Jin-Hwa Kim
- Computational Mechanics top 2%
- Fluid Dynamics and Turbulent Flows 35
- Computational Fluid Dynamics and Aerodynamics 9
- Combustion and flame dynamics 7
- Aerospace Engineering top 2%
- Aerodynamics and Acoustics in Jet Flows 33
- Plasma and Flow Control in Aerodynamics 19
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- Multimodal Machine Learning Applications 8
- Artificial Intelligence top 5%
- Imbalanced Data Classification Techniques 8
- Accounting top 10%
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- Data Mining Algorithms and Applications 7
- Co-authors
- Mo SamimyJung-Woo HaByoung‐Tak ZhangJae Kwon BaeJeff KastnerIgor AdamovichYurii UtkinMartin Kearney-Fischer
- Partner nations
- United StatesSouth KoreaCanada
In The Last Decade
Jin-Hwa Kim
74 papers receiving 1.0k citations
Peers
Comparison fields: 5 of 98
- Computational Mechanics 484
- Aerospace Engineering 503
- Computer Vision and Pattern Recognition 280
- Artificial Intelligence 329
- Accounting 74
Countries citing papers authored by Jin-Hwa Kim
This map shows the geographic impact of Jin-Hwa 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 Jin-Hwa Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jin-Hwa Kim more than expected).
Fields of papers citing papers by Jin-Hwa Kim
This network shows the impact of papers produced by Jin-Hwa 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 Jin-Hwa Kim. The network helps show where Jin-Hwa Kim may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jin-Hwa Kim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 3 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2022 | 11 | |
| 5 | 2022 | 4 | |
| 6 | 2022 | 0 | |
| 7 | Analysis of Online Conversations to Detect Cyberpredators Using Recurrent Neural Networks. | 2020 | 3 |
| 8 | 2019 | 35 | |
| 9 | Multimodal Residual Learning for Visual QA | 2016 | 55 |
| 10 | 2011 | 1 | |
| 11 | An Exploratory Study on Smart-Phone and Service Convergence | 2010 | 2 |
| 12 | 2010 | 10 | |
| 13 | 2009 | 17 | |
| 14 | 2008 | 11 | |
| 15 | 2008 | 4 | |
| 16 | 2007 | 168 | |
| 17 | 2007 | 59 | |
| 18 | [가솔린엔진부문] Effects of Injection Timing on Mixture Preparation in a Direct Fuel Injected CNG Engine | 1999 | 1 |
| 19 | An experimental study of mixing and noise in a supersonic rectangular jet with modified trailing edges / | 1998 | 6 |
| 20 | Mulit-Component Planar Doppler Velocimetry in High Speed Flows | 1996 | 1 |
About Jin-Hwa Kim
Jin-Hwa Kim is a scholar working on Computational Mechanics, Aerospace Engineering and Complementary and Manual Therapy, having authored 86 papers that have together received 1.1k indexed citations. Recurring topics across this work include Fluid Dynamics and Turbulent Flows (35 papers), Aerodynamics and Acoustics in Jet Flows (33 papers), Plasma and Flow Control in Aerodynamics (19 papers), Computational Fluid Dynamics and Aerodynamics (9 papers), Imbalanced Data Classification Techniques (8 papers), Multimodal Machine Learning Applications (8 papers), Data Mining Algorithms and Applications (7 papers) and Combustion and flame dynamics (7 papers). The work is most often cited by research in Computational Mechanics (484 citations), Aerospace Engineering (503 citations) and Computer Vision and Pattern Recognition (280 citations). Jin-Hwa Kim has collaborated with scholars based in United States, South Korea and Canada. Frequent co-authors include Mo Samimy, Jung-Woo Ha, Byoung‐Tak Zhang, Jae Kwon Bae, Jeff Kastner, Igor Adamovich, Yurii Utkin, Martin Kearney-Fischer, Saurabh Keshav and Sang-Woo Lee.
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.