Machine Learning for Complex EMI Prediction, Optimization and Localization

被引：0
|
作者：
Jin, Hang [1 ]
Zhang, Le [1 ]
Ma, Han-Zhi [1 ]
Yang, Si-Chen [1 ]
Yang, Xiao-Li [1 ]
Li, Er-Ping [1 ]
机构：
[1] Zhejiang Univ, Coll Informat Sci & Elect Engn, Key Lab Adv Micro Nano Elect Devices & Smart Syst, Hangzhou 310027, Zhejiang, Peoples R China
来源：
2017 IEEE ELECTRICAL DESIGN OF ADVANCED PACKAGING AND SYSTEMS SYMPOSIUM (EDAPS) | 2017年
基金：
中国国家自然科学基金;
关键词：
Artificial Intelligence; Bayesian optimization algorithm; deep neural network; electromagnetic interference; machine learning; sources localization; NEURAL-NETWORKS; DESIGN;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The electromagnetic interference (EMI) problem of extra-high speed electronic devices and systems is becoming more complex with an increase of operating frequency. The conventional analysis and design methods could not cope with the current EMI problems. Advanced analysis and design methods are desired. Deep neural network (DNN) and Bayesian optimization algorithm (BOA) based on machine learning are utilized in prediction of EMI radiation, optimization of design parameters and localization of EMI sources. The feasibility of DNN and BOA is investigated and validated. The steps of using DNN and BOA are proposed in the paper.
引用
收藏
页数：3
相关论文
共 50 条
  • [11] Enhancing property prediction and process optimization in building materials through machine learning: A review
    Stergiou, Konstantinos
    Ntakolia, Charis
    Varytis, Paris
    Koumoulos, Elias
    Karlsson, Patrik
    Moustakidis, Serafeim
    COMPUTATIONAL MATERIALS SCIENCE, 2023, 220
  • [12] Prediction of Protein Subcellular Localization using Machine Learning
    Upama, Paramita Basak
    Akhter, Shahin
    Bin Asad, Mohammad Imam Hasan
    2018 4TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2018,
  • [13] An Adaptive Framework for Optimization and Prediction of Air Traffic Management (Sub-)Systems with Machine Learning
    Reitmann, Stefan
    Schultz, Michael
    AEROSPACE, 2022, 9 (02)
  • [14] Optimization and comparison of machine learning algorithms for the prediction of the performance of football players
    Gianluca Morciano
    Andrea Zingoni
    Giuseppe Calabrò
    Neural Computing and Applications, 2024, 36 (31) : 19653 - 19666
  • [15] Clinical validation and optimization of machine learning models for early prediction of sepsis
    Liu, Xi
    Li, Meiyi
    Liu, Xu
    Luo, Yuting
    Yang, Dong
    Hui, Ouyang
    He, Jiaoling
    Xia, Jinyu
    Xiao, Fei
    FRONTIERS IN MEDICINE, 2025, 12
  • [16] Shale oil production prediction and fracturing optimization based on machine learning
    Lu, Chunhua
    Jiang, Hanqiao
    Yang, Jinlong
    Wang, Zhiqiang
    Zhang, Miao
    Li, Junjian
    JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2022, 217
  • [17] Performance Prediction and Experimental Optimization Assisted by Machine Learning for Organic Photovoltaics
    Zhao, Zhi-Wen
    Geng, Yun
    Troisi, Alessandro
    Ma, Haibo
    ADVANCED INTELLIGENT SYSTEMS, 2022, 4 (06)
  • [18] Machine learning-assisted early ignition prediction in a complex flow
    Popov, Pavel P.
    Buchta, David A.
    Anderson, Michael J.
    Massa, Luca
    Capecelatro, Jesse
    Bodony, Daniel J.
    Freund, Jonathan B.
    COMBUSTION AND FLAME, 2019, 206 : 451 - 466
  • [19] Machine Learning for Endometrial Cancer Prediction and Prognostication
    Bhardwaj, Vipul
    Sharma, Arundhiti
    Parambath, Snijesh Valiya
    Gul, Ijaz
    Zhang, Xi
    Lobie, Peter E.
    Qin, Peiwu
    Pandey, Vijay
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [20] Machine Learning for Earth System Observation and Prediction
    Bonavita, Massimo
    Arcucci, Rossella
    Carrassi, Alberto
    Dueben, Peter
    Geer, Alan J.
    Le Saux, Bertrand
    Longepe, Nicolas
    Mathieu, Pierre-Philippe
    Raynaud, Laure
    BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2021, 102 (04) : E710 - E716
12345