Feiyang Cai

508 citations
19 papers · 349 indexed · h-index 7
Topics
Anomaly Detection Techniques and Applications (7 papers)Adversarial Robustness in Machine Learning (5 papers)Fault Detection and Control Systems (4 papers)
Partner nations
United StatesCanadaChina

In The Last Decade

Feiyang Cai

19 papers receiving 344 citations

Peers

Feiyang Cai
Comparison fields: 5 of 41
  • Electrical and Electronic Engineering 235
  • Control and Systems Engineering 172
  • Artificial Intelligence 52
  • Computer Networks and Communications 25
  • Automotive Engineering 21
Replace M. Aiello with:
M. Aiello Italy
Wilhelm Kirchgässner Germany
Sunil Bhat India
A. Bień Poland
Lucio Ippolito Italy
Jason Wells United States
Mehdi Salay Naderi Iran
Paulo Márcio da Silveira Brazil
Ahmad Farid Abidin Malaysia
Shyh-Jier Huang Taiwan
Feiyang Cai relative to M. Aiello Italy M. Aiello's profile →
Citations per field
00.5×8.8×
M. Aiello · 1×
Citations per year

Countries citing papers authored by Feiyang Cai

Since Specialization
Citations

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

Fields of papers citing papers by Feiyang Cai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Feiyang Cai

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

All Works

19 of 19 papers shown
#WorkIndexed citations
1 5
2 2
3 1
4 2
5 1
6 14
7 6
8 2
9 2
10 2
11 37
12 2
13 7
14 166
15 75
16 7
17 13
18 1
19 4

About Feiyang Cai

Feiyang Cai is a scholar working on Signal Processing, Artificial Intelligence and Control and Systems Engineering, having authored 19 papers that have together received 349 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (7 papers), Adversarial Robustness in Machine Learning (5 papers) and Fault Detection and Control Systems (4 papers). The work is most often cited by research in Control and Systems Engineering (172 citations), Electrical and Electronic Engineering (235 citations) and Software (11 citations). Feiyang Cai has collaborated with scholars based in United States, Canada and China. Frequent co-authors include Hao Yan, Yongxiang Xu, Xenofon Koutsoukos, He Zhang, Chris Gerada, Weiduo Zhao, Yuan Fang, Jibin Zou, Guangrui Xia and Bradley Potteiger. Their work appears in journals such as Journal of Applied Physics, IEEE Transactions on Power Electronics and Semiconductor Science and Technology.

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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