Hao Lü

36 papers receiving 685 citations

Hao Lü's Hit Papers

A physics-informed deep learning approach for bearing fault detection 2021 · 203 citations
2030+1+3Years since publication50100150200

Peers

Hao Lü
Comparison fields: 5 of 100
  • Human-Computer Interaction 84
  • Control and Systems Engineering 259
  • Computer Science Applications 26
  • Mechanical Engineering 161
  • Cognitive Neuroscience 60
Replace Jingwei Wang with:
Jingwei Wang China
Hasan U. Zaman Bangladesh
Jian Wan United Kingdom
Hao Cheng China
Shenglin Mu Japan
Wenjing Li China
Mujtaba Hussain Jaffery Pakistan
Abdallah Kassem Lebanon
Natividad Duro Spain
Hao Lü relative to Jingwei Wang China Jingwei Wang's profile →
Citations per field
00.5×8.4×
Jingwei Wang · 1×
Citations per year

Countries citing papers authored by Hao Lü

Since Specialization
Citations

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

Fields of papers citing papers by Hao Lü

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A physics-informed deep learning approach for bearing fault detection
Hit paper breakdown →
2021203
2 202353
3 202140
4 202238
5 201137
6 201332
7 201430
8 201729
9 200423
10
Cascaded treemaps: examining the visibility and stability of structure in treemaps
200823
11 201519
12 202119
13 202317
14 202316
15 202314
16 202412
17 201911
18 202110
19 20148
20 20238

About Hao Lü

Hao Lü is a scholar working on Control and Systems Engineering, Mechanical Engineering, Electrical and Electronic Engineering, Human-Computer Interaction and Materials Chemistry, having authored 39 papers that have together received 699 indexed citations. Recurring topics across this work include Machine Fault Diagnosis Techniques (8 papers), Gear and Bearing Dynamics Analysis (7 papers), Interactive and Immersive Displays (5 papers), Tactile and Sensory Interactions (4 papers), Advanced Photocatalysis Techniques (2 papers), Spectroscopy and Chemometric Analyses (2 papers), Industrial Vision Systems and Defect Detection (2 papers) and Copper-based nanomaterials and applications (2 papers). The work is most often cited by research in Human-Computer Interaction (84 citations), Control and Systems Engineering (259 citations), Computer Science Applications (26 citations), Mechanical Engineering (161 citations) and Cognitive Neuroscience (60 citations). Hao Lü has collaborated with scholars based in China, United States and Switzerland. Frequent co-authors include Chao Hu, Venkat Pavan Nemani, Adam Thelen, Yang Li, James Fogarty, Mohammadkazem Sadoughi, Andrew T. Zimmerman, Keith Webster, Shawn Kenny and Matthew J. Darr. Their work appears in journals such as Mechanical Systems and Signal Processing, Engineering Applications of Artificial Intelligence, Journal of environmental chemical engineering, Materials Today Chemistry 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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