Dianliang Wu

453 total citations
32 papers, 279 citations indexed

About

Dianliang Wu is a scholar working on Industrial and Manufacturing Engineering, Automotive Engineering and Computational Mechanics. According to data from OpenAlex, Dianliang Wu has authored 32 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Industrial and Manufacturing Engineering, 6 papers in Automotive Engineering and 6 papers in Computational Mechanics. Recurrent topics in Dianliang Wu's work include Manufacturing Process and Optimization (15 papers), Mobile Crowdsensing and Crowdsourcing (5 papers) and Auction Theory and Applications (5 papers). Dianliang Wu is often cited by papers focused on Manufacturing Process and Optimization (15 papers), Mobile Crowdsensing and Crowdsourcing (5 papers) and Auction Theory and Applications (5 papers). Dianliang Wu collaborates with scholars based in China and United Kingdom. Dianliang Wu's co-authors include Xiumin Fan, Qichang He, Yong Hu, Yucheng Ding, Jun Hong, Hongbo Lan, Jinsong Bao, Dongming Huang, Yu Zheng and Xiangyu Bao and has published in prestigious journals such as International Journal of Production Research, IEEE Internet of Things Journal and The International Journal of Advanced Manufacturing Technology.

In The Last Decade

Dianliang Wu

30 papers receiving 267 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Dianliang Wu China 9 176 68 57 39 38 32 279
Peter Mitrouchev France 10 152 0.9× 94 1.4× 39 0.7× 65 1.7× 29 0.8× 36 275
Martin Manns Germany 10 128 0.7× 56 0.8× 35 0.6× 87 2.2× 45 1.2× 45 281
Ferruccio Mandorli Italy 11 194 1.1× 146 2.1× 27 0.5× 24 0.6× 58 1.5× 54 323
Anil Kumar Gulivindala India 8 207 1.2× 54 0.8× 33 0.6× 27 0.7× 77 2.0× 9 280
Sang-Uk Cheon South Korea 12 293 1.7× 145 2.1× 59 1.0× 31 0.8× 48 1.3× 27 400
Franco Failli Italy 9 317 1.8× 106 1.6× 62 1.1× 55 1.4× 66 1.7× 16 468
M. Eswaran India 7 209 1.2× 48 0.7× 27 0.5× 48 1.2× 180 4.7× 8 409
Borhen Louhichi Tunisia 12 258 1.5× 128 1.9× 125 2.2× 12 0.3× 47 1.2× 60 416

Countries citing papers authored by Dianliang Wu

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Chen, Liang, et al.. (2024). Abnormal pattern recognition for online inspection in manufacturing process based on multi-scale time series classification. Journal of Manufacturing Systems. 76. 457–477. 3 indexed citations
2.
Wu, Dianliang, et al.. (2024). A rapid generation method of models in machining processes for real-time human–machine interaction with virtual-real fusion. The International Journal of Advanced Manufacturing Technology. 132(11-12). 6115–6130.
3.
Bao, Xiangyu, et al.. (2024). A self-supervised contrastive change point detection method for industrial time series. Engineering Applications of Artificial Intelligence. 133. 108217–108217. 7 indexed citations
4.
Wu, Dianliang, et al.. (2024). Online Non-Stationary Pricing Incentives for Budget-Limited Crowdsensing. IEEE Transactions on Big Data. 11(4). 2025–2035.
5.
Lv, Chaofan, et al.. (2024). AR-assisted assembly method based on instance segmentation. International Journal of Computer Integrated Manufacturing. 38(2). 271–287. 7 indexed citations
6.
Zheng, Yu, et al.. (2023). Assembly sequence planning method for optimum assembly accuracy of complex products based on modified teaching–learning based optimization algorithm. The International Journal of Advanced Manufacturing Technology. 126(3-4). 1681–1699. 6 indexed citations
7.
Wu, Dianliang, et al.. (2023). Augmented reality-based virtual-real fusion commissioning: a novel approach to production commissioning. The International Journal of Advanced Manufacturing Technology. 131(11). 5527–5541. 2 indexed citations
8.
Wu, Dianliang, et al.. (2023). Reconstruction Algorithm for Complex Dexel Models Based on Composite Block Partition. Journal of Computing and Information Science in Engineering. 24(4). 2 indexed citations
9.
Wu, Dianliang, et al.. (2023). Research on the Driving Simulation Method of a Manned Lunar Rover System for Somatosensory Representation. Microgravity Science and Technology. 35(6). 1 indexed citations
10.
Wu, Dianliang, et al.. (2023). Contextual-Feature-Based Budget-Limited Online Pricing for Heterogeneous Sensing Tasks. IEEE Internet of Things Journal. 11(9). 15783–15791. 3 indexed citations
11.
Wu, Dianliang, et al.. (2022). Online Truthful Incentives for Heterogeneous and $K$-Submodular Crowdsensing. IEEE Systems Journal. 17(2). 3083–3092. 4 indexed citations
12.
Cheng, Huanchong, Dianliang Wu, & Xiumin Fan. (2018). Modeling and simulation of sheet-metal part deformation in virtual assembly. Journal of Ambient Intelligence and Humanized Computing. 10(3). 1231–1240. 4 indexed citations
13.
Bao, Jinsong, et al.. (2013). Scheduling Ship Hull Assembly Using Lagrangian Relaxation Method. IFAC Proceedings Volumes. 46(9). 239–244. 1 indexed citations
14.
Wu, Dianliang, et al.. (2012). Construction of Generalized Ricci Flow Based Virtual Coordinates for Wireless Sensors Network. IEEE Sensors Journal. 12(6). 2109–2112. 4 indexed citations
15.
Wu, Dianliang, Yong Hu, & Xiumin Fan. (2009). Visual simulation for granular rocks crush in virtual environment based on fractal geometry. Simulation Modelling Practice and Theory. 17(7). 1254–1266. 9 indexed citations
16.
Wu, Dianliang, et al.. (2009). Distributed parallel virtual assembly environment for automobile development. Assembly Automation. 29(3). 279–289. 5 indexed citations
17.
Wu, Dianliang, et al.. (2007). Research on constraint-based virtual assembly technologies. Frontiers of Mechanical Engineering in China. 2(2). 243–249. 4 indexed citations
18.
Huang, Dongming, et al.. (2006). Multi-objective planning of cone crusher chamber, output and size reduction. Minerals Engineering. 20(2). 163–172. 11 indexed citations
19.
Fan, Xiumin, et al.. (2006). Virtual assembly technologies based on constraint and DOF analysis. Robotics and Computer-Integrated Manufacturing. 23(4). 447–456. 39 indexed citations
20.
Wu, Dianliang. (2004). CONSTRAINTS NAVIGATION IN INTEGRATED VIRTUAL ASSEMBLY ENVIRONMENT. Journal of Mechanical Engineering. 40(11). 47–47. 4 indexed citations

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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