Publications/Presentations

Peer-reviewed Journal Publications (†: Corresponding Author, #: Equal Contribution)

  1. Kim, Gyeongho, Jae Gyeong Choi, and Sunghoon Lim†. "Using Transformer and a Reweighting Technique to Develop a Remaining Useful Life Estimation Method for Turbofan Engines." Engineering Applications of Artificial Intelligence (Accepted for Publication)
  2. Kim, Gyeongho, Soyeon Park, Jae Gyeong Choi, Sang Min Yang, Hyung Wook Park, and Sunghoon Lim†. “Developing a Data-driven System for Grinding Process Parameter Optimization Using Machine Learning and Metaheuristic Algorithms.” CIRP Journal of Manufacturing Science and Technology 51 (2024): 20-35.
  3. Choi, Jae Gyeong#, Dong Chan Kim#, Miyoung Chung#, Sunghoon Lim†, and Hyung Wook Park†. “Multimodal 1D CNN for delamination prediction in CFRP drilling process with industrial robots.” Computers & Industrial Engineering 190 (2024): 110074.
  4. Kim, Gyeongho, Sang Min Yang, Sinwon Kim, Do Young Kim, Jae Gyeong Choi, Hyung Wook Park, and Sunghoon Lim†. "A multi-domain mixture density network for tool wear prediction under multiple machining conditions." International Journal of Production Research (2023): https://doi.org/10.1080/00207543.2023.2289076
  5. Kim, Gyeongho#, Sang Min Yang#, Dong Min Kim, Sinwon Kim, Jae Gyeong Choi, Minjoo Ku, Sunghoon Lim†, and Hyung Wook Park†. "Bayesian-Based Uncertainty-Aware Tool-Wear Prediction Model in End-Milling Process of Titanium Alloy." Applied Soft Computing 148 (2023): 110922.
  6. Vania, Malinda#, Bayu Adhi Tama#, Hasan Maulahela, and Sunghoon Lim†. “Recent Advances in Applying Machine Learning and Deep Learning to Detect Upper Gastrointestinal Tract Lesions.” IEEE Access 11 (2023): 66544 - 66567.
  7. Kim, Gyeongho, Jae Gyeong Choi, Minjoo Ku, and Sunghoon Lim†. "Developing a semi-supervised learning and ordinal classification framework for quality level prediction in manufacturing." Computers & Industrial Engineering 181 (2023): 109286.
  8. Tama, Bayu Adhi, Malinda Vania, Seungchul Lee†, and Sunghoon Lim†. "Recent Advances in the Application of Deep Learning Techniques for Fault Detection Using Vibration Signals: A Systematic Review." Artificial Intelligence Review 5 (2023): 4667–4709.
  9. Kim, Gyeongho#, Dong-hyun Shin#, Jae Gyeong Choi, and Sunghoon Lim†. "A Deep Learning-Based Cryptocurrency Price Prediction Model That Uses On-chain Data." IEEE Access 10 (2022): 56232 -56248.
  10. Kim, Kyudong, Heena No, Kijung Park†, Hyun Woo Jeon†, and Sunghoon Lim. "Characterization of Power Demand and Energy Consumption for Fused Filament Fabrication Using CFR-PEEK." Rapid Prototyping Journal 28, no. 7 (2022): 1394-1406.
  11. Kim, Gyeongho, and Sunghoon Lim†. "Development of an Interpretable Maritime Accident Prediction System Using Machine Learning Techniques." IEEE Access 10 (2022): 41313 - 41329.
  12. Tama, Bayu Adhi#, Malinda Vania#, Iljung Kim, and Sunghoon Lim†. "An EfficientNet-based Weighted Ensemble Model for Industrial Machine Malfunction Detection Using Acoustic Signals." IEEE Access 10 (2022): 34625-34636.
  13. Hwang, Seong Wook, and Sunghoon Lim†. “The Charging Infrastructure Design Problem with Electric Taxi Demand Prediction Using Convolutional LSTM.” European Journal of Industrial Engineering 16, no. 6 (2022): 1.
  14. Chatterjee, Sujoy, and Sunghoon Lim†. "A TOPSIS-inspired ranking method using constrained crowd opinions for urban planning." Entropy 24, no. 3 (2022): 371.
  15. Kim, Gyeongho#, Jae Gyeong Choi#, Minjoo Ku, Hyewon Cho, and Sunghoon Lim†. "A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process." IEEE Access 9 (2021): 132455-132467.
  16. Tuarob, Suppawong, Poom Wettayakorn, Ponpat Phetchai, Siripong Traivijitkhun, Sunghoon Lim, Thanapon Noraset, and Tipajin Thaipisutikul†. "DAViS: A Unified Solution for Data Collection, Analyzation, and Visualization in Real-time Stock Market Prediction." Financial Innovation 7 (2021): 56.
  17. Lim, Sunghoon†, Sun Jun Kim, YoungJae Park, and Nahyun Kwon. "A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic." Expert Systems with Applications 184 (2021): 115532.
  18. Choi, Jae Gyeong, Chan Woo Kong, Gyeongho Kim, and Sunghoon Lim†. "Car crash detection using ensemble deep learning and multimodal data from dashboard cameras." Expert Systems with Applications 183 (2021): 115400.
  19. Tama, Bayu Adhi, and Sunghoon Lim†. "Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation." Computer Science Review 39 (2021): 100357.
  20. Nkenyereye, Lewis, Bayu Adhi Tama, and Sunghoon Lim†. "A Stacking-based Deep Neural Network Approach for Effective Network Anomaly Detection." Computers, Materials & Continua 66, no. 2 (2021): 2217-2227.
  21. Tama, Bayu Adhi, and Sunghoon Lim†. "A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems." Mathematics 8, no. 10 (2020): 1814.
  22. Chatterjee, Sujoy, and Sunghoon Lim†. "A Multi-objective Differential Evolutionary Method for Constrained Crowd Judgment Analysis." IEEE Access 8 (2020): 87647-87664.
  23. Lim, Sunghoon, and Conrad S. Tucker†. "Mining Twitter data for causal links between tweets and real-world outcomes." Expert Systems with Applications: X 3 (2019): 100007.
  24. Lim, Sunghoon, Conrad S. Tucker†, Kathryn Jablokow, and Bart Pursel. "A semantic network model for measuring engagement and performance in online learning platforms." Computer Applications in Engineering Education 26, no. 5 (2018): 1481-1492.
  25. Tuarob, Suppawong, Sunghoon Lim, and Conrad S. Tucker†. "Automated Discovery of Product Feature Inferences within Large Scale Implicit Social Media Data."  Journal of Computing and Information Science in Engineering 18, no. 2 (2018): 021017.
  26. Lim, Sunghoon, and Conrad S. Tucker†. "Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers." Journal of Mechanical Design 139, no. 11 (2017): 111409.
  27. Lim, Sunghoon, Conrad S. Tucker†, and Soundar Kumara. "An unsupervised machine learning model for discovering latent infectious diseases using social media data." Journal of Biomedical Informatics 66 (2017): 82-94.  (PubMed)
  28. Lim, Sunghoon, and Conrad S. Tucker†. "A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data." Journal of Mechanical Design 138, no. 6 (2016): 061403.

