| Application of monitoring, diagnosis, and prognosis in thermal performance analysis for nuclear power plants H Kim, MG Na, G Heo Nuclear Engineering and Technology 46 (6), 737-752, 2014 | 62 | 2014 |
| Reliability data update using condition monitoring and prognostics in probabilistic safety assessment H Kim, SH Lee, JS Park, H Kim, YS Chang, G Heo Nuclear Engineering and Technology 47 (2), 204-211, 2015 | 56 | 2015 |
| Smart support system for diagnosing severe accidents in nuclear power plants KH Yoo, JH Back, MG Na, S Hur, H Kim Nuclear Engineering and Technology, 2018 | 48 | 2018 |
| A methodology for diagnosing FAC induced pipe thinning using accelerometers and deep learning models YH Chae, SG Kim, H Kim, JT Kim, PH Seong Annals of Nuclear Energy 143, 107501, 2020 | 43 | 2020 |
| Diagnosis of feedwater heater performance degradation using fuzzy inference system YK Kang, H Kim, G Heo, SY Song Expert Systems with Applications 69, 239-246, 2017 | 35 | 2017 |
| Failure rate updates using condition-based prognostics in probabilistic safety assessments H Kim, JT Kim, G Heo Reliability Engineering & System Safety 175, 225-233, 2018 | 30 | 2018 |
| Recent research towards integrated deterministic-probabilistic safety assessment in Korea G Heo, S Baek, D Kwon, H Kim, J Park Nuclear Engineering and Technology 53 (11), 3465-3473, 2021 | 28 | 2021 |
| Probabilistic deep learning model as a tool for supporting the fast simulation of a thermal–hydraulic code S Ryu, H Kim, SG Kim, K Jin, J Cho, J Park Expert Systems with Applications 200, 116966, 2022 | 27 | 2022 |
| Application of a Deep Learning Technique to the Development of a Fast Accident Scenario Identifier. H KIM, J CHO, J PARK IEEE Access, 2020 | 22 | 2020 |
| Prognostics for integrity of steam generator tubes using the general path model H Kim, JT Kim, G Heo Nuclear Engineering and Technology 50 (1), 88-96, 2018 | 12 | 2018 |
| Application of particle filtering for prognostics with measurement uncertainty in nuclear power plants G Kim, H Kim, E Zio, G Heo Nuclear Engineering and Technology 50 (8), 1314-1323, 2018 | 10 | 2018 |
| An approach to constructing effective training data for a classification model to evaluate the reliability of a passive safety system K Jin, H Kim, S Ryu, S Kim, J Park Reliability Engineering & System Safety 222, 108446, 2022 | 6 | 2022 |
| Influence of the spatial Pu variation for evaluating the Pu content in spent nuclear fuel using Support Vector Regression SM Woo, H Kim, SS Chirayath Annals of Nuclear Energy 135, 106997, 2020 | 6 | 2020 |
| Diagnosis of Feedwater Heater Performance Degradation using Fuzzy Approach H Kim, YK Kang, G Heo, SY Song | 6 | 2014 |
| Preprocessing Energy Intervals on Spectrum for Real-Time Radionuclide Identification I Kwon, D Shin, J Oh, CH Kim, H Kim IEEE Transactions on Nuclear Science 68 (8), 2202-2209, 2021 | 5 | 2021 |
| Reproduction strategy of radiation data with compensation of data loss using a deep learning technique W Cho, H Kim, D Kim, SH Kim, I Kwon Nuclear Engineering and Technology 53 (7), 2229-2236, 2021 | 5 | 2021 |
| Enhancing the Explainability of AI Models in Nuclear Power Plants with Layer-wise Relevance Propagation SG Kim, S Ryu, H Kim, K Jin, J Cho Proceedings of the Transactions of the Korean Nuclear Society Virtual Autumn …, 2021 | 4 | 2021 |
| Prognostics for steam generator tube rupture using Markov chain model G Kim, H Kim, G Heo Korean Nuclear Society 2, 2016 | 4 | 2016 |
| Data-Driven prognostics for major piping in nuclear power plants G Kim, H Kim, YS Chang, S Jung, G Heo PHM Society Asia-Pacific Conference 1 (1), 2017 | 3 | 2017 |
| Survey on prognostics techniques for updating initiating event frequency in PSA H Kim, G Heo Korean Nuclear Society 1, 2015 | 3 | 2015 |
Gyunyoung HeoKyung Hee University確認したメール アドレス: khu.ac.kr