Deep learning for anomaly detection in multivariate time series: Approaches, applications, and challenges
Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …
based on deep learning have been proposed for the analysis of multivariate time series. In …
A survey of unmanned aerial vehicle flight data anomaly detection: Technologies, applications, and future directions
Flight data anomaly detection plays an imperative role in the safety and maintenance of
unmanned aerial vehicles (UAVs). It has attracted extensive attention from researchers …
unmanned aerial vehicles (UAVs). It has attracted extensive attention from researchers …
Detecting cyberattacks using anomaly detection in industrial control systems: A federated learning approach
In recent years, the rapid development and wide application of advanced technologies have
profoundly impacted industrial manufacturing, leading to smart manufacturing (SM) …
profoundly impacted industrial manufacturing, leading to smart manufacturing (SM) …
LSTM-autoencoder-based anomaly detection for indoor air quality time-series data
Anomaly detection for indoor air quality (IAQ) data has become an important area of
research as the quality of air is closely related to human health and well-being. However …
research as the quality of air is closely related to human health and well-being. However …
Unsupervised anomaly detection with LSTM autoencoders using statistical data-filtering
To address one of the most challenging industry problems, we develop an enhanced
training algorithm for anomaly detection in unlabelled sequential data such as time-series …
training algorithm for anomaly detection in unlabelled sequential data such as time-series …
Variational transformer-based anomaly detection approach for multivariate time series
X Wang, D Pi, X Zhang, H Liu, C Guo - Measurement, 2022 - Elsevier
Due to the strategic importance of satellites, the safety and reliability of satellites have
become more important. Sensors that monitor satellites generate lots of multivariate time …
become more important. Sensors that monitor satellites generate lots of multivariate time …
Do deep neural networks contribute to multivariate time series anomaly detection?
J Audibert, P Michiardi, F Guyard, S Marti… - Pattern Recognition, 2022 - Elsevier
Anomaly detection in time series is a complex task that has been widely studied. In recent
years, the ability of unsupervised anomaly detection algorithms has received much attention …
years, the ability of unsupervised anomaly detection algorithms has received much attention …
opengauss: An autonomous database system
Although learning-based database optimization techniques have been studied from
academia in recent years, they have not been widely deployed in commercial database …
academia in recent years, they have not been widely deployed in commercial database …
On the importance of building high-quality training datasets for neural code search
The performance of neural code search is significantly influenced by the quality of the
training data from which the neural models are derived. A large corpus of high-quality query …
training data from which the neural models are derived. A large corpus of high-quality query …
A novel orthogonal self-attentive variational autoencoder method for interpretable chemical process fault detection and identification
X Bi, J Zhao - Process Safety and Environmental Protection, 2021 - Elsevier
Industrial processes are becoming increasingly large and complex, thus introducing
potential safety risks and requiring an effective approach to maintain safe production …
potential safety risks and requiring an effective approach to maintain safe production …