A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: Methods, applications, and directions

P Yan, A Abdulkadir, PP Luley, M Rosenthal… - IEEE …, 2024 - ieeexplore.ieee.org
Automating the monitoring of industrial processes has the potential to enhance efficiency
and optimize quality by promptly detecting abnormal events and thus facilitating timely …

CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community

Y Liu, B Guo, N Li, Y Ding, Z Zhang… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …

Deep Learning for Anomaly Detection in Time-Series Data: An Analysis of Techniques, Review of Applications, and Guidelines for Future Research

UA Usmani, IA Aziz, J Jaafar, J Watada - IEEE Access, 2024 - ieeexplore.ieee.org
Industries are generating massive amounts of data due to increased automation and
interconnectedness. As data from various sources becomes more available, the extraction of …

Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks

N Noonari, D Corujo, RL Aguiar, FJ Ferrao - arXiv preprint arXiv …, 2024 - arxiv.org
The rapid growth in mobile broadband usage and increasing subscribers have made it
crucial to ensure reliable network performance. As mobile networks grow more complex …

Model-Based Transfer Learning Framework for Time Series Analysis: A Field Application to HVAC Units

A Taffari - 2024 - search.proquest.com
Abstract Design of a Model-Based Transfer Learning Framework for Time Series Analysis: a
guideline for the application of knowledge transfer for time series data and its application to …

[PDF][PDF] AComprehensive SURVEY OF DEEP TRANSFER LEARNING FOR ANOMALY DETECTION IN INDUSTRIAL TIME SERIES: METHODS, APPLICATIONS, AND …

P Yan, A Abdulkadir, PP Luley, M Rosenthal… - stdm.github.io
Automating the monitoring of industrial processes has the potential to enhance efficiency
and optimize quality by promptly detecting abnormal events and thus facilitating timely …