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

页数：3

共 50 条

[31] Prediction of subcellular localization of proteins using machine learning techniques and evolutionary information
Raghava, G. P. S.
AMINO ACIDS, 2007, 33 (03) : X - XI
[32] Machine learning and Bayesian optimization for performance prediction of membrane fuel cells
Echabarri, Soufian
Do, Phuc
Vu, Hai-Canh
Bornand, Bastien
ENERGY AND AI, 2024, 17
[33] Machine Learning Assisted Tensile Strength Prediction and Optimization of Ti Alloy
Fatriansyah, Jaka Fajar
Aqila, Muhamad Rafi
Suhariadi, Iping
Federico, Andreas
Ajiputro, Dzaky Iman
Pradana, Agrin Febrian
Andreano, Yossi
Rizky, Muhammad Ali Yafi
Dhaneswara, Donanta
Lockman, Zainovia
Hur, Su-Mi
IEEE ACCESS, 2024, 12 : 119660 - 119670
[34] Antenna Structure Prediction and Optimization Based on Machine Learning and Grid Search
Huang, Chung-Hao
Tsao, Han-Hsiang
Hsu, Chang-Chen
Ali, Amir
Liao, Sen-Yu
2024 IEEE INTERNATIONAL WORKSHOP ON ELECTROMAGNETICS: APPLICATIONS AND STUDENT INNOVATION COMPETITION, IWEM 2024, 2024,
[35] Improve robustness of machine learning via efficient optimization and conformal prediction
Yan, Yan
AI MAGAZINE, 2024, 45 (02) : 270 - 279
[36] Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization
Liang, Mang
An, Bingxing
Li, Keanning
Du, Lili
Deng, Tianyu
Cao, Sheng
Du, Yueying
Xu, Lingyang
Gao, Xue
Zhang, Lupei
Li, Junya
Gao, Huijiang
BIOLOGY-BASEL, 2022, 11 (11):
[37] Application of machine learning in ultrasonic pretreatment of sewage sludge: Prediction and optimization
Zhang, Jie
Long, Zeqing
Ren, Zhijun
Xu, Weichao
Sun, Zhi
Zhao, He
Zhang, Guangming
Gao, Wenfang
ENVIRONMENTAL RESEARCH, 2024, 263
[38] Boosting ant colony optimization via solution prediction and machine learning
Sun, Yuan
Wang, Sheng
Shen, Yunzhuang
Li, Xiaodong
Ernst, Andreas T.
Kirley, Michael
COMPUTERS & OPERATIONS RESEARCH, 2022, 143
[39] A hybrid machine learning optimization algorithm for multivariable pore pressure prediction
Song Deng
HaoYu Pan
HaiGe Wang
ShouKun Xu
XiaoPeng Yan
ChaoWei Li
MingGuo Peng
HaoPing Peng
Lin Shi
Meng Cui
Fei Zhao
Petroleum Science, 2024, 21 (01) : 535 - 550
[40] Torrefied biomass quality prediction and optimization using machine learning algorithms
Naveed, Muhammad Hamza
Gul, Jawad
Khan, Muhammad Nouman Aslam
Naqvi, Salman Raza
Stepanec, Libor
Ali, Imtiaz
CHEMICAL ENGINEERING JOURNAL ADVANCES, 2024, 19

← 1 2 3 4 5 →