Conference

Conferences that iAI Attends

1.  대한기계학회

2.  대한기계학회 신뢰성부문, CAE 및 응용역학

3.  한국소음진동공학회

4.  한국PHM학회

5.  한국금속재료공학회

6.  Inter Noise (International Congress and Exposition on Noise Control Engineering)

7.  NAMRC/MSEC (North American Manufacturing Research Conference/Manufacturing Science and Engineering Conference)

8.  MLSE (Machine Learning in Science & Engineering)

International Conferences

2023

1. Jongmok Lee, Keonhyeok Park, Hyunjoon Chung and Seungchul Lee, 2023, "Deep Learning-based Human Action Recognition with Exo-Suit," 20th International Conference on Ubiquitous Robots (UR), Honolulu, USA.

2. Hyunsuk Huh, Jeong-Jung Kim, Iljeok Kim, Iljoo Jeong, Doo-Yeol Koh, Chang-Hyun Kim, and Seungchul Lee, 2023, "Estimation of Simulated Physical Properties of an Object using Robot with Tactile Sensors and Attention Mechanism," 20th International Conference on Ubiquitous Robots (UR), Honolulu, USA.

3. Taewan Kim, Jaejung Park, Sebin Lee, and Seungchul Lee, 2023, "Multi-Agent Deep Reinforcement Learning for Efficient Traffic Signal Control," 20th International Conference on Ubiquitous Robots (UR), Honolulu, USA.

4. Sun Im, Heekyu Kim, DoGyeon Park, Hae-Yeon Park, and Seungchul Lee, 2023, "Non-invasive Way to Diagnose Dysphagia using Deep Learning Model with Voice Spectrograms," 31st DRS Annual Meeting, San Francisco, CA, USA.

5. Iljeok Kim, Juwon Na, Jong Pil Yun and Seungchul Lee, 2023, "Feature Selection for Process Optimization using Interpretable AI: Case Study on Injection Molding," Proceeding of the 9th International Conference on Manufacturing, Machine Design and Tribology (ICMDT2023), Jeju, Korea. 

6. Seongwook Choi+, Jinge Yang+, Soo Young Lee+, Jiwoong Kim, Byullee Park, Seungchul Lee, and Chulhong Kim, 2023, "Improvement of Spatiotemporal Resolution based on Deep Learning in 3D Photoacoustic Tomography," The Conference on Optoacoustic Methods and Applications in Biophotonics, part of European Conferences on Biomedical Optics, Munich, Germany.

7. Seongwook Choi, Jinge Yang, Soo Young Lee, Jiwoong Kim, Seungchul Lee, and Chulhong Kim, 2023, "3D Photoacoustic Computed Tomography Enhanced by 3D Progressive U-shaped Enhancement Network (3D-pU-net)," The conference on Photons Plus Ultrasound: Imaging and Sensing 2023, 123790I (2023) https://doi.org/10.1117/12.2648107, part of SPIE BiOS, San Francisco, USA.

8. Jongbeom Kim, Gyuwon Kim, Lei Li, Pengfei Zhang, Jin Young Kim, Yeonggeun Kim, Hyung Ham Kim, Lihong V. Wang, Seungchul Lee, and Chulhong Kim, 2023, "Fast Superresolution Multiscale Photoacoustic Imaging via a Deep Learning Approach," The conference on Photons Plus Ultrasound: Imaging and Sensing 2023, part of SPIE BiOS, San Francisco, USA.

2022

9. Hyunsuk Huh, Jeong-Jung Kim, Doo-Yeol Koh, Chang-Hyun Kim, and Seungchul Lee, 2022, "Deep Learning-based Physical Property Estimation of an Object using Gripper with Tactile Sensor," The 22nd International Conference on Control, Automation and Systems (ICCAS 2022), Busan, Korea.

10. Kyung Seok Choi, Han Hee Lee, DoGyeom Park, Seungchul Lee, Dae Young Cheung, Bo‐In Lee, Young-Seok Cho, Jin Il Kim, and Myung‐Gyu Choi, 2022, "Reanalysis of Negative Small Bowel Capsule Endoscopy by Deep Convolutional Neural Network Model Improved Diagnostic Yield and Could Change the Outcome," UEG (United European Gastroenterology) Week, Vienna, Austria.

