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

Featured Application: Selection of most diverse light spectra from a larger set of possible candidates to be used in subject studies or for machine learning to find correlations between photometric and other parameters such as psychological, physiological, or preference-based outcome measures. Abstract: 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

Modeling of individual lighting preferences depending on various influencing parameters

Inspired by the evolutionary adapted visual systems of humans towards the natural changing sunlight of a day and year, office lighting should likewise not be static. Due to the possible changeability of illuminance and color temperature of multi-channel luminaires, such dynamic conditions can be realized in offices. Since the light is designed for people, their preferences should additionally be addressed in the selection process of a dynamic light scenario. However, by the fact that each person has a different perception of the current environment through different psychological and physiological states, the dynamic preference must be determined with the associated influencing parameters. The questions that arise are, how does a system have to be designed to model the individual user light preferences based on certain influencing parameters and in particular what are the impacting parameters. To address these questions, this doctoral thesis introduces a 32 months long-term field study with 30 in the research phase developed floor lamps. Through the specialized hard- and software, participants are able to insert the current state-of-mind as well as the current light preference with an illuminance and color temperature selection out of 100 combinations within a web interface. Furthermore, environmental data of the room as well as the prevailing weather condition are gathered during the period of use. In order to enhance the psychological rated light spectra space, each day a new set of 100 white light settings out of a collection with 5,781 spectra is automatically distributed to the floor lamp users. More diverse light spectra are able to be rated in a so-called training process, in which the participants perform a self-conducted subject study each day with five diverse light settings. The maximized space of rated light settings in combination with the current mood, indoor sensor values and weather data forms the ground truth data set for the evaluation. During the evaluation, a novel static and dynamic preference lighting models (PLMs) were defined. The static PLM introduces two equations, one for illuminance and one for correlated color temperature (CCT) that can be fitted with a low amount of data to the preferences of each individual participant. Whereby the illuminance preference is modelled only with the time of day and in contrast the CCT preference integrate time of day, week of year, indoor temperature and humidity. Thus, these models incorporate the dynamic light preference behavior of a user with certain environmental parameters and can be personalized with few data points. Whereas, the dynamic PLM is based on a data-driven approach. A contextual multi armed bandit (CMAB) is stated as the main component with 32 environmental input features. Since multi armed bandits include only the current environment in the prediction of a light setting, the sensor and weather input features are classified into cluster-labels based on the time-series characteristics of the last six hours. Light spectra with the highest preference are thus predicted for a certain environment and individual user based on the ground truth data set gathered in the long-term field study. A novel reward function for the lighting domain forms the main component for a user rating estimation. This estimation of non-existing user ratings enables a fully automatic learning process, and a generalized user model which is able to react to preference changes. Furthermore, a novel preference rating function is defined and enables the comparability between users by abstracting the absolute light rating with quantile ranges and including usage parameters. In particular, the three involved parameters are: (i) Ten quantile ranges of the individual user ratings, (ii) the illumination durations of each spectrum as well as (iii) the frequency of how often a spectrum was illuminated to abstract each user’s individual rating behavior in a utility function. This likewise enables, by discarding the absolute user ratings from the equation, to obtain a fully usage based preference rating, and a full automated learning process without any user interaction is made possible. As application, this preference rating function is included in a user-based collaborative filtering approach that suggests light ratings based on other participants with a similar light behavior in a certain environment. Therefore, knowledge about the light similarity between the participants in environments are revealed. As result, light preferences of other users with a similar light preference in a given environment can thus be suggested to enhance the learning process of the dynamic PLM. The fitted static PLM equations of the combined ten users from the long-term floor lamp study resulted in a high coefficient of determination for both the illuminance and CCT equations of R2 = 0.97 and R2 = 0.98 respectively and demonstrated the meaningfulness of these functions. A four-month evaluation experiment with the trained dynamic PLM stated that high quality light suggestions are predicted, which have a higher median rating as the manual adjusted lights settings previous to the evaluation study for spectra that are illuminated for longer than ten minutes. Manual light adjustments during the study have a similar median rating as the smart predictions by the CMAB. The satisfaction of the participants as well as the meaningfulness of the predictions in a real environment could therefore be demonstrated. The feasibility of the user-based collaborative filtering approach, which was enabled by the high amount of 24,261 gathered light rating data, is presented with light suggestions for the participants in a certain environment. In 21 most rated environments, on average five light rating predictions are suggested for the ten users. This approach enables an active learning process of new, unseen light settings for users of the same light preference group per environment. With the presented framework and approaches of this doctoral thesis, it is now possible to predict dynamic changing illuminances and color temperatures with respect to influencing environmental parameters even with very little knowledge of a user based on the static PLM. If a large data set with multiple users exists, the user-based collaborative filtering approach can be consulted to actively enhance the suggested light settings. As soon as a sufficient amount of data is present, the introduced dynamic preference lighting model (PLM) is able to predict preferred light settings based on more environmental impacts and learn dynamically varying light preferences per user. Due to the novel light preference rating function which can also integrate only the usage of the luminaire, a minimal invasive system is introduced in which subjects only have to use the lamp with the provided predictions or manually adjusting it for unique conditions and the PLM dynamically adapts to these new preferences. This results in a comprehensive framework for individual light spectra predictions per user with respect to dynamically changing environmental parameters

