[International Conference]
Soomin Lee, Jeongin Koo, and Dongil Kim (2025). Unsupervised learning for tool condition monitoring: Reconstruction error analysis, Manufacturing Science and Engineering Conference 2025, Greenvile, South Carolina, USA.
Soomin Lee, and Dongil Kim (2024). Unsupervised Anomaly Detection with Multivariate Time Series: Leveraging Mixture of Experts and Time Series Decomposition, INFORMS Annual Meeting 2024, Seattle, Washington, USA.
Seongyoung Kim, and Dongil Kim (2024). Semi-Supervised Time Series Domain Adaptation Based On Contrastive Learning Using Momentum Encoder, INFORMS Annual Meeting 2024, Seattle, Washington, USA.
Wonkeun Jo, and Dongil Kim (2024). Neural Forecasting Layer: Straightforward connected interpretable neural forecaster for multivariate time-series, INFORMS Annual Meeting 2024, Seattle, Washington, USA.
Juheong Kwak, and Dongil Kim (2024). Deep Learning-based White-box Adversarial Attack using Multivariate Time Series Classification Model, INFORMS Annual Meeting 2024, Seattle, Washington, USA.
Juhee Kim, and Dongil Kim (2024). Learning From Imbalanced Data With SMOTE-Like Oversampling for Mixed Type and High-Dimensional data, INFORMS Annual Meeting 2024, Seattle, Washington, USA.
Seonyeong Park, and Dongil Kim (2024). Deep Learning-based Bio-signal Multivariate Time-series Forecasting, INFORMS Annual Meeting 2024, Seattle, Washington, USA.
Sungmin Lim, Juheon Kwak, and Dongil Kim (2024). Relationship between language model and data quality analyzed through personality type prediction, INFORMS Annual Meeting 2024, Seattle, Washington, USA.
Juheon Kwak and Dongil Kim (2023). Time series perturbation based on gradient of deep learning models, INFORMS Annual Meeting 2023, Phoenix, Arizona, USA.
Sungmin Lim, Juheon Kwak, and Dongil Kim (2023). Personality classification with reddit data using natural language processing, INFORMS Annual Meeting 2023, Phoenix, Arizona, USA.
Soomin Lee, Juheon Kwak, Wonkeun Jo, Sungsu Lim, and Dongil Kim (2023). Transformer-based unsupervised anomaly detection with multivariate time series, INFORMS Annual Meeting 2023, Phoenix, Arizona, USA.
Geonhee Jang, Wonkeun Jo, Sungsu Lim, and Dongil Kim (2023). Diffusion model-based oversampling for class imbalanced problems, INFORMS Annual Meeting 2023, Phoenix, Arizona, USA.
Hyemin Lim, Geonhee Jang, Wonkeun Jo, Yeong Jun Koh, and Dongil Kim (2023). Clustering morphological properties of cells using neural network-based image segmentation models, INFORMS Annual Meeting 2023, Phoenix, Arizona, USA.
Yeseul Choi, Soomin Lee, and Dongil Kim (2022). Deep learning-based anomaly detection in multivariate time series data, INFORMS Annual Meeting 2022, Indianapolis, USA.
Wonkeun Jo, and Dongil Kim (2022). The explaiable neural networks for nowcasting, INFORMS Annual Meeting 2022, Indianapolis, USA.
Soomin Lee, Hyein Kim, Jeongin Koo, and Dongil Kim (2022). Multi-task learning for regression with multivariate time-series, INFORMS Annual Meeting 2022, Indianapolis, USA.
Hoguen Koo, Soomin Lee, Wonkeun Jo, Hyein Kim, Jeongin Koo, Juheon Kwak, and Dongil Kim (2002). Prediction of tool wear based on machine learning for cutting force, INFORMS Annual Meeting 2022, Indianapolis, USA.
Juheon Kwak, Soomin Lee, and Dongil Kim (2022). Deep learning-based adversarial attacks for time series data, INFORMS Annual Meeting 2022, Indianapolis, USA.
