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 …
Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
LLNet: A deep autoencoder approach to natural low-light image enhancement
In surveillance, monitoring and tactical reconnaissance, gathering visual information from a
dynamic environment and accurately processing such data are essential to making informed …
dynamic environment and accurately processing such data are essential to making informed …
Anomaly detection and fault disambiguation in large flight data: A multi-modal deep auto-encoder approach
Flight data recorders provide large volumes of heterogeneous data from arrays of sensors
on-board to perform fault diagnosis. Challenges such as large data volumes, lack of labeled …
on-board to perform fault diagnosis. Challenges such as large data volumes, lack of labeled …
Deep learning in aircraft design, dynamics, and control: Review and prospects
In recent decades, deep learning (DL) has become a rapidly growing research direction,
redefining the state-of-the-art performances in a wide range of techniques, such as object …
redefining the state-of-the-art performances in a wide range of techniques, such as object …
Detection of precursors of combustion instability using convolutional recurrent neural networks
Many combustors are prone to Thermoacoustic Instabilities (TAI). Being able to avoid TAI is
mandatory to efficiently operate a system without sacrificing neither performance nor safety …
mandatory to efficiently operate a system without sacrificing neither performance nor safety …
Flame image processing and classification using a pre-trained VGG16 model in combustion diagnosis
Z Omiotek, A Kotyra - Sensors, 2021 - mdpi.com
Nowadays, despite a negative impact on the natural environment, coal combustion is still a
significant energy source. One way to minimize the adverse side effects is sophisticated …
significant energy source. One way to minimize the adverse side effects is sophisticated …
Monitoring combustion instabilities of stratified swirl flames by feature extractions of time-averaged flame images using deep learning method
Y Zhou, C Zhang, X Han, Y Lin - Aerospace Science and Technology, 2021 - Elsevier
The present article investigates the application of deep learning methods to monitor
combustion instabilities based on time-averaged flame images. Experiments on BASIS …
combustion instabilities based on time-averaged flame images. Experiments on BASIS …
Segmentation of schlieren images of flow field in combustor of scramjet based on improved fully convolutional network
L Li, Y Tian, X Deng, M Guo, J Le, H Zhang - Physics of Fluids, 2022 - pubs.aip.org
Extraction of the wave structure of the flow field in the combustor of the scramjet is important
for main flow control and performance evaluation of the scramjet. In this study, a deep …
for main flow control and performance evaluation of the scramjet. In this study, a deep …
Deep learning for structural health monitoring: A damage characterization application
Structural health monitoring (SHM) is usually focused on damage detection (eg, Yes/No) or
approximate estimation of damage size. Any additional details of the damage such as …
approximate estimation of damage size. Any additional details of the damage such as …