Kui Hu

1.4k citations
59 papers · 1.0k · 1 hit paper · h-index 13

Impact in

Papers in

Kui Hu

49 papers receiving 1.0k citations

Kui Hu's Hit Papers

Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network 2019 · 277 citations
2770+2+4Years since publication50100150200250

Peers

Kui Hu
Comparison fields: 5 of 89
  • Control and Systems Engineering 634
  • Medical Laboratory Technology 32
  • Safety, Risk, Reliability and Quality 168
  • Mechanical Engineering 356
  • Algebra and Number Theory 39
Replace Shaojiang Dong with:
Shaojiang Dong China
Lijie Zhang China
Haizhou Chen China
Jichao Zhuang China
Daoming She China
Shaopeng Dong China
Hao Su China
Zexian Wei China
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Kui Hu relative to Shaojiang Dong China Shaojiang Dong's profile →
Citations per field
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Shaojiang Dong · 1×
Citations per year

Countries citing papers authored by Kui Hu

Since Specialization
Citations

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

Fields of papers citing papers by Kui Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 59 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network
Hit paper breakdown →
2019277
2 2021129
3 202166
4 202165
5 202364
6 202164
7 201952
8 202049
9 201939
10 202334
11 202128
12 202321
13 202121
14 202312
15 201312
16 201911
17 201910
18 20258
19 20247
20 20247

About Kui Hu

Kui Hu is a scholar working on Control and Systems Engineering, Algebra and Number Theory, Geometry and Topology, Mechanical Engineering and Mechanics of Materials, having authored 59 papers that have together received 1.0k indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (13 papers), Commutative Algebra and Its Applications (13 papers), Algebraic structures and combinatorial models (13 papers), Rings, Modules, and Algebras (11 papers), Fault Detection and Control Systems (8 papers), Advanced Topics in Algebra (7 papers), Engineering Diagnostics and Reliability (5 papers) and Robotic Mechanisms and Dynamics (5 papers). The work is most often cited by research in Control and Systems Engineering (634 citations), Medical Laboratory Technology (32 citations), Safety, Risk, Reliability and Quality (168 citations), Mechanical Engineering (356 citations) and Algebra and Number Theory (39 citations). Kui Hu has collaborated with scholars based in China, South Korea and Hong Kong. Frequent co-authors include Jun Wu, Yiwei Cheng, Haiping Zhu, Xinyu Shao, Yuanhang Wang, Qingbo He, Yanzhi Wang, Chao Deng, Zuoyi Chen and Yongmin Liu. Their work appears in journals such as Advanced Engineering Informatics, IEEE Access, Frontiers in Plant Science, The International Journal of Advanced Manufacturing Technology and Applied Intelligence.

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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