Efficient Discharge Waveform Distribution Measurement Using Active Machine Learning

被引：1

作者：

Xie, Yuting ^{[1
]}

Zhang, Ling ^{[1
]}

Chen, Junhui ^{[1
]}

Li, Da ^{[1
]}

Yang, Zhenzhong ^{[2
,3
]}

Ren, Dan ^{[2
,3
]}

Li, Er-Ping ^{[1
]}

机构：

[1] Zhejiang Univ, Coll Informat Sci & Elect Engn, Hangzhou, Peoples R China

[2] China Acad Engn Phys, Microsyst & Terahertz Res Ctr, Chengdu, Peoples R China

[3] China Acad Engn Phys, Inst Elect Engn, Mianyang, Sichuan, Peoples R China

来源：

2022 IEEE ELECTRICAL DESIGN OF ADVANCED PACKAGING AND SYSTEMS (EDAPS) | 2022年

关键词：

Near-field scanning (NFS); active learning; query-by-committee (QBC);

D O I：

10.1109/EDAPS56906.2022.9995150

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：

Near-field scanning (NFS) is a promising method to capture the current propagation in an electronic system through an automated scanning system. This article presents a novel and efficient measurement method for discharge waveform distribution based on active machine learning using NFS. Implicitly, the query-by-committee (QBC) active learning method is adopted to select scanning points with high uncertainty. The proposed approach is computationally efficient in real-time NFS, demonstrates higher reconstruction accuracy than random sampling using the same amount of sparse samples, and is much more efficient than full scanning.

引用

页数：3

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