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김성일
Kim, Sungil

김성일

산업공학과

Research Interests
Industrial AI;Quality Engineering;Industrial Statistics;Data Science;산업인공지능;품질공학;산업통계;데이터사이언스
Lab Description
Welcome to the Data Analytics Lab at the Ulsan National Institute of Science and Technology (UNIST). Our research focuses on development of novel statistical methods for solving complex engineering problems. Our team pursues leading-edge research in the field of data science and business analytics with industry, government, and community partners. Our research can be characterized by three aspects: i) statistics as a research methodology, ii) motivation from real data, and iii) applications to industry.
Researcher ID Research Gate ORCiD Scopus Google Scholar
Previous
Predicting non-recurrent congestion impact: A pattern-based approach for speed drop ratio prediction using weighted K-nearest neighbors File

Oh, YongKyung , Kwak, Jiin , Kim, Sungil

Article Issue Date2026-03 View17
TSSI: Time Series as Screenshot Images for multivariate time series classification using convolutional neural networks File

Oh, Yongkyung , Kim, Heeyoung , Kim, Sungil

Article Issue Date2025-11 View32
TAOD-Net: Automated detection and analysis of thyroid-associated orbitopathy in facial imagery File

Oh, Yongkyung , Koh, Giheon , Kwak, Jiin , Shin, Kyubo , Kim, Gi-Soo , Lee, Min Joung , Choung, Hokyung , Kim, Namju , Moon, Jae Hoon , Kim, Sungil

Article Issue Date2025-05 View136
Comparative evaluation of VAE-based monitoring statistics for real-time anomaly detection in AIS data File

Oh, YongKyung , Yoon, Kwonin , Park, Jaemin , Kim, Sungil

Article Issue Date2025-04 View145
Grid-Based Bayesian Bootstrap Approach for Real-Time Detection of Abnormal Vessel Behaviors From AIS Data in Maritime Logistics File

Oh, YongKyung , Kim, Sungil

Article Issue Date2024-10 View193
Multi-modal Lifelog Data Fusion for Improved Human Activity Recognition: A Hybrid Approach File

Oh, YongKyung , Kim, Sungil

Article Issue Date2024-10 View206
Multichannel convolution neural network for gas mixture classification File

Oh, YongKyung , Lim, Chiehyeon , Lee, Junghye , Kim, Sewon , Kim, Sungil

Article Issue Date2024-08 View185
Domain-knowledge-informed functional outlier detection for line quality control systems File

Mun, Jong Hwan , Yoo, Jitae , Kim, Heesun , Ryu, Nayi , Kim, Sungil

Article Issue Date2024-03 View116
Spatially-correlated time series clustering using location-dependent Dirichlet process mixture model File

Jung, Junsub , Kim, Sungil , Kim, Heeyoung

Article Issue Date2024-02 View125
Sensor drift compensation for gas mixture classification in batch experiments File

Oh, YongKyung , Lee, Juhui , Kim, Sungil

Article Issue Date2023-10 View124
Chemistry-informed machine learning: Using chemical property features to improve gas classification performance File File

Kim, Yeram , Lim, Chiehyeon , Lee, Junghye , Kim, Sungil , Kim, Sewon , Seo, Dong-Hwa

Article Issue Date2023-06 View145
Quantifying incident impacts and identifying influential features in urban traffic networks File

Lee, JuYeong , Kwak, JiIn , Oh, YongKyung , Kim, Sungil

Article Issue Date2023-03 View115
Time delay estimation of traffic congestion propagation due to accidents based on statistical causality File File

Oh, YongKyung , Kwak, JiIn , Kim, Sungil

Article Issue Date2023-02 View144
Maximum feasibility estimation File

Kim, Sungil

Article Issue Date2021-10 View100
시뮬레이션 데이터 생성을 통한 기계학습 기반 원자로 노심 이상 탐지 방법론 File

오용경 , 김한주 , 이덕중 , 김성일

Article Issue Date2021-04 View96
혼합 가스 분류를 위한 전이학습 기반 방법론 File

오용경 , 김남우 , 김성일

Article Issue Date2021-04 View108
Multiresolution spatial generalized linear mixed model for integrating multi-fidelity spatial count data without common identifiers between data sources File

Kim, Sungil , Duan, Rong , Ma, Guang-Qin , Kim, Heeyoung

Article Issue Date2020-10 View93
선박 이상 탐지를 위한 VAE-CUSUM 기반 모니터링 방법론 File

박재민 , 김성일

Article Issue Date2020-08 View93
Revealing household characteristics using connected home products File

Kim, Sungil

Article Issue Date2019-06 View93
A practical approach to measuring the impacts of stockouts on demand File

Kim, Sungil , Kim, Heeyoung , Lu, Jye-Chyi

Article Issue Date2019-06 View98
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