Sebastian Babilon

This study explores a novel approach to monitor the spectral emission of LEDs by estimating the spectral power distribution from the spectral sensor responses during an accelerated ageing experiment. Two methods for reconstructing the actual LED spectra from sensor responses are presented and tested, one solely requires sensor datasheet information and the other uses a full spectral characterisation of the sensor’s spectral sensitivities. The reconstruction results show that a spectral sensor can provide accurate spectral estimates even after severe LED degradation. Only for an LED that suffered a phosphor crack, affecting its spatial radiation characteristics, limited ability to estimate the true spectral power distribution without prior assumptions about the spectral changes must be reported. Overall, the use of a spectral sensor, even without detailed characterisation of the sensor itself, allows for an accurate monitoring of the true emission of LEDs, with a maximum radiometric error of 0.73 %, a maximum colormetric error of 0.0017Δ u ′ v ′ and a maximum spectral nRMSE error of 0.0097 compared to a spectroradiometric measurement. This advance holds great promise for improving lighting technology, particularly in applications that require constant radiometric output and stable color.

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.

Cortisol secretion has a fundamental role in human circadian regulation. The cortisol awakening response (CAR) can be observed as a daily recurring sharp increase in cortisol concentration within the first hour after awakening and is influenced by environmental light conditions. The current work provides the study protocol for an ongoing research project that is intended to explore the spectral dependencies and to discuss measures of emotional state and cognitive functioning potentially related to the CAR. Based on a controlled within-subjects sleep laboratory study, the impact of a two-hour, (quasi-)monochromatic, post-awakening light exposure of different peak wavelength (applied from 6:00 to 8:00 am) on resulting CAR levels should be investigated in a systematic manner to eventually derive a corresponding spectral sensitivity model. As a secondary outcome, it should be explored whether a potentially light-enhanced cortisol secretion might also impact different measures of sleepiness, mood, and vigilance for certain wavelengths. The study protocol described in the present work discusses the various protocol steps using pilot data collected for two different wavelength settings (i.e., short-wavelength blue-light at λ_max = 476 nm and long-wavelength red-light at λ_max = 649 nm) experienced by a group of four healthy male adults at an average ± SD age of 25.25 ± 3.59 years.

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.

Vertical farming is considered to play a crucial role in future food supply. Until today, the high amount of electrical energy required for artificial lighting has been problematic in this context. Various possibilities for increasing efficiency through adapted lighting conditions have been and are being investigated. However, comparably little attention is paid to increasing utilance, i.e., the amount of photons that can effectively be used by the plant. In this work, a novel targeted lighting strategy is therefore proposed that allows for a dynamic adaptation of the luminaires’ light distribution to match the effective crop size at each stage of plant growth in a fully-automated manner. It is shown that the resulting utilance can significantly be increased compared to standard full-coverage lighting. Moreover, it is found that the proposed strategy is likely to consume less than half of the electrical energy usually required for the latter. An additional increase in system efficiency can be prognosticated and the potential energy savings are estimated based on assumptions of future LED generations derived from literature.

Provoking high user acceptance in lighting can be a very challenging task and demands suitable tools for properly modelling and predicting the users’ perception of the lit environment. Recently, a new model formalism based on the perceptually relevant attributes of perceived brightness, visual clarity, and color preference has been introduced and successfully applied in some preliminary studies. However, a proof of the model’s applicability from a lighting practitioner’s point of view for realistic lighting scenarios and use-cases is still pending and should be performed as part of this work. For this purpose, results of two dedicated lighting condition rating experiments representing different lighting contexts are reported. It is shown that the model predictions for all three perceptual attributes exhibit excellent linear correlations with the respective subject mean ratings and, in all cases, correctly predict the test light sources’ rank order. These results clearly emphasize the applicability and practical relevance of the model and confirm the suitability of its multi-dimensional approach.

