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Publication

In Preparation or Submitted

  1. “Mechanical Property Estimation for FDM 3D Printed Parts using Gaussian Process Regression,” submitted
  2. “New Approach for Fault Identification using Observer-based Residual,” submitted
  3. “Wavelet-like CNN Structure for Time-Series Data Classification,” submitted
  4. “Stochastic Degradation-based Optimal Swapping for Fleet-level Battery Utilization,” submitted

Journals

  1. 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]
  2. 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]
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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
  16. 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.
  17. 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.
  18. 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).
  19. 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.
  20. 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.

International Conferences

  1. 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.
  2. 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.
  3. S. Park, S. Kim and S. Lee, 2017, “Wavelet-like CNN Structure for Time-Series Data Classification,” PHM Asia Pacific 2017, Jeju, Korea.
  4. 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.
  5. 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.
  6. S Lee, 2016, “Machine Learning and Data Visualization in Manufacturing,” the 2nd Pacific Rim Statistical Conference for Production Engineering, Seoul, Korea.
  7. H. Jeong, S. Park, and S. Lee, 2016, “Deep Learning based Diagnostics for Rotating Machinery on Orbit Analysis (slides),” Asian Conference Experimental Mechanics 2016, Jeju, Korea.
  8. H. Jeong, S. Woo, B. Park, and S. Lee, 2016, “PHM for Manufacturing Industry with IoT and Cloud Platform (slides),” Asian Conference Experimental Mechanics 2016, Jeju, Korea.
  9. H. Jeong, S. Woo, S. Kim, S. Park, H. Kim, and S. Lee, 2016, “Deep Learning based Diagnostics of Orbit Patterns in Rotating Machinery (slides),” PHM Conference 2016, Denver, CO, USA.
  10. H. Jeong, S. Park, S. Woo, and S. Lee, 2016, “Rotating Machinery Diagnostics using Deep Learning on Orbit Plot Images (slides),” SME NAMRC 44, Blacksburg, VA, USA.
  11. S. Park, H. Jeung, H. Min, and S. Lee, 2015, “System Diagnostics using Kalman Filter Estimation Error (slides),” The 3rd International Conference on Materials and Reliability, Jeju, Korea.
  12. 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)
  13. 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)
  14. S. Lee, 2012, “Hidden Markov Model with Independent Component Analysis,” US-Korea Conference on Science, Technology and Entrepreneurship, Los Angeles, CA. (UKC2012-131)
  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. 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)
  20. 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)
  21. 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)
  22. 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)
  23. 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.
  24. 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)
  25. 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. 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.
  2. H. Jeong, S. Park, and S. Lee, 2017, “Observer-based Fault Detection and Isolation for Rotating Machinery (slides),” The Korean Society for Noise and Vibration Engineering, Gwangju, Korea.
  3. H. Lee, S. Park, and S. Lee, 2017, “Vibration Comparison between High Speed Trains (KTX and SRT) in Korea (slides),” The Korean Society for Noise and Vibration Engineering, Gwangju, Korea.
  4. H. Jeong, S. Park, and S. Lee, 2017, “Rotating Machinery Diagnostics using Model-based Fault Detection and Isolation (slides),” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  5. B. Park, H. Jeong, and S. Lee, 2017, “Servo Motor Diagnostics using Anomaly Detection (slides),” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  6. S. Kim, S. Park, and S. Lee, 2017, “Deep Learning Structures for Time Series Data in Manufacturing (slides),” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  7. 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.
  8. 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 (slides),” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  9. M. Kim, H. Jeong, B. Park, and S. Lee, 2017, “Development of Vision-based Quality Assurance System in 3D Printing (slides),” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.
  10. S. Lee, 2016, “Mechanical Systems with Artificial Intelligence (slides),” the Korean Society of Mechanical Engineers 2016, Jeongseon, Korea, Invited.
  11. 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)
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. S. Lee, 2014, “Intelligent Fault Detection and Prediction System on Wind Turbine Gearboxes,” The Korean Society for Noise and Vibration Engineering, Gangchon, Korea.
  20. S. Lee, 2014, “Diagnostics of Automated Manufacturing Processes Using Event Time Durations,” Korean Society of CAD CAM Engineers, Pyeongchang, Korea.

