Shaohua Tian

2.1k citations
68 papers · 1.7k indexed · 1 hit paper · h-index 21

Shaohua Tian

64 papers receiving 1.7k citations

Hit Papers

Deep Transfer Learning Based on Sparse Autoencoder for Re...4222018202620202023100200300400

Peers

Shaohua Tian
Comparison fields: 5 of 77
  • Civil and Structural Engineering 801
  • Statistics, Probability and Uncertainty 165
  • Control and Systems Engineering 511
  • Mechanics of Materials 471
  • Mechanical Engineering 623
Replace Jyoti K. Sinha with:
Jyoti K. Sinha United Kingdom
Wang‐Ji Yan China
Yongteng Zhong China
Luigi Garibaldi Italy
Shizhe Feng China
Michel Salaün France
Ioannis Antoniadis Greece
Baijie Qiao China
Ashish K. Darpe India
Shuangwen Sheng United States
Shaohua Tian relative to Jyoti K. Sinha United Kingdom Jyoti K. Sinha's profile →
Citations per field
00.5×3.4×
Jyoti K. Sinha · 1×
Citations per year

Countries citing papers authored by Shaohua Tian

Since Specialization
Citations

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

Fields of papers citing papers by Shaohua Tian

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20254
2 20250
3 20245
4 20242
5 20240
6 20233
7 20231
8 202326
9 202326
10 202216
11 20224
12 20224
13 202137
14 202119
15 202124
16 202120
17 202112
18 202086
19 202013
20 201710

About Shaohua Tian

Shaohua Tian is a scholar working on Civil and Structural Engineering, Statistics, Probability and Uncertainty and Mechanics of Materials, having authored 68 papers that have together received 1.7k indexed citations. Recurring topics across this work include Structural Health Monitoring Techniques (38 papers), Bladed Disk Vibration Dynamics (36 papers), Ultrasonics and Acoustic Wave Propagation (11 papers), Turbomachinery Performance and Optimization (9 papers), Gear and Bearing Dynamics Analysis (8 papers), Probabilistic and Robust Engineering Design (8 papers), Machine Fault Diagnosis Techniques (8 papers) and Optical measurement and interference techniques (7 papers). The work is most often cited by research in Civil and Structural Engineering (801 citations), Statistics, Probability and Uncertainty (165 citations) and Control and Systems Engineering (511 citations). Shaohua Tian has collaborated with scholars based in China, United Kingdom and Sweden. Frequent co-authors include Xuefeng Chen, Zhibo Yang, Ruqiang Yan, Zhibin Zhao, Chuang Sun, Meng Ma, Shuming Wu, Haoqi Li, Caibin Xu and Yong Xie. Their work appears in journals such as Analytical Chemistry, Chemical Engineering Journal and IEEE Transactions on Industrial Electronics.

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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