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Polychromatic lighting sources that are composed of at least four different colored light-emitting diodes (LEDs) offer versatility in color quality of illumination. In this paper, different methods of assessment of color quality of white light are discussed and a general approach to the solution of the color-mixing problem by means of optimization in respect of several color rendition characteristics is considered. Spectral power distributions of model tetrachromatic solid-state sources obtained by maximizing various figures of merit, such as color rendering index, gamut area index, color quality scale, and indices based on the statistical analysis of the just perceivable chromaticity differences for a large number of test color samples, are demonstrated. A concept tetrachromatic lighting source that can be operated within a dynamical trade-off between two opposing color rendition characteristics, the ability to render colors with high fidelity and the ability to render colors with increased chromatic saturation, is introduced. Such "smart" sources with tailored color quality can meet individual needs and preferences of color vision and find numerous applications in lighting.

Light emitting diodes (LEDs) have become an effective lighting solution because of the characteristics of energy efficiency, flexible controllability and extended lifetime. They find use in numerous lighting systems for residents, industries, enterprises and street lighting applications. The efficiency and trustworthiness of the LED systems considerably based on the thermal mechanical loading improved several degradation schemes and respective interfaces. The complication of the LED systems limits the theoretic interpretation of the core reasons for the luminous variation or the formation of the direct correlation among the thermal aging loading and the luminous output. Therefore, this paper designs a new hybrid Henry gas solubility optimization with deep learning (HHGSO-DL) algorithm for LED driver system design. The presented HHGSO-DL technique mainly concentrates on the derivation of empirical relationships among the design parameters, thermal aging loading and luminous outcomes of the LED product. In the presented HHGSO-DL technique, bidirectional long short-term memory (BiLSTM) algorithm is executed for examining the empirical relationship and its hyperparameters can be tuned by the HHGSO algorithm. In this work, the HHGSO algorithm is derived by the integration of traditional HGSO algorithm with oppositional-based learning (OBL) concept. The performance of the HHGSO-DL technique can be investigated on LED chip packaging and LED luminaire with thermal aging loading. The extensive results demonstrate the promising performance of the HHGSO-DL technique over other state-of-the-art approaches.