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IEEE Transactions on Electromagnetic Compatibility最新文献

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IF 2 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-19 DOI: 10.1109/TEMC.2024.3504888
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引用次数: 0
IEEE Electromagnetic Compatibility Society Information IEEE电磁兼容协会信息
IF 2 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-19 DOI: 10.1109/TEMC.2024.3504875
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引用次数: 0
Toward Enhancing Soil Resistivity Measurement and Modelling for Limited Interelectrode Spacing 加强有限电极间距土壤电阻率测量与建模
IF 2.1 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-18 DOI: 10.1109/temc.2024.3514772
Omar Kherif, Stephen Robson, Salah Mousa, Noureddine Harid, Huw Griffiths, David Thorpe, Abderrahmane Haddad
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引用次数: 0
Response of Metal-Oxide Varistors Excited by Consecutive Early-Time and Intermediate-Time HEMP Conducted Currents 金属氧化物压敏电阻在连续早期和中期HEMP传导电流激励下的响应
IF 2.1 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-18 DOI: 10.1109/temc.2024.3514685
Dao-zhong Zhang, Yan-zhao Xie, Yi Zhou, Yun-peng Zhang
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引用次数: 0
Practice and Evaluation for Ceiling-Mounted MIMO Indoor Base Stations With Antenna Optimization and SAR Touch Compliance 天线优化与SAR触控的吸顶式MIMO室内基站实践与评价
IF 2.1 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-18 DOI: 10.1109/temc.2024.3511700
Wenfu Fu, Stanislav Stefanov Zhekov, Carla Di Paola, Sailing He, Bo Xu
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引用次数: 0
Passive Intermodulation Model Based on Conductors Cross-Section for Printed Transmission Line 基于印刷传输线导体截面的无源互调模型
IF 2.1 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-16 DOI: 10.1109/temc.2024.3511254
Mingtai Li, Tuanjie Li
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引用次数: 0
Design of an LISN for Low-Frequency Conducted Emissions Measurement 设计用于低频传导发射测量的 LISN
IF 2.1 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-16 DOI: 10.1109/temc.2024.3510390
Lu Wan, Arun D. Khilnani, Xinglong Wu, Xiaokang Liu, Sergio A. Pignari, David W. P. Thomas, Mark Sumner, Flavia Grassi
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引用次数: 0
MIMO Performance and Uncertainty Analysis in a Reverberation Chamber With Phase Stirring 相位搅拌混响室内MIMO性能及不确定度分析
IF 2.1 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-13 DOI: 10.1109/temc.2024.3507287
Yifan Wang, Hanzhi Ma, Gangzha Liu, Er-Ping Li
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引用次数: 0
GHz-Range Modeling of Power Integrity in an Array of Simultaneously Switching Power Converters 同时开关的功率转换器阵列的 GHz 范围功率完整性建模
IF 2.1 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-13 DOI: 10.1109/temc.2024.3509375
Gabriel Nobert, Nicolas G. Constantin, Yves Blaquière
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引用次数: 0
Intelligent Prediction Method for Radiation Emission of Printed Circuit Board by Using Deep Learning 基于深度学习的印刷电路板辐射发射智能预测方法
IF 2 3区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-12-13 DOI: 10.1109/TEMC.2024.3491822
Kai Wei;Dan Shi;Chengyu Li;Yanchi Liu;Na Sun;Dan Xiao;Xingguo Jiang
This article introduces a novel deep learning approach, utilizing a seq2seq model, to predict far-field radiation emissions (REs) in high-density, multilayer, and complex printed circuit boards (PCBs). To address the challenges of weak long-term dependencies and information ambiguity arising from excessively long network sequences in RE prediction for complex PCBs, our method begins by tokenizing and modeling each network combination. These combinations are then fed into the seq2seq model for training, supplemented with a multihead attention mechanism to enhance information connectivity. To validate the model's accuracy and efficiency, we compared our prediction results with those obtained from traditional full-wave electromagnetic simulations. Our approach significantly reduces the RE prediction time for the entire board from several hours to nearly one second. Moreover, the model exhibits high performance in RE prediction, with a mean absolute error of approximately 0.001 V/m. To verify the model's reliability, we applied the trained model to predict RE for brand-new entire boards and key networks, demonstrating its ability to accurately predict RE in previously unseen PCBs. Therefore, this method can be implemented for intelligent, full-automatic RE prediction in practical applications.
本文介绍了一种新的深度学习方法,利用seq2seq模型来预测高密度、多层和复杂印刷电路板(pcb)中的远场辐射发射(REs)。为了解决在复杂pcb的RE预测中由过长的网络序列引起的弱长期依赖性和信息模糊性的挑战,我们的方法首先对每个网络组合进行标记和建模。然后将这些组合输入seq2seq模型进行训练,并辅以多头注意机制来增强信息连通性。为了验证模型的准确性和效率,我们将模型的预测结果与传统的全波电磁模拟结果进行了比较。我们的方法显著地将整个电路板的RE预测时间从几个小时减少到近一秒。此外,该模型在RE预测中表现出很高的性能,平均绝对误差约为0.001 V/m。为了验证模型的可靠性,我们将训练好的模型应用于预测全新整个电路板和关键网络的RE,证明其能够准确预测以前未见过的pcb的RE。因此,该方法在实际应用中可以实现智能化、全自动的RE预测。
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引用次数: 0
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IEEE Transactions on Electromagnetic Compatibility
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