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

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

Reducing the stroboscopic effects of LED luminaires with pulse width modulation control

Pulse width modulation for dimming the light output of LEDs has become common. When pulse width modulation is used at low frequencies unwanted visual artefacts including flicker perception and stroboscopic effects may occur. These artefacts need to be avoided or at least reduced to a minimum in order to obtain high user acceptance. In this paper, an optimized phase-shifted pulse width modulation method is described, implemented and validated in a visual experiment. The method is intended to minimize the stroboscopic effect on a reference surface by first optimizing the LED units of a single LED luminaire and then co-optimizing several of these luminaires. The optimized pulse width modulation waveforms are then compared to standard pulse width modulation dimming methods. In the visual experiment, 13 subjects rated the extent of the stroboscopic effect of standard and optimized waveforms in a white painted experimental room. The results indicate that the optimized waveforms are indistinguishable from constant light

Evaluation system of adaptive lighting systems indynamic situations at night-time

In the recent years, one of the key areas of research on automotive lighting has been focused on developing adaptive lighting systems. The performance of such systems depends not only on their photometric characteristics but also due to driving situation, roadway geometry and vehicle kinematics. Hence, objective evaluation and characterization of adaptive lighting systems require driving test under usual road conditions. An evaluation system, that provides accurate and reliable measurement results in various environments, presents multifaceted technological challenges. The system is intended to provide a simple software adaptation in case of additional or new hardware components offering versatile test procedures. Additional challenges are imposed by the need of synchronization of several measurement systems. All these various aspects have been taken into account in the developed system

Über die Wirkung spektral variierender Farbtemperaturen auf die kognitive Leistung des Menschen

Die ähnlichste Farbtemperatur (CCT) wird derzeit in einer Vielzahl von Untersuchungen als Marker für die biologische Lichtwirkung genutzt. Moderne mehrkanal-LED- Lichtsysteme bieten die Möglichkeit die spektrale Zusammensetzung bei gleicher CCT zu variieren. Mit diesem Beitrag wird die Auswirkungen von ipRGC-optimierten Spektren verschiedener Farbtemperaturen in einem Arbeitsumfeld auf die akute Beeinflussung von kognitiven Parametern aufgezeigt. Dazu werden Daten anhand von Fragebögen, Leistungstests und physiologischen Parametern des kardiovaskulären Systems sowie durch Pupillen- und Blickdaten in einer Studie erhoben und auf Korrelation überprüft. Die Ergebnisse dienen als fundamentale Grundlagen für zukünftige dynamischen Beleuchtungsanlagen in Büroräumen

Sky-like interior light settings: a preference study

This paper explores human observer preferences for various sky-like interior lighting scenarios realized by a combination of a blue-enriched indirect uplight component with a correlated color temperature (CCT) of 6,500 K up to 30,000 K and a 4,000 K or 5,500 K direct downlight component. Variations in the natural sky were mimicked by the indirect uplight component reflected from the ceiling of the experimental room. The settings for the direct lighting component, on the other hand, were selected based on the reported outcomes of previous preference studies in the field of interior lighting. The resulting lighting conditions were evaluated by a total of 29 observers, from which subjective ratings of brightness, sky-likeness, satisfaction, pleasantness, and general appeal were collected in an office workplace environment. In this experimental setting, the most preferred lighting conditions exhibited a direct-to-indirect lighting ratio of 50:50 with a CCT of 4,000 K in the direct component and 6,500, 7,500, and 9,000 K in the indirect component. For all examined combinations, none was rated as truly sky-like. Nonetheless, the study results showed that only the combination of a warmer CCT in the direct component and a cooler, blue-enriched CCT in the indirect lighting component leads to a maximum in the subjects’ preference ratings. In summary, the subjects preferred light settings with a white appearance on the work surface without any intense or noticeable blue cast or tint