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 …
[HTML][HTML] 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 …
A hybrid approach for forecasting ship motion using CNN–GRU–AM and GCWOA
MW Li, DY Xu, J Geng, WC Hong - Applied Soft Computing, 2022 - Elsevier
The motion of a ship, which has six degrees of freedom, is a complex nonlinear dynamic
process with variable periodicity and chaotic characteristics. With the development of smart …
process with variable periodicity and chaotic characteristics. With the development of smart …
Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals
RFR Junior, IA dos Santos Areias, MM Campos… - Measurement, 2022 - Elsevier
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 …
Spatio-temporal feature encoding for traffic accident detection in VANET environment
Z Zhou, X Dong, Z Li, K Yu, C Ding… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In the Vehicular Ad hoc Networks (VANET) environment, recognizing traffic accident events
in the driving videos captured by vehicle-mounted cameras is an essential task. Generally …
in the driving videos captured by vehicle-mounted cameras is an essential task. Generally …
Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network
Z Shen, X Fan, L Zhang, H Yu - Ocean Engineering, 2022 - Elsevier
Wind speed is a key factor for unmanned sailboats, and accurate prediction of wind speed is
of great significance to the safety and performance of unmanned sailboats. In this study, a …
of great significance to the safety and performance of unmanned sailboats. In this study, a …
Mutual information based anomaly detection of monitoring data with attention mechanism and residual learning
Due to the damage of sensors or transmission equipment, abnormal monitoring data
inevitably exists in the measured raw data, and it significantly impacts the condition …
inevitably exists in the measured raw data, and it significantly impacts the condition …