Circadian lighting in a space daylit by a tubular daylight device

Jain, Sneha; Fernandes, Luís; Regnier, Cynthia; Garg, Vishal

doi:10.1088/1755-1315/238/1/012030

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Malet-Damour et al (Malet-Damour et al, 2019) show spatial and temporal illuminance distributions for clear and overcast sky on Reunion Island, also studying the effects of adding and varying the position of a reflector inside the dome and of adding a cyclone-resistant subdome under the main dome. Recently, several researchers have investigated the circadian impacts of the light provided by TDDs (Jain et al, 2019;Malet-Damour and Fakra, 2021).…”

Section: Tubular Daylight Devicesmentioning

confidence: 99%

Lighting and visual comfort performance of commercially available tubular daylight devices

Fernandes

Regnier

2023

Solar Energy

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Section: Tubular Daylight Devicesmentioning

confidence: 99%

Lighting and visual comfort performance of commercially available tubular daylight devices

Fernandes

Regnier

2023

Solar Energy

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“…A sensor calibration method is suggested by Juan Sebastian Botero et al [14] to address the above issue. Sneha Jain et al [15] photographically obtained CIE XYZ values using HDR images. McCamy's method is used to calculate CCT values from CIE XYZ values.…”

Section: Introductionmentioning

confidence: 99%

Development of Bayesian Neural Network Model to Predict the Correlated Color Temperature Using Digital Camera and Macbeth ColorChecker Chart

2022

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Information about correlated color temperature influencing the scene due to the surrounding lighting is vital, especially for circadian lighting and photography. This paper proposes a novel image-based machine learning model to predict the correlated color temperature in a scene with the help of the Macbeth ColorChecker color rendition chart and a DSLR camera. In the proposed technique, the researcher fixes the white balance setting in the camera, thereby forcing color difference in the captured image of the Macbeth ColorChecker chart placed in the scene. The Bayesian neural network model considers the color difference values of the six spectrally neutral patches of the Macbeth ColorChecker chart as inputs for CCT prediction. The color differences are calculated using the CIEDE2000 color difference formula. Fours models with white balance settings in the camera as 5000 K, 6500 K, 8000 K, and 10000 K were developed and analyzed. It is experimentally found that the correlated color temperature prediction error is less than five percent by the proposed model with white balance setting in the DSLR camera as 10000 K. The proposed model performed consistently during varied lighting levels and mixed CCT lighting conditions set up with LED, incandescent, and fluorescent lamps.

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“…In a precedent study, a device that applied a small image sensor was used to measure the SPD and calculate the correlated color temperature (CCT) [ 18 ]. Additionally, it presented a method of calculating the color temperature using the RGB pixel values of light source images taken by an HDR (high dynamic range) camera [ 20 ]. However, these methods also have limitations in that a relatively expensive image sensor and HDR camera need to be used.…”

Section: Introductionmentioning

confidence: 99%

Method of Calculating Short-Wavelength-Ratio-Based Color Temperature Supporting the Measurement of Real-Time Natural Light Characteristics through RGB Sensor

Jeon

Lim

2020

Sensors

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The characteristics of natural light are mostly collected through specialized measuring equipment, such as a spectroradiometer, and some suggested measurement methods through a small RGB sensor. However, specialized measuring equipment presents difficulty in its high cost, and the RGB-sensor-based method has the limitation of being unable to measure the wavelength characteristics of natural light that are needed to implement lighting that supports circadian rhythms. This paper presents a method for calculating the short-wavelength-ratio-based color temperature of natural light in real time. First, an analysis of the correlation between the characteristics of natural light collected through a spectroradiometer was performed to determine the factors that were needed to accurately measure the color temperature of natural light. Then, the short-wavelength ratio of natural light was calculated through chromaticity coordinates (x and y), which are output values of the RGB sensor, and an equation for calculating the color temperature of natural light was derived through the short-wavelength ratio. Furthermore, after producing an RGB-sensor-based device, the derived equation was applied to calculate the color temperature of real-time natural light that reflects the wavelength characteristics. Then, as a result of the performance evaluation of the proposed method, the color temperature of natural light was accurately calculated within 1% of the average error rate.

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Circadian lighting in a space daylit by a tubular daylight device

Cited by 3 publications

References 5 publications

Lighting and visual comfort performance of commercially available tubular daylight devices

Lighting and visual comfort performance of commercially available tubular daylight devices

Development of Bayesian Neural Network Model to Predict the Correlated Color Temperature Using Digital Camera and Macbeth ColorChecker Chart

Method of Calculating Short-Wavelength-Ratio-Based Color Temperature Supporting the Measurement of Real-Time Natural Light Characteristics through RGB Sensor

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