Variational LSTM enhanced anomaly detection for industrial big data
With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly
discussed topic in digital and intelligent industry field. The security problem existing in the …
discussed topic in digital and intelligent industry field. The security problem existing in the …
Lightweight long short-term memory variational auto-encoder for multivariate time series anomaly detection in industrial control systems
Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong
impact on the physical world in recent decades. Connecting devices to the internet enables …
impact on the physical world in recent decades. Connecting devices to the internet enables …
An attention-based ConvLSTM autoencoder with dynamic thresholding for unsupervised anomaly detection in multivariate time series
As a substantial amount of multivariate time series data is being produced by the complex
systems in smart manufacturing (SM), improved anomaly detection frameworks are needed …
systems in smart manufacturing (SM), improved anomaly detection frameworks are needed …
FL-MGVN: Federated learning for anomaly detection using mixed gaussian variational self-encoding network
D Wu, Y Deng, M Li - Information processing & management, 2022 - Elsevier
Anomalous data are such data that deviate from a large number of normal data points, which
often have negative impacts on various systems. Current anomaly detection technology …
often have negative impacts on various systems. Current anomaly detection technology …
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …
presence of irrelevant features can conceal the presence of anomalies. This problem, known …
LSTM learning with Bayesian and Gaussian processing for anomaly detection in industrial IoT
The data generated by millions of sensors in the industrial Internet of Things (IIoT) are
extremely dynamic, heterogeneous, and large scale and pose great challenges on the real …
extremely dynamic, heterogeneous, and large scale and pose great challenges on the real …
Integrated generative model for industrial anomaly detection via bidirectional LSTM and attention mechanism
For emerging industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to
build smart industry. Especially, explosive time-series data pose enormous challenges to the …
build smart industry. Especially, explosive time-series data pose enormous challenges to the …
Machine learning for anomaly detection: A systematic review
Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …
components from data. Many techniques have been used to detect anomalies. One of the …
Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach
Since edge device failures (ie, anomalies) seriously affect the production of industrial
products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming …
products in Industrial IoT (IIoT), accurately and timely detecting anomalies are becoming …
ITran: A novel transformer-based approach for industrial anomaly detection and localization
X Cai, R Xiao, Z Zeng, P Gong, Y Ni - Engineering Applications of Artificial …, 2023 - Elsevier
Anomaly detection is currently an essential quality monitoring process in industrial
production. It is often affected by factors such as under or over reconstruction of images and …
production. It is often affected by factors such as under or over reconstruction of images and …
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