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Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments
This work surveys and illustrates multiple open challenges in the field of industrial Internet of
Things (IoT)-based big data management and analysis in cloud environments. Challenges …
Things (IoT)-based big data management and analysis in cloud environments. Challenges …
Secure architecture for Industrial Edge of Things (IEoT): A hierarchical perspective
Abstract The Industrial Internet of Things (IIoT) is an application of the IoT specifically
tailored for industrial manufacturing, characterized by its heightened requirements for …
tailored for industrial manufacturing, characterized by its heightened requirements for …
Augmented industrial data-driven modeling under the curse of dimensionality
The curse of dimensionality refers to the problem of increased sparsity and computational
complexity when dealing with high-dimensional data. In recent years, the types and …
complexity when dealing with high-dimensional data. In recent years, the types and …
A new distributed echo state network integrated with an auto-encoder for dynamic soft sensing
As dynamic industrial processes become increasingly complicated, it tends to be difficult to
develop accurate soft sensors. Echo state networks (ESNs) as dynamic neural network (NN) …
develop accurate soft sensors. Echo state networks (ESNs) as dynamic neural network (NN) …
Transfer adversarial attacks across industrial intelligent systems
As indispensable parts of industrial production control, data-driven industrial intelligent
systems (IIS) achieve efficient executions of significant tasks such as fault classification (FC) …
systems (IIS) achieve efficient executions of significant tasks such as fault classification (FC) …
Advances in Bayesian networks for industrial process analytics: Bridging data and mechanisms
Data analytics plays a vital role in Industry 4.0, guiding decisions and operations in key
areas including process monitoring, reliability assessment and soft sensing. Bayesian …
areas including process monitoring, reliability assessment and soft sensing. Bayesian …
Adversarial learning from imbalanced data: A robust industrial fault classification method
Data-driven models are revealed to be vulnerable to adversarial examples, so improving the
model's adversarial robustness has attracted extensive research. However, in real-world …
model's adversarial robustness has attracted extensive research. However, in real-world …
Adversarial Weight Prediction Networks for Defense of Industrial FDC Systems
In recent years, more and more open environment have led to confidential links and data
exposure, which seriously threatens the security of industrial systems. Adversarial attacks …
exposure, which seriously threatens the security of industrial systems. Adversarial attacks …
Attacks on data-driven process monitoring systems: Subspace transfer networks
With the rapid development of information technology, intelligent upgrading of the
manufacturing industry has broken the closed environment of traditional industrial control …
manufacturing industry has broken the closed environment of traditional industrial control …
Robust Adversarial Attacks on Imperfect Deep Neural Networks in Fault Classification
In recent years, deep neural networks (DNNs) have been widely applied in fault
classification tasks. Their adversarial security has received attention, but little consideration …
classification tasks. Their adversarial security has received attention, but little consideration …