Seoung Bum Kim
- Artificial Intelligence top 2%
- Control and Systems Engineering top 2%
- Statistics, Probability and Uncertainty top 0.5%
- Industrial and Manufacturing Engineering top 1%
- Information Systems top 2%
- Co-authors
- Victoria C. P. ChenKwok‐Leung TsuiSangmin LeeHan Kyu LeeYoung-Hoon KimChan Hee ParkSeulki LeeThomas R. Ziegler
- Topics
- Advanced Statistical Process Monitoring (31 papers)Industrial Vision Systems and Defect Detection (30 papers)Fault Detection and Control Systems (23 papers)
- Cited by
- Statistics, Probability and UncertaintyIndustrial and Manufacturing EngineeringStatistics and Probability
- Partner nations
- South KoreaUnited StatesJapan
In The Last Decade
Seoung Bum Kim
167 papers receiving 2.7k citations
Peers
Comparison fields: 5 of 183
- Artificial Intelligence 765
- Control and Systems Engineering 605
- Statistics, Probability and Uncertainty 490
- Industrial and Manufacturing Engineering 365
- Information Systems 315
Countries citing papers authored by Seoung Bum Kim
This map shows the geographic impact of Seoung Bum Kim'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 Seoung Bum Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seoung Bum Kim more than expected).
Fields of papers citing papers by Seoung Bum Kim
This network shows the impact of papers produced by Seoung Bum Kim. 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 Seoung Bum Kim. The network helps show where Seoung Bum Kim may publish in the future.
Co-authorship network of co-authors of Seoung Bum Kim
This figure shows the co-authorship network connecting the top 25 collaborators of Seoung Bum Kim. A scholar is included among the top collaborators of Seoung Bum Kim 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 Seoung Bum Kim. Seoung Bum Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 4 | |
| 7 | 1 | |
| 8 | 3 | |
| 9 | 0 | |
| 10 | 3 | |
| 11 | 4 | |
| 12 | 7 | |
| 13 | 2 | |
| 14 | 13 | |
| 15 | 14 | |
| 16 | 85 | |
| 17 | 2 | |
| 18 | Comparison of exponential smoothing methods for prediction under non-trendy data | 1 |
| 19 | 95 | |
| 20 | A review and analysis of the Mahalanobis-Taguchi system | 2 |
About Seoung Bum Kim
Seoung Bum Kim is a scholar working on Statistics, Probability and Uncertainty, Industrial and Manufacturing Engineering and Statistics and Probability, having authored 197 papers that have together received 2.9k indexed citations. Recurring topics across this work include Advanced Statistical Process Monitoring (31 papers), Industrial Vision Systems and Defect Detection (30 papers) and Fault Detection and Control Systems (23 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (490 citations), Industrial and Manufacturing Engineering (365 citations) and Statistics and Probability (276 citations). Seoung Bum Kim has collaborated with scholars based in South Korea, United States and Japan. Frequent co-authors include Victoria C. P. Chen, Kwok‐Leung Tsui, Sangmin Lee, Han Kyu Lee, Young-Hoon Kim, Chan Hee Park, Seulki Lee, Thomas R. Ziegler, Dean P. Jones and Jay Michael Rosenberger. Their work appears in journals such as PLoS ONE, Technometrics and European Journal of Operational Research.
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.