Estimation of Light Source Color Rendition with Low-Cost Sensors Using Multilayer Perceptron and Extreme Learning Machine

Botero, Juan Sebastián; Valencia-Aguirre, Juliana; Vargas-Bonilla, Jesús Francisco

doi:10.1080/15502724.2020.1755306

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Using low-cost sensors, Botero et al . 31 proposed an approach for estimating R normalf , R normalg and CRI through SPD. The absolute inaccuracy is less than 1%.…”

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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“…Using low-cost sensors, Botero et al . 31 proposed an approach for estimating R normalf , R normalg and CRI through SPD. The absolute inaccuracy is less than 1%.…”

Section: Introductionmentioning

confidence: 99%

“…The validation of luminous data from multiple LED chip packaging reveals that the maximum differences in CCT and colour coordinate are 2.6% and 1.0%, respectively. Using low-cost sensors, Botero et al 31 proposed an approach for estimating R f , R g and CRI through SPD. The absolute inaccuracy is less than 1%.…”

Section: Introductionmentioning

confidence: 99%

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

Lokesh

Padmasali

Mahesha

et al. 2023

Lighting Research & Technology

View full text Add to dashboard Cite

show abstract

“…Scientists in various countries have made countless achievements in this algorithm technology. Deep learning is based on many levels of processing calculation data to establish the model and ultimately achieve the prediction and analysis of the actual results [ 2 , 3 ]. When the back propagation technology in the neural network is combined with the development of the artificial neural network, the traditional mode of connecting data calculation and optimization algorithm began to change [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Error Correction of College English Spoken Grammar Based on the GA‐MLP‐NN Algorithm

2021

Computational Intelligence and Neuroscience

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With the development of neural networks in deep learning, artificial intelligence machine learning has become the main focus of researchers. In College English grammar detection, oral grammar is the most error rate content. So, this paper optimizes MLP based on GA in the deep learning neural network and then studies the intelligent image correction of College English spoken grammar. The main direction is to discuss and analyze GA-MLP-NN algorithm technology first and then predict the error correction model of spoken language grammar by combining the optimized algorithm. The results show that GA-MLP-NN provides excellent accuracy for the prediction of the whole syntax error correction model. Then, the paper studies the deep learning technology to build an intelligent image error correction model of College English spoken grammar. The results show that the effect of intelligent correction of spoken grammar is very fast and accurate.

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“…Amirazar et al [36] applied this principle to build a device for monitoring a person's individual lighting exposure from an 18-channel spectral sensor, again relying on an artificial neural network for the SPD reconstruction. Instead of an SPD reconstruction, Botero et al [37] also investigated the feasability of directly estimating certain color rendition features, such as TM 30-18 R f and R g values or the CIE R a color rendering index, from spectral sensor responses.…”

mentioning

confidence: 99%

LED Degradation Monitoring Using a Multi-Channel Spectral Sensor

Myland,

Herzog,

Babilon

et al. 2024

IEEE Access

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This study explores a novel approach to monitor the spectral emission of LEDs by estimating the spectral power distribution from the spectral sensor responses during an accelerated ageing experiment. Two methods for reconstructing the actual LED spectra from sensor responses are presented and tested, one solely requires sensor datasheet information and the other uses a full spectral characterisation of the sensor's spectral sensitivities. The reconstruction results show that a spectral sensor can provide accurate spectral estimates even after severe LED degradation. Only for an LED that suffered a phosphor crack, affecting its spatial radiation characteristics, limited ability to estimate the true spectral power distribution without prior assumptions about the spectral changes must be reported. Overall, the use of a spectral sensor, even without detailed characterisation of the sensor itself, allows for an accurate monitoring of the true emission of LEDs, with a maximum radiometric error of 0.73 %, a maximum colormetric error of 0.0017 ∆u ′ v ′ and a maximum spectral nRMSE error of 0.0097 compared to a spectroradiometric measurement. This advance holds great promise for improving lighting technology, particularly in applications that require constant radiometric output and stable color.

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Estimation of Light Source Color Rendition with Low-Cost Sensors Using Multilayer Perceptron and Extreme Learning Machine

Cited by 5 publications

References 21 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

Intelligent Error Correction of College English Spoken Grammar Based on the GA‐MLP‐NN Algorithm

LED Degradation Monitoring Using a Multi-Channel Spectral Sensor

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