Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms
This paper presents a novel deep learning framework that integrates experimental measurements with advanced modeling techniques to predict key optical parameters, including luminous flux, correlated color temperature (CCT), and chromaticity coordinates of white-red light-emitting diodes (LED) config...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10999086/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850269656074420224 |
|---|---|
| author | Songsheng Lin Huanting Chen Yin Zheng Quanji Xie Xuehua Shen Huichuan Lin Shuo Lin Yan Li |
| author_facet | Songsheng Lin Huanting Chen Yin Zheng Quanji Xie Xuehua Shen Huichuan Lin Shuo Lin Yan Li |
| author_sort | Songsheng Lin |
| collection | DOAJ |
| description | This paper presents a novel deep learning framework that integrates experimental measurements with advanced modeling techniques to predict key optical parameters, including luminous flux, correlated color temperature (CCT), and chromaticity coordinates of white-red light-emitting diodes (LED) configurations under diverse operating conditions. The heatsink temperature, white LED driving current, and red LED driving current were each varied systematically to generate a comprehensive set of 5,166 spectral power distribution (SPD) measurements. This dataset, partitioned into training (4,182 data sets) and testing (984 data sets) sets, encapsulates the complex physical mechanisms influencing LED performance, such as temperature-induced spectral shifts and current-dependent optical behavior. Four deep learning algorithms were evaluated. Each model was trained to reconstruct the SPD curves and predict the corresponding optical and chromatic parameters. Our results indicate that Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Autoencoder (AE) outperform Backpropagation Neural Network (BP-NN), with CNN achieving the highest accuracy in predicting SPD curves and LSTM achieving the highest accuracy in predicting the optical and chromatic parameters. Furthermore, By mimicking the effects of varying red phosphor ratios through independent control of red LED output, our approach can provide deeper insights into the underlying physical phenomena governing LED spectral behavior. This integrated methodology not only enhances our understanding of the interplay between operating conditions and LED performance but also offers a robust predictive tool for the design and optimization of next-generation LED lighting technologies. |
| format | Article |
| id | doaj-art-5c8cd203ad91467480d01fb45ff04caf |
| institution | OA Journals |
| issn | 1943-0655 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-5c8cd203ad91467480d01fb45ff04caf2025-08-20T01:53:00ZengIEEEIEEE Photonics Journal1943-06552025-01-011731710.1109/JPHOT.2025.356907910999086Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning AlgorithmsSongsheng Lin0https://orcid.org/0009-0009-3564-1944Huanting Chen1https://orcid.org/0000-0001-7231-8454Yin Zheng2Quanji Xie3Xuehua Shen4https://orcid.org/0000-0002-7150-5212Huichuan Lin5Shuo Lin6Yan Li7Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province, College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaKey Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province, College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaKey Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province, College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaKey Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province, College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaCollege of Intelligent Manufacturing, Guangzhou Maritime University, Guangzhou, ChinaKey Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province, College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaKey Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province, College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaKey Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province, College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, ChinaThis paper presents a novel deep learning framework that integrates experimental measurements with advanced modeling techniques to predict key optical parameters, including luminous flux, correlated color temperature (CCT), and chromaticity coordinates of white-red light-emitting diodes (LED) configurations under diverse operating conditions. The heatsink temperature, white LED driving current, and red LED driving current were each varied systematically to generate a comprehensive set of 5,166 spectral power distribution (SPD) measurements. This dataset, partitioned into training (4,182 data sets) and testing (984 data sets) sets, encapsulates the complex physical mechanisms influencing LED performance, such as temperature-induced spectral shifts and current-dependent optical behavior. Four deep learning algorithms were evaluated. Each model was trained to reconstruct the SPD curves and predict the corresponding optical and chromatic parameters. Our results indicate that Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Autoencoder (AE) outperform Backpropagation Neural Network (BP-NN), with CNN achieving the highest accuracy in predicting SPD curves and LSTM achieving the highest accuracy in predicting the optical and chromatic parameters. Furthermore, By mimicking the effects of varying red phosphor ratios through independent control of red LED output, our approach can provide deeper insights into the underlying physical phenomena governing LED spectral behavior. This integrated methodology not only enhances our understanding of the interplay between operating conditions and LED performance but also offers a robust predictive tool for the design and optimization of next-generation LED lighting technologies.https://ieeexplore.ieee.org/document/10999086/White-red LED configurationsspectral power distributionoptical and chromatic parametersdeep learning algorithms |
| spellingShingle | Songsheng Lin Huanting Chen Yin Zheng Quanji Xie Xuehua Shen Huichuan Lin Shuo Lin Yan Li Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms IEEE Photonics Journal White-red LED configurations spectral power distribution optical and chromatic parameters deep learning algorithms |
| title | Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms |
| title_full | Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms |
| title_fullStr | Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms |
| title_full_unstemmed | Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms |
| title_short | Forward Predicting Chromatic-Optical Parameters of the Mixed Light of White-Red Light-Emitting Diode Configurations Based on Deep Learning Algorithms |
| title_sort | forward predicting chromatic optical parameters of the mixed light of white red light emitting diode configurations based on deep learning algorithms |
| topic | White-red LED configurations spectral power distribution optical and chromatic parameters deep learning algorithms |
| url | https://ieeexplore.ieee.org/document/10999086/ |
| work_keys_str_mv | AT songshenglin forwardpredictingchromaticopticalparametersofthemixedlightofwhiteredlightemittingdiodeconfigurationsbasedondeeplearningalgorithms AT huantingchen forwardpredictingchromaticopticalparametersofthemixedlightofwhiteredlightemittingdiodeconfigurationsbasedondeeplearningalgorithms AT yinzheng forwardpredictingchromaticopticalparametersofthemixedlightofwhiteredlightemittingdiodeconfigurationsbasedondeeplearningalgorithms AT quanjixie forwardpredictingchromaticopticalparametersofthemixedlightofwhiteredlightemittingdiodeconfigurationsbasedondeeplearningalgorithms AT xuehuashen forwardpredictingchromaticopticalparametersofthemixedlightofwhiteredlightemittingdiodeconfigurationsbasedondeeplearningalgorithms AT huichuanlin forwardpredictingchromaticopticalparametersofthemixedlightofwhiteredlightemittingdiodeconfigurationsbasedondeeplearningalgorithms AT shuolin forwardpredictingchromaticopticalparametersofthemixedlightofwhiteredlightemittingdiodeconfigurationsbasedondeeplearningalgorithms AT yanli forwardpredictingchromaticopticalparametersofthemixedlightofwhiteredlightemittingdiodeconfigurationsbasedondeeplearningalgorithms |