11. Soo Young Lee, Jiho Chang, and Seungchul Lee, 2022, "Non-Contact Boundary Condition Quantification Using Data-Driven Beamforming Approach," International Congress on Acoustics (ICA 2022), Gyeongju, Korea.

12. Iljoo Jeong, Injee Jung, Keonhyeok Park, Soo Young Lee, Jiho Chang, and Seungchul Lee, 2022, "Robust Sound Source Localization Against Background Noise By Deep Reconstruction Method," International Congress on Acoustics (ICA 2022), Gyeongju, Korea.

13. Jongmok Lee, Junbum Park, Sungmin Kim, Seokman Sohn, and Seungchul Lee, "Deep learning-based Human Safety Management," ifip International Conference APMS (Advances in Production Management Systems) 2022, Gyeongju, Korea.

14. G. Kim, J. Kim, W. J. Choi, C. Kim, and S. Lee, 2022, "Integrated Deep Learning Framework for Accelerated Optical Coherence Tomography Angiography," the International Society for Optics and Photonics (SPIE) Photonics West 2022, San Francisco, USA.

2021

15. Hyuckcheol Kwon, Jongwhan Lee, Seungchul Lee, Taekyu An, Youngseok Cho, and Jeasook Chung, 2021, "Analysis and Control of Bar Warping in Roughing Mill of HSM," ASIA STEEL 2021, Gyeongju, Korea.

16. Iljeok Kim, Sung Wook Kim, Jeongsan Kim, Taegyu Choi, Jungchan Kim, and Seungchul Lee, 2021, "A Novel Interpretable and Generalized Learning Method for Bearing Fault Diagnosis," PHMAP 2021, Seoul, Korea.

17. Soo Young Lee, Jiho Chang, and Seungchul Lee, 2021, "Point-level Deep Learning Approach for 3D Sound Source Localization in Spherical Microphone Array," PHMAP 2021, Seoul, Korea.

18. Sung Wook Kim, Ki-Yong Oh, and Seungchul Lee, 2021, "A Novel Physics-Infused Recurrent Neural Network for Health Monitoring of Lithium-Ion Batteries," PHMAP 2021, Seoul, Korea.

19. Taewan Kim and Seungchul Lee, 2021, "Deep Learning-based Bearing Health Indicator Construction for Better RUL Prediction," PHMAP 2021, Seoul, Korea.

20. Soo Young Lee, Jiho Chang, and Seungchul Lee, 2021, "Deep Learning-enhanced Single Point Sound Source Localization for Spherical Microphone Array," inter-noise 2021, Online.

21. Hyunsuk Huh, and Seungchul Lee, 2021, "Background Noise Removal Technique using Deep Learning Segmentation Network without Segmentation Map," inter-noise 2021, Online.

22. Gyuwon Kim, and Seungchul Lee, 2021, "Generative Adversarial Neural Network for Unsupervised Bearing Fault Detection," inter-noise 2021, Online.

23. Taewan Kim and Seungchul Lee, 2021, "Deep Learning-based Health Indicator for Better Bearing RUL Prediction," inter-noise 2021, Online.

2020

24. Soo Young Lee, Yunseob Hwang, and Seungchul Lee, 2020, “Frequency-driven Convolutional Neural Network for Enhancing Noise-robustness of Bearing Fault Detection,” inter-noise 2020, Seoul, Korea.

25. 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-noise 2020, Seoul, Korea.

26. 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.

27. 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-noise 2020, Seoul, Korea.

28. 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-noise 2020, Seoul, Korea.

29. Juhyeong Jeon, HyunBum Kim, Yeon Jae Han, YoungHoon Joo, Sun Im, and Seungchul Lee, 2020, “Artificial Intelligence Approach for Detecting Pathological Voice,” inter-noise 2020, Seoul, Korea.

2019

30. 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.

31. 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.

32. 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)

33. 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.

2017

34. 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.

