Trustworthy Diagnostics With Out-of-Distribution Detection: A Novel Max-Consistency and Min-Similarity Guided Deep Ensembles for Uncertainty Estimation-Reference-Cited by-同舟云学术

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Trustworthy Diagnostics With Out-of-Distribution Detection: A Novel Max-Consistency and Min-Similarity Guided Deep Ensembles for Uncertainty Estimation

Published:2024-07-01 Issue:13 Volume:11 Page:23055-23067
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhang Xiaochen¹^ORCID,Wang Chen¹,Zhou Wei²^ORCID,Xu Jiajia²,Han Te³^ORCID

Affiliation:

1. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China

2. Industrial Product Solutions Core Development Department, Ling Yang Industrial Internet Company Ltd., Anhui, China

3. Center for Energy and Environmental Policy Research and the School of Management, Beijing Institute of Technology, Beijing, China

Funder

National Natural Science Foundation of China

Young Elite Scientists Sponsorship Program by CAST

Anhui Provincial Science and Technology Major Project

Natural Science Foundation of Fujian Province

Education and Research Project for Middle and Young Teachers in Fujian Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10570342/10497096.pdf?arnumber=10497096

Reference46 articles.

1. Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0

2. IoT-Enabled Fault Prediction and Maintenance for Smart Charging Piles

3. Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice

4. Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals

5. Simulation-driven deep learning for locating faulty insulators in a power line

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