Slow down to go better: A survey on slow feature analysis
Temporal data contain a wealth of valuable information, playing an essential role in various
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …
machine-learning tasks. Slow feature analysis (SFA), one of the most classic temporal …
Energy efficiency evaluation of complex petrochemical industries
As the most effective indicator for energy saving and emission reduction, energy efficiency
evaluation is widely used in complex petrochemical industries. It is nowadays common to …
evaluation is widely used in complex petrochemical industries. It is nowadays common to …
Interpretable machine learning-assisted advanced exergy optimization for carbon-neutral olefins production
Q Yang, L Zhao, R Bao, Y Fan, J Zhou, D Rong… - … and Sustainable Energy …, 2025 - Elsevier
The CO 2-to-light olefins technology represents a significant approach to mitigating the
greenhouse effect and advancing green energy solutions. However, little literature …
greenhouse effect and advancing green energy solutions. However, little literature …
Variational bayesian approach to nonstationary and oscillatory slow feature analysis with applications in soft sensing and process monitoring
Extraction of underlying patterns from measured variables is central to various data-driven
control applications, such as soft-sensor modeling, statistical process monitoring, and fault …
control applications, such as soft-sensor modeling, statistical process monitoring, and fault …
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 …
Two-stage stacking heterogeneous ensemble learning method for gasoline octane number loss prediction
Gasoline is the main fuel for small vehicles, and the exhaust emissions from its combustion
have a major impact on the atmospheric environment. In the cumbersome process of …
have a major impact on the atmospheric environment. In the cumbersome process of …
Neural networks with upper and lower bound constraints and its application on industrial soft sensing modeling with missing values
Soft sensors estimate quality indicators that are difficult to measure online so that they are
important in industrial processes. The sensors may malfunction so that some data may be …
important in industrial processes. The sensors may malfunction so that some data may be …
Concurrent monitoring strategy for static and dynamic deviations based on selective ensemble learning using slow feature analysis
Slow feature analysis (SFA) has been extensively adopted for process monitoring. Since the
prominent ability of exploring dynamic information of the industrial process, SFA could …
prominent ability of exploring dynamic information of the industrial process, SFA could …
A data‐driven soft sensor based on weighted probabilistic slow feature analysis for nonlinear dynamic chemical processes
M Zhang, B Xu, L Zhou, H Zheng… - Journal of …, 2023 - Wiley Online Library
Modeling high‐dimensional dynamic processes is a challenging task. In this regard,
probabilistic slow feature analysis (PSFA) is revealed to be advantageous for dynamic soft …
probabilistic slow feature analysis (PSFA) is revealed to be advantageous for dynamic soft …
Model-agnostic meta-learning with optimal alternative scaling value and its application to industrial soft sensing
Y Lu, X Peng, D Yang, M Yang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In soft sensing, relationship variation of process variables and quality indicators may cause
the model trained from the training datasets unsuitable for the prediction on the testing …
the model trained from the training datasets unsuitable for the prediction on the testing …