The Defect Identification of LED Chips Based on Bayesian Classifier

Wei Shi; Yao Wu Pei; Liang Sun; Jian Guo Wang; Shao Qing Ren

doi:10.4028/www.scientific.net/AMM.333-335.1564

Paper Titles

Research on the Application of Weighted Grey Relational Analysis on the Failure Diagnosis of Complicated Electronic Equipment
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Design and Implementation of an On-Board Automatic Test System Based on CPCI Bus for Aerial Fire Control Equipment
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Fault Diagnostic Equipment of Electrical System Based on Embedded System
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Study on Grain Moisture Detection System Based on the Theory of Dielectric Properties
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The Defect Identification of LED Chips Based on Bayesian Classifier
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The Development of a Realtime Remote Monitoring System for the Subsidence of Building
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Research and Application of Data Monitoring on Digital Campus
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The Development of Electrochemical Hypergolic Vapor Monitoring Instrument
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Study on PPP-BOTDA Technology for Concrete Crack Monitoring
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 333-335The Defect Identification of LED Chips Based on...

The Defect Identification of LED Chips Based on Bayesian Classifier

Abstract:

The types, causes and image properties of the defective and damaged chips are been analyzes. On the basis of analyzing the image properties, Bayesian classifier and identification method are proposed. Based on the Bayesian decision theory, five kinds of image features such as the number of light spot, dark spot, edge spot, block and area are applied. Normal distribution is made use to building classifier and studying the correction strategy of model. In the process of identification, dynamical correction and model parameters completion make the classifier have some degree of self-learning feature and higher identification accuracy. Keywords: defect; feature; classifier; decision

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Periodical:

Applied Mechanics and Materials (Volumes 333-335)

Pages:

1564-1568

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.333-335.1564

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Online since:

July 2013

Authors:

Wei Shi, Yao Wu Pei, Liang Sun, Jian Guo Wang, Shao Qing Ren

Keywords:

Classifier, Decision, Defect, Feature

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