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LED spectrum optimal modelization
Dupuis P., Purwanto E.b,c, Sinisuka N.I.b, Zissis G.
a Université de Toulouse, LAPLACE, UMR 5213 (CNRS, INPT, UPS), Toulouse, 31062, France
b School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, 40133, Indonesia
c PT PLN (Persero), Jakarta, 12160, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2018 IEEELED lamps market share is continuously growing since a few years, replacing traditional products due to their higher efficiency and expected lifetime. Yet some aspects such as the dependence between photometric and colorimetric parameters and the applied electrical power still require additional investigations and modeling. In lighting, every parameter is defined as some weighting function applied to the radiometric spectrum. A detailed knowledge of the spectrum sensitivity is thus the key to predict derived parameters. After measuring LED spectrum at different command levels, it was found that the whole set of curves can be predicted from a very small number of generating functions highly correlated with the current level. A new approach is thus proposed in order to predict the light spectrum from a low-order polynomial with the current as the only input.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Colorimetric shift,Generating functions,LED efficiencies,Led spectrums,Low-order polynomials,Thermal drifts,Traditional products,Weighting functions[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Aging,Colorimetric shift,LED CCT,LED efficiency,LED spectrum,Thermal drift[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IAS.2018.8544471[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]