PUBLICATION

Journals

  1. Yunseob Hwang, Han Hee Lee, Chunghyun Park, Bayu Adhi Tama, Jin Su Kim, Dae Young Cheung, Woo Chul Chung, Young-Seok Cho, Kang-Moon Lee, Myung-Gyu Choi, Seungchul Lee*, and Bo-In Lee*, “An Improved Classification and Localization Approach to Small Bowel Capsule Endoscopy Using Convolutional Neural Network,” accepted to Digestive Endoscopy.
  2. Chihun Lee, Juwon Na, Kyongho Park, Hyeonjae Yu, Jongsun Kim, Kwonil Choi, Dongyong Park, Seongjin Park, Junsuk Rho* and Seungchul Lee*, 2020, “Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries,” accepted to Advanced Intelligent Systems.
  3. Andrew Glaeser, Vignesh Selvaraj, Kangsan Lee, Namjeong Lee, Yunseob Hwang, Sooyoung Lee, Seungchul Lee, and Sangkee Min*, 2020, “Remote Machine Mode Detection in Cold Forging using Vibration Signal,” Procedia Manufacturing, Vol. 48, pp. 908-914
  4. Bayu Adhi Tama, Sung Won Kim, Gyuwon Kim, Dohyun Kim*, and Seungchul Lee*, 2020, “Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology – Head and Neck Surgery,” accepted to Clinical & Experimental Otorhinolaryngology.
  5. Sung Wook Kim, Young Gon Lee, Bayu Adhi Tama, and Seungchul Lee*, 2020, “Reliability-enhanced Camera Lens Module Classification using Semi-supervised Regression Method,” Applied Sciences, 10(11), 3832, https://doi.org/10.3390/app10113832.
  6. Bayu Adhi Tama, Sun Im, and Seungchul Lee*, 2020, “Improving an Intelligent Detection System for Coronary Heart Disease using a Two-tier Classifier Ensemble,” BioMed Research International, Vol. 2020, https://doi.org/10.1155/2020/9816142.
  7. Namjeong Lee, Sungmin Kim, Iljoo Jeong, Seokman Sohn, and Seungchul Lee*, 2020, “Ensemble Method of Rule-Based and Deep Learning for Rotating Machine Diagnostics,” The Korean Society for Noise and Vibration Engineering. [In Korean]
  8. Kangsan Lee, Juwon Na, Jongduk Sohn, Seokman Sohn, and Seungchul Lee*, 2020, “Image Recognition Algorithm for Maintenance Data Digitization: CNN and FCN,” The Korean Society for Noise and Vibration Engineering. [In Korean]
  9. Jeong-Won Lee, Seongmin Kim, Seungchul Lee, and Woonbong Hwang*, 2020, “Exponential Promotion and Suppression of Bubble Nucleation in Carbonated Liquid by Modification of Surface Wettability,” Applied Surface Science, Vol. 512, https://doi.org/10.1016/j.apsusc.2020.145709.
  10. Soo Young Lee, Bayu Adhi Tama, Changyun Choi, Jeong Yeon Hwang, Jonggeun Bang, and Seungchul Lee*, 2020, “Spatial and Sequential Deep Learning Approach for Predicting Temperature Distribution in a Steel-making Continuous Casting Process,” IEEE Access, 8(1), pp. 2169-3536, 10.1109/ACCESS.2020.2969498.
  11. Soo Young Lee, Bayu Adhi Tama, Seok Jun Moon, and Seungchul Lee*, 2019, “Steel Surface Defect Diagnostics using Deep Convolutional Neural Network and Class Activation Map,” Applied Sciences, 9(24), 5449, https://doi.org/10.3390/app9245449.
  12. Gi Woung Song, Bayu Adhi Tama, Jaewan Park, Jeong Yeon Hwang, Jonggeun Bang, Seong Jin Park, and Seungchul Lee*, 2019, “Temperature Control Optimization in a Steel‐Making Continuous Casting Process Using Multimodal Deep Learning Approach,” Steel Research International, https://doi.org/10.1002/srin.201900321. [Top Downloaded Paper 2018-2019]
  13. Woosung Choi+, Hyunsuk Huh+, Bayu Adhi Tama, Gyusang Park, and Seungchul Lee*, 2019, “A Neural Network Model for Material Degradation Detection and Diagnosis Using Microscopic Images,” IEEE Access, 7, pp.92151-92160. (+ equally contributed)
  14. Juhyeong Jeon, Yeon Jae Han, Geun-Young Park, Deung Gyun Sohn, Seungchul Lee*, and Sun Im*, 2019, “Artificial Intelligence in the Field of Electrodiagnosis – A New Threat or Heralding a New Era in Electromyography?,” Clinical Neurophysiology, 130(10), https://doi.org/10.1016/j.clinph.2019.06.005.
  15. D. Shin, C. Lee, S. Kim, D. Park, J. Oh, C. Gal, J. Koo, S. Park and S. Lee*, 2019, “Analysis of Cold Compaction for Fe-C, Fe-C-Cu Powder Design based on Constitutive Relation and Artificial Neural Networks,” Powder Technology, 353, https://doi.org/10.1016/j.powtec.2019.05.042.
