Deep neural networks for spatial-temporal cyber-physical systems: A survey
Cyber-physical systems (CPS) refer to systems that integrate communication, control, and
computational elements into physical processes to facilitate the control of physical systems …
computational elements into physical processes to facilitate the control of physical systems …
Machine learning for the detection and identification of Internet of Things devices: A survey
The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a
variety of emerging services and applications. However, the presence of rogue IoT devices …
variety of emerging services and applications. However, the presence of rogue IoT devices …
A spatial-temporal approach for multi-airport traffic flow prediction through causality graphs
Accurate airport traffic flow estimation is crucial for the secure and orderly operation of the
aviation system. Recent advances in machine learning have achieved promising prediction …
aviation system. Recent advances in machine learning have achieved promising prediction …
Take an irregular route: Enhance the decoder of time-series forecasting transformer
L Shen, Y Wei, Y Wang, H Qiu - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
With the development of Internet of Things (IoT) systems, precise long-term forecasting
method is requisite for decision makers to evaluate current statuses and formulate future …
method is requisite for decision makers to evaluate current statuses and formulate future …
FCM-GCN-based upstream and downstream dependence model for air traffic flow networks
Y Zhang, Z Lu, J Wang, L Chen - Knowledge-Based Systems, 2023 - Elsevier
The upstream and downstream dependence of air traffic flow networks (ATFN) is a key step
in identifying the changing characteristics and interaction patterns of air traffic flow. This …
in identifying the changing characteristics and interaction patterns of air traffic flow. This …
Deep learning architecture for UAV traffic-density prediction
A Alharbi, I Petrunin, D Panagiotakopoulos - Drones, 2023 - mdpi.com
The research community has paid great attention to the prediction of air traffic flows.
Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft …
Nonetheless, research examining the prediction of air traffic patterns for unmanned aircraft …
Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm
Weather disaster related emergency operations pose a great challenge to air mobility in
both aircraft and airport operations, especially when the impact is gradually approaching …
both aircraft and airport operations, especially when the impact is gradually approaching …
GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration
The current National Airspace System (NAS) is reaching capacity due to increased air traffic,
and is based on outdated pre-tactical planning. This study proposes a more dynamic …
and is based on outdated pre-tactical planning. This study proposes a more dynamic …
Machine learning-enabled adaptive air traffic recommendation system for disaster evacuation
Extreme weather conditions, such as floods, hurricanes and wildfires, cause large-scale
human population movements and evacuations in the world. Taking flights to evacuate the …
human population movements and evacuations in the world. Taking flights to evacuate the …
Distillation knowledge-based space-time data prediction on industrial IoT edge devices
Y Zhang, Y **ng, Y Liu, T Zhang - Ad Hoc Networks, 2022 - Elsevier
Abstract In industrial Internet of Things (IIoT), the space–time data prediction algorithm is
considered as one of the key technologies for supporting real-time monitoring and intelligent …
considered as one of the key technologies for supporting real-time monitoring and intelligent …