Presentations and Talks

  1. [July 2017] Tutorial on Deep Learning for PHM, PHM Asia Pacific Conference, Jeju, Korea.
  2. [April 2017] Tutorial on Coding for Machine Learning and Deep Learning (link), KSNVE, Gwangju, Korea.
  3. [April 2017] Make IT Smarter via Deep Learning (slides), the department of Mechanical Engineering at POSTECH, Pohang, Korea.
  4. [April 2017] Intelligent Mechatronic Systems with Signal Processing, Control, and Optimization (slides), Hongik University, Seoul, Korea.
  5. [April 2017] How to Teach Engineering Mechanics as a recipient of outstanding teaching award at UNIST, Ulsan, Korea.
  6. [April 2017] Mechatronics with Machine Learning and Deep Learning (slides), Inha University, Incheon, Korea.
  7. [Mar. 2017] Machine Learning and Deep Learning in Manufacturing (slides), Korea Institute of Machinery and Materials (KIMM), Daejeon, Korea.
  8. [Jan. 2017] Bayesian Machine Learning and Data Visualization in PHM (slides), Korea Atomic Energy Research Institute (KAERI), Daejeon, Korea.
  9. [Nov. 2016] Machine Learning and Data Visualization in Manufacturing (slides), the department of Industrial and Management Engineering at POSTECH, Pohang, Korea.
  10. [Aug. 2016] Intelligent Mechanical Systems with Machine Learning (slides), KIMM, Daejeon, Korea.
  11. [July. 2016] IoT and Cloud Platform for Monitoring (slides), Signallink, Daegeon, Korea.
  12. [July. 2016] IoT-based PHM in Power Plants (slides), Korea Electric Power Corporation (KEPCO), Daejeon, Korea.
  13. [Dec. 2015] Introduction to PHM and Big Data Visualization, the Korea Aerospace University, Seoul, Korea.
  14. [Sep. 2015] Machine Learning for Machine Healthcare Systems, the Korea Certification Institute for Machine Diagnostics, Gwangju, Korea.
  15. [Aug. 2015] Big Data Mining and IoT-based PHM, Seoul National University, Seoul, Korea.
  16. [May. 2015] Big Data Visualization, ASPM Business Analytic Program, UNIST, Ulsan, Korea.
  17. [Mar. 2015] Big Data Visualization in Manufacturing, UNIST Big Data Symposium, UNIST, Ulsan, Korea.
  18. [Sep. 2014] Issues on Intelligent PHM, the Korea Institute for Machine Diagnostics, Kyeongju, Korea.
  19. [Dec. 2013] Diagnostics and Prognostics of Battery Management Systems, Samsung Advanced Institute of Technology, Suwon, Korea.
  20. [Nov. 2013] Guest Lecture on Self-Healing Engineering Systems, Ajou University, Suwon, Korea.
  21. [Oct. 2013] Issues on Intelligent Prognostics, KEPCO, Daejeon, Korea.
  22. [Oct. 2013] Introduction to iSystems Design Laboratory, UNIST, Ulsan, Korea.
  23. [May 2013] Die Monitoring in Progressive Stamping Process, IAB 25, P&G Mason Business Center, Mason, OH.
  24. [Mar. 2013] Diagnostics, Prognostics, and Decision-Making for Next Generation Manufacturing Factories, University of Maryland, College Park, MD.
  25. [Feb. 2013] Diagnostics, Prognostics, and Decision-Making for Next Generation Manufacturing Factory, University of Toronto, Toronto, ON, Canada.
  26. [Jan. 2013] Introduction to Intelligent Maintenance with Industrial Case Studies, Samsung Electro-mechanics, Suwon, Korea.
  27. [Jan. 2013] Smart Factory of the Future: Diagnostics, Prognostics, and Decision-Making, UNIST, Ulsan, Korea.