  16. B. Park, H. Jeong, H. Huh, M. Kim and S. Lee*, 2018, “Experimental Study on the Life Prediction of Servo Motors through Model-based System Degradation Assessment and Accelerated Degradation Testing,” Journal of Mechanical Science and Technology, 32(11), 5105-5110.
  17. S. Park, H. Jeong and S. Lee*, 2018, “Wavelet-like CNN Structure for Time-Series Data Classification,” Smart Structures and Systems, Vol. 22, No. 2 (2018) 175-183.
  18. H. Jeong, B. Park, S. Park, and S. Lee*, 2018, “Fault Detection and Identification Method using Observer-based Residuals,” Reliability Engineering and System Safety, Vol. 184, pp 27-40.
  19. S. Kim, S. Park, S. Woo, and S. Lee*, 2017, “Development and Analysis of the Interchange Centrality Evaluation Index Using Network Analysis,” J. Korean Soc. Transp. Vol.35, No.6, pp.525-544. [in Korean]
  20. H. Jeong, S. Kim, S. Woo, S. Kim and S. Lee*, 2017, “Real-time Monitoring System for Rotating Machinery with IoT-based Cloud Platform,” Transactions of the KSME A. [in Korean]
  21. H. Jeong, S. Park , S. Woo, and S. Lee*, 2016, “Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images,” Procedia Manufacturing, Vol. 5, pp. 1107-1118.
  22. L. Cui, Y. Zhang, F. Zhang*, J. Zhang, and S. Lee, 2016, “Vibration Response Mechanism of Faulty Outer Race Rolling Element Bearings for Quantitative Analysis,” Journal of Sound and Vibration, 364, pp. 67-76.
  23. Z. Zhang, S. Wu*, L. Binfeng, and S. Lee, 2015, “(n,N) Type Maintenance Policy for Multi-component Systems with Failure Interactions,” International Journal of Systems Science, 46(6), pp. 1051-1064.
  24. Z. Zhang, S. Wu, S. Lee*, and J. Ni, 2014, “Modified Iterative Aggregation Procedure for Maintenance Optimization of Multi-component Systems with Failure Interaction,” International Journal of Systems Science, 45(12), pp. 2480-2489.
  25. A. Almuhtady, S. Lee*, E. Romeijn, M. Wynblatt, and J. Ni, 2014, “A Degradation-Informed Battery Swapping Policy for Fleets of Electric or Hybrid-Electric Vehicles,” Transportation Science, 48(4), pp. 609-618.
  26. W. Cheng, Z. Zhang*, S. Lee, and Z. He, 2014, “Investigations of Denoising Source Separation Technique and Its Application to Source Separation and Identification of Mechanical Vibration Signals,” Journal of Vibration and Control, 20(14), pp. 2100-2117.
  27. L. Cui*, J. Wang, S. Lee, 2014, “Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis,” Journal of Sound and Vibration, 333(10), pp. 2840-2862.
  28. S. Lee, J. Ko, X. Tan, I. B. Patel, R. Balkrishnan, J. Chang*, 2014, “Markov Chain Modeling and Analysis of HIV/AIDS Progression: A Race-based Forecast in the United States,” Indian Journal of Pharmaceutical Sciences, 76(2), pp. 107-115.
  29. Zhang, S. Wu, L. Binfeng, and S. Lee*, 2013, “Optimal Maintenance Policy for Multi-Component Systems under Markovian Environment Changes,” Expert Systems With Applications, 40(18), pp. 7391-7399.
  30. S. Lee*, X. Gu, M. Garcellano, M. Diederichs, and J. Ni, 2013, “Discovery of Hidden Opportunities in Manufacturing Systems: MOW and GMOW,” International Journal of Advanced Manufacturing Technology, 68(9), pp. 2611-2623.
  31. S. Lee*, X. Gu, and J. Ni, 2013, “Stochastic Maintenance Opportunity Windows for Unreliable Two-Machine One-Buffer System,” Expert Systems With Applications, 40(13), pp. 5385-5394.
  32. X. Gu, S. Lee*, X. Liang, M. Garcellano, M. Diederichs, and J. Ni, 2013, “Hidden Maintenance Opportunities in Discrete and Complex Production Lines,” Expert Systems with Application, 40(11), pp. 4353-4361.
  33. S. Lee, L. Li*, and J. Ni, 2013, “Markov-based Maintenance Planning Considering Repair Time and Periodic Inspection,” ASME Journal of Manufacturing Science and Engineering, 135(3), 031013 (12 pages), DOI:10.1115/1.4024152
  34. S. Lee* and J. Ni, 2012, “Joint Decision Making for Maintenance and Production Scheduling of Production Systems,” International Journal of Advanced Manufacturing Technology, 66(5-8), pp. 1135-1146.
  35. W. Cheng, S. Lee, Z. Zhang*, and Z. He, 2012, “Independent Component Analysis based Source Number Estimation and Its Comparison for Mechanical Systems,” Journal of Sound and Vibration, 331(2012), pp. 5153-5167.
  36. W. Cheng, Z. Zhang*, S. Lee, and Z. He, 2011, “Source Contribution Evaluation of Mechanical Vibration Signals via Enhanced Independent Component Analysis,” ASME Journal of Manufacturing Science and Engineering, 134(2), pp. 021014 (9 pages).
