Publications

International Conferences

  • S. Park*, H. Jeong*, T. Djanibekov*, G. Jeon, J. Seol, and J. Choi, 2025 “On the Relationship between Populated Regions and Adversarial Robustness in Deep Neural Networks,” IEEE International Conference on Data Mining, ICDM-25. (*contributed equally)
  • G. Jeon, H. Jeong, and J. Choi, 2023 “Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling,” Proceedings of the IEEE/CVF International Conference on Computer Vision, ICCV-23.
  • G. Jeon*, H. Jeong*, and J. Choi, 2022 “Distilled gradient aggregation: Purify features for input attribution in the deep neural network,” Advances in Neural Information Processing Systems, NeurIPS-22. (*contributed equally)
  • H. Jeong, J. Han, and J. Choi, 2022 “An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks,” AAAI Conference on Artifact Intelligence, AAAI-22.
  • A. Tousi*, H. Jeong*, J. Han, H. Choi, and J. Choi, 2021, “Automatic Correction of Internal Units in Generative Neural Networks,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR21. (*contributed equally)
  • G. Jeon*, H. Jeong*, and J. Choi, 2020, “An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks,” AAAI Conference on Artificial Intelligence, AAAI-20. (*contributed equally, Oral)
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • S. Park, H. Jeong, H. Min, and S. Lee, 2015, “System Diagnostics using Kalman Filter Estimation Error,” The 3rd International Conference on Materials and Reliability, Jeju, Korea.

International Journals

  • 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.
  • H. Jeong, B. Park, S. Park, and S. Lee, 2018, “Fault Detection and Identification Method using Observerbased Residuals,” Reliability Engineering and System Safety, Vol. 184, pp 27-40.
  • 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.
  • 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.

Domestic Conferences

  • 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.
  • 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.
  • 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.
  • 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.
  • 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)
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Domestic Journals

  • 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]