| Power flow control in multi-active-bridge converters: Theories and applications Y Chen, P Wang, H Li, M Chen 2019 IEEE Applied Power Electronics Conference and Exposition (APEC), 1500-1507, 2019 | 75 | 2019 |
| MagNet: A machine learning framework for magnetic core loss modeling H Li, SR Lee, M Luo, CR Sullivan, Y Chen, M Chen 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics …, 2020 | 47 | 2020 |
| MagNet: An open-source database for data-driven magnetic core loss modeling H Li, D Serrano, T Guillod, E Dogariu, A Nadler, S Wang, M Luo, V Bansal, ... 2022 IEEE Applied Power Electronics Conference and Exposition (APEC), 588-595, 2022 | 42 | 2022 |
| Why magnet: Quantifying the complexity of modeling power magnetic material characteristics D Serrano, H Li, S Wang, T Guillod, M Luo, V Bansal, NK Jha, Y Chen, ... IEEE Transactions on Power Electronics, 2023 | 32 | 2023 |
| Transfer learning methods for magnetic core loss modeling E Dogariu, H Li, DS López, S Wang, M Luo, M Chen 2021 IEEE 22nd Workshop on Control and Modelling of Power Electronics …, 2021 | 29 | 2021 |
| Neural network as datasheet: Modeling BH loops of power magnetics with sequence-to-sequence lstm encoder-decoder architecture D Serrano, H Li, T Guillod, S Wang, M Luo, CR Sullivan, M Chen 2022 IEEE 23rd Workshop on Control and Modeling for Power Electronics …, 2022 | 22 | 2022 |
| How MagNet: Machine learning framework for modeling power magnetic material characteristics H Li, D Serrano, T Guillod, S Wang, E Dogariu, A Nadler, M Luo, V Bansal, ... IEEE Transactions on Power Electronics, 2023 | 19 | 2023 |
| Magnet-AI: Neural network as datasheet for magnetics modeling and material recommendation H Li, D Serrano, S Wang, M Chen IEEE Transactions on Power Electronics, 2023 | 12 | 2023 |
| Machine learning methods for feedforward power flow control of multi-active-bridge converters M Liao, H Li, P Wang, T Sen, Y Chen, M Chen IEEE Transactions on Power Electronics 38 (2), 1692-1707, 2022 | 10 | 2022 |
| Predicting the BH loops of power magnetics with transformer-based encoder-projector-decoder neural network architecture H Li, D Serrano, S Wang, T Guillod, M Luo, M Chen 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), 1543-1550, 2023 | 9 | 2023 |
| Calculation of ferrite core losses with arbitrary waveforms using the composite waveform hypothesis T Guillod, JS Lee, H Li, S Wang, M Chen, CR Sullivan 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), 1586-1593, 2023 | 6 | 2023 |
| A simplified dc-bias injection method with mirror transformer for magnetic material characterization S Wang, D Serrano, H Li, A Lin, T Guillod, M Luo, CR Sullivan, M Chen 2023 IEEE Applied Power Electronics Conference and Exposition (APEC), 1565-1571, 2023 | 6 | 2023 |
| Interphase Resonance and Stability Analysis of Series-Capacitor Buck Converters P Wang, D Zhou, H Li, DM Giuliano, G Szczeszynski, S Allen, M Chen IEEE Transactions on Power Electronics 38 (5), 5680-5687, 2023 | 5 | 2023 |
| Machine learning methods for power flow control of multi-active-bridge converters M Liao, H Li, P Wang, Y Chen, M Chen 2021 IEEE 22nd Workshop on Control and Modelling of Power Electronics …, 2021 | 4 | 2021 |
| Circuits and magnetics co-design for ultra-thin vertical power delivery: A snapshot review Y Elasser, H Li, P Wang, J Baek, K Radhakrishnan, S Jiang, H Gan, ... MRS Advances 9 (1), 12-24, 2024 | 1 | 2024 |
| MagNet-AI: Neural network as datasheet for magnetics modeling and material recommendation M Chen, H Li, D Serrano, S Wang Authorea Preprints, 2023 | 1 | 2023 |
| A Simplified Dc-Bias Injection Method for Characterizing Power Magnetics using a Voltage Mirror Transformer S Wang, H Li, D Serrano, T Guillod, J Li, C Sullivan, M Chen IEEE Transactions on Power Electronics, 2024 | | 2024 |
| Investigating the Mutual Impact of Waveform, Temperature, and Dc-Bias on Magnetic Core Loss using Neural Network Models J Li, E Deleu, W Lee, H Li, M Chen, S Wang 2024 IEEE Applied Power Electronics Conference and Exposition (APEC), 391-395, 2024 | | 2024 |
| Multi-Material Power Magnetics Modeling with a Modular and Scalable Machine Learning Framework E Deleu, H Li, J Li, W Lee, T Guillod, CR Sullivan, S Wang, M Chen 2024 IEEE Applied Power Electronics Conference and Exposition (APEC), 370-377, 2024 | | 2024 |
| Quantifying the Complexity of Modeling Power Magnetic Material Characteristics M Chen Authorea Preprints, 2023 | | 2023 |
Minjie ChenAssistant Professor of Electrical Engineering, Princeton University在 princeton.edu 的电子邮件经过验证