  37. S. Lee* and J. Ni, 2012, “Genetic Algorithm for Job Scheduling with Maintenance Consideration in Semiconductor Manufacturing Process,” Mathematical Problems in Engineering, Volume 2012, Article ID 875641, 16 pages, DOI:10.1155/2012/875641.
  38. S. Lee, L. Li*, and J. Ni, 2010, “Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model,” ASME Journal of Manufacturing Science and Engineering, 132(2), pp. 021010-11.

In Preparation or Submitted

  1. Gyuwon Kim, and Seungchul Lee, “Attention-Based LSTM Network for Road Profile Regression and Sensor Optimization,” in preparation
  2. Gyuwon Kim, Yunseob Hwang, Changyun Choi, Jeongho Jeon, Dohyun Kim, Seungchul Lee, and Sung Won Kim, “Classification and Localization of Functional hNTSCs using CNN-Based Methods,” in preparation
  3. Bayu Adhi Tama, Soo Young Lee, and Seungchul Lee, “Noise and Vibration Engineering Meets Deep Learning,” in preparation
  4. Hyunsuk Huh, Hyoungcheol Kwon, and Seungchul Lee, “Deep Learning-based Etching Mask Design in Semiconductor Manufacturing,” in preparation
  5. Soo Young Lee, Jiho Chang, and Seungchul Lee, “Multiple Acoustic Source Localization in 2D using Deep Learning,” in preparation
  6. Juwon Na, Jaimyun Jung, Gyuwon Kim, Seungchul Lee, and HyoungSeop Kim, “Deep Learning-based Super-Resolution for EBSD Images,” in preparation
  7. Juwon Na, Gyuwon Kim, Sejong Kim, and Seungchul Lee, “Deep Learning-based Refocusing of SEM Images,” in preparation
  8. Juhyeong Jeon+, HyunBum Kim+, , Yeon Jae Han, YongHoon Joo, Jonghwan Lee, Seungchul Lee*, and Sun Im*, “Computer-aided Diagnosis of Malignant Voice Change in Larygneal Cancer via Deep Learning based on Acousitc Signals,” submitted (+ equally contributed)
  9. Andrew Glaeser, Soo Young Lee, Yunseob Hwang, Vignesh Selvaraj, Kangsan Lee, Namjeong Lee, Seungchul Lee, Sangkee Min*, “Applications of Deep Learning for Fault Detection in Industrial Cold Forging,” submitted
  10. HyunBum Kim+, Juhyeong Jeon+, Yeon Jae Han, YoungHoon Joo, Jonghwan Lee, Seungchul Lee*, and Sun Im*, “Can Artificial Intelligence Help Distinguish Voice Changes in Laryngeal Cancer Patients?,” submitted. (+ equally contributed)
  11. Kyung Ho Sun+, Hyunsuk Huh+, Bayu Adhi Tama, Soo Young Lee, Joon Ha Jung, and Seungchul Lee*, “Vision-based Fault Diagnostics using Explainable Deep Learning with Class Activation Map,” submitted with multimedia (+ equally contributed)
  12. Bayu Adhi Tama, Soo Young Lee, and Seungchul Lee*, “Machine Learning and Deep Learning Techniques for Intrusion Detection Systems in Industrial Control Networks: A Systematic Mapping Study,” submitted
  13. Da Seul Shin, Chi Hun Lee, U. Kühn, Seungchul Lee, Seong Jin Park, H. Schwab, Sergio Scudino and K. Kosiba*, “Additive Manufacturing Meets Artificial Intelligence: Selective Laser Melting of Metals Optimized by Deep Learning,” submitted
  14. Bayu Adhi Tama, Hyunsuk Huh, Sooyoung Lee, and Seungchul Lee*, “A Fine-grained Feature Engineering Technique for Faulty Detection using Fully Convolutional Network,” submitted
  15. “Self-tunable Mechanosensing Ability of Spider by Changing Tension of Slit Organ on Demand : better understanding through artificial slit organ,” submitted

International Conferences

  1. Soo Young Lee, Yunseob Hwang, and Seungchul Lee, 2020, “Frequency-driven Convolutional Neural Network for Enhancing Noise-robustness of Bearing Fault Detection,” inter-noise2020, Seoul, Korea.
  2. Soo Young Lee, Jiho Chang, and Seungchul Lee, 2020, “Acoustic Source Localization for a Single Point Source using Convolutional Neural Network and Weighted Frequency Loss,” inter-noise2020, Seoul, Korea.
  3. Bayu Adhi Tama, Soo Young Lee, and Seungchul Lee, 2020, “An Overview of Deep Learning Techniques for Fault Detection using Vibration Signal,” inter-noise2020, Seoul, Korea.
  4. Hyunsuk Huh, Kyung Ho Sun, Soo Young Lee, Joon Ha Jung, and Seungchul Lee, 2020, “New Way of Detecting Vibration of Mechanical Systems by Explainable Deep Learning,” inter-noise2020, Seoul, Korea.
  5. GyuWon Kim, and Seungchul Lee, 2020, “Attention-Based LSTM Network for Unknown Road Input Estimation and Sensor Selection for Applications in Vehicle Suspension Control,” inter-noise2020, Seoul, Korea.
