Lin Shao

1.1k citations
55 papers · 627 indexed · h-index 12
Topics
Robot Manipulation and Learning (15 papers)Reinforcement Learning in Robotics (8 papers)Neural Networks Stability and Synchronization (5 papers)

In The Last Decade

Lin Shao

47 papers receiving 612 citations

Peers

Lin Shao
Comparison fields: 5 of 82
  • Control and Systems Engineering 240
  • Public Health, Environmental and Occupational Health 208
  • Clinical Psychology 116
  • Artificial Intelligence 114
  • Computer Vision and Pattern Recognition 94
Replace Neelam Sanjeev Kumar with:
Neelam Sanjeev Kumar India
Santokh Singh Canada
Chih-Chieh Yang Taiwan
Somayeh Bakhtiari Ramezani United States
Eun-Hee Jeong South Korea
Charles W. Warren United States
Mark Hempstead United States
Janusz Wróbel Poland
Paul D. Sutton Ireland
Heeseung Choi South Korea
Lin Shao relative to Neelam Sanjeev Kumar India Neelam Sanjeev Kumar's profile →
Citations per field
00.5×10×16×
Neelam Sanjeev Kumar · 1×
Citations per year

Countries citing papers authored by Lin Shao

Since Specialization
Citations

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

Fields of papers citing papers by Lin Shao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lin Shao

This figure shows the co-authorship network connecting the top 25 collaborators of Lin Shao. A scholar is included among the top collaborators of Lin Shao 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 Lin Shao. Lin Shao 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 1
2 0
3 2
4 2
5 6
6 0
7 1
8 2
9 13
10 2
11 0
12 1
13
Generative 3D Part Assembly via Dynamic Graph Learning
4
14 35
15 14
16 112
17
Own-learning Fuzzy Neural Network PID Heating Furnace Pressure Control
1
18
Intelligent ultrasonic distance measurement system of CAN bus based on P87C591
1
19 4
20
NUMERICAL SIMULATIONS OF HYPERVELOCITY LAUNCHERS
1

About Lin Shao

Lin Shao is a scholar working on Control and Systems Engineering, Computer Graphics and Computer-Aided Design and Computational Mechanics, having authored 55 papers that have together received 627 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (15 papers), Reinforcement Learning in Robotics (8 papers) and Neural Networks Stability and Synchronization (5 papers). The work is most often cited by research in Control and Systems Engineering (240 citations), Obstetrics and Gynecology (72 citations) and Public Health, Environmental and Occupational Health (208 citations). Lin Shao has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Megan V. Smith, Kimberly A. Yonkers, Heather B. Howell, Jeannette Bohg, Toki Migimatsu, Haiqun Lin, Hanyong Shao, Hong Wang, Karalee Poschman and Qiang Zhang. Their work appears in journals such as SHILAP Revista de lepidopterología, The Science of The Total Environment and IEEE Transactions on Antennas and Propagation.

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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