Modelling the Voltage Degradation of High-Power LEDs Using approach based on Bayesian-Optimized Bidirectional Long Short-Term Neural Network

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60162694%3AG43__%2F26%3A00564666" target="_blank" >RIV/60162694:G43__/26:00564666 - isvavai.cz</a>

Result on the web

<a href="http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11061248" target="_blank" >http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11061248</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICMT65201.2025.11061320" target="_blank" >10.1109/ICMT65201.2025.11061320</a>

Result language

angličtina

Original language name

Modelling the Voltage Degradation of High-Power LEDs Using approach based on Bayesian-Optimized Bidirectional Long Short-Term Neural Network

Original language description

High-power LEDs have become integral components in modern systems, including lighting, signalling, visible communication, medical applications, and other critical fields. In addition to their practical applications, these LEDs have garnered significant attention in reliability research. Key objectives in this domain include data collection, degradation modelling, reliability prediction, and comprehensive reliability assessment. This study introduces a novel methodology based on Bayesian optimized-Bidirectional Long Short-Term Memory Neural Network to model and predict the degradation of LEDs under ageing test conditions. The proposed approach leverages the strengths of these techniques to capture the nonlinear and complex degradation behaviours of LEDs effectively. The performance of the model is rigorously verified using widely accepted metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R}^{2}). The results indicate that the proposed method offers robust and accurate predictions, showcasing its potential as a reliable approach for modelling and predicting the reliability of high-power LEDs. This approach contributes to advancing the field of LED reliability research and supports the development of innovative solutions for predicting the performance and lifespan of these critical devices.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2025 10th International Conference on Military Technologies, ICMT 2025 - Proceedings

ISBN

979-8-3315-2338-1

ISSN

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e-ISSN

2996-4474

Number of pages

7

Pages from-to

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Publisher name

Institute of Electrical and Electronics Engineers Inc.

Place of publication

Brno

Event location

Brno, Czech Republic

Event date

May 27, 2025

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

001545807300061