  6. Juhyeong Jeon, HyunBum Kim, Yeon Jae Han, YoungHoon Joo, Sun Im, and Seungchul Lee, 2020, “Artificial Intelligence Approach for Detecting Pathological Voice,” inter-noise2020, Seoul, Korea
  7. Vignesh Selvaraj, Andrew Glaeser, Kangsan Lee, Namjeong Lee, Yunseob Hwang, Sooyoung Lee, Seungchul Lee, and Sangkee Min, 2020, “Detection of Process Variation in a Cold Forging Process through Smart Manufacturing,” PRESM2020 (International Symposium on Precision Engineering and Sustainable Manufacturing), Seoul, Korea.
  8. Vignesh Selvaraj, Andrew Glaeser, Kangsan Lee, Namjeong Lee, Yunseob Hwang, Sooyoung Lee, Seungchul Lee and Sangkee Min, 2020, “Remote Machine Mode Detection in Cold Forging using Vibration Signal,” SME NAMRC 48, Cincinnati, OH, USA.
  9. Soo Young Lee, Chunghyun Park, and Seungchul Lee, 2019, “Classification of the Steel Surface Defects via Machine Learning and Deep Learning,” ICMR 2019 (the 5th International Conference on Materials and Reliability), Jeju, Korea.
  10. Han Hee Lee, Chunghyun Park, Yunseob Hwang, Seungchul Lee, Seung-Jun Kim, Jin Su Kim, Bo-In Lee, Young-Seok Cho, and Myung-Gyu Choi, 2019, “A Convolutional Neural Network Algorithm with Class Activation Map for Detection of Various Lesions during Small Bowel Capsule Endoscopy,” UEG (United European Gastroenterology) Week 2019, Fira Gran Via, Barcelona, Spain.
  11. S. Kim, Y. Lee and S. Lee, 2019, “Camera Lens Module Classification and Recommendation Model based on Deep Neural Network,” 2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2019), Zhangjiajie, Hunan, China. (Best Paper Award)
  12. B. A. Tama, H. Huh, K. Sun, and S. Lee, 2019, “A CNN-based Fault Detection Method using Vibration Video,” The International Conference on the Interface between Statistics and Engineering (ICISE2019), Seoul, Korea.
  13. H. Jeong, S. Park, B. Park, and S. Lee, 2017, “New Approach for Fault Identification using Observer-based Residual,” PHM Asia Pacific 2017, Jeju, Korea.
  14. S. Park, S. Kim and S. Lee, 2017, “Wavelet-like CNN Structure for Time-Series Data Classification,” PHM Asia Pacific 2017, Jeju, Korea.
  15. H. Kim, S. Park, E. Park, N. Kim, and S. Lee, 2017, “Mechanical Property Estimation  for FDM 3D Printed Parts using Gaussian Process Regression,” PHM Asia Pacific 2017, Jeju, Korea.
  16. H. Jeong, M. Kim, B. Park, and S. Lee, 2017, “Vision-based Real-time Layer Error Quantification for Additive Manufacturing,” SME NAMRC 45, Los Angeles, CA, USA.
  17. H. Kim, E. Park, S. Kim, B. Park, N. Kim, and S. Lee, 2017, “Experimental Study on Mechanical Properties of Single- and Dual-Material 3D Printing,” SME NAMRC 45, Los Angeles, CA, USA.
  18. S Lee, 2016, “Machine Learning and Data Visualization in Manufacturing,” the 2nd Pacific Rim Statistical Conference for Production Engineering, Seoul, Korea.
  19. H. Jeong, S. Park, and S. Lee, 2016, “Deep Learning based Diagnostics for Rotating Machinery on Orbit Analysis,” Asian Conference Experimental Mechanics 2016, Jeju, Korea.
  20. H. Jeong, S. Woo, B. Park, and S. Lee, 2016, “PHM for Manufacturing Industry with IoT and Cloud Platform,” Asian Conference Experimental Mechanics 2016, Jeju, Korea.
  21. H. Jeong, S. Woo, S. Kim, S. Park, H. Kim, and S. Lee, 2016, “Deep Learning based Diagnostics of Orbit Patterns in Rotating Machinery,” PHM Conference 2016, Denver, CO, USA.
  22. H. Jeong, S. Park, S. Woo, and S. Lee, 2016, “Rotating Machinery Diagnostics using Deep Learning on Orbit Plot Images,” SME NAMRC 44, Blacksburg, VA, USA.
  23. S. Park, H. Jeung, H. Min, and S. Lee, 2015, “System Diagnostics using Kalman Filter Estimation Error,” The 3rd International Conference on Materials and Reliability, Jeju, Korea.