35. S. Park, S. Kim and S. Lee, 2017, “Wavelet-like CNN Structure for Time-Series Data Classification,” PHM Asia Pacific 2017, Jeju, Korea.

36. 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.

37. 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.

38. 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.

2016

39. S Lee, 2016, “Machine Learning and Data Visualization in Manufacturing,” the 2nd Pacific Rim Statistical Conference for Production Engineering, Seoul, Korea.

40. 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.

41. 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.

42. 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.

43. 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.

2015

44. 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.

2013

45. 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)

46. 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)

2012

47. S. Lee, 2012, “Hidden Markov Model with Independent Component Analysis,” US-Korea Conference on Science, Technology and Entrepreneurship, Los Angeles, CA. (UKC2012-131)

48. S. Lee, H. Cui, M. Rezvanizaniani, and J. Ni, 2012, “Battery Prognostics: SoC and SoH Prediction,” Proc. of ASME 2012 International Manufacturing Science and Engineering Conference, Notre Dame, IN. (MSEC2012-7345)

49. 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)

50. 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)

51. 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)

2011

52. 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)

53. 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)

54. 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)

2010

55. 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)

2009

56. 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.

57. 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)

2007

58. 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

2023

1. 김민선, 박재정, 김희규, 이재준, 이인효, 이승철, 민경민, 2023, "차세대 칼슘 이온 배터리 양극재 설계 및 선별을 위한 기계학습 플랫폼," 대한기계학회 본부추계학술대회, 송도.

2. 박재정, 이재준, 이인효, 민경민, 이승철, 2023, "High-entropy Doping Strategy with Machine Learning for Maximizing Stability and Energy Density of Co-free Layered Cathode Materials," 대한금속재료학회 추계학술대회, 대구.

3. 이세빈, 김태완, 이승철, 홍성호, 2023, "딥러닝 기반 마모 흔적 측정 방법," 한국트라이볼로지학회 추계학술대회, 경주.

4. 이지훈, 김태완, 박형식, 이승철, 2023, "디지털 전환을 위한 필요조건: 가상 데이터 생성과 시뮬레이션 계산 가속화," 한국소음진동공학회 추계학술대회, 여수.

5. 김일적, 원홍인, 윤종필, 이승철, 2023, "학습되지 않은 작동 환경에 대한 웨이브 제너레이터 베어링 고장 진단," 한국소음진동공학회 추계학술대회, 여수.

6. 박건혁, 이수영, 정일주, 박준형, 이형진, 박춘수, 이승철, 2023, "고품질 및 빠른 초음파 영상 변환을 위한 딥러닝 기반 접근법," 한국소음진동공학회, 삼척.

7. 정일주, 연인모, 최정우, 이승철, 2023, "메타버스 구현을 위한 음향 에코 기반 딥러닝 방 구조 예측," 한국소음진동공학회, 삼척.

8. 이재준, 박재정, 이승철, 2023, "RL-GPT: 금속-유기 골격체 역설계를 위한 강화학습 기반 생성 사전학습 모델," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

9. 박재정, 김민선, 김희규, 이재준, 이인효, 민경민, 이승철, 2023, "제1원리 계산과 머신러닝을 활용한 기계적으로 견고한 MXene 식별," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

10. 이종목, 신승민, 김태완, 이승철, 2023, "물성 변화를 고려한 이중 열 교환 모사 물리기반 인공지능," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

11. 신승민, 이종목, 김태완, 이창환, 예인수, 최호, 이승철, 2023, "유동 해석을 위한 물리기반 인공신경망 재초기화 전략," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

12. 김태완, 신승민, 이종목, 이창환, 예인수, 최호, 이승철, 2023, "빠른 수치 시뮬레이션 보간을 위한 물리지식기반 컨볼루션 프레임워크," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

13. 김희규, 김민선, 박재정, 이재준, 이인효, 민경민, 이승철, 2023, "능동적 학습법과 결합한 기계학습 모델을 사용한 높은 평균전압을 갖는 배터리 검색 가속화: 배터리 데이터 부족 문제의 극복," 대한금속재료학회 춘계학술대회, 제주.

