Yejin Kang
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Also affiliated: Seoul National University (2025); Yonsei University (2023)
Research Areas
Biography and Research Information
OverviewAI-generated summary
Yejin Kang's research focuses on the application of machine learning techniques to complex data for various domains. This includes work on randomized learning-based classification of sound quality using spectrogram images and time-series data, exploring a practical perspective for its implementation. Kang has also contributed to the development of datasets for autonomous navigation, specifically the MOANA dataset for maritime odometry and autonomous navigation applications, with publications in both 2024 and 2025. Further research involves understanding human interaction with automated systems, exemplified by studies on explaining takeover requests in conditionally automated driving, considering hazard types and explanation strategies. Additionally, Kang has investigated the development of scales for risk attitudes in automated driving through sentence elicitation and clustering studies. Kang has a h-index of 2 with 6 total publications and 9 total citations, and collaborates with Dongje Lee, Hanguen Kim, Jin‐Bum Park, and Jaeseong Koh at Hendrix College.
Metrics
- h-index: 2
- Publications: 6
- Citations: 15
Selected Publications
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Explaining Takeover Requests: The Role of Hazard Type and Explanation Strategy in Conditionally Automated Driving (2025)
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Developing Scale for Risk Attitudes in Conditionally Automated Driving: A Sentence Elicitation and Clustering Study (2025)
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MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application (2025)
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MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application (2024)
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Randomized learning-based classification of sound quality using spectrogram image and time-series data: A practical perspective (2023)
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국내 사람유두종바이러스백신 접종 후 자발적 이상반응 보고사례의 Brighton Collaboration 기준 활용 가능성 연구 (2020)
Collaboration Network
Top Collaborators
- MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application
- MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application
- MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application
- MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application
- MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application
- MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application
- MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application
- MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- MOANA: Multi-radar dataset for maritime odometry and autonomous navigation application
- MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
- 국내 사람유두종바이러스백신 접종 후 자발적 이상반응 보고사례의 Brighton Collaboration 기준 활용 가능성 연구
- 국내 사람유두종바이러스백신 접종 후 자발적 이상반응 보고사례의 Brighton Collaboration 기준 활용 가능성 연구
- 국내 사람유두종바이러스백신 접종 후 자발적 이상반응 보고사례의 Brighton Collaboration 기준 활용 가능성 연구
- 국내 사람유두종바이러스백신 접종 후 자발적 이상반응 보고사례의 Brighton Collaboration 기준 활용 가능성 연구
- 국내 사람유두종바이러스백신 접종 후 자발적 이상반응 보고사례의 Brighton Collaboration 기준 활용 가능성 연구
- 국내 사람유두종바이러스백신 접종 후 자발적 이상반응 보고사례의 Brighton Collaboration 기준 활용 가능성 연구
- Randomized learning-based classification of sound quality using spectrogram image and time-series data: A practical perspective
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