  24. A. Almuhtady, S. Lee, and J. Ni, 2013, “Planning by Maintenance-optimal Swapping for System-level Manufacturing Utilization,” Proc. of ASME 2013 International Manufacturing Science and Engineering Conference, Madison, WI. (MSEC2013-1075)
  25. A. Almuhtady, S. Lee, E. Romeijn and J. Ni, 2013, “A Maintenance-optimal Swapping Policy for a Fleet of Electric or Hybrid-Electric Vehicles,” The 2nd International Conference on Operations Research and Enterprise Systems (ICORES 2013), Barcelona, Spain. (ICORES 2013 best student paper award)
  26. S. Lee, 2012, “Hidden Markov Model with Independent Component Analysis,” US-Korea Conference on Science, Technology and Entrepreneurship, Los Angeles, CA. (UKC2012-131)
  27. S. Lee, H. Cui, M. Rezvanizaniani, and J. Ni, 2012, “Battery Prognositics: SoC and SoH Prediction,” Proc. of ASME 2012 International Manufacturing Science and Engineering Conference, Notre Dame, IN. (MSEC2012-7345)
  28. X. Gu, S. Lee, X. Liang, and J. Ni, 2012, “Extension of Maintenance Opportunity Windows to General Manufacturing Systems,” Proc. of ASME 2012 International Manufacturing Science and Engineering Conference, Notre Dame, IN. (MSEC2012-7346)
  29. W. Cheng, S. Lee, Z. Zhang, and Z. He, 2012, “Dissimilarity Measures for ICA-Based Source Number Estimation,” Proc. of ASME 2012 International Manufacturing Science and Engineering Conference, Notre Dame, IN. (MSEC2012-7340)
  30. A. Almuhtady, and S. Lee, and J. Ni, 2012, “Degradation-based Swapping Policy with Application to System-Level Manufacturing Utilization,” Proc. of ASME 2012 International Manufacturing Science and Engineering Conference, Notre Dame, IN. (MSEC2012-7280)
  31. S. Lee, 2011, “Development and Implementation of Optimal Maintenance Strategies at Automotive Assembly Plants,” US-Korea Conference on Science, Technology and Entrepreneurship, Park City, UT. (UKC2011-423)
  32. M. Rezvani, S. Lee, M. AbuAli, J. Lee, and J. Ni, 2011, “A Comparative Analysis of Techniques for Electric Vehicle Battery Prognostics and Health Management (PHM),” SAE 2011 Commercial Vehicle Engineering Congress and Exhibition, Rosemont, IL. (11CV-0191)
  33. S. Lee, A. Brzezinski, and J. Ni, 2011, “Plant Layout Optimization Considering the Effect of Maintenance,” Proc. ASME International Conference on Manufacturing Science and Engineering, Corvallis, OR. (MSEC2011-50233)
  34. S. Lee, L. Li, and J. Ni, 2010, “Adaptive Anomaly Detection Using a Hidden Markov Model,” Proc. ASME International Conference on Manufacturing Science and Engineering, Erie, PA. (MSEC2010-34169)
  35. J. Ni, S. Lee, and L. Li, 2009, “Predictive Modeling for Intelligent maintenance in Complex Semiconductor Manufacturing Processes,” Proc. of Advanced Equipment Control/Advanced Process Control Symposium Asia, Tokyo, Japan.
  36. S. Lee, L. Li, and J. Ni, 2009, “Modeling of Degradation Processes to Obtain an Optimal Solution for Maintenance and Performance,” Proc. ASME International Conference on Manufacturing Science and Engineering, West Lafayette, IN. (MSEC2009-84166)
  37. S. Lee, D. Djurdjanovic, and J. Ni, 2007, “Optimal Condition-Based Maintenance Decision-Making For a Cluster Tool,” Proc. of 9th Semiconductor Research Cooperation Technical Conference (SRC TechCon).

Domestic Conferences

  1. Seungchul Lee, 2020, “AI for Healthcare in Industrial AI Lab.,” The Korean Society for Prognostics & Health Management, Seoul, Korea.
  2. Seungchul Lee, 2020, “PHM and Industrial AI,” The Korean Society for Prognostics & Health Management, Seoul, Korea. (invited)
  3. Taewan Kim, Yunseob Hwang, Hanhee Lee, and Seungchul Lee, 2020, “Deep Learning-based Smart Reading System for Small Bowel Capsule Endoscopy,” The Korean Society for Prognostics & Health Management, Seoul, Korea.
  4. Juwon Na, Se Jong Kim, Jaimyun Jung, and Seungchul Lee, 2020, “Deep Learning-based Refocusing and Super-resolution for Microstructural Image,” The Korean Society for Prognostics & Health Management, Seoul, Korea.
  5. Hyunsuk Huh, Hyoungcheol Kwon, and Seungchul Lee, 2020, “Deep Learning for Inverse Problem: Etching-Mask Design,” The Korean Society for Prognostics & Health Management, Seoul, Korea.
  6. Gyuwon Kim, Changyun Choi, Do Hyun Kim, Sung Won Kim, and Seungchul Lee, 2020, “Deep Learning-based Stem Cell Image Analysis: Cell Classification and Functional Structure Interpretation,” The Korean Society for Prognostics & Health Management, Seoul, Korea.
  7. Seungchul Lee, 2020, “Industrial AI,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  8. Sung Wook Kim, Young Gon Lee, Bayu Adhi Tama, and Seungchul Lee, 2020, “Camera Lens Module Classification using Semi-supervised Regression Method,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  9. Juwon Na, Se Jong Kim, Jaimyun Jung, and Seungchul Lee, 2020, “Deep Learning-based Image Quality Enhancement for Materials Science: Super-resolution for Microstructure Image and Refocusing for SEM Image,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  10. Iljeok Kim, Juwon Na, Kyongho Park, Hyeonjae Yu, Jongsun Kim, Kwonil Choi, Seungchul Lee, 2020, “AI-based Optimization for Process Conditions of Injection Molding and Feature Relevance Analysis,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  11. Changyun Choi, Sooyoung Lee, Bayu Adhi Tama, Seungchul Lee, 2020, “Prediction for Temperature Distribution and Coolant Quantity in Steel-Making Continuous Casting Process using Deep Learning,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  12. Changyun Choi, SooYoung Lee, and Seungchul Lee, 2020, “AI-based Temperature Prediction Model for Continuous Casting Secondary Cooling,” The Korean Society for Noise and Vibration Engineering, E-Conference, Korea
  13. Hyunsuk Huh, Hyoungcheol Kwon, and Seungchul Lee, 2020, “Etch-Mask Design using Generative Adversarial Network,” The Korean Society for Noise and Vibration Engineering, E-Conference, Korea.
