Gaussian Mixture Model and Double-Weighted Deep Neural Networks for Data Augmentation Soft Sensing-Reference-Cited by-同舟云学术

Gaussian Mixture Model and Double-Weighted Deep Neural Networks for Data Augmentation Soft Sensing

Published:2023-05-12 Issue: Volume: Page:
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Container-title:2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)
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Author:

Jiang Xiaoyu¹,Yao Le²,Yang Zeyu³,Song Zhihuan¹,Shen Bingbing²

Affiliation:

1. College of Control Science and Engineering, Zhejiang University,State Key Laboratory of Industrial Control Technology,Hangzhou,Zhejiang,PR China,310027

2. School of Mathematics, Hangzhou Normal University,Hangzhou,P. R. China,311121

3. School of Engineering, Huzhou University,Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems,Huzhou,China,313000

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10165684/10165785/10166693.pdf?arnumber=10166693

Reference18 articles.

1. Enhanced Soft Sensor with Qualified Augmented Data Using Centroid Measurement Criterion

2. Data supplement for a soft sensor using a new generative model based on a variational autoencoder and Wasserstein GAN

3. A Gaussian mixture model based virtual sample generation approach for small datasets in industrial processes

4. Efficient Volume Exploration Using the Gaussian Mixture Model

5. Augmented Multidimensional Convolutional Neural Network for Industrial Soft Sensing

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