利用机器学习技术预测LED结温

M. Merenda, Carlo Porcaro, F. D. Della Corte
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引用次数: 3

摘要

发光二极管(led)是持续时间最长的人工照明光源,其持续时间可超过50,000个连续工作小时。然而,由于器件温度的升高,它们显示出光通量逐渐降低。在这项工作中,将介绍和讨论一种机器学习算法,该算法能够在最终用户电路连接时实时预测LED的结温值,同时考虑到器件中流动的电流和电压,以及LED的实际模型和老化。该算法在微控制器上实现,显示了在微小但功能强大的设备上执行边缘机器学习的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LED junction temperature prediction using machine learning techniques
Light Emitting Diodes (LEDs) are the longest lasting source of artificial illumination whose duration can exceed 50.000 continuous working hours. Nevertheless, they show a gradual reduction of the luminous flux due to the increase of the device temperature. In this work, a Machine Learning algorithm will be introduced and discussed, able to predict the junction temperature value of a LED in real-time while connected in the end-user circuit, taking into account current and voltage flowing in the device and, further, the actual model and aging of the LED. The algorithm was implemented on a microcontroller, showing the feasibility of performing edge machine learning on tiny yet powerful devices.
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