Peng Yan
- Biomedical Engineering
- Computer Networks and Communications top 10%
- Polymers and Plastics
- Artificial Intelligence
- Electrical and Electronic Engineering
- Co-authors
- Hitay ÖzbayYing GongHengyu GuoZhong Lin WangQin ZhangChen CaoZhongjie LiThilo Stadelmann
- Topics
- Industrial Vision Systems and Defect Detection (3 papers)Machine Learning and Data Classification (2 papers)Big Data and Business Intelligence (1 paper)
- Journals
- Energy & Environmental ScienceIEEE Transactions on Pattern Analysis and Machine IntelligenceScientific Reports
- Partner nations
- ChinaSwitzerlandItaly
In The Last Decade
Peng Yan
9 papers receiving 230 citations
Hit Papers
Peers
Comparison fields: 5 of 59
- Biomedical Engineering 92
- Computer Networks and Communications 69
- Polymers and Plastics 63
- Artificial Intelligence 44
- Electrical and Electronic Engineering 44
Countries citing papers authored by Peng Yan
This map shows the geographic impact of Peng Yan'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 Peng Yan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peng Yan more than expected).
Fields of papers citing papers by Peng Yan
This network shows the impact of papers produced by Peng Yan. 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 Peng Yan. The network helps show where Peng Yan may publish in the future.
Co-authorship network of co-authors of Peng Yan
This figure shows the co-authorship network connecting the top 25 collaborators of Peng Yan. A scholar is included among the top collaborators of Peng Yan 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 Peng Yan. Peng Yan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionsbreakdown → | 70 |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 2 | |
| 9 | 1 | |
| 10 | 8 | |
| 11 | 95 | |
| 12 | 1 | |
| 13 | 52 |
About Peng Yan
Peng Yan is a scholar working on Industrial and Manufacturing Engineering, Management Information Systems and Geology, having authored 13 papers that have together received 233 indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (3 papers), Machine Learning and Data Classification (2 papers) and Big Data and Business Intelligence (1 paper). The work is most often cited by research in Polymers and Plastics (63 citations), Computer Networks and Communications (69 citations) and Biomedical Engineering (92 citations). Peng Yan has collaborated with scholars based in China, Switzerland and Italy. Frequent co-authors include Hitay Özbay, Ying Gong, Hengyu Guo, Zhong Lin Wang, Qin Zhang, Chen Cao, Zhongjie Li, Thilo Stadelmann, Fan Shen and Benjamin F. Grewe. Their work appears in journals such as Energy & Environmental Science, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.
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.