Soiling and value of cleaning for low-tilt PV systems in temperate climates: A Swiss case study
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: 2014شناسه الکترونیک: 10.1109/IRS.2014.6869280
کلیدواژه(گان): electromagnetic wave scattering,n feature extraction,n neural nets,n radar signal processing,n signal classification,n FSR,n Z-score,n forward scattering radar,n frequency 151 MHz,n frequency 434 MHz,n frequency 64 MHz,n ground target classification,n neural network modelling,n target signature feature extraction,n vehicle size classification technique,n Artificial neural networks,n Feature extraction,n Radar,n Testing,n Training,n Vehi
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Soiling and value of cleaning for low-tilt PV systems in temperate climates: A Swiss case study
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contributor author | Alet, P.-J. | |
contributor author | Cuony, P. | |
contributor author | Dutoit, J. | |
contributor author | Scharrenberger, L. | |
contributor author | Currat, B. | |
contributor author | Perret-Aebi, L.-E. | |
contributor author | Ballif, C. | |
date accessioned | 2020-03-12T22:06:31Z | |
date available | 2020-03-12T22:06:31Z | |
date issued | 2014 | |
identifier other | 6993275.pdf | |
identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/1071701 | |
format | general | |
language | English | |
publisher | IEEE | |
title | Soiling and value of cleaning for low-tilt PV systems in temperate climates: A Swiss case study | |
type | Conference Paper | |
contenttype | Metadata Only | |
identifier padid | 8207777 | |
subject keywords | electromagnetic wave scattering | |
subject keywords | n feature extraction | |
subject keywords | n neural nets | |
subject keywords | n radar signal processing | |
subject keywords | n signal classification | |
subject keywords | n FSR | |
subject keywords | n Z-score | |
subject keywords | n forward scattering radar | |
subject keywords | n frequency 151 MHz | |
subject keywords | n frequency 434 MHz | |
subject keywords | n frequency 64 MHz | |
subject keywords | n ground target classification | |
subject keywords | n neural network modelling | |
subject keywords | n target signature feature extraction | |
subject keywords | n vehicle size classification technique | |
subject keywords | n Artificial neural networks | |
subject keywords | n Feature extraction | |
subject keywords | n Radar | |
subject keywords | n Testing | |
subject keywords | n Training | |
subject keywords | n Vehi | |
identifier doi | 10.1109/IRS.2014.6869280 | |
journal title | enewable Power Generation Conference (RPG 2014), 3rd | |
filesize | 391936 | |
citations | 0 |