14. 박재정, 김희규, 이재준, 박해선, 이승철, 2023, "능동적 학습과 결합된 제1원리 계산을 사용한 열역학적으로 안정된 MXenes 탐색," 대한금속재료학회 춘계학술대회, 제주.

15. 이재준, 이인효, 김희규, 박재정, 김민선, 민경민, 이승철, 2023, "피쳐 조합을 통한 기계적으로 안정적인 Metal-organic Frameworks 발견 가속화," 대한금속재료학회 춘계학술대회, 제주.

16. 이승철, 2023, "딥러닝 기반 재료 연구: 재료 이미지 개선, 역설계, 실험식 예측," 대한금속재료학회 춘계학술대회, 제주.

17. 박형식, 이지훈, 이승철, 2023, "진동신호의 크기와 위상 성분을 모두 생성하는 GANs," 대한기계학회 신뢰성 부문 춘계학술대회, 제주도.

18. 이지훈, 박형식, 이승철, 2023, "딥러닝 기반 인버전 및 속성 조작을 이용한 정상 신호로부터의 결함 진동 신호 생성 방법," 대한기계학회 신뢰성 부문 춘계학술대회, 제주도.

19. 이세빈, 이종목, 이승철, 2023, "세그멘테이션을 활용한 오간 모듈 좌표 인식," 대한기계학회 신뢰성 부문 춘계학술대회, 제주도.

20. 이승철, 2023, "ChatGPT 활용해서 연구하기: 교수와 대학원생 입장에서," 대한기계학회 신뢰성 부문 춘계학술대회, 제주도.

21. 허현석, 김정중, 고두열, 김창현, 이승철, 2023, "딥러닝을 이용한 촉각센서 기반 물체 특성 예측 및 로봇 중요 동작 선택," 제18회 한국로봇종합학술대회, 평창.

2022

22. 이정아, 박재정, 최연택, 김래언, 서민홍, 정재면, 이승철, 김형섭, 2022, "설명가능 기계학습을 결합한 생산적 적대 대치법 신경망을 사용한 구멍확장성 분석," 한국소성가공학회 추계학술대회, 제주.

23. 이승철, 2022, "기계공학자를 위한 인공지능 교육은 뭐가 달라야 하는가?" 대한기계학회 추계학술대회, 제주.

24. 이수영, 조완호, 정인지, 장지호, 이승철, 2022, "딥러닝 기반 음향 암호 기법," 한국소음진동공학회 추계학술대회, 제주.

25. 이정아, 박재정, 최연택, 김래언, 서민홍, 정재면, 이승철, 김형섭, "생산적 적대 대치법 신경망과 설명 가능한 인공지능을 활용한 구멍 확장성 영향 요소 분석," 대한금속재료학회 추계학술대회, 제주.

26. 이종목, 박준범, 김성민, 손석만, 이승철, 2022, "이상치 탐지를 이용한 산업 현장 내 위험 행동 분류," 대한기계학회 IT융합 부문 춘계학술대회, 안동.

27. 이승철, 2022, "물리지식기반 인공지능에 대한 소개와 고찰," 한국소음진동공학회 춘계학술대회, 창원.

28. 이수영, 이지훈, 이중석, 이승철, 2022, "딥러닝 기반 메타포러스 물질의 흡음 성능 예측 및 해석," 한국소음진동공학회 춘계학술대회, 창원.

29. 이지훈, 이수영, 이중석, 이승철, 2022, "적대적 생성 신경망을 활용한 층 구조 메타포러스 역설계," 한국소음진동공학회 춘계학술대회, 창원.

30. 박건혁, 이수영, 정일주, 박춘수, 이형진, 이승철, 2022, "음향 산란 역설계를 위한 물리 기반 딥러닝 접근법," 한국소음진동공학회 춘계학술대회, 창원.

31. 정일주, 정인지, 이승철, 2022, "물리 기반 인공신경망을 활용한 3차원 음향인텐시티의 위치추정 오차," 한국소음진동공학회 춘계학술대회, 창원.