Hyojeong Kang, Juheon Kwak, Wonkeun Jo, and Dongil Kim (2022). Machine learning-based sleep quality prediction with body signal data, INFORMS Annual Meeting 2022, Indianapolis, USA.
Juheon Kwak, Wonkeun Jo, Soomin Lee, Hyein Kim, Jeongin Koo, and Dongil Kim (2022). Cutting force similarity calculation in milling process using siamese LSTM structure, The 11th International Conference on Industrial Technology and Management (ICITM), Oxford, UK.
Junwon Park, Wonkeun Jo, Hogeun Koo, Hyungtaik Oh, Kyhyup Oh, and Dongil Kim (2021). Blood pressure prediction based on deep learning with PPG and ECG signal data, INFORMS Annual Meeting 2021, Anaheim, USA.
Seonyoung Kim, Taewon Go, Dongil Kim (2021). Deep learning-based cancer diagnostic biomarker detection with metabolomics data, INFORMS Annual Meeting 2021, Anaheim, USA.
Soomin Lee, Wonkeun Jo, Dongil Kim, Hyein Kim, and Jeongin Koo (2021). Deep learning-based cutting force prediction with machining process monitoring data, INFORMS Annual Meeting 2021, Anaheim, USA.
Wonkeun Jo and Dongil Kim (2020). Improvement of class imbalanced classification performance with generative adversarial network, INFORMS Annual Meeting 2020, Virtual Conference, USA.
Seonyoung Kim and Dongil Kim (2020). Deep neural network based multivariate time series anomaly detection using robust statistics, INFORMS Annual Meeting 2020, Virtual Conference, USA.
Jeongin Koo and Dongil Kim (2019). A study on the cutting force prediction model of milling process, INFORMS Annual Meeting 2019, Seattle, USA.
Na Young Lee, Minjung Kwak, Miri Jeong, Eun Jeong Choi, Eunjung Lim, Il Bum Kwon, Wonpyo Lee, Hanwool Ku, Dongil Kim, Haesung Nam, Junsik Na, Myonghwa Park (2019). Pilot testing of an ICT-based care management support system to deliver integrated community care, Medinfo 2019, Lyon, France.
Eun Jeong Choi, Eunjung Lim, Minjung Kwak, Miri Jeong, Na Young Lee, Il Bum Kwon, Wonpyo Lee, Hanwool Ku, Dongil Kim, Haesung Nam, Junsik Na, Myonghwa Park (2019). Development of ICT-based comprehensive heath and social-needs assessment system to enhance person-centered community care, Medinfo 2019, Lyon, France.
Hyein Kim, Jeongin Koo, and Dongil Kim (2019). The prediction model of time series cutting forces in end milling based on the machine learning algorithm, PRESM 2019, Da Nang, Vietnam. (Best Poster Award)
Hyein Kim, Dongil Kim, and Jeongin Koo (2018). A study on data preprocessing method for relationship and characterization of cutting process monitoring signals , INFORMS Annual Meeting 2018, Phoenix, Arizona, USA.
Dongil Kim, Hyein Kim, Jeongin Koo, Sungsoo Choi, Sang-Hyun Lee, and Jeong Tae Kang. (2018). Machine learning-based quality prediction for smart manufacturing in surface mount technology process, INFORMS Annual Meeting 2018, Phoenix, Arizona, USA.
Dongil Kim, Seokho Kang, and Sunzoon Cho. (2017). Margin-based pattern selection for support vector machines, INFORMS Annual Meeting 2017, Houston, Texas, USA.
Sungsoo Choi, Jun-yong Lee, Jeong Tae Kang, YoungJai Oh, Dongil Kim, and SangHyung Lee. (2017). Design of process data analysis system for automotive parts manufacturers, BIGDAS 2017, Jeju, Korea.
Dongil Kim and Sungzoon Cho. (2016). Response modeling with semi-supervised support vector regression. INFORMS Annual Meeting 2016, Nashville, Tennessee, USA.