  14. Iljeok Kim, Juwon Na, Kyongho Park, Hyeonjae Yu, Jongsun Kim, Kwonil Choi, and Seungchul Lee, 2020, “AI-based Optimization for process conditions of Injection Molding and Feature Relevance Analysis,” The Korean Society for Noise and Vibration Engineering, E-Conference, Korea. (Best Paper Award)
  15. Juwon Na, Se Jong Kim, Jaimyun Jung, and Seungchul Lee, 2020, “Deep Learning-based Image Restoration for Materials Science: Super-resolution and Refocusing of SEM Image,” The Korean Society for Noise and Vibration Engineering, E-Conference, Korea.
  16. Seungchul Lee, Juhyeong Jeon, Sooyoung Lee, Kangsan Lee, Taegyu Choi, Jungchan Kim, 2019, “Deep Learning-based Anomaly Detection of Bearing Faults,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  17. Kangsan Lee, Juwon Na, Jongduk Sohn, Sukman Sohn, Seungchul Lee, 2019, “Image Recognition to Digitalize Maintenance Logs: CNN and FCN,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  18. Seungchul Lee, 2019, “Artificial Intelligence Applications to Mechanical Engineering,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  19. Juwon Na, Sung Wook Kim, Kyongho Park, Hyeonjae Yu, Jongsun Kim, Kwonil Choi, Seungchul Lee, 2019, “Domain Adaptation from Simulation Data to Experimental Data via Transfer Learning: Case Study on Injection Molding,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  20. Juwon Na, Sung Wook Kim, Kyongho Park, Hyeonjae Yu, Jongsun Kim, Kwonil Choi, Seungchul Lee, 2019, “AI-based Recommender System for Process Conditions of Injection Molding,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  21. Hyunsuk Huh, Sooyoung Lee, Junha Jeong, Kyunho Sun, Seungchul Lee, 2019, “Study on Localizing the Most Vibrating Regime on Images using Explainable Deep Learning,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  22. Soo Young Lee, Ju Hyeong Jeon, Kang San Lee, Jung Chan Kim, Tae Gyu Choi, Seungchul Lee, 2019, “Data Preprocessing and Machine Learning Techniques for Detection and Classification of Bearing Faults,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  23. Soo Young Lee, Ju Hyeong Jeon, Kang San Lee, Jung Chan Kim, Tae Gyu Choi, Seungchul Lee, 2019, “Transfer Learning for Enhancing Bearing Fault Detection Performance under Time-varying Speed,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  24. Yunseob Hwnag, Han Hee Lee, Seungchul Lee, Bo-In Lee, 2019, “Explainable Deep Learning-based Smart Diagnostics for Capsule Endoscopy Images,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  25. Sung Wook Kim, Juwon Na, Se Jong Kim, Seungchul Lee, 2019, “Phase Analysis of Multi-phase Steel using Unsupervised Deep Learning,” The Korean Society of Mechanical Engineers, Jeju, Korea.
  26. Juwon Na, Seungchul Lee, 2019, “AI-based Recommender System for Process Conditions of Injection Molding,” The Korean Society of Die & Mold Engineers, Incheon, Korea
  27. J. Jeon, H. Huh, D. Lim, and Seungchul Lee, 2019, “Deep learning based diagnostics algorithm for rotating machinery,” The Korean Society for Prognostics & Health Management, Seoul, Korea.
  28. S. W. Kim, H. Huh, and Seungchul Lee, 2019, “Deep Learning based Diagnostics and Prediction for Camera Lens Module Assembly,” The Korean Society for Prognostics & Health Management, Seoul, Korea.
  29. H. Huh, D. Lim, S. W. Kim, J. Jeon and Seungchul Lee, 2019, “Sensor Selection in Time Series Data using Class Activation Map,” The Korean Society for Prognostics & Health Management, Seoul, Korea.
  30. Seungchul Lee, Kangsan Lee, Juwon Na, Jongduk Sohn and Sukman Sohn, 2019, “Image Recognition to Digitalize Maintenance Logs: CNN and FCN,” The Korean Society for Noise and Vibration Engineering, Jeju, Korea.
  31. Seungchul Lee, Juhyeong Jeon, Yunseob Hwang, Iljoo Jeong, Yeonjae Han and Sun Im, 2019, “Pathological Voice Diagnostics using Deep Learning,” The Korean Society for Noise and Vibration Engineering, Jeju, Korea.
  32. Seungchul Lee, Juhyeong Jeon, Sooyoung Lee, Kangsan Lee, Taegyu Choi and Jungchan Kim, 2019, “Deep Learning-based Anomaly Detection of Bearing Faults,” The Korean Society for Noise and Vibration Engineering, Jeju, Korea.
