Min Liu

148 papers receiving 2.8k citations

Min Liu's Hit Papers

Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting 2019 · 255 citations
2550+2+4Years since publication50100150200250

Peers

Min Liu
Comparison fields: 5 of 115
  • Ceramics and Composites 337
  • Aerospace Engineering 1.3k
  • Mechanical Engineering 926
  • Automotive Engineering 240
  • Surfaces, Coatings and Films 133
Replace Zhiqian Zhang with:
Zhiqian Zhang China
Wei Xu China
Chuan Huang China
Kaifeng Zhang China
Luhan Wang China
C.J. Bennett United Kingdom
Dong Jiang China
Kamran Behdinan Canada
Zhiguo Yan China
Dušan P. Sekulić United States
Min Liu relative to Zhiqian Zhang China Zhiqian Zhang's profile →
Citations per field
00.5×7.3×
Zhiqian Zhang · 1×
Citations per year

Countries citing papers authored by Min Liu

Since Specialization
Citations

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

Fields of papers citing papers by Min Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 162 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting
Hit paper breakdown →
2019255
2 2020134
3 2019128
4 202096
5 200891
6 201882
7 201876
8 202070
9 200670
10 202268
11 201467
12 202267
13 202265
14 200951
15 201349
16 201543
17 201935
18 202034
19 202231
20 201231

About Min Liu

Min Liu is a scholar working on Aerospace Engineering, Electrical and Electronic Engineering, Mechanical Engineering, Materials Chemistry and Computer Networks and Communications, having authored 162 papers that have together received 2.9k indexed citations. Recurring topics across this work include High-Temperature Coating Behaviors (67 papers), Advanced materials and composites (30 papers), Nuclear Materials and Properties (28 papers), UAV Applications and Optimization (14 papers), Cooperative Communication and Network Coding (13 papers), Privacy-Preserving Technologies in Data (12 papers), Catalytic Processes in Materials Science (10 papers) and Advanced ceramic materials synthesis (10 papers). The work is most often cited by research in Ceramics and Composites (337 citations), Aerospace Engineering (1.3k citations), Mechanical Engineering (926 citations), Automotive Engineering (240 citations) and Surfaces, Coatings and Films (133 citations). Min Liu has collaborated with scholars based in China, France and United States. Frequent co-authors include Chunming Deng, Zhongcheng Li, Xiaofeng Zhang, Renzhuo Wan, Fan Yang, Jun Wang, Xingchen Yan, Chaoyue Chen, Sheng Sun and Hanlin Liao. Their work appears in journals such as Ceramics International, Surface and Coatings Technology, IEEE Transactions on Mobile Computing, Transactions of Nonferrous Metals Society of China and Applied Surface Science.

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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