Eunjeong Park, Dongil Kim, Taehoon Ko, Sungzoon Cho, and Young-Hak Lee. (2012). Feature Selection for Identifying High Defect Density Sensors in Semiconductor Manufacturing. INFORMS International, Beijing, China.
Dongil Kim, and Sungzoon Cho. (2010). A hybrid customer score for response modeling using support vector regression with pattern selection. INFORMS Annual Meeting 2010, Austin, TX, USA.
Dongil Kim, and Sungzoon Cho. (2008). Bootstrap based pattern selection for support vector regression. Paci.c Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2008, Osaka, Japan.
Youngjoo Lee, Joon Beom Seo, Bokyoung Kang, Dongil Kim, June Goo Lee, Song Soo Kim, Namkug Kim and Suh Ho Kang. (2007). Performance comparison of classifiers for differentiation among obstructive lung diseases based on features of texture analysis at HRCT. International Society for Optics and Photonics (SPIE) 6512, Medical Imaging 2007: Image Processing, 651249.
Dongil Kim, and Sungzoon Cho. (2006). e–tube based pattern selection for support vector machines. Paci.c Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006, Singapore.
[Korean Conference]
Seonyeong Park, and Dongil Kim (2025). Deep learning-based multivariate electrocardiogram prognosis prediction model, Korea Society of Computer and Information Conference, Cheonan, South Korea.
Juhee Kim, Juheon Kwak, and Dongil Kim (2025). k-nearest neighbors-based oversampling for high dimensional mixed data, Korea Society of Computer and Information Conference, Cheonan, South Korea.
Soomin Lee, Geonhee Jang, Wonkeun Jo, and Dongil Kim (2024). Deep learning-based tool remaining useful life prediction with machining monitoring data, Korea Computer Conference 2024 (KCC2024), Jeju, South Korea.
Sungmin Lim, Juheon Kwak, and Dongil Kim (2024). Analysis on prediction performances of LLMs based on data quality, , Korea Computer Conference 2024 (KCC2024), Jeju, South Korea.
Hyojeong Kang, and Dongil Kim (2024). Vienna code classification from brand image data, Korea Computer Conference 2024 (KCC2024), Jeju, South Korea.
Seonyoung Kim, and Dongil Kim (2024). Uncertainty-aware label selection and manifold mixup-based unsupervised time-series domain adaptation for medical bio singal data, Korea Computer Conference 2024 (KCC2024), Jeju, South Korea. (Best Paper)
Wonkeun Jo, and Dongil Kim (2024). Interpretable deep learning for short-term time series forecasting, Korea Data Mining Society Conference, Seoul, South Korea. (Best Paper)
Jyheon Kwak, Soomin Lee, Geonhee Jang, Wonkeun Jo, Sungmin Lim, Hyein Kim, Jeongin Koo, and Dongil Kim (2023). Deep learning-based tool wear and remaining useful life prediction, Korea Society for Precision Engineering Conference, Samcheok, South Korea.
Geonhee Jang, Wonkeun Jo, Sungsu Lim, and Dongil Kim (2023). GAN and diffusion model-based oversampling for class imbalanced problems, Korea Data Mining Society Conference, Seoul, South Korea.
Seonyoung Kim, and Dongil Kim (2023). Domain Adaptation for Biomedical Time Series Classification using wavelet manifold mixup, Korea Data Mining Society Conference, Seoul, South Korea.
Juheon Kwak, Wonkeun Jo, Soomin Lee, and Dongil Kim (2023). Adversarial attacks of regression model based on deep learning using multivariate time series, Korea Data Mining Society Conference, Gangneung, South Korea.
Geonhee Jang, Wonkeun Jo, Sungsu Lim, and Dongil Kim (2023). Diffusion model-based oversampling for class imbalanced problems, Korea Computer Conference 2023 (KCC2023), Jeju, South Korea.
