Fu Song

1.5k citations
64 papers · 576 indexed · 1 hit paper · h-index 13
Topics
Adversarial Robustness in Machine Learning (17 papers)Security and Verification in Computing (16 papers)Advanced Malware Detection Techniques (16 papers)

In The Last Decade

Fu Song

57 papers receiving 556 citations

Hit Papers

FD-LLM: Large language model for fault diagnosis of compl...20252026202551015

Peers

Fu Song
Comparison fields: 5 of 47
  • Artificial Intelligence 271
  • Signal Processing 253
  • Information Systems 213
  • Software 160
  • Computer Networks and Communications 122
Replace Omar Chowdhury with:
Omar Chowdhury United States
Hong Hu China
Chung Hwan Kim United States
Dongkwan Kim South Korea
Chan-Gun Lee South Korea
Byoungyoung Lee South Korea
Jianqi Shi China
Wuu Yang Taiwan
Bharat B. Madan United States
Shigeki Goto Japan
Fu Song relative to Omar Chowdhury United States Omar Chowdhury's profile →
Citations per field
00.5×2.9×
Omar Chowdhury · 1×
Citations per year

Countries citing papers authored by Fu Song

Since Specialization
Citations

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

Fields of papers citing papers by Fu Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fu Song

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2
FD-LLM: Large language model for fault diagnosis of complex equipmentbreakdown →
17
3 0
4 0
5 3
6 1
7 7
8 2
9 3
10 0
11 1
12 6
13 6
14 1
15 10
16
Things You May Not Know About Adversarial Example: A Black-box Adversarial Image Attack.
3
17 103
18
Verifying pushdown multi-agent systems against strategy logics
1
19 2
20
Nonlinear Internal Model Control Based on Support Vector Machine αth-order Inverse System Method
6

About Fu Song

Fu Song is a scholar working on Software, Hardware and Architecture and Artificial Intelligence, having authored 64 papers that have together received 576 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (17 papers), Security and Verification in Computing (16 papers) and Advanced Malware Detection Techniques (16 papers). The work is most often cited by research in Software (160 citations), Signal Processing (253 citations) and Information Systems (213 citations). Fu Song has collaborated with scholars based in China, United Kingdom and Singapore. Frequent co-authors include Yang Liu, Tayssir Touili, Bihuan Chen, Zhengzi Xu, Mahinthan Chandramohan, Zhe Zhao, Lingling Fan, Taolue Chen, Xiaoning Du and Sen Chen. Their work appears in journals such as IEEE Transactions on Software Engineering, IEEE Transactions on Fuzzy Systems 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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