scholarly journals Satellite to Ground Station, Attenuation Prediction for 2.4-72GHz Using LTSM, An Artificial Recurrent Neural Network Technology

2021 ◽  
Author(s):  
Menachem Domb ◽  
Guy Leshem
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Satellite Communication ◽  
Signal Strength ◽  
Direct Access ◽  
Network Technology ◽  
Fixed Amount ◽  
Satellite Transmitter ◽  
Ground Station ◽  
Neural Network Technology

Free-space communication is a leading component in global communications. Its advantages relate to a broader signal spread, no wiring, and ease of engagement. Satellite communication services became recently attractive to mega-companies that foresee an excellent opportunity to connect disconnected remote regions, serve emerging machine-to-machine communication, Internet-of-things connectivity, and more. Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. In addition, when signals approach the ground station, it has to overcome buildings blocking the direct access to the ground station. Therefore, satellites commonly use redundant signal strength to ensure constant and continuous signal transmission, resulting in excess energy consumption, challenging the limited power capacity generated by solar energy or the fixed amount of fuel. This research proposes LTSM, an artificial recurrent neural network technology that provides a time-dependent prediction of the expected attenuation level due to rain and fog and the signal strength that remained after crossing physical obstacles surrounding the ground station. The satellite transmitter is calibrated accordingly. The satellite outgoing signal strength is based on the predicted signal strength to ensure it will remain strong enough for the ground station to process it. The instant calibration eliminates the excess use of energy resulting in energy savings.

scholarly journals UNORGANIZED AIR RELEASES WITH RADIOACTIVE AEROSOLS FROM THE NEW SAFE CONFINIMENT OF CHNPP INTO THE ENVIRONMENT

2021 ◽  
pp. 181-186
Author(s):  
P.G. Krukovskyi ◽  
Ye.V. Diadiushko ◽  
D.J. Skliarenko ◽  
I.S. Starovit
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Network Technology ◽  
Building Structures ◽  
Flow Rates ◽  
Radioactive Aerosols ◽  
Computational Performance ◽  
Lack Of Information ◽  
Chernobyl Npp ◽  
Neural Network Technology

The New Safe Confinement (NSC) of the Chernobyl NPP, which isolates the destroyed reactor and the “Shelter Object” from the environment, is not airtight, so the problem is the lack of information on the flow of unorganized air with radioactive aerosols outside the NSC. This work presents computational model of the hydraulic state of the NSC, which allows to determine these flow rates through the leaks in the shells and building structures under the walls of the NSC. In addition to the developed model, the NSC hydraulic state model, created by neural network technology, was tested, which showed similar results and much higher computational performance, which allows its use for analysis and prediction of NSC`s hydraulic state in real time.

Prospects for the implementation of neural network technologies in radar information processing systems

2021 ◽  
Author(s):  
E.V. Egorova ◽  
A.N. Rybakov ◽  
M.H. Aksyaitov
Keyword(s):  
Neural Network ◽  
Information Processing ◽  
Network Technology ◽  
Network Methods ◽  
External Conditions ◽  
Network Technologies ◽  
Radar Information ◽  
Neural Network Technologies ◽  
Neural Network Technology

Conducted studies of the phased implementation of neural network technologies in the practice of processing radar information, providing for a gradual increase in the level of neural network methods in processing systems, have shown that the use of neural network technologies can improve the quality of radar information processing in the most difficult conditions that require high computing power, when the dynamics of changes in external conditions is very is high and traditional approaches to the creation of processing systems are not able to provide the required level of efficiency. The need to develop theoretical provisions for neural network processing of radar information was revealed, while the main features of information processing in radars determine the relevance of research devoted to preventing the reduction in the quality of radar images in conditions of a large number of targets and a complex «jamming» environment based on the rational use of neural network technology. Analysis of the phased implementation of neural network technologies in radar information processing systems, as well as the use of neural network technology for processing radar information in terms of search and research, makes it possible to increase the efficiency of neural network methods for all processing tasks. Assessment of the required performance of computational tools allows us to single out the main neural network paradigms, the use of which gives a tangible increase in the efficiency of radar information processing, such as multilayer perceptron, Hopfield associative memory and self-organizing Kohonen network, while it is possible to rank the proposed methods in accordance with the required performance, undemanding to computing power and implemented on existing or promising computing facilities with software implementation of neural network paradigms. The analysis of possible directions for improving the quality of radar information processing does not claim to fully cover the entire multifaceted area of such studies. In this paper, only the most universal and widespread neural network paradigms are considered and the main part of possible areas of their application is analyzed. However, the proposed options show that the use of neural network technologies in critical tasks will improve the efficiency of radar information processing for complex, rapidly changing external conditions. The use of the principles of self-learning and the developed apparatus for the synthesis of neural network methods will reduce the duration and complexity of theoretical research, the conduct of which is a necessary and mandatory part of the traditional approach. In the course of further research, some of the proposed methods can be refined, as well as the emergence of new methods that make it possible to more fully use the advantages of neural network technology. Carrying out further research work in these areas will give a powerful stimulating impetus for the creation in the future of highly efficient methods for processing radar information, which can be implemented on the available element base.

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