Hyemin Lim, Wonkeun Jo, Yeongjun Koh, and Dongil Kim (2023). Clustering morphological properties of cells using neural networks image segmentation models, Korean Institute of Intelligent Systems (KIIS) 2023 Spring Conference, Jeju, South Korea.
Hyojeong Kang, Soomin Lee, Wonkeun Jo, and Dongil Kim (2023). Machine learning-based prediction of user state on the computer tasking, Korea Conference on Software Engineering, Pyeongchang, South Korea.
Wonkeun Jo, and Dongil Kim (2022). Neural additive models for nowcasting, Korea Data Mining Society Conference, Busan, South Korea.
Hyungtaik Oh, and Dongil Kim (2022). Attention-based sequential recommendation system using multi-modal, Korea Data Mining Society Conference, Busan, South Korea.
Taewon Go, Sungwoo Goo, Changseon Ryu, Hwiyeol Yun, and Dongil Kim (2022). HTTK Characteristics Prediction Model Based on SMILES Data using ChemBERTa Model, Korea Computer Conference 2022 (KCC2022), Jeju, South Korea.
Hyojeong Kang, Juheon Kwak, Wonkeun Jo, and Dongil Kim (2022). Sleep Quality Prediction with E4 Wireless Dataset based on Machine Learning, Korea Computer Conference 2022 (KCC2022), Jeju, South Korea.
Wonkeun Jo, and Dongil Kim (2022). Interpretable Neural Network for Time Series Nowcasting, Korea Computer Conference 2022 (KCC2022), Jeju, South Korea.
Soomin Lee, Hyein Kim, Jeongin Koo, and Dongil Kim (2022). Study of Multi-task Learning-based Regression Model with Multi-variate Time Series Data, Korea Computer Conference 2022 (KCC2022), Jeju, South Korea.
Junwon Park, and Dongil Kim (2022). Blood pressure prediction method based on deep learning with stable PPG, Korea Computer Conference 2022 (KCC2022), Jeju, South Korea.
Hyungtaik Oh, Wonkeun Jo, and Dongil Kim (2021). Recommendation System Based on Graph Collaborative Filtering Using Explicit Information, Korea Software Conference 2021 (KSC2021), Jeju, South Korea.
Hogeun Koo, Soomin Lee, Wonkeun Jo, and Dongil Kim (2021). Hybrid Sequential Recommendation System Using k-NN and Graph Neural Networks, Korea Software Conference 2021 (KSC2021), Jeju, South Korea.
Yeseul Choi, Soomin Lee, and Dongil Kim (2021). Pattern-Wise Outlier Detection of Time-series using Shingling-based Forest, Korea Software Conference 2021 (KSC2021), Jeju, South Korea.
Wonkeun Jo, Hogeun Koo, Hyein Kim, Jeongin Koo, and Dongil Kim (2021). Milling tool wear prediction based on sensor and time-series feature extraction, Korea Software Conference 2021 (KSC2021), Jeju, South Korea.
Juheon Kwak, Wonkeun Jo, Soomin Lee, Hyein Kim, Jeongin Koo, and Dongil Kim (2021). Methodology of Cutting Force Similarity Calculation in Milling Process using Siamese LSTM Structure, Korea Software Conference 2021 (KSC2021), Jeju, South Korea.
Jungil Lee, and Dongil Kim (2021). A study on short-term electricity demand prediction using stacking Ensemble of machine learning and deep learning Ensemble models, Annual Conference of KIPS (ACK) 2021, Yeosu, South Korea. (우수논문상)
Soomin Lee, Wonkeun Jo, Hyein Kim, Jeongin Koo, and Dongil Kim (2021). Deep learning-based cutting force prediction with machining process monitoring data, Korea Computer Conference 2020 (KCC2021), Jeju, South Korea.
Junwon Park, Wonkeun Jo, Kyhyup Oh, and Dongil Kim (2021). Blood pressure prediction based on deep learning with PPG and ECG, Korea Computer Conference 2020 (KCC2021), Jeju, South Korea.
