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A survey on anomaly detection for technical systems using LSTM networks
Anomalies represent deviations from the intended system operation and can lead to
decreased efficiency as well as partial or complete system failure. As the causes of …
decreased efficiency as well as partial or complete system failure. As the causes of …
Deep learning for time series forecasting: a survey
Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …
increasing in recent years. Deep neural networks have proved to be powerful and are …
Dcdetector: Dual attention contrastive representation learning for time series anomaly detection
Time series anomaly detection is critical for a wide range of applications. It aims to identify
deviant samples from the normal sample distribution in time series. The most fundamental …
deviant samples from the normal sample distribution in time series. The most fundamental …
Multi-input CNN-GRU based human activity recognition using wearable sensors
Abstract Human Activity Recognition (HAR) has attracted much attention from researchers in
the recent past. The intensification of research into HAR lies in the motive to understand …
the recent past. The intensification of research into HAR lies in the motive to understand …
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
[HTML][HTML] Potential, challenges and future directions for deep learning in prognostics and health management applications
Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …
domains, including computer vision and natural language understanding. The drivers for the …
Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals
Fault detection and diagnosis in time series data are becoming mainstream in most
industrial applications since the increase of monitoring sensors in machinery. Traditional …
industrial applications since the increase of monitoring sensors in machinery. Traditional …
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 …
Attention induced multi-head convolutional neural network for human activity recognition
Deep neural networks, including convolutional neural networks (CNNs), have been widely
adopted for human activity recognition in recent years. They have attained significant …
adopted for human activity recognition in recent years. They have attained significant …
Deep learning-based anomaly detection in cyber-physical systems: Progress and opportunities
Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS).
However, due to the increasing complexity of CPSs and more sophisticated attacks …
However, due to the increasing complexity of CPSs and more sophisticated attacks …