Deep learning for time series anomaly detection: A survey
Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …
applications, including financial markets, economics, earth sciences, manufacturing, and …
[HTML][HTML] Leveraging IoT-aware technologies and AI techniques for real-time critical healthcare applications
AT Shumba, T Montanaro, I Sergi, L Fachechi… - Sensors, 2022 - mdpi.com
Personalised healthcare has seen significant improvements due to the introduction of health
monitoring technologies that allow wearable devices to unintrusively monitor physiological …
monitoring technologies that allow wearable devices to unintrusively monitor physiological …
Tranad: Deep transformer networks for anomaly detection in multivariate time series data
Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …
importance for modern industrial applications. However, building a system that is able to …
Tsmae: a novel anomaly detection approach for internet of things time series data using memory-augmented autoencoder
With the development of communication, the Internet of Things (IoT) has been widely
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …
deployed and used in industrial manufacturing, intelligent transportation, and healthcare …
GRU-based interpretable multivariate time series anomaly detection in industrial control system
C Tang, L Xu, B Yang, Y Tang, D Zhao - Computers & Security, 2023 - Elsevier
Interpretable multivariate time series anomaly detection is an important technology to
prevent accidents and ensure the reliable operation of Industrial Control Systems. A key …
prevent accidents and ensure the reliable operation of Industrial Control Systems. A key …
BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data
M Ma, L Han, C Zhou - Advanced Engineering Informatics, 2023 - Elsevier
In the context of big data, if the task of multivariate time series data anomaly detection cannot
be performed efficiently and accurately, it will bring great security risks to industrial systems …
be performed efficiently and accurately, it will bring great security risks to industrial systems …
Channel attention for sensor-based activity recognition: embedding features into all frequencies in DCT domain
During recent years, channel attention has attracted great interest in deep learning
community. Despite significant success, it has been rarely exploited in ubiquitous human …
community. Despite significant success, it has been rarely exploited in ubiquitous human …
A comprehensive survey of deep transfer learning for anomaly detection in industrial time series: Methods, applications, and directions
Automating the monitoring of industrial processes has the potential to enhance efficiency
and optimize quality by promptly detecting abnormal events and thus facilitating timely …
and optimize quality by promptly detecting abnormal events and thus facilitating timely …
Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data
M Zheng, J Man, D Wang, Y Chen, Q Li, Y Liu - Reliability Engineering & …, 2023 - Elsevier
The maintenance cost and unplanned downtime caused by faults are an important part of
the operation cost of wind turbines. Supervisory control and data acquisition (SCADA) data …
the operation cost of wind turbines. Supervisory control and data acquisition (SCADA) data …
Anomaly detection in aerial videos with transformers
Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search,
and rescue operations by the virtue of low-cost, large-coverage, real-time, and high …
and rescue operations by the virtue of low-cost, large-coverage, real-time, and high …