首页|Findings from Benha University Provide New Insights into Artificial Intelligence (A Complete Artificial Intelligence Pipeline for Radio Frequency Energy Predict ion In Cellular Bands for Energy Harvesting Systems)
Findings from Benha University Provide New Insights into Artificial Intelligence (A Complete Artificial Intelligence Pipeline for Radio Frequency Energy Predict ion In Cellular Bands for Energy Harvesting Systems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Artificial Intelligen ce have been presented. According to news reporting originating in Banha, Egypt, by NewsRx journalists, research stated, “Radio Frequency (RF) energy harvesting has been used to power wireless and low-powered devices. However, RF energy har vesting has limitations in terms of the amount of power that can be collected ba sed on signal availability.” The news reporters obtained a quote from the research from Benha University, “He nce, energy prediction is essential to improve energy harvesting circuits ‘ perf ormance. Previous research has mainly focused on improving power harvesting poli cies or theoretically estimating the harvested energy. Very few works have consi dered the prediction of the RF signal as time series data using real RF measurem ents. Moreover, challenges such as the power consumed by the circuit ‘ s harvest ing decisions and the impact of outliers on the model performance haven ‘ t been addressed yet. This paper presents a complete pipeline for developing the best predictive model for RF energy in cellular frequency bands. Real -time measureme nts are taken in different frequency bands using software-defined radio technolo gy. We use four artificial intelligence techniques to model the RF energy signal . Additionally, we propose an optimized model with an enhanced loss function, wh ich makes the model more resilient to anomalies, saving computational power and time consumed in cleaning the data. The four algorithms are investigated, and th eir prediction accuracies are compared. The average power of a period of 5 min i s accurately forecasted. Numerical results in the 1960 MHz band show that long s hort -term memory has the best performance, followed by the DeepAR algorithm wit h prediction accuracies of 95.76% and 95.02%, respect ively.”
BanhaEgyptAfricaArtificial Intelli genceEmerging TechnologiesMachine LearningBenha University
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