Pi-Yun Chen
- Artificial Intelligence top 10%
- Electrical and Electronic Engineering
- Radiology, Nuclear Medicine and Imaging
- Computer Vision and Pattern Recognition top 10%
- Renewable Energy, Sustainability and the Environment
- Co-authors
- Chia‐Hung LinKuei‐Hsiang ChaoJian‐Xing WuNeng‐Sheng PaiChien‐Ming LiHer‐Terng YauChung‐Dann KanChao‐Lin Kuo
- Topics
- Advanced Steganography and Watermarking Techniques (6 papers)Chaos-based Image/Signal Encryption (6 papers)COVID-19 diagnosis using AI (5 papers)
- Partner nations
- TaiwanUnited StatesYemen
In The Last Decade
Pi-Yun Chen
46 papers receiving 394 citations
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 112
- Electrical and Electronic Engineering 107
- Radiology, Nuclear Medicine and Imaging 93
- Computer Vision and Pattern Recognition 80
- Renewable Energy, Sustainability and the Environment 61
Countries citing papers authored by Pi-Yun Chen
This map shows the geographic impact of Pi-Yun Chen'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 Pi-Yun Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pi-Yun Chen more than expected).
Fields of papers citing papers by Pi-Yun Chen
This network shows the impact of papers produced by Pi-Yun Chen. 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 Pi-Yun Chen. The network helps show where Pi-Yun Chen may publish in the future.
Co-authorship network of co-authors of Pi-Yun Chen
This figure shows the co-authorship network connecting the top 25 collaborators of Pi-Yun Chen. A scholar is included among the top collaborators of Pi-Yun Chen 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 Pi-Yun Chen. Pi-Yun Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 16 | |
| 3 | 10 | |
| 4 | 11 | |
| 5 | 19 | |
| 6 | 15 | |
| 7 | 2 | |
| 8 | 11 | |
| 9 | 22 | |
| 10 | 1 | |
| 11 | 10 | |
| 12 | 1 | |
| 13 | 8 | |
| 14 | 49 | |
| 15 | SENSORLESS DIRECT TORQUE CONTROL INDUCTION MOTOR DRIVE WITH ADAPTIVE MOTOR RESISTANCE PARAMETERS IDENTIFICATION | 1 |
| 16 | 7 | |
| 17 | 5 | |
| 18 | 8 | |
| 19 | 1 | |
| 20 | Transient characteristics analysis based-on circuit models for a high-speed rail system | 2 |
About Pi-Yun Chen
Pi-Yun Chen is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Control and Systems Engineering, having authored 46 papers that have together received 415 indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (6 papers), Chaos-based Image/Signal Encryption (6 papers) and COVID-19 diagnosis using AI (5 papers). The work is most often cited by research in Automotive Engineering (48 citations), Radiology, Nuclear Medicine and Imaging (93 citations) and Neurology (33 citations). Pi-Yun Chen has collaborated with scholars based in Taiwan, United States and Yemen. Frequent co-authors include Chia‐Hung Lin, Kuei‐Hsiang Chao, Jian‐Xing Wu, Neng‐Sheng Pai, Chien‐Ming Li, Her‐Terng Yau, Chung‐Dann Kan, Chao‐Lin Kuo, Menghui Wang and K. Kirk Shung. Their work appears in journals such as IEEE Access, IEEE Internet of Things Journal and IEEE Sensors Journal.
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.