Seonyoung Kim and Dongil Kim (2020). Deep neural network based multivariate time series anomaly detection using robust statistics, Korea Software Conference 2020 (KSC2020), Virtual Conference, South Korea.
Soomin Lee, Seonyoung Kim, Wonkeun Jo, Jinyoung Choi, Hyungshin Kim, and Dongil Kim (2020). Machine learning-based elderly depression prediction with pattern-of-life data, Korea Software Conference 2020 (KSC2020), Virtual Conference, South Korea.
Seonyoung Kim, Soomin Lee, Junsang Lee, Junwon Park, Jinyoung Choi, Hyungshin Kim, and Dongil Kim (2020). Supervised learning-based elderly depression prediction with wearable devices, Korea Data Mining Society Conference, Seoul, South Korea.
Wonkun Cho and Dongil Kim (2020). Intelligent data generation with generative adversarial nets for class-imbalanced problems, Korea Computer Conference 2020 (KCC2020), Virtual Conference, South Korea.
Jeongin Koo and Dongil Kim (2019). A study on the cutting force prediction based on the learning model, Korean Society of Precision Engineering Conference, Jeju, South Korea.
Junho Song, Jeongin Koo, Hyunchul Tae, and Dongil Kim (2018). Investigation of the cause of failure by injection molding process parameter analysis, Korean Institute of Industrial Engineering Conference, Seoul, South Korea.
Hyein Kim, Dongil Kim, Junho Song, and Jeongin Koo (2018). A study on the cutting force estimation model using the simulation data and ensemble method , Korean Society of Precision Engineering Conference, Gunsan, South Korea.
Hyein Kim, Jooseong Yoon, Ilha Park, Dongil Kim, and Jeongin Koo (2018). A study on data preprocessing method for relationship and characterization of cutting process monitoring signals , Korean Society of Precision Engineering Conference, Jeju, South Korea.
Dongil Kim, Jeongin Koo, Bohyun Kim, SangHyun Lee, Sungsoo Choi, Jun-yong Lee, and Jeong Tae Kang. (2017). Data-driven quality diagnosis for smart manufacturing, Korean Institute of Industrial Engineering Conference, Daejeon, South Korea.
Soonchan Kwon, Dongil Kim, Kyunghee Park, Jooseong Yoon, and Jaeyoon Choi. (2017). Machine learning-based novelty detection for thin blisk manufacturing, Korean Institute of Industrial Engineering Conference, Daejeon, South Korea.
Dongil Kim, Kyunghee Park, Jooseong Yoon, Dongyoon Lee, Youngjae Choi, and Jaeyoon Choi. (2017). Machine learning-based power monitoring data analysis, Korean Society for Precision Engineering Conference, Jeju, South Korea.
Dongil Kim, Seokho Kang, and Sungzoon Cho. (2017). An efficient pattern selection method for support vector machines, Korean Institute of Industrial Engineering Conference, Yeosu, South Korea.
Dongil Kim, Seokho Kang, and Sungzoon Cho. (2016). Pattern selection for support vector machines and its application to quality prediction, Korean Institute of Industrial Engineering Conference, Seoul, South Korea.
Chanmo Cheon, Jooyun Lee, Dongil Kim, Bohyun Kim. (2016). Big data platform implementing data analytics library for manufacturing, Korean Institute of Industrial Engineering Conference, Jeju, South Korea.
Dongil Kim, Seokho Kang, and Sungzoon Cho. (2016). Pattern selection for support vector classifiers, Korean Institute of Industrial Engineering Conference, Jeju, South Korea.
Dongil Kim, Seokho Kang, and Sungzoon Cho. (2016). Pattern selection for one-class support vector machines, Korea Business Intelligence Data Mining Society Conference, Seoul, South Korea.
