Flexible Clockwork Recurrent Neural Network for multirate industrial soft sensor
Data-driven-based soft sensors play significant roles in predicting key quality and optimizing
the production process. Considering the difficulty and cost of variable acquisition, these …
the production process. Considering the difficulty and cost of variable acquisition, these …
Quality variable prediction for nonlinear dynamic industrial processes based on temporal convolutional networks
Soft sensors have been extensively developed to estimate the difficult-to-measure quality
variables for real-time process monitoring and control. Process nonlinearities and dynamics …
variables for real-time process monitoring and control. Process nonlinearities and dynamics …
Dual attention-based encoder–decoder: A customized sequence-to-sequence learning for soft sensor development
L Feng, C Zhao, Y Sun - IEEE Transactions on Neural …, 2020 - ieeexplore.ieee.org
Soft sensor techniques have been applied to predict the hard-to-measure quality variables
based on the easy-to-measure process variables in industry scenarios. Since the products …
based on the easy-to-measure process variables in industry scenarios. Since the products …
A Soft Sensor for Multirate Quality Variables Based on MC-CNN
B Song, Y Zhou, H Shi, Y Tao… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In recent years, data-driven soft sensor modeling methods have been widely used in
industrial production, chemistry, and biochemical. In industrial processes, the sampling rates …
industrial production, chemistry, and biochemical. In industrial processes, the sampling rates …
A dynamic soft sensor based on hybrid neural networks to improve early off-spec detection
Soft sensors are widely used to predict hard-to-measure quality variables in industrial
processes. For efficient quality control, prediction of quality dynamics is essential to prevent …
processes. For efficient quality control, prediction of quality dynamics is essential to prevent …
Stacked enhanced auto-encoder for data-driven soft sensing of quality variable
X Yuan, S Qi, Y Wang - IEEE Transactions on Instrumentation …, 2020 - ieeexplore.ieee.org
Data-driven soft sensors have been widely used in industrial processes. Traditional soft
sensors are mostly shallow networks, which cannot easily describe the complicated process …
sensors are mostly shallow networks, which cannot easily describe the complicated process …
Data-driven soft sensor development based on deep learning technique
In industrial process control, some product qualities and key variables are always difficult to
measure online due to technical or economic limitations. As an effective solution, data …
measure online due to technical or economic limitations. As an effective solution, data …
Variational progressive-transfer network for soft sensing of multirate industrial processes
Deep-learning-based soft sensors have been extensively developed for predicting key
quality or performance variables in industrial processes. However, most approaches …
quality or performance variables in industrial processes. However, most approaches …
Nonlinear dynamic soft sensor modeling with supervised long short-term memory network
X Yuan, L Li, Y Wang - IEEE transactions on industrial …, 2019 - ieeexplore.ieee.org
Soft sensor has been extensively utilized in industrial processes for prediction of key quality
variables. To build an accurate virtual sensor model, it is very significant to model the …
variables. To build an accurate virtual sensor model, it is very significant to model the …
Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach
HK Mohanta, AK Pani - Applied Soft Computing, 2022 - Elsevier
Real time estimation of target quality variables using soft sensor relevant to time varying
process conditions will be a significant step forward in effective implementation of Industry …
process conditions will be a significant step forward in effective implementation of Industry …