Digital twin intelligent system for industrial internet of things-based big data management and analysis in cloud environments

C Stergiou, K Psannis - Virtual Reality & Intelligent Hardware, 2022‏ - Elsevier
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 …

Secure architecture for Industrial Edge of Things (IEoT): A hierarchical perspective

P Li, J **a, Q Wang, Y Zhang, M Wu - Computer Networks, 2024‏ - Elsevier
Abstract The Industrial Internet of Things (IIoT) is an application of the IoT specifically
tailored for industrial manufacturing, characterized by its heightened requirements for …

Augmented industrial data-driven modeling under the curse of dimensionality

X Jiang, X Kong, Z Ge - IEEE/CAA Journal of Automatica Sinica, 2023‏ - ieeexplore.ieee.org
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 …

A new distributed echo state network integrated with an auto-encoder for dynamic soft sensing

YL He, L Chen, Y Xu, QX Zhu… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
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) …

Transfer adversarial attacks across industrial intelligent systems

Z Yin, Y Zhuo, Z Ge - Reliability Engineering & System Safety, 2023‏ - Elsevier
As indispensable parts of industrial production control, data-driven industrial intelligent
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

J Zheng, Y Zhuo, X Jiang, L Zeng, Z Ge - Expert Systems with Applications, 2025‏ - Elsevier
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 …

Adversarial learning from imbalanced data: A robust industrial fault classification method

Z Yin, X Zhang, Z Song, Z Ge - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
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 …

Adversarial Weight Prediction Networks for Defense of Industrial FDC Systems

Z Yin, L Ye, Z Ge - IEEE Transactions on Industrial Informatics, 2024‏ - ieeexplore.ieee.org
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 …

Attacks on data-driven process monitoring systems: Subspace transfer networks

X Jiang, Z Ge - IEEE Transactions on Artificial Intelligence, 2022‏ - ieeexplore.ieee.org
With the rapid development of information technology, intelligent upgrading of the
manufacturing industry has broken the closed environment of traditional industrial control …

Robust Adversarial Attacks on Imperfect Deep Neural Networks in Fault Classification

X Jiang, X Kong, J Zheng, Z Ge… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
In recent years, deep neural networks (DNNs) have been widely applied in fault
classification tasks. Their adversarial security has received attention, but little consideration …