Dianliang Wu
- Industrial and Manufacturing Engineering top 2%
- Mechanical Engineering
- Automotive Engineering top 10%
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Topics
- Manufacturing Process and Optimization (15 papers)Mobile Crowdsensing and Crowdsourcing (5 papers)Auction Theory and Applications (5 papers)
- Cited by
- Industrial and Manufacturing EngineeringAutomotive EngineeringManagement of Technology and Innovation
- Journals
- International Journal of Production ResearchIEEE Internet of Things JournalThe International Journal of Advanced Manufacturing Technology
- Partner nations
- ChinaUnited Kingdom
In The Last Decade
Dianliang Wu
30 papers receiving 267 citations
Peers
Comparison fields: 5 of 59
- Industrial and Manufacturing Engineering 176
- Mechanical Engineering 68
- Automotive Engineering 57
- Control and Systems Engineering 39
- Computer Vision and Pattern Recognition 38
Countries citing papers authored by Dianliang Wu
This map shows the geographic impact of Dianliang Wu'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 Dianliang Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dianliang Wu more than expected).
Fields of papers citing papers by Dianliang Wu
This network shows the impact of papers produced by Dianliang Wu. 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 Dianliang Wu. The network helps show where Dianliang Wu may publish in the future.
Co-authorship network of co-authors of Dianliang Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Dianliang Wu. A scholar is included among the top collaborators of Dianliang Wu 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 Dianliang Wu. Dianliang Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 7 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 6 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | 3 | |
| 10 | 4 | |
| 11 | 4 | |
| 12 | 1 | |
| 13 | 4 | |
| 14 | 6 | |
| 15 | 5 | |
| 16 | 9 | |
| 17 | 4 | |
| 18 | 11 | |
| 19 | 39 | |
| 20 | 4 |
About Dianliang Wu
Dianliang Wu is a scholar working on Industrial and Manufacturing Engineering, Computer Science Applications and Computer Graphics and Computer-Aided Design, having authored 32 papers that have together received 279 indexed citations. Recurring topics across this work include Manufacturing Process and Optimization (15 papers), Mobile Crowdsensing and Crowdsourcing (5 papers) and Auction Theory and Applications (5 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (176 citations), Automotive Engineering (57 citations) and Management of Technology and Innovation (25 citations). Dianliang Wu has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Xiumin Fan, Qichang He, Yong Hu, Hongbo Lan, Yucheng Ding, Jun Hong, Jinsong Bao, Dongming Huang, Yu Zheng and Xiangyu Bao. Their work appears in journals such as International Journal of Production Research, IEEE Internet of Things Journal and The International Journal of Advanced Manufacturing Technology.
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.