Development of a measuring device for measurements of correlated colour temperature of daylight

Svetloba ima velik vpliv na naše obnašanje in počutje. Z umetno razsvetljavo želimo nadomestiti dnevno svetlobo v času, ko je te premalo ali je sploh ni. Vendar pa vsaka razsvetljava ni ustrezna za življenje in delo ljudi v notranjih prostorih. S tem magistrskim delom želimo zasnovati orodje za nadzor in meritev zunanje (dnevne) svetlobe z namenom posnemanja le-te v nadzorovanem okolju. Eden izmed načinov zaznavanja in opredelitev učinka barv je merjenje podobne barvne temperature (ang. CCT). V ta namen smo razvili sistem senzorjev, ki je sposoben meriti CCT in podatek o tem posredovati krmilnemu sistemu, ki lahko spreminja CCT svetilk za umetno razsvetljavo. Naš sistem najprej pretvori surove podatke petih RGBsenzorjev v barvni prostor XYZ, nato pa s pomočjo tega izračuna CCT. Iz tako pridobljene vrednosti CCT lahko nato ustvarimo umetno svetlobo, ki kar se da dobro posnema naravno. Pomemben del računanja CCT je umerjanje senzorjev oziroma transformacijska matrika, ki se uporablja pri prehodu iz enega barvnega prostora v drugega. To umerjanje smo opravili s pomočjo strojnega učenja. Rezultati vrednotenja 1080 meritev, narejenih s spektrometrom in senzorji, so pokazali, da lahko s strojnim učenjem uspešno umerimo delovanje nizkocenovnih RGB-senzorjev.Light has a significant impact on our behavior and the way we feel. Using artificial light, we aim to substitute daylight in a time, when the amount of it is insufficient, although not every type of lighting is suitable for our everyday needs, living and working indoors. With this masters thesis we aim to design a tool for controlling and measuring the external (day)light rays, resulting in a copy of them in a controlled environment. One of the ways to perceive colors and define their effect is called CCT – Correlated Color Temperature. In order to do so we developed a system of sensors, which is able to perform CCT value measurements and then send the information to the steering system which in the end changes the CCT of the artificial lamps. Firstly, our system transforms the raw data from the five RGB sensors into a XYZ color space, from which we then get a CCT value reading. From this value reading we can then create artificial light which successfully imitates its natural source. One of the most important parts of our work when calculating the CCT is calibrating the sensors. Specifically, setting up the transformation matrix from one color space to another. We did this using machine learning. The results of the evaluation of 1080 measurements (which were done using a spectoradiometer) showed us, that using machine learning is a successful way to calibrate the utility of low-cost RGB sensors