The purpose of this work is to determine as a function of velocity the minimal roadway luminance that is required to be judged as being bright enough for a driver to perform a nighttime driving task with an adequate feeling of safety. In this context, it shall also be evaluated which areas of the vehicle forefield are most crucial for the driver’s general brightness perception. A field study with 23 subjects and dimmable LED headlights was conducted, in which the subjects were given the task to assess their perceived brightness for different luminance levels caused by the headlights’ low-beam distribution in the vehicle’s forefield on a 5-step rating scale. The experiments were repeated for three different driving velocities of 0 km h⁻¹ (static case), 30 km h⁻¹, and 60 km h⁻¹, respectively. Results for the static case indicate that, for the roadway to be perceived as bright enough by 50% of the subjects, an average roadway luminance of 0.88 cd m⁻² is required in an area up to 32 m in front of the vehicle. Furthermore, a significant effect of driving speed is observed. For example, at 60 km h⁻¹, the luminance must be increased to 1.54 cd m⁻² to be still perceived as sufficiently bright by 50% of the subjects.

Residents of nursing homes often have a very limited access to natural daylight, which is the main environmental cue for circadian entrainment that usually cannot be compensated by standard indoor lighting. For the development of adequate modernization strategies with regard to a more human-centred lighting approach, reliable field measurements of the circadian effectiveness of the prevalent lighting conditions are required. To ease the task for the lighting practitioner, our lab recently developed a simplified method of estimating the biologically meaningful circadian stimulus (CS) metric using standard measurement equipment. In this work, the proposed method is applied for the first time in field measurements to assess the circadian effectiveness of the indoor light conditions including daylight entry of a nursing home in the Frankfurt Rhine-Main area. Reference thresholds for low, moderate and high circadian stimuli derived from the literature were used for subsequent data analysis. Mostly, moderate to rather low CS values were found. It is concluded that the amount of circadian-effective light is insufficient to provoke proper circadian stimulation, which may lead to progressive circadian disruptions manifesting in adverse effects on the residents’ health and well-being. Further evidence is provided that the insufficient-lighting-situation in nursing homes is a serious problem in long-term care and needs urgent consideration in health policy.

Current subject studies and data-driven approaches in lighting research often use manually selected light spectra, which usually exhibit a large bias due to the applied selection criteria. This paper, therefore, presents a novel approach to minimize this bias by using a data-driven framework for selecting the most diverse candidates from a given larger set of possible light spectra. The spectral information per wavelength is first reduced by applying a convolutional autoencoder. The relevant features are then selected based on Laplacian Scores and transformed to a two-dimensional embedded space for subsequent clustering. The low dimensional embedding, from which the required diversity follows, is done with respect to the locality of the features. In a second step, photometric parameters are considered and a second clustering is performed. As a result of this algorithmic pipeline, the most diverse selection of light spectra complying with a given set of relevant photometric parameters can be extracted and used for further experiments or applications.

Light Emitting Diodes are an integral part of modern illumination systems. While their long-term stability in terms of lumen maintenance used to be in the focus leading to drastically increased nominal lifetimes over the past decade, the color shift or respectively chromaticity shift is lately attracting more and more attention especially in lighting applications that demand for a high chromatic stability. Common ways of displaying and reporting color shifts in LEDs and LED products provide only limited possibilities to accurately classify changes in color according to the current understanding of the underlying degradation processes. This paper therefore presents a methodology to overcome the observed discrepancies in analyzing time-dependent chromaticity data in terms of their shift direction and distance within the CIE 1976 ( $u',v'$ ) chromaticity diagram. The use of an extrapolation function extending the shift towards the spectral locus enables the determination of the corresponding nearest spectral locus intersection wavelength. Thereby, it is possible to classify changes in color according to different shift regions, which basically allows for performing an automated comprehensive color shift mode analysis. As part of this paper five different algorithmic approaches for determining the intersection wavelength and shift region are discussed and compared in terms of their accuracy and ease in implementation.