Duo Qiu

583 total citations
15 papers, 450 citations indexed

About

Duo Qiu is a scholar working on Computational Mechanics, Computer Vision and Pattern Recognition and Computational Mathematics. According to data from OpenAlex, Duo Qiu has authored 15 papers receiving a total of 450 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computational Mechanics, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Computational Mathematics. Recurrent topics in Duo Qiu's work include Sparse and Compressive Sensing Techniques (9 papers), Tensor decomposition and applications (6 papers) and Image and Signal Denoising Methods (4 papers). Duo Qiu is often cited by papers focused on Sparse and Compressive Sensing Techniques (9 papers), Tensor decomposition and applications (6 papers) and Image and Signal Denoising Methods (4 papers). Duo Qiu collaborates with scholars based in China, Australia and Hong Kong. Duo Qiu's co-authors include Hongming Yang, Jing Qiu, Zhao Yang Dong, Xiongjun Zhang, Minru Bai, Michael K. Ng, Mingyong Lai, Zhaoyang Dong, Shiming Zhang and Junhua Zhao and has published in prestigious journals such as Applied Energy, IEEE Transactions on Power Systems and IEEE Transactions on Industrial Informatics.

In The Last Decade

Duo Qiu

13 papers receiving 437 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Duo Qiu China 8 301 98 89 80 73 15 450
Haotian Liu China 10 379 1.3× 253 2.6× 14 0.2× 15 0.2× 13 0.2× 49 521
Zongyan Li China 7 473 1.6× 101 1.0× 13 0.1× 19 0.3× 25 581
Mohammad Esmaeil Hassanzadeh Iran 11 419 1.4× 204 2.1× 8 0.1× 24 0.3× 13 484
Ittetsu Taniguchi Japan 11 186 0.6× 63 0.6× 7 0.1× 63 0.9× 120 415
Parameswaran Kamalaruban United Kingdom 6 198 0.7× 92 0.9× 8 0.1× 17 0.2× 11 331
Fangyuan Xu China 11 231 0.8× 128 1.3× 5 0.1× 43 0.6× 36 395
Ashutosh Bhadoria India 9 226 0.8× 79 0.8× 10 0.1× 27 0.4× 17 495
Yuxuan Yuan United States 13 607 2.0× 301 3.1× 9 0.1× 1 0.0× 29 0.4× 29 721

Countries citing papers authored by Duo Qiu

Since Specialization
Citations

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

Fields of papers citing papers by Duo Qiu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Duo Qiu

This figure shows the co-authorship network connecting the top 25 collaborators of Duo Qiu. A scholar is included among the top collaborators of Duo Qiu 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 Duo Qiu. Duo Qiu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
2.
Qiu, Duo, et al.. (2024). Robust Tensor Completion via Dictionary Learning and Generalized Nonconvex Regularization for Visual Data Recovery. IEEE Transactions on Circuits and Systems for Video Technology. 34(11). 11026–11039. 6 indexed citations
3.
Qiu, Duo, et al.. (2023). Tensor factorization via transformed tensor-tensor product for image alignment. Numerical Algorithms. 95(3). 1251–1289. 4 indexed citations
4.
Qiu, Duo, et al.. (2023). Robust Low Transformed Multi-Rank Tensor Completion With Deep Prior Regularization for Multi-Dimensional Image Recovery. IEEE Transactions on Big Data. 9(5). 1288–1301. 6 indexed citations
5.
Qiu, Duo, et al.. (2023). Low-Rank Matrix Completion with Poisson Observations via Nuclear Norm and Total Variation Constraints. Journal of Computational Mathematics. 42(6). 1427–1451. 2 indexed citations
6.
Qiu, Duo, Minru Bai, Michael K. Ng, & Xiongjun Zhang. (2021). Nonlocal robust tensor recovery with nonconvex regularization *. Inverse Problems. 37(3). 35001–35001. 39 indexed citations
7.
Qiu, Duo, Minru Bai, Michael K. Ng, & Xiongjun Zhang. (2021). Robust low-rank tensor completion via transformed tensor nuclear norm with total variation regularization. Neurocomputing. 435. 197–215. 52 indexed citations
8.
Qiu, Duo, Minru Bai, Michael K. Ng, & Xiongjun Zhang. (2021). Robust Low Transformed Multi-Rank Tensor Methods for Image Alignment. Journal of Scientific Computing. 87(1). 12 indexed citations
9.
10.
Qiu, Duo, Xiongjun Zhang, & Lei Zhang. (2017). Distributed Optimization Operation Strategy for Charging Stations Under Demand Response. International Journal of Emerging Electric Power Systems. 18(4). 2 indexed citations
11.
Yang, Hongming, Shiming Zhang, Duo Qiu, et al.. (2017). Distributionally Robust Optimal Bidding of Controllable Load Aggregators in the Electricity Market. IEEE Transactions on Power Systems. 33(1). 1089–1091. 40 indexed citations
12.
Yang, Hongming, et al.. (2017). CVaR-Constrained Optimal Bidding of Electric Vehicle Aggregators in Day-Ahead and Real-Time Markets. IEEE Transactions on Industrial Informatics. 13(5). 2555–2565. 99 indexed citations
13.
Lai, Mingyong, et al.. (2017). Optimal decisions for a dual-channel supply chain under information asymmetry. Journal of Industrial and Management Optimization. 14(3). 1023–1040. 13 indexed citations
14.
Yang, Hongming, et al.. (2015). Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response. Applied Energy. 167. 353–365. 167 indexed citations
15.

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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