 

Peer-reviewed Journal Publications: Under Review (†: Corresponding Author, #: Equal Contribution)

  1. Choi, Jae Gyeong, Dong Chan Kim, Miyoung Chung, Gyeongho Kim, Hyung Wook Park, and Sunghoon Lim†. “BLIND” Expert Systems with Applications (Under Review)
  2. Jeon, Sujin, Soyeon Park, Joonbum Bae, and Sunghoon Lim†, “Applying multistep classification techniques to recognize static and dynamic hand gestures in a soft sensor-embedded glove.” IEEE Sensors Journal (Under Review)
  3. Kim, Gyeongho#, Sang Min Yang#, Dong Min Kim, Jae Gyeong Choi, Sunghoon Lim†, and Hyung Wook Park†. "Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V." Journal of Manufacturing Systems (Under Review)

 

Peer-reviewed Conference Proceedings (†: Corresponding Author)

  1. Kim, Gyeongho, Sang Min Yang, Sinwon Kim, Dong Min Kim, Sunghoon Lim†, and Hyung Wook Park†. “Tool Wear Prediction in the End Milling Process of Ti-6Al-4V using Bayesian Learning.” In 2022 International Conference on Advanced Mechatronic Systems, Institute of Electrical and Electronics Engineers (IEEE), 2022.
  2. Chatterjee, Sujoy, and Sunghoon Lim†. “A TOPSIS-based Multi-objective Model for Constrained Crowd Judgment Analysis”, In Eighth AAAI Human Computation and Crowdsourcing (HCOMP-2020), 2020. [Presentation/Poster]
  3. Lim, Sunghoon, Conrad S. Tucker†, Kathryn Jablokow, and Bart Pursel. "Quantifying the Mismatch between Course Content and Students’ Dialogue in Online Learning Environments." In ASME 2017 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2017. [DEC Technical Committee Best Paper Award]

