TimeDDPM: Time Series Augmentation Strategy for Industrial Soft Sensing-Reference-Cited by-同舟云学术

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TimeDDPM: Time Series Augmentation Strategy for Industrial Soft Sensing

Published:2024-01-15 Issue:2 Volume:24 Page:2145-2153
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Dai Yun¹^ORCID,Yang Chao²^ORCID,Liu Kaixin³,Liu Angpeng¹,Liu Yi¹^ORCID

Affiliation:

1. Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou, China

2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

3. Shanxi Key Laboratory of Signal Capturing and Processing, North University of China, Taiyuan, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10400385/10353969.pdf?arnumber=10353969

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1. A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology;Advanced Engineering Informatics;2024-10

2. Dynamic data reconciliation for enhancing the prediction performance of long short-term memory network;Measurement Science and Technology;2024-09-02

3. Temporal graph convolutional network soft sensor for molecular weight distribution prediction;Chemometrics and Intelligent Laboratory Systems;2024-09

4. SA-MSIFF: Soft sensing the cement f-CaO content with a self-adaptive multisource information fusion framework in clinker burning process;Journal of Process Control;2024-09

5. Global Dependency Graph Network for Soft Sensing in Process Industry;IEEE Sensors Journal;2024-08-15

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