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Machine-learning assisted prediction of spectral power distribution for full-spectrum white light-emitting diode
摘要： The full-spectrum white light-emitting diode (LED) emits light with a broad wavelength range by mixing all lights from multiple LED chips and phosphors. Thus, it has great potentials to be used in healthy lighting, high resolution displays, plant lighting with higher color rendering index close to sunlight and higher color fidelity index. The spectral power distribution (SPD) of light source, representing its light quality, is always dynamically controlled by complex electrical and thermal loadings when the light source operates under usage conditions. Therefore, a dynamic prediction of SPD for the full-spectrum white LED has become a hot but challenging research topic in the high quality lighting design and application. This paper proposes a dynamic SPD prediction method for the full-spectrum white LED by integrating the SPD decomposition approach with the artificial neural network (ANN) based machine learning method. Firstly, the continuous SPDs of a full-spectrum white LED driven by an electrical-thermal loading matrix are discretized by the multi-peak fitting with Gaussian model as the relevant spectral characteristic parameters. Then, the Back Propagation (BP) and Genetic Algorithm-Back Propagation (GA-BP) NNs are proposed to predict the spectral characteristic parameters of LEDs operated under any usage conditions. Finally, the dynamically predicted spectral characteristic parameters are used to reconstruct the SPDs. The results show that: (1) The spectral characteristic parameters obtained by fitting with the Gaussian model can be used to represent the emission lights from multiple chips and phosphors in a full-spectrum white LED; (2) The prediction errors of both BP NN and GA-BP NN can be controlled at low level, that is to say, our proposed method can achieve a highly accurate SPD dynamic prediction for the full-spectrum white LED when it operates under different operation mission profiles.
关键词： Machine learning,Spectral power distribution,Genetic algorithm,Full-spectrum white LED,BP neural network
更新于2025-09-16 10:30:52
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[IEEE 2019 Second Balkan Junior Conference on Lighting (Balkan Light Junior) - Plovdiv, Bulgaria (2019.9.19-2019.9.21)] 2019 Second Balkan Junior Conference on Lighting (Balkan Light Junior) - The LED spectral power distribution modelled by different functions - how spectral matching quality affected computed LED color parameters
摘要： The paper is describing different alternatives for mathematical modelling of LED’s spectral power distribution. logistic power peak and asymmetric double Gaussians, sigmoidal functions are tested with different shape and peak wavelength LEDs. Article shows how SPD accuracy modeling is affecting color quality parameters and luminous efficiency of LED. Consequences of applying always single SPD model for different LED emitters are shown for chosen parameters.
关键词： colorimetry,spectral power distribution,light emitting diode,mathematical modeling
更新于2025-09-11 14:15:04
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[IEEE 2018 VII. Lighting Conference of the Visegrad Countries (Lumen V4) - Trebic, Czech Republic (2018.9.18-2018.9.20)] 2018 VII. Lighting Conference of the Visegrad Countries (Lumen V4) - Public Lighting, Public Health
摘要： Impact of artificial light at night on sleep and health of humans and other living species is discussed among scientists. This paper analyses the properties of several commercially available light sources with regards to their effects on wildlife, human sleep, and health. A novel, environmentally considerate LED light source is introduced. Further, integration of this light source into the pilot biodynamic street lighting system is described. Based on the season and time of day, a control system changes the spectral composition of the light in lighting without compromising on safety, psychological needs or energy savings.
关键词： advanced control system,spectral power distribution,light at night,LED,biodynamic lighting,non-image forming light perception,Circadian rhythm
更新于2025-09-10 09:29:36