Assessment of Tool Wear with Insufficient and Unbalanced Data Using Improved Conditional Generative Adversarial Net and High-quality Optimization Algorithm-Reference-Cited by-同舟云学术

Assessment of Tool Wear with Insufficient and Unbalanced Data Using Improved Conditional Generative Adversarial Net and High-quality Optimization Algorithm

Published:2022 Issue: Volume: Page:1-10
ISSN:0278-0046
Container-title:IEEE Transactions on Industrial Electronics
language:
Short-container-title:IEEE Trans. Ind. Electron.

Author:

Liu Tianyu¹,Chen Bojian²^ORCID,Huang Weiguo²^ORCID,Jackson Lisa³^ORCID,Mao Lei¹^ORCID,He Qingbo⁴^ORCID,Wu Qiang¹^ORCID

Affiliation:

1. Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China

2. School of Rail Transportation, SooChow University, Suzhou, China

3. Department of Aeronautical and Automotive Engineering, Loughborough University, UK

4. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

key R&D plan of Anhui Province

Natural Science Foundation of Anhui Province

Hefei Municipal Natural Science Foundation

CAS Pioneer Hundred Talents Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/41/4387790/10002203.pdf?arnumber=10002203

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