He Kong

1.6k citations
84 papers · 1.0k · h-index 18

Impact in

Papers in

He Kong

73 papers receiving 1.0k citations

Peers

He Kong
Comparison fields: 5 of 90
  • Small Animals 152
  • Control and Systems Engineering 271
  • Animal Science and Zoology 110
  • Food Science 186
  • Computer Vision and Pattern Recognition 201
Replace Daobilige Su with:
Daobilige Su Australia
Christos Tachtatzis United Kingdom
Philip Valencia Australia
Daihee Park South Korea
Thi Thi Zin Japan
Andrew Hamilton United Kingdom
Henrik Karstoft Denmark
Daniel Riordan Ireland
Reza Arablouei Australia
He Kong relative to Daobilige Su Australia Daobilige Su's profile →
Citations per field
00.5×5.2×
Daobilige Su · 1×
Citations per year

Countries citing papers authored by He Kong

Since Specialization
Citations

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

Fields of papers citing papers by He Kong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside He Kong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with He Kong Line = papers co-authored together He Kong links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 84 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2021127
2 2021104
3 201971
4 202147
5 202143
6 201439
7 201935
8 199834
9 202132
10 199632
11 202029
12 202124
13 202022
14 202119
15 202119
16 201819
17 202218
18 202017
19 202216
20 201815

About He Kong

He Kong is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing and Mechanical Engineering, having authored 84 papers that have together received 1.0k indexed citations. Recurring topics across this work include Control Systems and Identification (15 papers), Fault Detection and Control Systems (14 papers), Advanced Control Systems Optimization (14 papers), Speech and Audio Processing (9 papers), Target Tracking and Data Fusion in Sensor Networks (8 papers), Animal Behavior and Welfare Studies (7 papers), Indoor and Outdoor Localization Technologies (6 papers) and Robotics and Sensor-Based Localization (5 papers). The work is most often cited by research in Small Animals (152 citations), Control and Systems Engineering (271 citations), Animal Science and Zoology (110 citations), Food Science (186 citations) and Computer Vision and Pattern Recognition (201 citations). He Kong has collaborated with scholars based in China, Australia and Hong Kong. Frequent co-authors include Salah Sukkarieh, Daobilige Su, Yongliang Qiao, Cameron Clark, Sabrina Lomax, Ling Guan, Marı́a M. Seron, Graham C. Goodwin, Guang‐Ren Duan and Shoudong Huang. Their work appears in journals such as Automatica, IEEE Transactions on Automatic Control, IEEE Transactions on Circuits and Systems I Regular Papers, Animals and IEEE Transactions on Cybernetics.

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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