  33. Seungchul Lee, Namjeong Lee, Iljoo Jeong, Sungmin Kim and Sukman Shon, 2019, “Ensemble Methods of Rule-based Expert System and Data-driven AI Model: Case Study of Rotating Machinery,” The Korean Society for Noise and Vibration Engineering, Jeju, Korea.
  34. Seungchul Lee, Hyunsuk Huh, Sooyoung Lee, Junha Jeong, Kyungho Sun, 2019, “Study on Localizing the Most Vibrating Regime on Images using Explainable Deep Learning,” The Korean Society for Noise and Vibration Engineering, Jeju, Korea.
  35. Seungchul Lee, Soo Young Lee, Juhyeong Jeon, Kangsan Lee, Jungchan Kim and Taegyu Choi, 2019, “Data Preprocessing and Machine Learning Techniques for Detection and Classification of Bearing Faults,” The Korean Society for Noise and Vibration Engineering, Jeju, Korea.
  36. Seungchul Lee, Soo Young Lee, Juhyeong Jeon, Kangsan Lee, Jungchan Kim and Taegyu Choi, 2019, “Transfer Learning for Enhancing Bearing Fault Detection Performance under Time-varying Speed,” The Korean Society for Noise and Vibration Engineering, Jeju, Korea.
  37. J. Na, C. Lee and S. Lee, 2019, “Development of Process Recommender System for Injection Molding Based on AI,” the Korea Society of Die & Mold Engineering, Gongju, Korea.
  38. Juhyeong Jeon, Hyunsuk Huh, Dohyeong Lim, Seungchul Lee, 2019, “Smart Diagnostics System: Deep Learning Model for Time Series Analysis of Rotating Machinery, Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  39. S. Kim, and S. Lee, 2017, “Artificial Intelligence in Mechanical Engineering,” CAE and Applied Mechanics Division of KSME conference, Busan, Korea
  40. S. Park, S. Kim, and S. Lee, 2017, “Deep Learning Classification Model for Sequential Data,” The Korean Society for Noise and Vibration Engineering, Gwangju, Korea.
  41. H. Jeong, S. Park, and S. Lee, 2017, “Observer-based Fault Detection and Isolation for Rotating Machinery,” The Korean Society for Noise and Vibration Engineering, Gwangju, Korea.
  42. H. Lee, S. Park, and S. Lee, 2017, “Vibration Comparison between High Speed Trains (KTX and SRT) in Korea,” The Korean Society for Noise and Vibration Engineering, Gwangju, Korea.
  43. H. Jeong, S. Park, and S. Lee, 2017, “Rotating Machinery Diagnostics using Model-based Fault Detection and Isolation,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  44. B. Park, H. Jeong, and S. Lee, 2017, “Servo Motor Diagnostics using Anomaly Detection,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  45. S. Kim, S. Park, and S. Lee, 2017, “Deep Learning Structures for Time Series Data in Manufacturing,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  46. S. Park, S. Kim, and S. Lee, 2017, “Interpretable CNN Structure for Time Series Data in Manufacturing,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  47. H. Kim, S. Kim, E. Park, N. Kim, and S. Lee, 2017, “Experimental Study on Improvement and Estimation of Mechanical Properties of FDM-based 3D Printing Products,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  48. M. Kim, H. Jeong, B. Park, and S. Lee, 2017, “Development of Vision-based Quality Assurance System in 3D Printing,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  49. S. Lee, 2016, “Mechanical Systems with Artificial Intelligence,” the Korean Society of Mechanical Engineers 2016, Jeongseon, Korea, Invited.
  50. H. Jeong, and S. Lee, 2016, “Real-time Monitoring System for Power Plant with IoT-based Cloud Platform,” Reliability Division in the Korean Society of Mechanical Engineers, Pusan, Korea. (Best Student Paper Award)
  51. H. Jeong, and S. Lee, 2016, “Real-time Monitoring for Rotating Machinery with IoT and Cloud Platform,” The Korean Society for Noise and Vibration Engineering, Gyeongju, Korea.
  52. S. Woo, and S. Lee, 2016, “Visualization Method of PCA Algorithm for Machine Health Diagnostics,” The Korean Society for Noise and Vibration Engineering, Gyeongju, Korea.
  53. S. Lee, H. Min, H. Jeong, S. J. Lee, and C. Kim, 2015, “Anomaly Detection in Rotating Machinery based on Orbit Image Eigen-analysis,” The Korean Society for Noise and Vibration Engineering, Jeju, Korea.
  54. H. Min, H. Jeong, S. Park, and S. Lee, Y. Lee, 2015, “Misalignment Detection Algorithm in Stacking Processes,” Korean Institute of Industrial Engineering, Jeju, Korea.
  55. H. Jeong, S. Park, H. Min, S. Lee, R. Koo, Y. Bae, 2015, “Rotational Machinery Diagnostics via Singular Value Decomposition of Orbit Images,” Korean Institute of Industrial Engineering, Jeju, Korea.
  56. H. Min, H. Jeong, S. Park, and S. Lee, S. J. Lee, 2015, “Anomaly Detection in Rotating Machinery based on Machine Learning of Orbits’ Eigenvalues,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  57. H. Min, Y. Lee, H. Jeong, S. Park, and S. Lee, 2014, “Condition Monitoring in Multilayer Stacking Processes,” The Korean Society for Noise and Vibration Engineering, Mokpo, Korea.