 

International Conference Presentations (*: Presenter, †: Corresponding Author)

  1. Choi, Jae Gyeong*, Dong Chan Kim, Miyoung Chung, Hyung Wook Park, and Sunghoon Lim†. "BLIND" IISE Annual Conference & Expo 2023, New Orleans, Louisiana, 2023.
  2. Jeon, Sujin*, Soyeon Park, Hyewon Cho, and Sunghoon Lim†. “Hand gesture recognition without-of-distribution gesture detection using a soft sensor embedded glove." IISE Annual Conference & Expo 2023, New Orleans, Louisiana, 2023.
  3. Kim, Gyeongho*, and Sunghoon Lim†. “Development of a Deep Learning-based Uncertainty-aware Predictive Maintenance Method." IISE Annual Conference & Expo 2023, New Orleans, Louisiana, 2023.
  4. Cho, Hyewon*, Nurbolat Aimakov, Inwoo Park, Myeonghoon Choi, Yerim Kim, Geosong Na, Sunghoon Lim, and Woonggyu Jung†. "Glomerulus quantification with deep learning based on novel multi-modal label-free quantitative phase imaging from a near-infrared (Conference Presentation)." In Quantitative Phase Imaging IX, p. PC123890A. SPIE, 2023.
  5. Kim, Gyeongho*, Sang Min Yang, Sinwon Kim, Dong Min Kim, Sunghoon Lim†, Hyung Wook Park†. “Tool Wear Prediction in the End Milling Process of Ti-6Al-4V using Bayesian Learning.” 2022 International Conference on Advanced Mechatronic Systems, Toyama, Japan, 2022.
  6. Ku, Minjoo*, Gyeongho Kim, and Sunghoon Lim†. “Developing a quality level prediction framework with semi-supervised learning and ordinal classification for UV lamps.” IISE Annual Conference & Expo 2022, Seattle, Washington, 2022.
  7. Cho, Hyewon*, Sujin Jeon, and Sunghoon Lim†. “Type 2 Diabetes Risk Scoring via Bayesian Neural Networks.” IISE Annual Conference & Expo 2022, Seattle, Washington, 2022.
  8. Hwang, Seong Wook*, and Sunghoon Lim†. “The Charging Infrastructure Design Problem with Electric Taxi Demand Prediction Using Convolutional LSTM.” INFORMS Annual Meeting, Seattle, Washington, 2019.

 

Domestic Conference Presentations (*: Presenter, †: Corresponding Author)            

