stress Responses and Cortisol (1 paper)
Partner nations
ChinaHong Kong

In The Last Decade

Kun Yu

15 papers receiving 1.2k citations

Hit Papers

DenseASPP for Semantic Segmentation in Street Scenes201820262020202320182505007501000

Peers

Kun Yu
Comparison fields: 5 of 119
  • Computer Vision and Pattern Recognition 839
  • Artificial Intelligence 267
  • Media Technology 192
  • Radiology, Nuclear Medicine and Imaging 118
  • Ocean Engineering 107
Replace Panqu Wang with:
Panqu Wang United States
Chao Peng China
Pablo Martínez-González Spain
Zehua Huang China
Guangliang Cheng China
Amit Agrawal United States
David Acuna Canada
Ambrish Tyagi United States
Alexander Hermans Germany
Kun Yu relative to Panqu Wang United States Panqu Wang's profile →
Citations per field
00.5×1.5×
Panqu Wang · 1×
Citations per year

Countries citing papers authored by Kun Yu

Since Specialization
Citations

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

Fields of papers citing papers by Kun Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kun Yu

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

All Works

15 of 15 papers shown
#WorkIndexed citations
1 2
2 10
3 15
4 12
5 8
6 18
7 8
8 8
9 4
10 1
11
DenseASPP for Semantic Segmentation in Street Scenesbreakdown →
1102
12 2
13 1
14
Studies on the influence of loads on voltage stability in the urban power grid
1
15 2

About Kun Yu

Kun Yu is a scholar working on Energy Engineering and Power Technology, Biological Psychiatry and Instrumentation, having authored 15 papers that have together received 1.2k indexed citations. Recurring topics across this work include Power Systems and Renewable Energy (2 papers), Face and Expression Recognition (2 papers) and Stress Responses and Cortisol (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (839 citations), Media Technology (192 citations) and Artificial Intelligence (267 citations). Kun Yu has collaborated with scholars based in China and Hong Kong. Frequent co-authors include Chi Zhang, Zhiwei Li, Kuiyuan Yang, Chunxia Zhao, Dao Wen Wang, Chengcheng Zhao, Ling Zhou, Jia Cheng, Feng Wang and Liping Jiao. Their work appears in journals such as Hypertension, Frontiers in Immunology and Frontiers in Pharmacology.

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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