Reliability testing and machine learning approach for modelling high-power light-emitting diode reliability
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F26%3A0201476" target="_blank" >RIV/00216305:26210/26:0201476 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1051/matecconf/202541303005" target="_blank" >https://doi.org/10.1051/matecconf/202541303005</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1051/matecconf/202541303005" target="_blank" >10.1051/matecconf/202541303005</a>
Alternative languages
Result language
angličtina
Original language name
Reliability testing and machine learning approach for modelling high-power light-emitting diode reliability
Original language description
The high-power Light Emitting Diode (LED) is a specialized type of LED that has found extensive use in a wide array of fields, particularly in areas such as lighting, signalling, and medical applications due to their cost-effectiveness and replace-ability. As a result of significant technological advancements, high-power LEDs have undergone rapid development, leading to improvements in quality, variety, and application. Within the realm of reliability research, high-power LEDs have garnered considerable attention. The primary aim of this paper is to conduct a comprehensive exploration and analysis of the existing methodologies for testing the reliability of high-power LEDs. This endeavour will involve a thorough investigation into the types of objects utilized for testing, the diverse testing methods employed, the techniques for data collection, and the parameters measured during testing. Furthermore, the paper aims to delve into the potential application of machine learning techniques for modelling, estimating, and predicting the reliability of high-power LEDs. The anticipated outcomes of this paper are intended to establish the foundation for the adoption of innovative approaches in reliability testing and to enhance the prediction and estimation of high-power LEDs reliability.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
21100 - Other engineering and technologies
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
MATEC web of conferences
ISSN
2274-7214
e-ISSN
2261-236X
Volume of the periodical
2025
Issue of the periodical within the volume
413
Country of publishing house
FR - FRANCE
Number of pages
6
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105018047449