Minyoung

Minyoung Lee (이민영)

Ph.D. student @ Applied Artificial Inteligence

About

I am Minyoung Lee, a M.Sc. student in Data eXperience Lab at Sungkyunkwan University, advised by Eunil Park.

Research Interests: Generative model, Speech recognition, Image processing

Education

Ph.D. in Applied Artificial Intelligence
Sungkyunkwan University @ Seoul, Republic of Korea - Mar 2021 ~ present

B.s in Library and Information Science & Statistics
Sungkyunkwan University @ Seoul, Republic of Korea - Mar 2017 ~ Aug 2021

Project

[2021.3-2021.11] Designing AI-based Diagnostic Platform for Heat Transfer Pipelines, The Commissioned Research, Korea Institute of Civil Engineering and Building Technology.
[2022.3-2022.9] AI-based Multimodal Diagnostic Systems for Heat Transfer Pipelines, The Commissioned Research, Korea Institute of Civil Engineering and Building Technology.
[2023.9-2024.8] (PI) FaceTTS: Face-based personalize multimodal Text-to-Speech synthesis model, Research Subsidies for Ph.D. Candidates (박사과정생연구장려금지원), NRF.

Publication
(* = co-corresponding author, ** = equal contribution)

[2021]

Determinants of customer brand loyalty in the retail industry: A comparison between national and private brands in South Korea.
Hwang, S., Lee, M., Park, E.*, & del Pobil, A. P.* (2021).
Journal of Retailing and Consumer Services, 63, 102684 (SSCI, JCR 2020 IF=7.135, Q1 in Business).

[2022]

Antecedents of consumer adoption of over-the-top services in South Korea.
Jeong, D., Lee, M., & Park, E.* (2022)
International Journal on Media Management, 24(3), 121-136.

[2023]

Real-time Korean Voice Phishing Detection based on Machine Learning Approaches.
Lee, M., & Park, E.* (2023).
Journal of Ambient Intelligence and Humanized Computing, 14, 8173-8184 (SCIE, JCR 2020 IF=7.104, Q1 in Computer Science, Artificial Intelligence).
Fused deep neural networks for sustainable and computational management of heat-transfer pipeline diagnosis.
Ji, H., An, C., Lee, M., Yang, J., & Park, E.* (2023).
Developments in the Built Environment, 14, 100144 (SCIE, JCR 2021 IF=5.563, Q1 in Engineering, Civil).

[2024]

DeepAUP: a deep neural network framework for abnormal underground heat transport pipelines.
Lee, M., Ji, H., & Park, E.* (2024).
IEEE Transactions on Automation Science and Engineering, 21(2), 2017-2026 (SCIE, JCR 2021 IF=6.636, Q1 in Automation & Control Systems).

[국내]

Development of AI-based Anomaly Detection Technology Model: the Case of Underground Facilities (AI를 적용한 구조물 이상탐지 기술 개발: 지하매설시설을 중심으로)
Lee, M., Lee, J., Ko, M., Han, J., & Park, E.* (2022).
In Proceedings of Korean Society of Civil Engineering (KSCE '22, Korean, pp. 1-2).
Development of a multimodal pipeline diagnosis model for heat-transfer pipelines (열수송관 이상 탐지를 위한 멀티 모달 인공지능 모델).
Lee, M., Lee, J., Kim, H., Lee, J., Park, M., An, C., Lim, J., Han, S., Lee, H., Hwang, I., Kim, Y., & Park, E.* (2022).
In Proceedings of Korea Software Congress 2022 (KSC '22, Korean, pp. 728-730).
A Pipeline Diagnosis Model for Heat-transfer Pipelines based on Multimodal Data (멀티모달 데이터를 활용한 열수송관 이상 탐지 인공지능 모델 개발).
Lee, M., An, C., Park, M., & Park, E.* (2023).
KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지), 29(12), pp. 571-576.

Patent&S/W