[International Journal]
Soomin Lee, Wonkeun Jo, Hogeun Koo, Juheon Kwak, Hyein Kim, Jeongin Koo, and Dongil Kim* (2025) A framework for tool wear and remaining useful life prediction in machining process using monitoring data and machine learning, The International Journal of Advanced Manufacturing Technology, 138(2), 491-509. (SCIE, IF=2.9, Q2)
Hyungtaik Oh, Wonkeun Jo, and Dongil Kim* (2024). Attention-based sequential recommendation system using multimodal data, arXiv: 2405.17959.
Wonkeun Jo, and Dongil Kim* (2024). NFCL: Simply interpretable nerual networks for a short-term multivariate forecasing, arXiv: 2405.13393.
Hangoo Kang, Dongil Kim*, Sungsu Lim* (2024). Machine learning-based anomaly detection on seawater temperature data with oversampling, Journal of Marine Science and Engineering, 12(5), 807. (SCIE, IF=2.7, Q1)
Ji-Hye Baek, Sujin Kim, Seonghwan Choi, Jongyeob Park, and Dongil Kim* (2024). Deep learning-based solar image captioning, Advances in Space Research, 73(6), 3270-3281. (SCIE, IF=2.6, Q1)
Wonkeun Jo, and Dongil Kim* (2023). Neural additive time-series models: Explainable deep learning for multivariate time-series prediction, Expert Systems with Applications 228, 120307. (SCIE, IF=8.5, Q1, Top 10%)
Soomin Lee, Wonkeun Jo, Hyein Kim, Jeongin Koo, and Dongil Kim* (2023). Deep learning-based cutting force prediction for machining process using monitoring data, Pattern Analysis and Applications 26(3), 1013-1025. (SCIE, IF=3.9, Q2)
Wonkeun Jo, and Dongil Kim* (2022). OBGAN: minority oversampling near borderline with generative adversarial networks, Expert System with Applications 197, 116694. (SCIE, IF=8.665, Q1, Top 10%)
Jinyoung Choi, Soomin Lee, Seonyoung Kim, Dongil Kim, and Hyungshin Kim* (2022). Depressed mood prediction of elderly people with a wearable band, Sensors 22(11), 4174. (SCIE, IF=3.9, Q2)
Hogul Song, Changshin Kang, Jungsoo Park*, Yeonho You, Yongnam In, Jinhong Min, Wonjoon Jeong, Yongchul Cho, Hongjoon Ahn, and Dongil Kim (2021). Intracranial pressure patterns and neurological outcomes in out-of-hospital cardiac arrest survivors after targeted temperature management: A retrospective observational Study, Journal of Clinical Medicine 10(23), 5697. (SCIE, IF=4.242, Q1)
Ji-Hye Baek, Sujin Kim, Seonghwan Choi, Jihun Kim, Wonkeun Jo, and Dongil Kim* (2021). Solar event detection using deep-learning-based object detection methods, Solar Physics 296, 160. (SCIE, IF=2.671, Q2)
Dongil Kim, Seokho Kang*, and Sungzoon Cho (2020). Expected margin-based pattern selection for support vector machines, Expert Systems with Applications 139, 112865. (SCIE, IF=6.954, Q1, Top 10%)
Myonghwa Park, Eun Jeong Choi, Miri Jeong, Nayoung Lee, Minjung Kwak, Mihyun Lee, Eun-Chung Lim, Haesung Nam, Dongil Kim, Hanwool Ku, Bong Seok Yang, Junsik Na, Joong Shik Jang, Ji Young Kim, and Wonpyo Lee (2019). ICT-based comprehensive health and social-needs assessment system for supporting person-centered community care, Healthcare Informatics Research 25(4), 338-343.