  58. S. Lee, 2014, “Intelligent Fault Detection and Prediction System on Wind Turbine Gearboxes,” The Korean Society for Noise and Vibration Engineering, Gangchon, Korea.
  59. S. Lee, 2014, “Diagnostics of Automated Manufacturing Processes Using Event Time Durations,” Korean Society of CAD CAM Engineers, Pyeongchang, Korea.

Patents

Presentations and Talks (in USA)

  1. [Nov. 2013] Guest Lecture on Self-Healing Engineering Systems, Ajou University, Suwon, Korea.
  2. [May 2013] Die Monitoring in Progressive Stamping Process, IAB 25, P&G Mason Business Center, Mason, OH.
  3. [Mar. 2013] Diagnostics, Prognostics, and Decision-Making for Next Generation Manufacturing Factories, University of Maryland, College Park, MD.
  4. [Feb. 2013] Diagnostics, Prognostics, and Decision-Making for Next Generation Manufacturing Factory, University of Toronto, Toronto, ON, Canada.
  5. [Jan. 2013] Introduction to Intelligent Maintenance with Industrial Case Studies, Samsung Electro-mechanics, Suwon, Korea.
  6. [Jan. 2013] Smart Factory of the Future: Diagnostics, Prognostics, and Decision-Making, UNIST, Ulsan, Korea.
  7. [Jan. 2013] Linear Systems Theory for Prediction with Industrial Applications, UNIST, Ulsan, Korea.
  8. [Nov. 2012] Self-diagnostic Module Development for MLCC Stacker, IAB 24, National Instruments, Austin, TX.
  9. [Oct. 2012] Diagnostics and prognostics for machine health and decision-making towards predictive manufacturing factory, Ajou University, Suwon, Korea.
  10. [Oct. 2012] IMS introduction with case studies, Samsung Electro-mechanics, Suwon, Korea.
  11. [Nov. 2011] Remaining Useful Life Prediction and Optimal Replacement Policy for Battery, 2011 INFORMS Annual Meeting Conference, Charlotte, NC.
  12. [Nov. 2011] Job Scheduling Considering the Effect of Maintenance in Semiconductor Manufacturing, 2011 INFORMS Annual Meeting Conference, Charlotte, NC.
  13. [Nov. 2011] Maintenance Opportunity Windows in Manufacturing Systems, KSEA MI Local Chapter Technical Seminar, Ann Arbor, MI.
  14. [Oct. 2011] Introduction of Intelligent Maintenance Systems – Advanced Prognostics for Smart Systems, LG Electronics, Seoul, Korea.
  15. [Sep. 2011] Introduction of Intelligent Maintenance Systems, Samsung SDS, Seoul, Korea.
  16. [May 2011] Development and Implementation of Maintenance Strategies for Assembly Line, IAB 21, Boeing, St Louis, MO.
  17. [Oct. 2010] Decision Making for Joint Maintenance and Product Policies, 2010 INFORMS Annual Meeting Conference,Austin, TX.
  18. [May 2010] Integrated Production and Maintenance Planning for a Multiple Product System, IAB 19, GE Aviation, Cincinnati, OH.
  19. [May 2010] Maintenance Strategies for Manufacturing Systems using Markov Models, Ph.D. Oral Defense, University of Michigan, Ann Arbor, MI.
  20. [Dec. 2009] Degradation Modeling, Fault Detection, and Maintenance Planning, Eaton Innovation Center, Southfield, MI.
  21. [Oct. 2009] Machine Degradation Estimation and Maintenance for Multiple Product System, IAB 18, Avetec, Springfield, OH.
  22. [May 2009] Online Self-Adaptive Fault Learning and Pattern Discovery Method, IAB 17, Ford, Dearborn, MI.
  23. [May 2009] An Overview of the Maintenance Decision Support Tool, IAB 17, Ford, Dearborn, MI.
  24. [Nov. 2008] Modeling of Degradation Processes to Obtain an Optimal Solution for Maintenance, Engineering Graduate Symposium, University of Michigan, Ann Arbor, MI.
  25. [April 2008] Degradation Modeling and Buffer Management: A Maintenance Perspective, IAB 15, Caterpillar, Peoria, IL.
  26. [Oct. 2007] Optimal Maintenance Solution for Degradation System, IAB 14, Chrysler, Warren, MI.
  27. [Sep. 2007] Modeling of Degradation Processes to Obtain an Optimal Solution for Maintenance and Performance, Ph.D. Preliminary Examination, University of Michigan, Ann Arbor, MI.
  28. [Sep. 2007] Optimal Condition-Based Maintenance Decision-Making for a Cluster Tool, 2007 Semiconductor Research Cooperation Technical Conference, Austin, TX.
  29. [July 2007] Predictive Modeling and Intelligent Maintenance Tools for High Yield Next Generation Fab, 2007 SRC FORCeII Research Review, Durham, NC.
  30. [May 2007] Optimal Condition-Based Maintenance Decision-Making and Production Dispatching, IAB 13, P&G, Cincinnati, OH.
  31. [Nov. 2006] Intelligent Maintenance Decision-Making, IAB 12, Boeing, Saint Louis, MO.