A Novel Few-Shot Deep Transfer Learning Method for Anomaly Detection: Deep Domain-Adversarial Contrastive Network With Time-Frequency Transferability Analytics-Reference-Cited by-同舟云学术

A Novel Few-Shot Deep Transfer Learning Method for Anomaly Detection: Deep Domain-Adversarial Contrastive Network With Time-Frequency Transferability Analytics

Published:2024-09-01 Issue:17 Volume:11 Page:28809-28823
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Wu Jianing¹,Mao Wentao²^ORCID,Zhang Yanna¹^ORCID,Fan Lilin¹,Zhong Zhidan³^ORCID

Affiliation:

1. School of Computer and Information Engineering, Henan Normal University, Xinxiang, China

2. Engineering Lab of Intelligence Business and Internet of Things, Xinxiang, Henan, China

3. School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, China

Funder

Key Technologies Research Development Joint Foundation of Henan Province

Major Science and Technology Projects of Henan Province

Joint Fund Project of Henan Provincial Science and Technology Research and Development Program, China

Graduate Student Research and Practice Innovation Project of Henan Normal University, China

Natural Science Foundation of Henan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10643444/10537045.pdf?arnumber=10537045

Reference39 articles.

1. IoT anomaly detection methods and applications: A survey

2. Data-Driven Diagnosis of Nonlinearly Mixed Mechanical Faults in Wind Turbine Gearbox

3. RiceTalk: Rice Blast Detection Using Internet of Things and Artificial Intelligence Technologies

4. VAE-based Deep SVDD for anomaly detection

5. Uninformed Students: Student-Teacher Anomaly Detection With Discriminative Latent Embeddings