Dongil Kim, and Seokho kang* (2019). Effect of irrelevant variables on faulty wafer detection in semiconductor manufacturing, Energies 12(13), 2530. (SCIE, IF=3.004, Q3)
Seokho Kang, Dongil Kim*, and Sungzoon Cho (2019). Approximate training of one-class support vector machines using expected margin, Computers & Industrial Engineering 130, 772-778. (SCIE, IF=5.431, Q1)
Dongil Kim, Jeongin Koo, Hyein Kim, Seokho Kang*, Sang Hyun Lee, and Jeong Tae Kang (2019). Rapid fault cause identification in surface mount technology processes based on factory-wide data analysis, International Journal of Distributed Sensor Networks 15(2), 1550147719832802. (SCIE, IF=1.640, Q4)
Seokho Kang, Dongil Kim*, and Sungzoon Cho (2016). Efficient feature selection based on random forward search for virtual metrology modeling, IEEE Transactions on Semiconductor Manufacturing 24(9), 391-398. (SCIE, IF=2.874, Q2)
Pilsung Kang, Dongil Kim*, and Sungzoon Cho (2016). Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing, Expert Systems with Application 51, 85-106. (SCIE, IF=6.954, Q1, Top 10%)
Dongil Kim*, Pilsung Kang, Seung-kyung Lee, Sungzoon Cho, Seokho Kang, and Seungyong Doh (2015). Improvement of virtual metrology performance by removing metrology noises in a training dataset, Pattern Analysis and Applications 18(1), 173-189. (SCIE, IF=2.580, Q3)
Pilsung Kang, Dongil Kim, and Sungzoon Cho* (2014). Evaluating the reliability level of virtual metrology results for flexible process control: a novelty detection-based approach, Pattern Analysis and Applications 17(4), 863-881. (SCIE, IF=2.580, Q3)
Dongil Kim, and Sungzoon Cho* (2012). Pattern selection for support vector regression based response modeling, Expert Systems with Applications 39 (10), 8975-8985. (SCIE, IF=6.954, Q1, Top 10%)
Dongil Kim, Pilsung Kang, Sungzoon Cho*, Hyung-joo Lee, and Seungyong Doh (2012). Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing, Expert Systems with Applications 39 (4), 4075-4083. (SCIE, IF=6.954, Q1, Top 10%)
Pilsung Kang, Dongil Kim, Hyoung-joo Lee, Seungyong Doh, and Sungzoon Cho* (2011). Virtual metrology for run-to-run control in semiconductor manufacturing. Expert Systems with Applications 38(3), 2508-2522. (SCIE, IF=6.954, Q1, Top 10%)
Pilsung Kang, Hyoung-joo Lee, Sungzoon Cho*, Dongil Kim, Jinwoo Park, Chan-kyoo Park,and Seungyong Doh (2009). A virtual metrology system for semiconductor manufacturing. Expert Systems with Applications 36(10), 12554-12561. (SCIE, IF=6.954, Q1, Top 10%)
Dongil Kim, and Sungzoon Cho* (2008). Bootstrap based pattern selection for support vector regression, Lecture Notes in Computer Science (LNCS) 5012, 608-615.
Dongil Kim, Hyung-joo Lee, and Sungzoon Cho* (2008). Response modeling with support vector regression, Expert Systems with Applications 34 (2), 1102-1108. (SCIE, IF=6.954, Q1, Top 10%)
Dongil Kim, and Sungzoon Cho* (2006). e–tube based pattern selection for support vector machines, Lecture Notes in Computer Science (LNCS) 3918, 215-224.
(*: Corresponding Author)
[Korean Journal]
Jeongin Koo, Joo-sung Yoon, and Dongil Kim* (2019). Fault diagnosis framework of the moldflow production processes based on univariative analysis, Journal of Korean Society of Manufacturing Technology Engineers 28(6), 391-399.
Dongil Kim* (2016). Data science for smart manufacturing, IE Magazine 23(3), 20-30.
Dongil Kim* (2014). Response modeling with semi-supervised support vector regression, Journal of the Korean Society of Computer and Information 19(9), 125-139.
Pilsung Kang, Dongil Kim, Seung-kyung Lee, and Sungzoon Cho* (2012). Reliability analysis of virtual metrology in semiconductor manufacturing: A Novelty Detection-based Approach. Journal of the Korean Institute of Industrial Engineers (JKIIE) 38 (1), 46-56.
[On-going works]
Multimodal recommendation system, submitted.
Neural forecasting models, submitted.
Transfer learning for RUL prediction.
Deep generative model for oversampling.
Semi- and Un-supervsied Domain adaptation.
Adversarial attack method.
Outlier detection in time-series data.