Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

Klir, Stefan; Fathia, Reda; Babilon, Sebastian; Benkner, Simon; Khanh, Tran Quoc

doi:10.3390/app11199062

Cited by 3 publications

(1 citation statement)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the issue of proper system integration and testing, ongoing research by our lab further addresses the lighting control problem in realistic application scenarios. For this purpose, a multi-channel IoT floor lamp prototype has recently been developed with contributions from some of the present authors [139] that allows for the implementation of additional color sensor devices to monitor the ambient lighting conditions as sketched in the Introduction. Thus, in order to account for potential changes in the ambient lighting conditions, color sensor feedback is intended to serve as suitable input for adaptively updating the lighting control parameters to achieve constant or pre-defined indoor lighting conditions that may for example keep the circadian effectiveness always at an optimal level.…”

Section: Discussionmentioning

confidence: 99%

Processing RGB Color Sensors for Measuring the Circadian Stimulus of Artificial and Daylight Light Sources

et al. 2022

Self Cite

View full text Add to dashboard Cite

The three main tasks of modern lighting design are to support the visual performance, satisfy color emotion (color quality), and promote positive non-visual outcomes. In view of large-scale applications, the use of simple and inexpensive RGB color sensors to monitor related visual and non-visual illumination parameters seems to be of great promise for the future development of human-centered lighting control systems. In this context, the present work proposes a new methodology to assess the circadian effectiveness of the prevalent lighting conditions for daylight and artificial light sources in terms of the physiologically relevant circadian stimulus (CS) metric using such color sensors. In the case of daylight, the raw sensor readouts were processed in such a way that the CIE daylight model can be applied as an intermediate step to estimate its spectral composition, from which CS can eventually be calculated straightforwardly. Maximal CS prediction errors of less than 0.0025 were observed when tested on real data. For artificial light sources, on the other hand, the CS approximation method of Truong et al. was applied to estimate its circadian effectiveness from the sensor readouts. In this case, a maximal CS prediction error of 0.028 must be reported, which is considerably larger compared to daylight, but still in an acceptable range for typical indoor lighting applications. The use of RGB color sensors is thus shown to be suitable for estimating the circadian effectiveness of both types of illumination with sufficient accuracy for practical applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Processing RGB Color Sensors for Measuring the Circadian Stimulus of Artificial and Daylight Light Sources

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

View full text Add to dashboard Cite

Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird's eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof's comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

show abstract

Reconstruction of spectral irradiance in a real application with a multi-channel spectral sensor using convolutional neural networks

et al. 2023

View full text Add to dashboard Cite

Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditions. Although mostly based on synthetic data, previous studies adopting small, low-cost, multi-band color sensors for this kind of parameter estimation have reported some promising preliminary results. Building up on these findings, the present work introduces a new methodology for estimating the absolute spectral irradiances of real-world lighting scenarios from the responses of a 10-channel spectral sensor by using a convolutional neural network approach. The lighting scenarios considered here are based on a tunable white floor lamp system set up at three different indoor locations and comprise combinations of LED, fluorescent, tungsten, and daylight lighting conditions. For white light mixtures of the various spectral components, the proposed reconstruction methodology yields estimates of the spectral power distribution with an average root-mean-square error of 1.6%, an average Δu′v′ of less than 0.001, and an average illuminance accuracy of 2.7%. Sensor metamerism is discussed as a limiting factor for the achievable spectral reconstruction accuracy with certain light mixtures.

show abstract

Unsupervised Clustering Pipeline to Obtain Diversified Light Spectra for Subject Studies and Correlation Analyses

Cited by 3 publications

References 49 publications

Processing RGB Color Sensors for Measuring the Circadian Stimulus of Artificial and Daylight Light Sources

Processing RGB Color Sensors for Measuring the Circadian Stimulus of Artificial and Daylight Light Sources

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

Reconstruction of spectral irradiance in a real application with a multi-channel spectral sensor using convolutional neural networks

Contact Info

Product

Resources

About