Ship-traffic control by means of neural networks applied to radar image sequences

1994 ◽  
Author(s):  
Alessandro Mecocci ◽  
Giuliano Benelli ◽  
Andrea Garzelli
Keyword(s):  
Neural Networks ◽  
Traffic Control ◽  
Image Sequences ◽  
Radar Image ◽  
Ship Traffic

Intraframe compression of radar image sequences for ship traffic control applications

1996 ◽  
Author(s):  
Alessandro Andreadis ◽  
Giuliano Benelli ◽  
Andrea Garzelli ◽  
S. Susini
Keyword(s):  
Traffic Control ◽  
Image Sequences ◽  
Radar Image ◽  
Control Applications ◽  
Ship Traffic

Radar image processing for ship-traffic control

1995 ◽  
Vol 13 (2) ◽  
pp. 119-128 ◽  
Author(s):  
A Mecocci ◽  
G Benelli ◽  
A Garzelli ◽  
S Bottalico
Keyword(s):  
Image Processing ◽  
Traffic Control ◽  
Radar Image ◽  
Ship Traffic

Application of neural networks in predictions of brake wear particulate matter emission

2021 ◽  
pp. 095440702110363
Author(s):  
Saša Vasiljević ◽  
Jasna Glišović ◽  
Nadica Stojanović ◽  
Ivan Grujić
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particulate Matter ◽  
Traffic Control ◽  
World Health ◽  
Vehicle Speed ◽  
System Pressure ◽  
The Neural Network ◽  
Vehicle Systems ◽  
Brake Wear

According to the World Health Organization, air pollution with PM10 and PM2.5 (PM-particulate matter) is a significant problem that can have serious consequences for human health. Vehicles, as one of the main sources of PM10 and PM2.5 emissions, pollute the air and the environment both by creating particles by burning fuel in the engine, and by wearing of various elements in some vehicle systems. In this paper, the authors conducted the prediction of the formation of PM10 and PM2.5 particles generated by the wear of the braking system using a neural network (Artificial Neural Networks (ANN)). In this case, the neural network model was created based on the generated particles that were measured experimentally, while the validity of the created neural network was checked by means of a comparative analysis of the experimentally measured amount of particles and the prediction results. The experimental results were obtained by testing on an inertial braking dynamometer, where braking was performed in several modes, that is under different braking parameters (simulated vehicle speed, brake system pressure, temperature, braking time, braking torque). During braking, the concentration of PM10 and PM2.5 particles was measured simultaneously. The total of 196 measurements were performed and these data were used for training, validation, and verification of the neural network. When it comes to simulation, a comparison of two types of neural networks was performed with one output and with two outputs. For each type, network training was conducted using three different algorithms of backpropagation methods. For each neural network, a comparison of the obtained experimental and simulation results was performed. More accurate prediction results were obtained by the single-output neural network for both particulate sizes, while the smallest error was found in the case of a trained neural network using the Levenberg-Marquardt backward propagation algorithm. The aim of creating such a prediction model is to prove that by using neural networks it is possible to predict the emission of particles generated by brake wear, which can be further used for modern traffic systems such as traffic control. In addition, this wear algorithm could be applied on other vehicle systems, such as a clutch or tires.

Application of neural networks to radar image classification

1994 ◽  
Vol 32 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Y. Hara ◽  
R.G. Atkins ◽  
S.H. Yueh ◽  
R.T. Shin ◽  
J.A. Kong
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Radar Image
Sign in / Sign up

Export Citation Format

Share Document