Unsupervised novelty detection for time series using a deep learning approach
Abstract In the Smart Homes and IoT devices era, abundant available data offers immense
potential for enhancing system intelligence. However, the need for effective anomaly …
potential for enhancing system intelligence. However, the need for effective anomaly …
A deep learning framework for quality control process in the motor oil industry
M Heydari, A Alinezhad, B Vahdani - Engineering Applications of Artificial …, 2024 - Elsevier
Given the advancements in the modern world, using Multivariate-Multistage Quality Control
(MVMSQC) patterns in continuous production industries is deemed crucial and essential …
(MVMSQC) patterns in continuous production industries is deemed crucial and essential …
Multi-node knowledge graph assisted distributed fault detection for large-scale industrial processes based on graph attention network and bidirectional LSTMs
Q Li, Y Wang, J Dong, C Zhang, K Peng - Neural Networks, 2024 - Elsevier
Modern industrial processes are characterized by extensive, multiple operation units, and
strong coupled correlation of subsystems. Fault detection of large-scale processes is still a …
strong coupled correlation of subsystems. Fault detection of large-scale processes is still a …
Intelligent optimal framework for the industrial mining plant-wide prediction control
Y Lei - IEEE Transactions on Instrumentation and …, 2023 - ieeexplore.ieee.org
In the underground mining industry, the deep cone system (DCS) is the core element for the
whole production and paste-filling process. Due to the coupled variables, large time delay …
whole production and paste-filling process. Due to the coupled variables, large time delay …
Anomaly detection in multivariate time series data using deep ensemble models
Anomaly detection in time series data is essential for fraud detection and intrusion
monitoring applications. However, it poses challenges due to data complexity and high …
monitoring applications. However, it poses challenges due to data complexity and high …
PAFormer: Anomaly Detection of Time Series With Parallel-Attention Transformer
Time-series anomaly detection is a critical task with significant impact as it serves a pivotal
role in the field of data mining and quality management. Current anomaly detection methods …
role in the field of data mining and quality management. Current anomaly detection methods …
[HTML][HTML] Anomaly Detection in Time Series: Current Focus and Future Challenges
F Arslan, A Javaid, MDZ Awan - 2023 - intechopen.com
Anomaly detection in time series has become an increasingly vital task, with applications
such as fraud detection and intrusion monitoring. Tackling this problem requires an array of …
such as fraud detection and intrusion monitoring. Tackling this problem requires an array of …
A Filtering-Based Stochastic Gradient Estimation Method for Multivariate Pseudo-Linear Systems Using the Partial Coupling Concept
P Ma, Y Liu, Y Chen - Processes, 2023 - mdpi.com
Solutions for enhancing parameter identification effects for multivariate equation-error
systems in random interference and parameter coupling conditions are considered in this …
systems in random interference and parameter coupling conditions are considered in this …
Classification Method of Load Pattern Based on Load Curve Image Information
L Wei, L Zhang, Y Wang, X Su… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Load pattern (LP) classification provides the foundation for demand side oriented power
system operation and control research. To address the problem that the nonlinear …
system operation and control research. To address the problem that the nonlinear …
Empowering Cybersecurity Using Enhanced Rat Swarm Optimization with Deep Stack-Based Ensemble Learning Approach
Cybersecurity is a vital technology and measures intended to protect networks, computers,
information, and programs from threats and illegal access, modification, or damage. A …
information, and programs from threats and illegal access, modification, or damage. A …