Zhanglin Peng

1.3k total citations
42 papers, 718 citations indexed

About

Zhanglin Peng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Zhanglin Peng has authored 42 papers receiving a total of 718 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 8 papers in Management Science and Operations Research. Recurrent topics in Zhanglin Peng's work include Advanced Image and Video Retrieval Techniques (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Product Development and Customization (5 papers). Zhanglin Peng is often cited by papers focused on Advanced Image and Video Retrieval Techniques (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Product Development and Customization (5 papers). Zhanglin Peng collaborates with scholars based in China, Hong Kong and Canada. Zhanglin Peng's co-authors include Qiang Zhang, Shanlin Yang, Ping Luo, Ruimao Zhang, Shuangyao Zhao, Xiaoan Tang, Xiaonong Lu, Anning Wang, Witold Pedrycz and Sufeng Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Cleaner Production and Energy.

In The Last Decade

Zhanglin Peng

41 papers receiving 700 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhanglin Peng China 16 194 185 114 97 81 42 718
Jun Guo China 20 236 1.2× 176 1.0× 58 0.5× 30 0.3× 152 1.9× 85 1.4k
Jeng-Fung Chen Taiwan 17 190 1.0× 162 0.9× 85 0.7× 36 0.4× 92 1.1× 36 1.1k
Francisco Velasco Morente Spain 15 147 0.8× 141 0.8× 121 1.1× 21 0.2× 60 0.7× 81 822
Giulia Bruno Italy 16 146 0.8× 62 0.3× 56 0.5× 35 0.4× 62 0.8× 75 997
Stefan Pickl Germany 13 145 0.7× 247 1.3× 107 0.9× 72 0.7× 428 5.3× 103 1000
Hadi Akbarzadeh Khorshidi Australia 17 326 1.7× 172 0.9× 58 0.5× 26 0.3× 78 1.0× 64 1.0k
Shunsheng Guo China 21 138 0.7× 179 1.0× 27 0.2× 51 0.5× 130 1.6× 74 1.5k
Robin G. Qiu United States 19 228 1.2× 56 0.3× 48 0.4× 34 0.4× 121 1.5× 103 1.3k
Ching-Chiang Yeh Taiwan 11 335 1.7× 273 1.5× 23 0.2× 77 0.8× 86 1.1× 25 1.0k
Hyerim Bae South Korea 17 146 0.8× 229 1.2× 27 0.2× 27 0.3× 51 0.6× 95 1.0k

Countries citing papers authored by Zhanglin Peng

Since Specialization
Citations

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

Fields of papers citing papers by Zhanglin Peng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhanglin Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Zhanglin Peng. A scholar is included among the top collaborators of Zhanglin Peng 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 Zhanglin Peng. Zhanglin Peng 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.
Zheng, Rui, et al.. (2024). Economic design of a self-healing policy with limited agents. Computers & Industrial Engineering. 199. 110740–110740.
2.
Zheng, Rui, et al.. (2024). Condition-based maintenance for a balanced system considering dependent soft and hard failures. Computers & Industrial Engineering. 197. 110550–110550. 7 indexed citations
3.
Peng, Zhanglin, et al.. (2024). An evaluation model for selection of large-scale product concept design schemes in design crowdsourcing environment. Advanced Engineering Informatics. 62. 102680–102680. 1 indexed citations
4.
Zhu, Xuhui, et al.. (2024). A novel integrated prediction method using adaptive mode decomposition, attention mechanism and deep learning for coking products prices. Engineering Applications of Artificial Intelligence. 139. 109504–109504. 3 indexed citations
5.
Peng, Zhanglin, et al.. (2024). Hybrid price prediction method combining TCN-BiGRU and attention mechanism for battery-grade lithium carbonate. Kybernetes. 54(14). 7662–7688. 1 indexed citations
6.
Peng, Zhanglin, et al.. (2022). Deep learning-based recommendation method for top-K tasks in software crowdsourcing systems. Journal of Industrial and Management Optimization. 19(9). 6478–6499. 13 indexed citations
7.
Zhao, Shuangyao, et al.. (2022). Product platform configuration for product families: Module clustering based on product architecture and manufacturing process. Advanced Engineering Informatics. 52. 101622–101622. 15 indexed citations
8.
Lu, Xiaonong, Qiang Zhang, Zhanglin Peng, et al.. (2020). Charging and relocating optimization for electric vehicle car-sharing: An event-based strategy improvement approach. Energy. 207. 118285–118285. 20 indexed citations
9.
Wang, Anning, Qiang Zhang, Shuangyao Zhao, Xiaonong Lu, & Zhanglin Peng. (2020). A review-driven customer preference measurement model for product improvement: sentiment-based importance–performance analysis. Information Systems and e-Business Management. 18(1). 61–88. 48 indexed citations
10.
Luo, Ping, et al.. (2019). Differentiable Dynamic Normalization for Learning Deep Representation. International Conference on Machine Learning. 4203–4211. 11 indexed citations
11.
Zhang, Zhaoyang, Jingyu Li, Wenqi Shao, et al.. (2019). Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks. 3541–3550. 31 indexed citations
12.
Feng, Nanping, et al.. (2019). The key role of dynamic capabilities in the evolutionary process for a startup to develop into an innovation ecosystem leader: An indepth case study. Journal of Engineering and Technology Management. 54. 81–96. 35 indexed citations
13.
Zhang, Qiang, et al.. (2019). An adaptive agent-based process model for optimizing innovative design. Optimization Letters. 15(2). 591–612. 1 indexed citations
14.
Zhang, Ruimao, Wei Yang, Zhanglin Peng, et al.. (2019). Progressively diffused networks for semantic visual parsing. Pattern Recognition. 90. 78–86. 9 indexed citations
15.
Luo, Ping, et al.. (2018). Differentiable Learning-to-Normalize via Switchable Normalization. International Conference on Learning Representations. 12 indexed citations
16.
Luo, Ping, Xinjiang Wang, Wenqi Shao, & Zhanglin Peng. (2018). Understanding Regularization in Batch Normalization. arXiv (Cornell University). 1 indexed citations
17.
Wang, Sufeng, Shuang Qiu, Shijian Ge, Jia Liu, & Zhanglin Peng. (2018). Benchmarking Toronto wastewater treatment plants using DEA window and Tobit regression analysis with a dynamic efficiency perspective. Environmental Science and Pollution Research. 25(32). 32649–32659. 25 indexed citations
18.
Peng, Zhanglin, Ruimao Zhang, Xiaodan Liang, Xiaobai Liu, & Liang Lin. (2016). Geometric scene parsing with hierarchical LSTM. International Joint Conference on Artificial Intelligence. 3439–3445. 1 indexed citations
19.
Peng, Zhanglin, et al.. (2011). Independent innovation capability evaluation and analysis on wanjiang city-zone. 2286–2289. 1 indexed citations
20.
Liu, Jianjun, et al.. (2010). Detection technique for cathay hickory grade based on machine vision. Acta Agriculturae Zhejiangensis. 22(6). 854–858. 2 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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