32. 신민철, 이승철, 2022, "진동 신호에서의 데이터 불균형 문제 완화를 위한 생성 모델 적용," 한국소음진동공학회 춘계학술대회, 창원.

33. 박도겸, 이승철, 2022, "에지 디바이스에서 크립그론 검출을 위한 경량 딥러닝 모델," 한국소음진동공학회 춘계학술대회, 창원.

34. 이승철, 2022, "물리지식기반 인공지능에 대한 소개와 고찰," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

35. 김정산, 이승철, 2022, "이상치 탐지를 위한 주목 메커니즘 기반 설명가능한 특징 추출 모델," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

36. 김일적, 이승철, 2022, "베어링 결함 진단을 위한 시뮬레이션 데이터 기반 도메인 일반화," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

37. 이수영, 이형진, 박춘수, 박건혁, 이승철, 2022, "물리기반 인공신경망을 활용한 파동 산란 예측 모델 구축 및 물리적 정합성 분석," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

38. 김승욱, 곽은지, 오기용, 이승철, 2022, "리튬이온전지 열폭주 시뮬레이션을 위한 다중물리기반 인공지능," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

39. 신승민, 이지훈, 김태완, 최호, 이승철, 2022, "원형 실린더 주변의 비정상 층류 유동 해석을 통한 물리기반 인공지능 고찰," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

40. 김태완, 이승철, 2022, "다중 좌표계를 이용한 물리기반 인공지능," 대한기계학회 CAE및응용역학 부문 춘계학술대회, 부산.

41. Juwon Na, Jaejun Lee, and Seungchul Lee, 2022, "AI Metallurgist: Data-driven Discovery of Mathematical Expressions via Natural Language Processing," 대한금속재료학회, 창원.

42. 신민철, 이승철, 2022, "진동 신호에서의 데이터 불균형 문제 완화를 위한 생성 모델 적용," 대한기계학회 신뢰성 부문 춘계학술대회, 제주도.

43. 김일적, 김정산, 이승철, 2022, "단일 도메인 일반화 및 물리적으로 설명 가능한 베어링 고장 진단," 대한기계학회 신뢰성부문 춘계학술대회, 제주도.

44. 박도겸, 나주원, 이승철, 2022, "에지 디바이스에서 크립그론 검출을 위한 경량 딥러닝 모델," 대한기계학회 신뢰성부문 춘계학술대회, 제주도.

2021

45. Seungchul Lee, 2021, "Deep Learning-based Sound Source Localization and its Applications," The Korean Society for Noise and Vibration Engineering, Jeju, Korea. 

46. Soo Young Lee, Hyoungjin Lee, Chunsoo Park and Seungchul Lee, 2021, "Deep Learning for Simulating the Wave Propagation and its Scattering Physics," The Korean Society for Noise and Vibration Engineering, Jeju, Korea. 

47. Iljoo Jeong, Mooyeob Lee and Seungchul Lee, 2021, "Wafer Failure Map Retrieval using Multiple Global Descriptors," The Korean Society for Noise and Vibration Engineering, Jeju, Korea. 

48. Taewan Kim, and Seungchul Lee, 2021, "Unsupervised Fault Clustering Method using Adversarial Autoencoder and Gaussian Mixture Model," The Korean Society for Noise and Vibration Engineering, Jeju, Korea. 

49. Jeongsan Kim, and Seungchul Lee, 2021, "Anomaly Detection in Planetary Gear via Generative Deep Learning Model using Short Time Fourier Transformation with Order Representation," The Korean Society for Noise and Vibration Engineering, Jeju, Korea. 

50. Seungchul Lee, 2021, "Recent Trends in Physics-informed Deep Learning," The Korean Society of Mechanical Engineers, Gwangju, Korea.

51. Juwon Na, Se-Jong Kim, and Seungchul Lee, 2021, "Weakly Supervised Microstructure Segmentation via Scribble Annotations," The Korean Society of Mechanical Engineers, Gwangju, Korea.