Jae-yoon Jung, Aekyung Kim, Kyuhyup Oh, Jinsung Lee, Seohyun Choi, Gwangjin Huh, Jooyun Lee, Dongil Kim, Chanmo Cheon, Bohyun Kim. (2016). Data mining library for manufacturing quality analysis in smart factory, Korea Society of CAD/CAM Engineers Conference, Pyungchang, South Korea.
Dongil Kim, Pilsung Kang, Seungkyung Lee, and Sungzoon Cho. (2012). Pattern generation and selection for semi-supervised support vector regression. Korean Operations Research & Management Science Society Conference, Seoul, South Korea.
Dongil Kim, Seungkyung Lee, and Sungzoon Cho. (2012). Pattern selection for semi-supervised support vector regression. Korean Institute of Industrial Engineers Conference, Seoul, South Korea.
Hyunjoong Kim, Dongil Kim, Wonyul Jung, and Sungzoon Cho. (2012). Clustering-based noise filtering for manually typed texts. Korean Institute of Industrial Engineers Conference, Seoul, South Korea.
Eunjeong Park, Dongil Kim, Taehoon Koh, and Sungzoon Cho. (2012). Feature selection for detecting faulty equipment parameters in semiconductor manufacturing process. Korean Institute of Industrial Engineers Conference, Seoul, South Korea.
Dongil Kim, Seungkyung Lee, and Sungzoon Cho. (2011). Pattern selection for semi-supervised support vector regression. Korean Data Mining Society Conference, Seoul, South Korea.
Seokho Kang, Dongil Kim, Seungkyung Lee, Sungzoon Cho, Pilsung Kang, and Seungyong Doh. (2011). Improving data quality for virtual metrology using novely detection methods. Korean Data Mining Society Conference, Seoul, South Korea.
Dongil Kim, and Sungzoon Cho. (2010). Pattern selection for support vector regression using expected margin. Korean Institute of Industrial Engineers Conference, Seoul, South Korea.
Dongil Kim, and Sungzoon Cho. (2010). Improving response modeling by support vector regression with pattern selection. Korean Data Mining Society Conference, Seoul, South Korea.
Taehoon Koh, Dongil Kim, Eunjeong Park, and Sungzoon Cho. (2010). Detecting faulty wafers based on random forest. Korean Data Mining Society Conference, Seoul, South Korea.
Dongil Kim, Pilsung Kang, Sungzoon Cho, Hyoung-joo Lee, and Seungyong Doh. (2009). Detection of Faulty Wafers based on Novelty Detection Approaches in Semiconductor Manufacturing. Korean Operations Research and Management Science Society Conference, Busan, South Korea.
Dongil Kim, and Sungzoon Cho. (2009). Customer scoring for response modeling with support vector regression. Korean Data Mining Society Conference, Seoul,
Pilsung Kang, Dongil Kim, Seungkyoung Lee, and Sungzoon Cho. (2009). Novelty Detection for Process Control in Semiconductor Manufacturing. Korean Data Mining Society Conference, Seoul, South Korea.
Pilsung Kang, Dongil Kim, and Sungzoon Cho. (2008). Development of Virtual Metrology and Run-to-Run Control System in Semiconductor Manufacturing based on Data Mining Techniques. Korean Institute of Industrial Engineers Conference, Seoul, South Korea.
Dongil Kim, and Sungzoon Cho. (2008). Multiple bootstrap based pattern selection for support vector regression. Korean Data Mining Society Conference, Seoul, South Korea.
Dongil Kim, and Sungzoon Cho. (2005). e–tube based pattern selection for support vector regression. Korean Institute of Industrial Engineers Conference, Seoul, South Korea.
Dongil Kim, and Sungzoon Cho. (2005). e–tube based pattern selection for support vector regression. Korean Data Mining Society Conference, Seoul, South Korea.
[Invited Talks]
eXplainable AI and Application to Manufacturing and Bio/Healthcare, SpaceAI 2005 Conference, 2025.
Introduction to machine learning, Workshop for KITECH-KRICT, KRICT, 2017.
Data science for smart factory, Workshop on Industrial Mathematics, Seogang University, 2016.