| Rotating machinery diagnostics using deep learning on orbit plot images H Jeong, S Park, S Woo, S Lee Procedia Manufacturing 5, 1107-1118, 2016 | 85 | 2016 |
| Fault detection and identification method using observer-based residuals H Jeong, B Park, S Park, H Min, S Lee Reliability Engineering & System Safety 184, 27-40, 2019 | 44 | 2019 |
| An efficient explorative sampling considering the generative boundaries of deep generative neural networks G Jeon, H Jeong, J Choi Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4288-4295, 2020 | 9 | 2020 |
| Experimental study on the life prediction of servo motors through model-based system degradation assessment and accelerated degradation testing B Park, H Jeong, H Huh, M Kim, S Lee Journal of Mechanical Science and Technology 32, 5105-5110, 2018 | 8 | 2018 |
| Automatic correction of internal units in generative neural networks A Tousi, H Jeong, J Han, H Choi, J Choi Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 7 | 2021 |
| Distilled gradient aggregation: Purify features for input attribution in the deep neural network G Jeon, H Jeong, J Choi Advances in Neural Information Processing Systems 35, 26478-26491, 2022 | 5 | 2022 |
| Vision-Based Real-Time Layer Error Quantification for Additive Manufacturing H Jeong, M Kim, B Park, S Lee International Manufacturing Science And Engineering Conference 50732 …, 2017 | 5 | 2017 |
| Wavelet-like convolutional neural network structure for time-series data classification S Park, H Jeong, H Min, H Lee, S Lee Smart structures and systems 22 (2), 175-183, 2018 | 4 | 2018 |
| Deep learning based diagnostics of orbit patterns in rotating machinery H Jeong, S Woo, S Kim, S Park, H Kim, S Lee Annual Conference of the PHM Society 8 (1), 2016 | 4 | 2016 |
| Real-Time Monitoring System for Rotating Machinery with IoT-based Cloud Platform H Jeong, S Kim, S Woo, S Kim, S Lee Transactions of the Korean Society of Mechanical Engineers A 41 (6), 517-524, 2017 | 3 | 2017 |
| An unsupervised way to understand artifact generating internal units in generative neural networks H Jeong, J Han, J Choi Proceedings of the AAAI Conference on Artificial Intelligence 36 (1), 1052-1059, 2022 | 2 | 2022 |
| 기계공학에서의 인공지능 적용 사례 ST Park, HD Jeong, SC Lee Journal of the KSME 57 (3), 30-33, 2017 | 1 | 2017 |
| 기계학습을 이용한 이상진단 기술에 관련된 이슈 SC Lee, HC Min, HD Jeong Journal of KSNVE 25 (1), 16-21, 2015 | 1 | 2015 |
| Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling G Jeon, H Jeong, J Choi Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | | 2023 |
| On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network S Park, H Jeong, G Jeon, J Choi arXiv preprint arXiv:2207.03400, 2022 | | 2022 |
| Example-based Methods to Explain the Internal Generative Mechanism of Deep Generative Neural Networks H Jeong Ulsan National Institute of Science and Technology, 2022 | | 2022 |
| Empirical Study of the Decision Region and Robustness in Deep Neural Networks S Park, H Jeong, G Jeon, J Choi | | 2021 |
| of KIISE K Taeksoon, H Kim, Y Choi, J Kim, S Yoo, Y Uh, G Jeon, H Jeong, J Choi, ... 정보과학회지, 6, 2021 | | 2021 |
| Wavelet-like CNN Structure for Time-Series Data Classification S Park, H Jeong, H Min, S Lee PHM Society Asia-Pacific Conference 1 (1), 2017 | | 2017 |
| New Approach for Fault Identification using Residual-based Fault Diagnosis H Jeong, B Park, S Park, H Min, S Lee PHM Society Asia-Pacific Conference 1 (1), 2017 | | 2017 |
Giyoung JeonUlsan National Institute of Science and TechnologyZweryfikowany adres z unist.ac.kr