52. Jongmok Lee, Changyun Choi, Junbum Park, Sungmin Kim, Seokman Sohn, and, Seungchul Lee, 2021, "Deep Learning-based Human Action Recognition in Real-Time to Prevent Industrial Workplace Accident," The Korean Society of Mechanical Engineers, Gwangju, Korea. 

53. 김세종, 강성훈, 정재면, 이호원, 오세혁, 오영석, 나주원, 이승철, 2021, "딥러닝을 이용한 광학 미세조직 결함 보정," 대한금속재료학회 추계학술대회, 제주도, 한국.

54. Juwon Na, Se-Jong Kim, and Seungchul Lee, 2021, "Deep Learning-based Grain Boundary Detection on Polycrystalline Materials in an Unsupervised Manner," The Korean Institute of Metals and Materials, Jeju, Korea.

55. Juwon Na, Se-Jong Kim, and Seungchul Lee, 2021, "Weakly Supervised Microstructure Segmentation via Scribble Annotations," The Korean Institute of Metals and Materials, Jeju, Korea. (Best Paper Award)

56. Jongmok Lee, Changyun Choi, Junbum Park, Sungmin Kim, Seokman Sohn, and Seungchul Lee, 2021, "Deep Learning-based Human Action Recognition to Prevent Industrial Workplace Accident," The Korea Society of Safety, Yeosu, Korea. 

57. Changyun Choi and Seungchul Lee, 2021, "Missing Marker Refinement for Motion Data using Directed Graph Neural Network," The Korean Society for Noise and Vibration Engineering, Pyeongchang, Korea.

58. Hyunsuk Huh and Seungchul Lee, 2021, "Important Feature Extraction for Classification using Deep Segmentation Model," The Korean Society for Noise and Vibration Engineering, Pyeongchang, Korea.

59. Iljeok Kim, Jeongsan Kim, Taegyu Choi, Jungchan Kim, and Seungchul Lee, 2021, "Bearing Fault Diagnosis using Explainable CNN and Signal Processing," The Korean Society for Noise and Vibration Engineering, Pyeongchang, Korea.

60. Soo Young Lee, Jiho Chang, and Seungchul Lee, 2021, "Super-resolved Sound Source Localization via Deep Learning," The Korean Society for Noise and Vibration Engineering, Pyeongchang, Korea.

61. Juwon Na, Seungchul Lee, and Se Jong Kim, 2021, "Deep Learning-based Grain Boundary Contrast Enhancement," Materials Research Society of Korea, Online.

62. Gyuwon Kim, Jongbeom Kim, Woo June Choi, Chulhong Kim, and Seungchul Lee, 2021, "Deep Learning-based Acceleration of Optical Coherence Tomography Angiography," The Korean Society of Medical & Biological Engineering, Online. (Best Paper Award)

63. Jiho Chang, Sooyoung Lee, and Seungchul Lee, 2021, "Deep Learning-based Sound Source Localization with High Resolution," The Acoustical Society of Korea, Jeju, Korea.

64. Sung Wook Kim, Ki-Yong Oh, and Seungchul Lee, 2021, "SOH Monitoring and RUL Estimation of Lithium-Ion Batteries using Physics Infused Deep Learning," Reliability Engineering Division in the Korean Society of Mechanical Engineers, Jeju, Korea.

65. Taewan Kim, and Seungchul Lee, 2021, "Bearing Health Indicator Construction using Adversarial Autoencoder," Reliability Engineering Division in the Korean Society of Mechanical Engineers, Jeju, Korea.

66. Jongmok Lee, Changyun Choi, Junbum Park, Sungmin Kim, Seokman Sohn, and Seungchul Lee, 2021, "Deep Learning-based Human Action Recognition to Prevent Industrial Workplace Accidents," Reliability Engineering Division in the Korean Society of Mechanical Engineers, Jeju, Korea.

67. Jeongsan Kim, Iljeok Kim, Jungchan Kim, Taegyu Choi, and Seungchul Lee, 2021, "Deep Learning-based Planetary Gear Fault Diagnosis using Frequency Domain Averaging," Reliability Engineering Division in the Korean Society of Mechanical Engineers, Jeju, Korea.

