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 …
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 …
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 …
Attention induced multi-head convolutional neural network for human activity recognition
ZN Khan, J Ahmad - Applied soft computing, 2021 - Elsevier
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 …
Deep learning in smart grid technology: A review of recent advancements and future prospects
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …
a promising landscape for high grid reliability and efficient energy management. This …
Deep transfer learning for industrial automation: A review and discussion of new techniques for data-driven machine learning
B Maschler, M Weyrich - IEEE Industrial Electronics Magazine, 2021 - ieeexplore.ieee.org
Deep learning has greatly increased the capabilities of" intelligent" technical systems over
the last years [1]. This includes the industrial automation sector [1]-[4], where new data …
the last years [1]. This includes the industrial automation sector [1]-[4], where new data …
Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects
Photovoltaic (PV) systems are subject to failures during their operation due to the aging
effects and external/environmental conditions. These faults may affect the different system …
effects and external/environmental conditions. These faults may affect the different system …
Time series anomaly detection for cyber-physical systems via neural system identification and bayesian filtering
C Feng, P Tian - Proceedings of the 27th ACM SIGKDD Conference on …, 2021 - dl.acm.org
Recent advances in AIoT technologies have led to an increasing popularity of utilizing
machine learning algorithms to detect operational failures for cyber-physical systems (CPS) …
machine learning algorithms to detect operational failures for cyber-physical systems (CPS) …