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Full-Text Articles in Engineering
Influence Of Neural Network Training Parameters On Short-Term Wind Forecasting, Adel Brka, Yasir M. Al-Abdeli, Ganesh Kothapalli
Influence Of Neural Network Training Parameters On Short-Term Wind Forecasting, Adel Brka, Yasir M. Al-Abdeli, Ganesh Kothapalli
Research outputs 2014 to 2021
This paper investigates factors which can affect the accuracy of short-term wind speed prediction when done over long periods spanning different seasons. Two types of neural networks (NNs) are used to forecast power generated via specific horizontal axis wind turbines. Meteorological data used are for a specific Western Australian location. Results reveal that seasonal variations affect the prediction accuracy of the wind resource, but the magnitude of this influence strongly depends on the details of the NN deployed. Factors investigated include the span of the time series needed to initially train the networks, the temporal resolution of these data, the …
The Ph Sensing Properties Of Rf Sputtered Ruo2 Thin-Film Prepared Using Different Ar/O2 Flow Ratio, Ali Sardarinejad, Devendra Kumar Maurya, Kamal Alameh
The Ph Sensing Properties Of Rf Sputtered Ruo2 Thin-Film Prepared Using Different Ar/O2 Flow Ratio, Ali Sardarinejad, Devendra Kumar Maurya, Kamal Alameh
Research outputs 2014 to 2021
The influence of the Ar/O2 gas ratio during radio frequency (RF) sputtering of the RuO2 sensing electrode on the pH sensing performance is investigated. The developed pH sensor consists in an RF sputtered ruthenium oxide thin-film sensing electrode, in conjunction with an electroplated Ag/AgCl reference electrode. The performance and characterization of the developed pH sensors in terms of sensitivity, response time, stability, reversibility, and hysteresis are investigated. Experimental results show that the pH sensor exhibits super-Nernstian slopes in the range of 64.33-73.83 mV/pH for Ar/O2 gas ratio between 10/0-7/3. In particular, the best pH sensing performance, in …