Improved Virtual Sample Generation Method Based on Conditional Regions Denoising Diffusion Probabilistic Model for Soft Sensors with Small Data-Reference-Cited by-同舟云学术

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Improved Virtual Sample Generation Method Based on Conditional Regions Denoising Diffusion Probabilistic Model for Soft Sensors with Small Data

Published:2024-05-17 Issue: Volume: Page:2264-2269
ISSN:
Container-title:2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Mo Wei-Tao¹,Zhu Qun-Xiong¹,Song Xiao-Lu¹,He Yan-Lin¹,Xu Yuan¹

Affiliation:

1. Beijing University of Chemical Technology (BUCT),College of Information Science and Technology,Beijing,China,100029

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10606543/10606546/10606870.pdf?arnumber=10606870

Reference27 articles.

1. Co-training based virtual sample generation for solving the small sample size problem in process industry

2. Novel space projection interpolation based virtual sample generation for solving the small data problem in developing soft sensor

3. Novel virtual sample generation using Gibbs Sampling integrated with GRNN for handling small data in soft sensing

4. A Data-Driven LSTM Soft Sensor Model Based on Bayesian Optimization for Hydraulic Pressure Measurement of Excavator

5. Novel virtual sample generation method based on data augmentation and weighted interpolation for soft sensing with small data

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