Truong, William Zhang-Wei-Lian ; Trinh, Vinh Quang ; Khanh, Tran Quoc (2023)
Circadian stimulus – A computation model with photometric and colorimetric quantities.
In: Lighting Research & Technology, 2020, 52 (6)
doi: 10.26083/tuprints-00017808
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | Circadian stimulus – A computation model with photometric and colorimetric quantities |
Language: | English |
Date: | 28 November 2023 |
Place of Publication: | Darmstadt |
Year of primary publication: | October 2020 |
Place of primary publication: | London |
Publisher: | SAGE Publications |
Journal or Publication Title: | Lighting Research & Technology |
Volume of the journal: | 52 |
Issue Number: | 6 |
DOI: | 10.26083/tuprints-00017808 |
Corresponding Links: | |
Origin: | Secondary publication DeepGreen |
Abstract: | The circadian stimulus is an important, validated and updated metric that describes the invisible influences of light on the human circadian system explicitly and scientifically. However, an absolute spectral power distribution must be supplied for its computation, which is only measurable by an expensive and complicated spectrometer. This paper proposes an alternative circadian stimulus computation model that is identified as the function CS(z, Eᵥ) for white light sources based on the most common and simplest parameters of illuminance Ev in lux and the chromaticity coordinate z. These parameters are well known and widely used in both colour science and lighting technology. In order to prove the accuracy and availability of the model, an internal validation was performed with the adapted method repeating split data to check the goodness of the model fit. The fitted model achieved a maximum residual of 0.058 in the circadian stimulus quantity (R²= 0.998). An external validation with the maximum residual of 0.030 (R² = 0.999) provided stronger evidence for the usability of the model in applications. |
Status: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-178082 |
Classification DDC: | 600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics |
Divisions: | 18 Department of Electrical Engineering and Information Technology > Adaptive Lighting Systems and Visual Processing |
Date Deposited: | 28 Nov 2023 10:37 |
Last Modified: | 01 Dec 2023 10:45 |
SWORD Depositor: | Deep Green |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/17808 |
PPN: | 513585257 |
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