Machine Learning to Predict Junction Temperature Based on Optical Characteristics in Solid-State Lighting Devices: A Test on WLEDs
Abstract
:1. Introduction
1.1. A. Junction Temperature Effect on Emission Characteristic
1.2. B. Commonality of SPD Response to Junction Temperature and Input Current Density Change
1.3. C. Luminous Flux and Correlated Color Temperature, Data Interpretations from SPD
1.4. D. Temperature Dependent Data Presentation by Manufacturers
1.5. E. Predictable Response to Temperature and Possibility of Algorithm Training
2. Method
3. Results
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Azarifar, M.; Ocaksonmez, K.; Cengiz, C.; Aydoğan, R.; Arik, M. Machine Learning to Predict Junction Temperature Based on Optical Characteristics in Solid-State Lighting Devices: A Test on WLEDs. Micromachines 2022, 13, 1245. https://doi.org/10.3390/mi13081245
Azarifar M, Ocaksonmez K, Cengiz C, Aydoğan R, Arik M. Machine Learning to Predict Junction Temperature Based on Optical Characteristics in Solid-State Lighting Devices: A Test on WLEDs. Micromachines. 2022; 13(8):1245. https://doi.org/10.3390/mi13081245
Chicago/Turabian StyleAzarifar, Mohammad, Kerem Ocaksonmez, Ceren Cengiz, Reyhan Aydoğan, and Mehmet Arik. 2022. "Machine Learning to Predict Junction Temperature Based on Optical Characteristics in Solid-State Lighting Devices: A Test on WLEDs" Micromachines 13, no. 8: 1245. https://doi.org/10.3390/mi13081245