Kai Qu

599 citations
36 papers · 405 · 1 hit paper · h-index 11

Impact in

Papers in

Kai Qu

33 papers receiving 403 citations

Kai Qu's Hit Papers

Finite-time safe reinforcement learning control of multi-player nonzero-sum game for quadcopter systems 2025 · 24 citations
240Years since publication5101520

Peers

Kai Qu
Comparison fields: 5 of 92
  • Artificial Intelligence 97
  • Energy Engineering and Power Technology 8
  • Control and Systems Engineering 51
  • Electrical and Electronic Engineering 124
  • Management Science and Operations Research 25
Replace Amedeo Buonanno with:
Amedeo Buonanno Italy
Yoshihiko Ozaki Japan
Li Tang China
Alexey Zaytsev Russia
Anderson Alvarenga de Moura Meneses Brazil
Xiaoliang Zheng China
Weifeng Shan China
Kai Qu relative to Amedeo Buonanno Italy Amedeo Buonanno's profile →
Citations per field
00.5×2.6×
Amedeo Buonanno · 1×
Citations per year

Countries citing papers authored by Kai Qu

Since Specialization
Citations

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

Fields of papers citing papers by Kai Qu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Kai Qu, 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 Kai Qu Line = papers co-authored together Kai Qu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202256
2 202255
3 202035
4 202234
5
Finite-time safe reinforcement learning control of multi-player nonzero-sum game for quadcopter systems
Hit paper breakdown →
202524
6 201220
7 202219
8 202318
9 201817
10 202316
11 201915
12 202510
13 20189
14 20208
15 20237
16 20117
17 20116
18 20196
19 20245
20 20215

About Kai Qu

Kai Qu is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering, Artificial Intelligence, Computer Networks and Communications and Molecular Biology, having authored 36 papers that have together received 405 indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (6 papers), Computational Physics and Python Applications (2 papers), Adaptive Control of Nonlinear Systems (2 papers), Advanced Memory and Neural Computing (2 papers), Adaptive Dynamic Programming Control (2 papers), UAV Applications and Optimization (2 papers), Seismic Waves and Analysis (2 papers) and Time Series Analysis and Forecasting (2 papers). The work is most often cited by research in Artificial Intelligence (97 citations), Energy Engineering and Power Technology (8 citations), Control and Systems Engineering (51 citations), Electrical and Electronic Engineering (124 citations) and Management Science and Operations Research (25 citations). Kai Qu has collaborated with scholars based in China, United States and New Zealand. Frequent co-authors include Gangquan Si, Zhou Zhou, Xianguo Tuo, Huailiang Li, Junkai Tan, Xin Chen, Feng Xu, Xiao‐Yuan Liu, Zhimin Xu and Xiao-Yong Yu. Their work appears in journals such as Energy Reports, Nonlinear Dynamics, Abdominal Radiology, Hepatogastroenterology and Neurocomputing.

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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