Anomaly identification with few labeled data in the distillation process based on semisupervised ladder networks-Reference-Cited by-同舟云学术

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Anomaly identification with few labeled data in the distillation process based on semisupervised ladder networks

Published:2020-10-24 Issue:2 Volume:40 Page:
ISSN:1066-8527
Container-title:Process Safety Progress
language:en
Short-container-title:Proc Safety Prog

Author:

Li Chuankun¹^ORCID,Xu Wei¹,Zhao Dongfeng²,Yuan Zhuang¹,Shi Jihao³,Wang Chunli¹

Affiliation:

1. State Key Laboratory of Safety and Control for Chemicals Sinopec Qingdao Research Institute of Safety Engineering Qingdao China

2. College of Chemical Engineering China University of Petroleum Huadong Qingdao China

3. Centre for Offshore Engineering and Safety Technology China University of Petroleum Huadong Qingdao China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Safety, Risk, Reliability and Quality,General Chemical Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/prs.12206

Reference20 articles.

1. Review of Recent Research on Data-Based Process Monitoring

2. Fault diagnosis for distillation process based on CNN–DAE

3. Deep convolutional neural network model based chemical process fault diagnosis

4. Deep neural network and random forest classifier for source tracking of chemical leaks using fence monitoring data;Cho J;J Loss Prev Process Ind,2018

5. Abnormal situation management: challenges and opportunities in the big data era;Shu Y;Comput Chem Eng,2016

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Completion-Attention Ladder Network for Few-Shot Underwater Acoustic Recognition;Neural Processing Letters;2023-03-11

2. Underwater acoustic object recognition with few shot SE_RseNet_Decoder semi-supervised learning;2022-08-12

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