TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light

This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color tem...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Seung-Taek Oh [verfasserIn] Deog-Hyeon Ga [verfasserIn] Jae-Hyun Lim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	TadGAN recursive TadGAN natural light CCT daily color temperature

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 20, p 7774
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:20, p 7774

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22207774

Katalog-ID:	DOAJ02165607X

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language	English
source	In Sensors 22(2022), 20, p 7774 volume:22 year:2022 number:20, p 7774
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topic_facet	TadGAN recursive TadGAN natural light CCT daily color temperature Chemical technology
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callnumber-first	T - Technology
author	Seung-Taek Oh
spellingShingle	Seung-Taek Oh misc TP1-1185 misc TadGAN misc recursive TadGAN misc natural light misc CCT misc daily color temperature misc Chemical technology TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light
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topic_title	TP1-1185 TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light TadGAN recursive TadGAN natural light CCT daily color temperature
topic	misc TP1-1185 misc TadGAN misc recursive TadGAN misc natural light misc CCT misc daily color temperature misc Chemical technology
topic_unstemmed	misc TP1-1185 misc TadGAN misc recursive TadGAN misc natural light misc CCT misc daily color temperature misc Chemical technology
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title	TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light
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title_sort	tadgan-based daily color temperature cycle generation corresponding to irregular changes of natural light
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title_auth	TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light
abstract	This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking sensors. Analysis results for the measured natural light showed that irregular color temperature cycles were observed for more than 90% of the year due to the influence of regional weather and atmospheric conditions. Regular color temperature cycles were observed only on some clear days. The color temperature cycle dramatically affects the health of the occupants. However, since irregular color temperatures are difficult to predict and cannot easily generate cycles, only the color temperatures of some clear days are currently used, and the actual color temperature of natural light cannot be reproduced. There is little research on deriving real-time periodic characteristics and lighting services targeting irregular color temperatures of natural light. Therefore, this paper proposes a TadGAN (Time Series Anomaly Detection Using Generative Adversarial Networks)-based daily color temperature cycle generation method that responds to irregular changes in the natural light color temperature. A TadGAN model for generating the natural light color temperature cycle was built, and learning was performed based on the dataset extracted through the measured natural light characteristic Database. After that, the generator of TadGAN was repeatedly applied to generate a color temperature cycle close to the change of natural light. In the performance test of the proposed method, it was possible to generate periodic characteristics of the irregular natural light color temperature distribution.
abstractGer	This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking sensors. Analysis results for the measured natural light showed that irregular color temperature cycles were observed for more than 90% of the year due to the influence of regional weather and atmospheric conditions. Regular color temperature cycles were observed only on some clear days. The color temperature cycle dramatically affects the health of the occupants. However, since irregular color temperatures are difficult to predict and cannot easily generate cycles, only the color temperatures of some clear days are currently used, and the actual color temperature of natural light cannot be reproduced. There is little research on deriving real-time periodic characteristics and lighting services targeting irregular color temperatures of natural light. Therefore, this paper proposes a TadGAN (Time Series Anomaly Detection Using Generative Adversarial Networks)-based daily color temperature cycle generation method that responds to irregular changes in the natural light color temperature. A TadGAN model for generating the natural light color temperature cycle was built, and learning was performed based on the dataset extracted through the measured natural light characteristic Database. After that, the generator of TadGAN was repeatedly applied to generate a color temperature cycle close to the change of natural light. In the performance test of the proposed method, it was possible to generate periodic characteristics of the irregular natural light color temperature distribution.
abstract_unstemmed	This study to develop lighting is advanced for reproducing natural light color temperature beneficial to humans. Methods were introduced to provide daily color temperature cycles through formulas based on the measured natural light characteristics or real-time reproduction of natural light color temperature linking sensors. Analysis results for the measured natural light showed that irregular color temperature cycles were observed for more than 90% of the year due to the influence of regional weather and atmospheric conditions. Regular color temperature cycles were observed only on some clear days. The color temperature cycle dramatically affects the health of the occupants. However, since irregular color temperatures are difficult to predict and cannot easily generate cycles, only the color temperatures of some clear days are currently used, and the actual color temperature of natural light cannot be reproduced. There is little research on deriving real-time periodic characteristics and lighting services targeting irregular color temperatures of natural light. Therefore, this paper proposes a TadGAN (Time Series Anomaly Detection Using Generative Adversarial Networks)-based daily color temperature cycle generation method that responds to irregular changes in the natural light color temperature. A TadGAN model for generating the natural light color temperature cycle was built, and learning was performed based on the dataset extracted through the measured natural light characteristic Database. After that, the generator of TadGAN was repeatedly applied to generate a color temperature cycle close to the change of natural light. In the performance test of the proposed method, it was possible to generate periodic characteristics of the irregular natural light color temperature distribution.
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title_short	TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light
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up_date	2024-07-03T22:07:29.334Z
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TadGAN-Based Daily Color Temperature Cycle Generation Corresponding to Irregular Changes of Natural Light

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