Semi-supervised variational bi-directional sampling on multi-class imbalanced electric power data for fault diagnosis-Reference-Cited by-同舟云学术

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Semi-supervised variational bi-directional sampling on multi-class imbalanced electric power data for fault diagnosis

Published:2024-01 Issue: Volume:155 Page:109512
ISSN:0142-0615
Container-title:International Journal of Electrical Power & Energy Systems
language:en
Short-container-title:International Journal of Electrical Power & Energy Systems

Author:

Qin Jia^ORCID,Yang Dongsheng^ORCID,Zhou Bowen^ORCID,Sun Yunhe

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Reference37 articles.

1. Learning from imbalanced data: Open challenges and future directions;Krawczyk;Progress Artif Intell,2016

2. Learning from imbalanced data;He;IEEE Trans Knowl Data Eng,2009

3. 10 Challenging problems in data mining research;Yang;Int J Inf Technol Decis Mak,2006

4. HS-KDNet: A lightweight network based on hierarchical-split block and knowledge distillation for fault diagnosis with extremely imbalanced data;Deng;IEEE Trans Instrum Meas,2021

5. Identification and application of machine learning algorithms for transformer dissolved gas analysis;Rao;IEEE Trans Dielectr Electr Insul,2021

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1. Design and Implementation of a Portable Digital Energy Measurement Field Diagnosis Instrument Based on DBN;2023 5th International Conference on Electrical Engineering and Control Technologies (CEECT);2023-12-15

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