Deep learning for time-series prediction in IIoT: progress, challenges, and prospects
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …
intelligent process control, analysis, and management, such as complex equipment …
A regularized cross-layer ladder network for intrusion detection in industrial internet of things
As part of Big Data trends, the ubiquitous use of the Internet of Things (IoT) in the industrial
environment has generated a significant amount of network traffic. In this type of IoT …
environment has generated a significant amount of network traffic. In this type of IoT …
[HTML][HTML] Anomaly classification in industrial Internet of things: A review
The fourth industrial revolution (Industry 4.0) has the potential to provide real-time, secure,
and autonomous manufacturing environments. The Industrial Internet of Things (IIoT) is a …
and autonomous manufacturing environments. The Industrial Internet of Things (IIoT) is a …
EID-GAN: Generative adversarial nets for extremely imbalanced data augmentation
Imbalanced data cause deep neural networks to output biased results, and it becomes more
serious when facing extremely imbalanced data regarding the outliers with tiny size (the …
serious when facing extremely imbalanced data regarding the outliers with tiny size (the …
Metaheuristic feature selection with deep learning enabled cascaded recurrent neural network for anomaly detection in Industrial Internet of Things environment
N Chander, M Upendra Kumar - Cluster Computing, 2023 - Springer
Abstract Industrial Internet of Things (IIoT) acts as essential part of the revolutionary
transition of conventional industries towards Industry 4.0. By the integration of instruments …
transition of conventional industries towards Industry 4.0. By the integration of instruments …
A framework for anomaly detection in IoT networks using conditional generative adversarial networks
I Ullah, QH Mahmoud - IEEE Access, 2021 - ieeexplore.ieee.org
While anomaly detection and the related concept of intrusion detection are widely studied,
detecting anomalies in new operating behavior in environments such as the Internet of …
detecting anomalies in new operating behavior in environments such as the Internet of …
Hybrid-order representation learning for electricity theft detection
Electricity theft is the primary cause of electrical losses in power systems, which severely
harms the economic benefits of electricity providers and threatens the safety of the power …
harms the economic benefits of electricity providers and threatens the safety of the power …
State-of-charge estimation and health prognosis for lithium-ion batteries based on temperature-compensated Bi-LSTM network and integrated attention mechanism
P Xu, C Wang, J Ye, T Ouyang - IEEE Transactions on Industrial …, 2023 - ieeexplore.ieee.org
The state-of-charge and health prognosis are important factors for electric vehicles. The long
short-term memory (LSTM) is used to estimate battery states, and it attracts a lot of attention …
short-term memory (LSTM) is used to estimate battery states, and it attracts a lot of attention …
Data-driven detection of stealthy false data injection attack against power system state estimation
Power system state estimation (PSSE) is the foundation of energy management system
applications. Hence, operators impose stringent requirements on PSSE data integrity. False …
applications. Hence, operators impose stringent requirements on PSSE data integrity. False …
UzADL: Anomaly detection and localization using graph Laplacian matrix-based unsupervised learning method
Visual inspection is an essential quality control process in industrial businesses. It is usually
automated due to its tedious procedure. An automated visual inspection (AVI) attempts to …
automated due to its tedious procedure. An automated visual inspection (AVI) attempts to …