68. Jong Hwan Lee, Eon Ho Im, Hyuck Cheol Kwon, Jea Sook Chung, and Seungchul Lee, 2021, "Deep Learning-based Analysis on Physical Factors of Bar Warping in Rolling Process," Reliability Engineering Division in the Korean Society of Mechanical Engineers, Jeju, Korea.

69. Il Joo Jeong and Seungchul Lee, 2021, "Wafer Map Failure Pattern Classification using Geometrical Transformation Invariant Convolutional Neural Network," Reliability Engineering Division in the Korean Society of Mechanical Engineers, Jeju, Korea.

70. Gyuwon Kim, Jongbeom Kim, Woo June Choi, Chulhong Kim, and Seungchul Lee, 2021, "Integrated Deep Learning Framework for Accelerated Optical Coherence Tomography Angiography," Bio Engineering Division in the Korean Society of Mechanical Engineers, Online.

71. Keonhyeok Park, Hyeonji Kim, Gyuwon Kim, Jinah Jang and Seungchul Lee, 2021, "Morphological Prediction of hTMSCs Lineage to Keratocyte using Deep Learning," Bio Engineering Division in the Korean Society of Mechanical Engineers, Online.

2020

72. Changyun Choi, Kyoungseok Noh, Kwang Woo Jeon, Seungchul Lee, Jae-Kwan Ryu and Hyun-Joon Chung, 2020, “AI-based Prediction of Gait Speed Using Insole Pressure Data,” Bio Engineering Division in the Korean Society of Mechanical Engineers, Gangneung, Korea. (Best Poster Award)

73. Seungchul Lee, 2020, “AI for Healthcare in Industrial AI Lab.,” The Korean Society for Prognostics & Health Management, Seoul, Korea.

74. Seungchul Lee, 2020, “PHM and Industrial AI,” The Korean Society for Prognostics & Health Management, Seoul, Korea. (invited)

75. 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.

76. 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.

77. 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.

78. 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.

79. Seungchul Lee, 2020, “Industrial AI,” Reliability Division in the Korean Society of Mechanical Engineers, Jeju, Korea.

80. 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.

81. 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.

82. 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.

83. 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.

84. 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

85. 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.

86. 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)

87. 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.

2019

88. 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.

89. 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.

90. Seungchul Lee, 2019, “Artificial Intelligence Applications to Mechanical Engineering,” The Korean Society of Mechanical Engineers, Jeju, Korea.

91. 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.

92. 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.

93. 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.

94. 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.

95. 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.

96. 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.

97. 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.

98. Seungchul Lee, 2019, “Deep Learning for Power Plant Equipment,”The Korean Society of PressureVessels and Piping, Changwon, Korea. (Invited)

99. Juwon Na, Seungchul Lee, 2019, “AI-based Recommender System for Process Conditions of Injection Molding,” The Korean Society of Die & Mold Engineers, Incheon, Korea

100. 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.

101. 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.

102. 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.

103. 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.

104. 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.

105. 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.

106. 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.

107. 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.

108. 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.

109. 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.

110. 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.

111. 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.

2017

112. S. Kim, and S. Lee, 2017, “Artificial Intelligence in Mechanical Engineering,” CAE and Applied Mechanics Division of KSME conference, Busan, Korea

113. 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.

114. 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.

115. 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.

116. 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.

117. 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.

118. 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.

119. 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.

120. 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.

121. 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.

2016

122. S. Lee, 2016, “Mechanical Systems with Artificial Intelligence,” the Korean Society of Mechanical Engineers 2016, Jeongseon, Korea, Invited.

123. 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)

124. 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.

125. 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.

2015

126. 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.

127. 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.

128. 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.

129. 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.

2014

130. 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.

131. S. Lee, 2014, “Intelligent Fault Detection and Prediction System on Wind Turbine Gearboxes,” The Korean Society for Noise and Vibration Engineering, Gangchon, Korea.

132. S. Lee, 2014, “Diagnostics of Automated Manufacturing Processes Using Event Time Durations,” Korean Society of CAD CAM Engineers, Pyeongchang, Korea.