Ao Ding

17 papers receiving 314 citations

Ao Ding's Hit Papers

Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems 2024 · 99 citations
990+1Years since publication255075

Peers

Ao Ding
Comparison fields: 5 of 43
  • Control and Systems Engineering 189
  • Industrial and Manufacturing Engineering 40
  • Mechanical Engineering 106
  • Mechanics of Materials 48
  • Safety, Risk, Reliability and Quality 16
Replace Xun Dong with:
Xun Dong China
Zuogang Shang China
Andongzhe Duan China
Renhe Yao China
Misael Lopez–Ramirez Mexico
Hongchun Sun China
Qiubo Jiang China
Hongdi Zhou China
Jin Uk Ko South Korea
Ao Ding relative to Xun Dong China Xun Dong's profile →
Citations per field
00.5×3.8×
Xun Dong · 1×
Citations per year

Countries citing papers authored by Ao Ding

Since Specialization
Citations

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

Fields of papers citing papers by Ao Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Evolvable graph neural network for system-level incremental fault diagnosis of train transmission systems
Hit paper breakdown →
202499
2 202251
3 202341
4 202334
5 202325
6 202413
7 202111
8 202211
9 20209
10 20247
11 20225
12 20225
13 20252
14 20232
15 20241
16 20211
17 20251
18 20250
19 20250
20 20250

About Ao Ding

Ao Ding is a scholar working on Control and Systems Engineering, Artificial Intelligence, Mechanical Engineering, Electrical and Electronic Engineering and Mechanics of Materials, having authored 21 papers that have together received 318 indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (6 papers), Innovative Energy Harvesting Technologies (3 papers), Wireless Power Transfer Systems (3 papers), Non-Destructive Testing Techniques (3 papers), Energy Harvesting in Wireless Networks (3 papers), Vehicle License Plate Recognition (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Reinforcement Learning in Robotics (2 papers). The work is most often cited by research in Control and Systems Engineering (189 citations), Industrial and Manufacturing Engineering (40 citations), Mechanical Engineering (106 citations), Mechanics of Materials (48 citations) and Safety, Risk, Reliability and Quality (16 citations). Ao Ding has collaborated with scholars based in China, United Kingdom and Hong Kong. Frequent co-authors include Yong Qin, Biao Wang, Xiaoqing Cheng, Limin Jia, Liang Guo, Mengzhou Liu, Hailing Fu, Eric M. Yeatman, Hongfeng Li and Dawei Chen. Their work appears in journals such as Mechanical Systems and Signal Processing, Measurement, IEEE Transactions on Industrial Electronics, Sensors and Actuators A Physical 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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