Prediction of channel characteristics can be of immense value in improving the quality of signals in high frequency satellite systems. Making prediction of rainfall rate (RR) using Markov theory and using that prediction in an intelligent system (IS) to maintain the quality of service (QoS) in channels impacted by attenuation due to weather is the object of this paper. The paper describes the method of prediction rainfall rate (RRp) using weather collected by environment agencies and applying the predictions to gateway and ground terminal for optimal control of channel characteristics. This novel method of predicting weather characteristics using Markov theory supplies valuable data to develop an enhanced back propagation-learning algorithm to iteratively tune the IS to adapt to changing weather conditions. The effectiveness of the algorithm was tested on a simulated model for activating the weighted modulation and codepoint control. It demonstrated marked improvements in channel parameter tuning and signal quality.
The anti-jam advantages of higher carrier frequencies for different applications and the necessity for wider information bandwidths have led system designers to look for operational frequencies above Ku band in order to support their next generation of communications satellite systems. The undesirable of characteristics of this band is that weather impact on signal attenuation and distortion on signal fidelity is a serious concern at frequencies above 10 GHz. This paper proposes a technique to achieve optimum signal quality when the impacts of weather attenuations have become significant. Our adaptive scheme is able to estimate these attenuations using predicted weather correlated database supplied by International Telecommunication Union-Radiocommunications (ITU-R) propagation models. The scheme is applied to gateway and ground terminal so that they collaborate to optimally control the channel characteristics. This paper describes a three dimensional relationships between rain attenuation (RA), frequency and rainfall rate (RR), and similarly between signal to noise ratio (SNR), weather attenuations and transmitted power. The relationships are exploited to develop an enhanced back propagation-learning algorithm that are used to iteratively tune the controller based on weather conditions and by means of SNR feedback values and other satellite parameters. The algorithm applied to a simulated model for activating the weighted Modulation/Codepoint control showed markedly improvements in ensuring optimal configuration settings and improved tolerance/safety margins for any given service level agreement (SLA) commitment.
The continuity of power supply is maintained by reconfiguring a power system as its operating state changes. The reconfiguration is done by opening and closing circuit breakers and isolators. An improper operation of a switch can damage equipment and/or adversely affect the quality of the supply to the customers.Switching operations are performed by operators (as well as by substation controllers) using pre-defined guidelines including interlocking of switches. A major obstacle in using computerized switching schemes is that interlocking logic has to be designed specifically for each substation. This paper describes the design of a software application that can be used to automatically generate interlocking schemes for substations. The software has two components, one to generate the interlocking scheme from the substation data and the other to generate switching instructions and implement them. Three examples of interlocking schemes are included in the paper.
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