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Circadian stimulus – A computation model with photometric and colorimetric quantities

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
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

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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