  28. [Jan. 2013] Linear Systems Theory for Prediction with Industrial Applications, UNIST, Ulsan, Korea.
  29. [Nov. 2012] Self-diagnostic Module Development for MLCC Stacker, IAB 24, National Instruments, Austin, TX.
  30. [Oct. 2012] Diagnostics and prognostics for machine health and decision-making towards predictive manufacturing factory, Ajou University, Suwon, Korea.
  31. [Oct. 2012] IMS introduction with case studies, Samsung Electro-mechanics, Suwon, Korea.
  32. [Nov. 2011] Remaining Useful Life Prediction and Optimal Replacement Policy for Battery, 2011 INFORMS Annual Meeting Conference, Charlotte, NC.
  33. [Nov. 2011] Job Scheduling Considering the Effect of Maintenance in Semiconductor Manufacturing, 2011 INFORMS Annual Meeting Conference, Charlotte, NC.
  34. [Nov. 2011] Maintenance Opportunity Windows in Manufacturing Systems, KSEA MI Local Chapter Technical Seminar, Ann Arbor, MI.
  35. [Oct. 2011] Introduction of Intelligent Maintenance Systems – Advanced Prognostics for Smart Systems, LG Electronics, Seoul, Korea.
  36. [Sep. 2011] Introduction of Intelligent Maintenance Systems, Samsung SDS, Seoul, Korea.
  37. [May 2011] Development and Implementation of Maintenance Strategies for Assembly Line, IAB 21, Boeing, St Louis, MO.
  38. [Oct. 2010] Decision Making for Joint Maintenance and Product Policies, 2010 INFORMS Annual Meeting Conference,Austin, TX.
  39. [May 2010] Integrated Production and Maintenance Planning for a Multiple Product System, IAB 19, GE Aviation, Cincinnati, OH.
  40. [May 2010] Maintenance Strategies for Manufacturing Systems using Markov Models, Ph.D. Oral Defense, University of Michigan, Ann Arbor, MI.
  41. [Dec. 2009] Degradation Modeling, Fault Detection, and Maintenance Planning, Eaton Innovation Center, Southfield, MI.
  42. [Oct. 2009] Machine Degradation Estimation and Maintenance for Multiple Product System, IAB 18, Avetec, Springfield, OH.
  43. [May 2009] Online Self-Adaptive Fault Learning and Pattern Discovery Method, IAB 17, Ford, Dearborn, MI.
  44. [May 2009] An Overview of the Maintenance Decision Support Tool, IAB 17, Ford, Dearborn, MI.
  45. [Nov. 2008] Modeling of Degradation Processes to Obtain an Optimal Solution for Maintenance, Engineering Graduate Symposium, University of Michigan, Ann Arbor, MI.
  46. [April 2008] Degradation Modeling and Buffer Management: A Maintenance Perspective, IAB 15, Caterpillar, Peoria, IL.
  47. [Oct. 2007] Optimal Maintenance Solution for Degradation System, IAB 14, Chrysler, Warren, MI.
  48. [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.
  49. [Sep. 2007] Optimal Condition-Based Maintenance Decision-Making for a Cluster Tool, 2007 Semiconductor Research Cooperation Technical Conference, Austin, TX.
  50. [July 2007] Predictive Modeling and Intelligent Maintenance Tools for High Yield Next Generation Fab, 2007 SRC FORCeII Research Review, Durham, NC.
  51. [May 2007] Optimal Condition-Based Maintenance Decision-Making and Production Dispatching, IAB 13, P&G, Cincinnati, OH.
  52. [Nov. 2006] Intelligent Maintenance Decision-Making, IAB 12, Boeing, Saint Louis, MO.