State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

2017 ◽  
Vol 19 (4) ◽  
pp. 2432-2455 ◽  
Author(s):  
Zubair Md. Fadlullah ◽  
Fengxiao Tang ◽  
Bomin Mao ◽  
Nei Kato ◽  
Osamu Akashi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Control Systems ◽  
Network Traffic ◽  
Traffic Control ◽  
State Of The Art ◽  
Machine Intelligence ◽  
Intelligent Network

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

A Novel Non-Supervised Deep-Learning-Based Network Traffic Control Method for Software Defined Wireless Networks

2018 ◽  
Vol 25 (4) ◽  
pp. 74-81 ◽  
Author(s):  
Bomin Mao ◽  
Fengxiao Tang ◽  
Zubair Md. Fadlullah ◽  
Nei Kato ◽  
Osamu Akashi ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Deep Learning ◽  
Network Traffic ◽  
Traffic Control ◽  
Control Method

Motorway network traffic control systems

2004 ◽  
Vol 152 (2) ◽  
pp. 321-333 ◽  
Author(s):  
Apostolos Kotsialos ◽  
Markos Papageorgiou
Keyword(s):  
Control Systems ◽  
Network Traffic ◽  
Traffic Control

scholarly journals SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems

IEEE Network ◽  
2018 ◽  
Vol 32 (6) ◽  
pp. 66-73 ◽  
Author(s):  
Anish Jindal ◽  
Gagangeet Singh Aujla ◽  
Neeraj Kumar ◽  
Rajat Chaudhary ◽  
Mohammad S. Obaidat ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Traffic Control ◽  
Cyber Physical Systems ◽  
Physical Systems

scholarly journals Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6281
Author(s):  
Teresa Pamuła ◽  
Wiesław Pamuła
Keyword(s):  
Deep Learning ◽  
Control Systems ◽  
Traffic Control ◽  
Region Of Interest ◽  
Weather Conditions ◽  
The State ◽  
Video Data ◽  
Sensor Data ◽  
Level Crossings ◽  
Safety Standards

The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.

The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective

2017 ◽  
Vol 24 (3) ◽  
pp. 146-153 ◽  
Author(s):  
Nei Kato ◽  
Zubair Md. Fadlullah ◽  
Bomin Mao ◽  
Fengxiao Tang ◽  
Osamu Akashi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Traffic Control ◽  
Heterogeneous Network ◽  
Future Perspective
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