  1. Park, Soyeon*, and Sunghoon Lim†. “Lightweight anomalous object detection in a fixed-camera environment.” KIIE Fall Conference, Ulsan, Republic of Korea, 2023.
  2. Kim, Gyeongho*, Sang Min Yang, Sin Won Kim, Do Young Kim, Jae Gyeong Choi, Hyung Wook Park, and Sunghoon Lim†. "Deep Learning-based Tool Wear Prediction under Multiple Machining Conditions." PHM Korea 2023, Seoul, Republic of Korea, 2023.
  3. Kim, Gyeongho*, Soyeon Park, Jae Gyeong Choi, Hyeokjoon Choi, and Sunghoon Lim†.“Grinding Process Parameter Optimization Using Machine Learning Techniques.” Korea Data Mining Society Summer Conference, Gangneung, Republic of Korea, 2023.
  4. Kim, Gyeongho*, Sangmin Yang, and Sunghoon Lim†. “Development of a Bayesian-based Uncertainty-aware Tool Wear Prediction Model in the End Milling Process.” KIIE Fall Conference, Incheon, Republic of Korea, 2022.
  5. Ku, Minjoo*, and Sunghoon Lim†. “Development of a deep learning-based anomaly detection model using multivariate time series manufacturing data.” KIIE Fall Conference, Incheon, Republic of Korea, 2022.
  6. Jeon, Sujin*, Soyeon Park, Hyewon Cho, and Sunghoon Lim†. “Multistep classification of static and dynamic finger gestures using a soft sensor embedded glove.” KIIE Fall Conference, Incheon, Republic of Korea, 2022.
  7. Kim, Gyeongho*, and Sunghoon Lim†. “Development of a Remaining Useful Life Estimation Method Using Transformer and a Reweighting Technique.” Korea Data Mining Society Summer Conference, Busan, Republic of Korea, 2022.
  8. Cho, Hyewon*, Sujin Jeon, Soyeon Park, and Sunghoon Lim†. “Development of a deep learning-based real-time gesture detection and classification model using a wearable sensing glove.” KIIE/KORMS Joint Spring Conference, Jeju, Republic of Korea, 2022.
  9. Choi, Jae Gyeong*, Dong Chan Kim, Miyoung Chung, Sunghoon Lim†, and Hyung Wook Park†. “BLIND”  KIIE/KORMS Joint Spring Conference, Jeju, Republic of Korea, 2022.
  10. Choi, Jae Gyeong*, Chan Woo Kong, Gyeongho Kim, and Sunghoon Lim†. “Car crash detection using ensemble deep learning and multimodal data from dashboard cameras.” Korea Safety Management & Science Fall Conference, Ulsan, Republic of Korea, 2021.
  11. Kim, Gyeongho, Jae Gyeong Choi, Minjoo Ku, Hyewon Cho, and Sunghoon Lim*,†. “Developing a deep learning-based fault detection model for plastic injection molding for car parts companies.” KSQM Spring Conference, Seoul, Republic of Korea, 2021.
  12. Kim, Sun Jun*, and Sunghoon Lim†. “A deep learning-based hybrid recommender system with fake review filtering for e-commerce customers.” KIIE Fall Conference, Seoul, Republic of Korea, 2020.
  13. Choi, Jae Gyeong*, Chan Woo Kong, and Sunghoon Lim†. “Developing machine-learning-based car crash detection systems
using video and audio data.” KIIE Fall Conference, Seoul, Republic of Korea, 2019.
  14. Baek, DaeSeon, and Sunghoon Lim*,†. “Smart farming: Developing growth programs and reforming environmental conditions for hog raising using machine vision and deep learning.” KIIE/KORMS/KSS Joint Spring Conference, Gwangju, Republic of Korea, 2019.

 

Patents          

  1. Lim, Sunghoon, Sujin Jeon, Soyeon Park, and Joonbum Bae. “Static and dynamic gesture recognition device using a soft sensor embedded glove and method thereof (소프트 센서가 부착된 장갑을 이용한 정적 및 동적 제스처 인식 장치 및 방법).” Pending, 2023.
  2. Lim, Sunghoon, Jae Gyeong Choi, Sun Jun Kim, Minjoo Ku. “SYSTEM AND METHOD FOR ESTIMATING THREE-DIMENSIONAL POSE USING VISIBILITY SCORE (가시성 지표를 활용한 3차원 포즈 추정 시스템 및 방법).” Pending, 2022.
  3. Kweon, Sang Jin, Yong Ung Kwon, and Sunghoon Lim. “DEVICE AND METHOD TO PREDICT MUSCLE INJURY DURING REPEATITIVE WORKING ACTIVITY OF WORKER (근로자의 반복적인 근무 활동 동안 근육 부상을 예측하는 방법 및 장치).” 10-2485242.
  4. Kweon, Sang Jin, Yong Ung Kwon, and Sunghoon Lim. “METHOD AND DEVICE FOR REHABILITATION TO PREVENT INJURIES DUE TO REPEATED ROTATION MOTION (반복 회전동작으로 인한 부상 방지 재활 방법 및 부상 방지 재활 장치).” 10-2413185.

 

Books     

  1. Chatterjee, Sujoy, Thipendra P Singh, Sunghoon Lim, Anirban Mukhopadhyay. “Social Media and Crowdsourcing: Application and Analytics.” CRC Press, 2023.
    9781032386874
  2. 김일중, 유승화, 임성훈, 김흥남. “제조AI빅데이터 분석기법.” 보민출판사, 2022.
    1650268435716.1_iqO_Screenshot_2022-04-18_at_16.52.10

 

Columns     

  1. Lim, Sunghoon. “AI and reformation of manufacturing cities, Pittsburgh and Ulsan (인공지능과 제조도시의 재도약, 피츠버그와 울산).” UNIST Magazine 2021 Autumn, 2021. [Link (English/Korean)]
    1