Deep Learning Method for Prediction of Planar Radiating Sources from Near-Field Intensity Data-Reference-Cited by-同舟云学术

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Deep Learning Method for Prediction of Planar Radiating Sources from Near-Field Intensity Data

Published:2019-07 Issue: Volume: Page:
ISSN:
Container-title:2019 IEEE International Symposium on Electromagnetic Compatibility, Signal & Power Integrity (EMC+SIPI)
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Author:

Ma Hanzhi,Li Er-Ping,Schutt-Aine Jose,Cangellaris Andreas C.

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8822234/8825188/08825271.pdf?arnumber=8825271

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Surrogate Modeling With Complex-Valued Neural Nets for Signal Integrity Applications;IEEE Transactions on Microwave Theory and Techniques;2024-01

2. Prediction of automotive radiated emission using machine learning;2023 International Symposium on Electromagnetic Compatibility – EMC Europe;2023-09-04

3. Deep Reinforcement Learning-Based Ground-Via Placement Optimization for EMI Mitigation;IEEE Transactions on Electromagnetic Compatibility;2023-04

4. Machine Learning for EMC/SI/PI – Blackbox, Physics Recovery, and Decision Making;IEEE Electromagnetic Compatibility Magazine;2023

5. Field Deviation in Radiated Emission Measurement in Anechoic Chamber for Frequencies up to 60 GHz and Deep Learning-Based Correction;International Journal of Antennas and Propagation;2022-12-09

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