Dongzhe Wang

1.2k total citations · 1 hit paper
18 papers, 907 citations indexed

About

Dongzhe Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Dongzhe Wang has authored 18 papers receiving a total of 907 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 3 papers in Computer Networks and Communications. Recurrent topics in Dongzhe Wang's work include Topic Modeling (5 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Image Retrieval and Classification Techniques (5 papers). Dongzhe Wang is often cited by papers focused on Topic Modeling (5 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Image Retrieval and Classification Techniques (5 papers). Dongzhe Wang collaborates with scholars based in Singapore, China and Japan. Dongzhe Wang's co-authors include Kezhi Mao, Ruqiang Yan, Fei Shen, Jinjiang Wang, Rui Zhao, Gang Xue, Gee-Wah Ng, Cheng‐Chew Lim, Guangyuan Zhao and Miaomiao Li and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Catalysis Today and Neurocomputing.

In The Last Decade

Dongzhe Wang

17 papers receiving 886 citations

Hit Papers

Machine Health Monitoring... 2017 2026 2020 2023 2017 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
    2017 675 citations Rui Zhao, Dongzhe Wang et al. IEEE Transactions on Industrial Electronics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dongzhe Wang Singapore 12 397 301 186 137 98 18 907
Cheng Shao China 17 661 1.7× 257 0.9× 146 0.8× 130 0.9× 60 0.6× 170 1.2k
Bei Sun China 20 548 1.4× 380 1.3× 181 1.0× 221 1.6× 50 0.5× 105 1.2k
Huixin Tian China 16 270 0.7× 227 0.8× 232 1.2× 157 1.1× 73 0.7× 61 784
Zhe Yang China 20 472 1.2× 337 1.1× 262 1.4× 145 1.1× 184 1.9× 74 1.1k
Haibo Liang China 12 185 0.5× 224 0.7× 148 0.8× 171 1.2× 128 1.3× 45 893
Jiacheng Xie China 19 232 0.6× 217 0.7× 60 0.3× 118 0.9× 65 0.7× 107 887
Jialing Zou China 10 168 0.4× 174 0.6× 120 0.6× 162 1.2× 88 0.9× 29 781
Ning Chen China 16 434 1.1× 217 0.7× 165 0.9× 198 1.4× 30 0.3× 125 1.1k

Countries citing papers authored by Dongzhe Wang

Since Specialization
Citations

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

Fields of papers citing papers by Dongzhe Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dongzhe Wang

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

All Works

18 of 18 papers shown
1.
Wang, Dongzhe, et al.. (2023). The characterization and mechanism of carbonated steel slag and its products under low CO2 pressure. Materials Today Communications. 35. 105827–105827. 21 indexed citations
2.
Wang, Dongzhe, et al.. (2022). Recycling iron from pickling sludge to activate peroxydisulfate for the degradation of phenol. Water Science & Technology. 85(8). 2332–2349. 1 indexed citations
3.
4.
Zaidi, Adeel, et al.. (2021). Distributed Observer-Based Leader Following Consensus Tracking Protocol for a Swarm of Drones. Journal of Intelligent & Robotic Systems. 102(3). 21 indexed citations
5.
Wang, Dongzhe, et al.. (2021). Tourmaline/Perovskite composite material as heterogeneous catalysts for activation peroxymonosulfate to remove organic dye in water. Journal of environmental chemical engineering. 9(3). 105221–105221. 29 indexed citations
6.
Li, Pengfei, Peixiang Zhong, Kezhi Mao, et al.. (2021). ACT: an Attentive Convolutional Transformer for Efficient Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 35(15). 13261–13269. 28 indexed citations
7.
Xie, Yuxi, Liangming Pan, Dongzhe Wang, Min‐Yen Kan, & Yansong Feng. (2020). Exploring Question-Specific Rewards for Generating Deep Questions. National University of Singapore. 2534–2546. 15 indexed citations
8.
Wang, Dongzhe, et al.. (2020). Goku’s Participation in WAT 2020. 135–141. 2 indexed citations
9.
Zhao, Guangyuan, Miaomiao Li, Liyin Wang, et al.. (2019). Environmentally-friendly tourmaline modified CeMnFeOx catalysts for low-temperature selective catalytic reduction of NOx with NH3. Catalysis Today. 355. 385–396. 27 indexed citations
10.
Wang, Dongzhe & Kezhi Mao. (2019). Learning Semantic Text Features for Web Text-Aided Image Classification. IEEE Transactions on Multimedia. 21(12). 2985–2996. 13 indexed citations
11.
Wang, Dongzhe & Kezhi Mao. (2018). Multimodal Object Classification Using Bidirectional Gated Recurrent Unit Networks. 685–690. 4 indexed citations
12.
Shi, Peng, et al.. (2018). Formation control for multi-robot systems with collision avoidance. International Journal of Control. 92(10). 2223–2234. 20 indexed citations
13.
Wang, Dongzhe & Kezhi Mao. (2018). Task-generic semantic convolutional neural network for web text-aided image classification. Neurocomputing. 329. 103–115. 17 indexed citations
14.
Wang, Dongzhe, Kezhi Mao, & Gee-Wah Ng. (2017). Convolutional neural networks and multimodal fusion for text aided image classification. 1–7. 13 indexed citations
15.
Zhao, Rui, Dongzhe Wang, Ruqiang Yan, et al.. (2017). Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks. IEEE Transactions on Industrial Electronics. 65(2). 1539–1548. 675 indexed citations breakdown →
16.
Wang, Dongzhe, Kezhi Mao, Gee-Wah Ng, & Tien Pham. (2016). Adaptive multimodal fusion with web resources for scene classification. International Conference on Information Fusion. 114–121. 3 indexed citations
17.
Wang, Dongzhe, Kezhi Mao, & Gee-Wah Ng. (2015). Improving scene classification by fusion of training data and web resources. International Conference on Information Fusion. 823–829. 4 indexed citations
18.
Fei, Zhongyang, Dongzhe Wang, Huijun Gao, & Yingwei Zhang. (2009). Discrete-time complex networks: a new synchronisation stability criterion. International Journal of Systems Science. 40(9). 931–936. 13 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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