JustusvLiebig/Soft_Sensor_Experiments

Soft Sensor Experiments

The code of Soft Sensor Experiments

Stacked Auto Encoder

This file is a pytorch realization of this papaer:

• A novel semi-supervised pre-training strategy for deep networks and its application for quality variable prediction in industrial processes

VW-SAE

This file is a pytorch realization of this papaer:

• Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

T-SAE(also known as SQAE)

This file is a pytorch realization of this papaer (part):

• Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application
• Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

GSTAE

This file is a pytorch realization of this papaer (part):

• Gated Stacked Target-Related Autoencoder: A Novel Deep Feature Extraction and Layerwise Ensemble Method for Industrial Soft Sensor Application

SLSTM

This file is a pytorch realization of this papaer:

• Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network

SIAE

This file is a pytorch realization of this papaer:

• Stacked isomorphic autoencoder based soft analyzer and its application to sulfur recovery unit

VA-LSTM

This file is a pytorch realization of this papaer:

• Deep learning for quality prediction of nonlinear dynamic processes with variable attention-based long short-term memory network

STA-LSTM

This file is a pytorch realization of these papaers:

• Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development
• Dual Attention-Based Encoder–Decoder: A Customized Sequence-to-Sequence Learning for Soft Sensor Development

Contact Me

Justus
12032042@zju.edu.cn
or
chenzhch7@mail3.sysu.edu.cn

Notice

Please cite the corresponding paper if the code shown aboved is used in your research.
如果在您的研究中使用了上述代码，请引用对应的论文。

Copyright

No reprobaiction without permission/ secondary creation(limited to articles). Please indicate the source if authorized.
未经许可, 严禁转载/二次创作(仅限于发文章)。 转载请说明出处。

Special Annuouncement

The data proposed in this repo is downloaded via this link. No confidential data is involved!
本仓库的数据是通过此链接下载得到。不涉及任何保密数据!