[HTML][HTML] Latent variable models in the era of industrial big data: Extension and beyond
A rich supply of data and innovative algorithms have made data-driven modeling a popular
technique in modern industry. Among various data-driven methods, latent variable models …
technique in modern industry. Among various data-driven methods, latent variable models …
When deep learning-based soft sensors encounter reliability challenges: a practical knowledge-guided adversarial attack and its defense
Deep learning-based soft sensors (DLSSs) have been demonstrated to exhibit significantly
improved sensing accuracy; however, their vulnerability to adversarial attacks affects their …
improved sensing accuracy; however, their vulnerability to adversarial attacks affects their …
Evasion Attack and Defense On Machine Learning Models in Cyber-Physical Systems: A Survey
Cyber-physical systems (CPS) are increasingly relying on machine learning (ML)
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
A self-interpretable soft sensor based on deep learning and multiple attention mechanism: From data selection to sensor modeling
For deep learning-based soft sensors, the lack of interpretability and the consequent
unreliability has become one of the most important problems. In this article, a neural network …
unreliability has become one of the most important problems. In this article, a neural network …
Cloud-fog automation: Vision, enabling technologies, and future research directions
The Industry 4.0 digital transformation envisages future industrial systems to be fully
automated, including the control, upgrade, and configuration processes of a large number of …
automated, including the control, upgrade, and configuration processes of a large number of …
Deep PLS: A lightweight deep learning model for interpretable and efficient data analytics
The salient progress of deep learning is accompanied by nonnegligible deficiencies, such
as: 1) interpretability problem; 2) requirement for large data amounts; 3) hard to design and …
as: 1) interpretability problem; 2) requirement for large data amounts; 3) hard to design and …
Adversarial attacks for neural network based industrial soft sensors: Mirror output attack and translation mirror output attack
L Chen, QX Zhu, YL He - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
Soft sensing using the neural network technique has been increasingly applied to industrial
processes. Recently, the security and robustness of neural network-based soft sensors have …
processes. Recently, the security and robustness of neural network-based soft sensors have …
Neural network weight comparison for industrial causality discovering and its soft sensing application
Due to the complex reaction mechanisms of industrial process units, causality and
correlations exist between industrial process variables. Causal discovery algorithms have …
correlations exist between industrial process variables. Causal discovery algorithms have …
Adversarial Attacks on Regression Systems via Gradient Optimization
Adversarial attack can fabricate imperceptible fake samples to cheat a well-trained artificial
intelligence (AI) model, and it has shown strong destructive power in many classification …
intelligence (AI) model, and it has shown strong destructive power in many classification …
A zero-shot soft sensor modeling approach using adversarial learning for robustness against sensor fault
Soft sensors are widely used in many industrial systems to monitor key variables that are
difficult to measure, using measurements from other available physical sensors. Because …
difficult to measure, using measurements from other available physical sensors. Because …