LED spectrum optimal modelization

Dupuis, Pascal; Purwanto, Edy; Sinisuka, Ngapuli I.; Zissis, Georges

doi:10.1109/ias.2018.8544471

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…However, Dupuis et al . 19 developed an SPD Prediction model using mathematical low order polynomial function considering only the current as input parameter. Chang et al .…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, these models are not used for the prediction of SPD for various electrical and thermal stress levels. However, Dupuis et al 19 developed an SPD Prediction model using mathematical low order polynomial function considering only the current as input parameter. Chang et al 20 proposed multiple peak fitting techniques to extract the SPD features.…”

Section: Introductionmentioning

confidence: 99%

“…The study found that the test duration of 1200 hours can be used to estimate the lifetime of a LED. Both Dupuis et al 19 and Chang et al 20 studies used luminous flux, extracted through SPD in estimation of lifetime. The SPD prediction follows mainly two steps.…”

Section: Introductionmentioning

confidence: 99%

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Comparison and validation of neural network models to estimate LED spectral power distribution

Lokesh

Padmasali

Mahesha

et al. 2023

Lighting Research & Technology

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The spectral power distribution (SPD) is the true fingerprint of a light source and is mainly dependent on electrical and thermal loading. Both the photometric and colorimetric quantities are originally extracted from SPD. Therefore, the dynamic prediction of SPD for LED has become an important aspect to evaluate the performance of LED during its time of operation. Generally, the statistical models are used to predict SPD. However, the statistical model with more than two input makes the system complex and time demanding. Artificial Neural Network (ANN) models, on the other hand, can help with this problem. The major goal of this research is to improve the utility of ANN in lighting applications. This is demonstrated by various neural network (NN) structures referred as models 1, 2 and 3 with combinations of varied neurons and hidden layers (HLs) to forecast SPD for various electrical and thermal stress levels at zero hours. The results are compared and based on absolute prediction error (APE) set to 5%, model 1 is considered as the best model for the SPD prediction. In addition, the time-based SPD prediction with model 1 is investigated using temperature, wavelength and time as input parameters for the LED luminaire and is validated.

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“…However, Dupuis et al . 19 developed an SPD Prediction model using mathematical low order polynomial function considering only the current as input parameter. Chang et al .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison and validation of neural network models to estimate LED spectral power distribution

Lokesh

Padmasali

Mahesha

et al. 2023

Lighting Research & Technology

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“…For example, the authors of [13] present a summary of different functions that can be used to model the spectral power emission of color LEDs with different levels of fitness, which include Gaussian, Pearson-VII and piece-wise third-order polynomial (Spline) functions, among others. Similarly, low-order polynomial functions were proposed in [19], split-Gaussian approximation were considered in [20], and merged exponential and Gaussian functions were presented in [21] to model the spectrum of different LED technologies. However, these spectral emission models were studied in these references from a purely empirical perspective, without a mathematical reasoning behind their derivation that justifies the shape of the function that was selected.…”

Section: Introductionmentioning

confidence: 99%

Closed Form Approximation of the Actual Spectral Power Emission of Commercial Color LEDs for VLC

Dowhuszko

Guzmán

2022

J. Lightwave Technol.

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Multi-color Light-Emitting Diode (LED) technology enables a simple approach to increase the throughput of a Visible Light Communications (VLC) system, by using Wavelength-Division Multiplexing (WDM) to transmit independent data streams on different colors. However, to compute the data rate that is achievable in such WDM VLC link, the optical power that leaks between the different colors needs to be estimated accurately, especially when low-cost optical filters are used in reception. So far, the approximations that have been reported in the literature to model the spectral power emission of different color LEDs are not good enough to perform these calculations. Starting from the theoretical spectral emission of a color LED, a closed form expression is derived based on an asymmetric Pearson type VII function, which is shown to approximate accurately the measured spectra of the color LEDs at different working regimes. In addition, the effect that the DC-bias current has on the key parameters of the approximated spectral power emissions, namely the peak and half-maximum wavelengths, as well as the peak spectral emission, are studied. Finally, a new approach is proposed to assess the level of fitness of the derived closed form approximations, using for this purpose the step-size of the MacAdam ellipses that corresponds to the different color LED spectral emissions in the CIE 1931 chromaticity diagram.

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“…It is found that the proposed model can accurately estimate the emission spectrum of the LED with mixed multiple phosphors. P. Dupuis et al [7] developed an SPD prediction method by using the low-order polynomial function with current as the only input parameter. Moreover, the SPD decomposition approach with statistical models is used to discretize the continuous spectrum of a LED and the statistical characteristic parameters is extracted to represent the whole information in an SPD.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Assisted Prediction of Spectral Power Distribution for Full-Spectrum White Light-Emitting Diode

Fan

Fryc

et al. 2020

IEEE Photonics J.

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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.

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LED spectrum optimal modelization

Cited by 7 publications

References 13 publications

Comparison and validation of neural network models to estimate LED spectral power distribution

Comparison and validation of neural network models to estimate LED spectral power distribution

Closed Form Approximation of the Actual Spectral Power Emission of Commercial Color LEDs for VLC

Machine-Learning Assisted Prediction of Spectral Power Distribution for Full-Spectrum White Light-Emitting Diode

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