[HTML][HTML] A review of the use of artificial intelligence methods in infrastructure systems
L McMillan, L Varga - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the
growth of digitalisation and has the potential to enable the 'system of systems' approach …
growth of digitalisation and has the potential to enable the 'system of systems' approach …
Convolutional neural network for behavioral modeling and predistortion of wideband power amplifiers
Power amplifier (PA) models, such as the neural network (NN) models and the multilayer NN
models, have problems with high complexity. In this article, we first propose a novel behavior …
models, have problems with high complexity. In this article, we first propose a novel behavior …
Deep learning for security problems in 5G heterogeneous networks
Z Lv, AK Singh, J Li - IEEE Network, 2021 - ieeexplore.ieee.org
With increasingly complex network structure, requirements for heterogeneous 5G are also
growing. The aim of this study is to meet the network security performance under the existing …
growing. The aim of this study is to meet the network security performance under the existing …
Deep learning-based cooperative automatic modulation classification method for MIMO systems
Automatic modulation classification (AMC) is one of the most essential algorithms to identify
the modulation types for the non-cooperative communication systems. Recently, it has been …
the modulation types for the non-cooperative communication systems. Recently, it has been …
Classification of high-spatial-resolution remote sensing scenes method using transfer learning and deep convolutional neural network
W Li, Z Wang, Y Wang, J Wu, J Wang… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
The deep convolutional neural network (DeCNN) is considered one of promising techniques
for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its …
for classifying the high-spatial-resolution remote sensing (HSRRS) scenes, due to its …
Deep neural network for robust modulation classification under uncertain noise conditions
Recently, classifying the modulation schemes of signals using deep neural network has
received much attention. In this paper, we introduce a general model of deep neural network …
received much attention. In this paper, we introduce a general model of deep neural network …
Dual cross-entropy loss for small-sample fine-grained vehicle classification
Fine-grained vehicle classification is a challenging topic in computer vision due to the high
intraclass variance and low interclass variance. Recently, considerable progress has been …
intraclass variance and low interclass variance. Recently, considerable progress has been …
Artificial intelligence to manage network traffic of 5G wireless networks
The deployment of 5G wireless communication systems is projected to begin in 2020. With
new scenarios, new technologies, and new network architectures, the traffic management for …
new scenarios, new technologies, and new network architectures, the traffic management for …
Fine-grained vehicle classification with channel max pooling modified CNNs
Convolutional neural networks (CNNs) have recently shown excellent performance on the
task of fine-grained vehicle classification, where the motivation is to identify the fine-grained …
task of fine-grained vehicle classification, where the motivation is to identify the fine-grained …
Machine learning aided air traffic flow analysis based on aviation big data
Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air
traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft …
traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft …