Zhongyi Pei

1.3k total citations · 2 hit papers
8 papers, 856 citations indexed

About

Zhongyi Pei is a scholar working on Artificial Intelligence, Computer Networks and Communications and Control and Systems Engineering. According to data from OpenAlex, Zhongyi Pei has authored 8 papers receiving a total of 856 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 2 papers in Control and Systems Engineering. Recurrent topics in Zhongyi Pei's work include Software System Performance and Reliability (2 papers), Software Engineering Research (2 papers) and Software Engineering Techniques and Practices (2 papers). Zhongyi Pei is often cited by papers focused on Software System Performance and Reliability (2 papers), Software Engineering Research (2 papers) and Software Engineering Techniques and Practices (2 papers). Zhongyi Pei collaborates with scholars based in China and Hong Kong. Zhongyi Pei's co-authors include Jianmin Wang, Zhangjie Cao, Mingsheng Long, Te Han, Chi Lin, Lin Yao, Mingchu Li, Guowei Wu, Feng Xia and Min Xie and has published in prestigious journals such as Information Sciences, Engineering Applications of Artificial Intelligence and Journal of Computer and System Sciences.

In The Last Decade

Zhongyi Pei

7 papers receiving 836 citations

Hit Papers

Multi-Adversarial Domain Adaptation 2018 2026 2020 2023 2018 2023 100 200 300 400 500
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. Multi-Adversarial Domain Adaptation
    2018 573 citations Zhongyi Pei, Zhangjie Cao et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
  2. Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine
    2023 145 citations Te Han, Zhongyi Pei et al. Information Sciences profile →

Peers

Zhongyi Pei
Xu Jia China
Donghyun Kim South Korea
Huijuan Xu United States
Binhui Xie China
Adrião Duarte Dória Neto Brazil
Yanshan Xiao China
Qing Tian China
Rahaf Aljundi Switzerland
Xu Jia China
Zhongyi Pei
Citations per year, relative to Zhongyi Pei Zhongyi Pei (= 1×) peers Xu Jia

Countries citing papers authored by Zhongyi Pei

Since Specialization
Citations

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

Fields of papers citing papers by Zhongyi Pei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhongyi Pei

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

All Works

8 of 8 papers shown
1.
Pei, Zhongyi, et al.. (2025). TimesBERT: A BERT-Style Foundation Model for Time Series Understanding. 10975–10983.
2.
Pei, Zhongyi, et al.. (2024). BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization. 2340–2351. 1 indexed citations
3.
Han, Te, et al.. (2023). Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine. Information Sciences. 648. 119496–119496. 145 indexed citations breakdown →
4.
Han, Te, et al.. (2023). A unified out-of-distribution detection framework for trustworthy prognostics and health management in renewable energy systems. Engineering Applications of Artificial Intelligence. 125. 106707–106707. 49 indexed citations
5.
Pei, Zhongyi, Lin Liu, Wang Chen, & Jianmin Wang. (2022). Requirements Engineering for Machine Learning: A Review and Reflection. arXiv (Cornell University). 1 indexed citations
6.
Pei, Zhongyi, Lin Liu, Chen Wang, & Jianmin Wang. (2022). Requirements Engineering for Machine Learning: A Review and Reflection. 166–175. 16 indexed citations
7.
Pei, Zhongyi, Zhangjie Cao, Mingsheng Long, & Jianmin Wang. (2018). Multi-Adversarial Domain Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 573 indexed citations breakdown →
8.
Lin, Chi, Guowei Wu, Feng Xia, et al.. (2011). Energy efficient ant colony algorithms for data aggregation in wireless sensor networks. Journal of Computer and System Sciences. 78(6). 1686–1702. 71 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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