[HTML][HTML] Advanced predictive control for GRU and LSTM networks
K Zarzycki, M Ławryńczuk - Information Sciences, 2022 - Elsevier
This article is concerned with Model Predictive Control (MPC) algorithms that use Short
Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for prediction. For …
Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for prediction. For …
DA-Net: Dual-attention network for multivariate time series classification
R Chen, X Yan, S Wang, G Xiao - Information Sciences, 2022 - Elsevier
Multivariate time series classification is one of the increasingly important issues in machine
learning. Existing methods focus on establishing the global long-range dependencies or …
learning. Existing methods focus on establishing the global long-range dependencies or …
Multi-classification recognition and quantitative characterization of surface defects in belt grinding based on YOLOv7
B Zhu, G Xiao, Y Zhang, H Gao - Measurement, 2023 - Elsevier
Due to the complex and varied surface defects of the belt grinding, the automatic recognition
and quantitative characterization of the surface defects is still an issue that needs to be …
and quantitative characterization of the surface defects is still an issue that needs to be …
A multimode structured prediction model based on dynamic attribution graph attention network for complex industrial processes
Complex industrial processes with dynamic and time-varying characteristics, as well as
diverse operating conditions, pose challenges in developing accurate real-time online …
diverse operating conditions, pose challenges in developing accurate real-time online …
A constrained multi-objective deep reinforcement learning approach for temperature field optimization of zinc oxide rotary volatile kiln
In the zinc oxide rotary volatile kiln (ZORVK), an optimal temperature field is essential to
balance the strong conflict between zinc recovery rate and carbon emissions. However, the …
balance the strong conflict between zinc recovery rate and carbon emissions. However, the …
Efficient JITL framework for nonlinear industrial chemical engineering soft sensing based on adaptive multi-branch variable scale integrated convolutional neural …
Y Chen, A Li, X Li, D Xue, J Long - Advanced Engineering Informatics, 2023 - Elsevier
Just-in-time Learning (JITL) is a soft measurement method commonly used in industrial
processes, which can update local models in real-time to solve the problem of inaccurate …
processes, which can update local models in real-time to solve the problem of inaccurate …
A process optimization method based on first principle model for the roasting process
Optimizing the roasting process is essential for achieving high-efficiency production.
However, due to lacking key data detection, limited studies are available in this area. To …
However, due to lacking key data detection, limited studies are available in this area. To …
Temperature field prediction model for zinc oxide rotary volatile kiln based on the fusion of thermodynamics and infrared images
The timely and accurate measurement of temperature field is of great significance for the low-
carbon and high-efficiency operation of the zinc oxide rotary volatile kiln (ZORVK). Due to …
carbon and high-efficiency operation of the zinc oxide rotary volatile kiln (ZORVK). Due to …
Nonlinear MPC based on elastic autoregressive fuzzy neural network with roasting process application
Because of the increasing complexity and nonlinearity of industrial processes, nonlinear
model predictive control (NMPC) has been rapidly developed owing to its fast response and …
model predictive control (NMPC) has been rapidly developed owing to its fast response and …
[PDF][PDF] 复杂生产流程协同优化与智能控制
阳春华， 孙备， 李勇刚， 黄科科， 桂卫华 - 自动化学报, 2023 - source.kongzhi.net
我国流程行业原料来源复杂, 如何优化调控工艺指标使复杂生产流程适应原料波动,
是保障产品质量, 降低物耗能耗的关键. 本文结合全流程, 工序, 反应器等不同生产层级的工艺 …
是保障产品质量, 降低物耗能耗的关键. 本文结合全流程, 工序, 反应器等不同生产层级的工艺 …