Intelligent LED Certification System in Mass Production
It is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/8/2891 |
| _version_ | 1797536962516615168 |
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| author | Galina Malykhina Dmitry Tarkhov Viacheslav Shkodyrev Tatiana Lazovskaya |
| author_facet | Galina Malykhina Dmitry Tarkhov Viacheslav Shkodyrev Tatiana Lazovskaya |
| author_sort | Galina Malykhina |
| collection | DOAJ |
| description | It is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The method for measuring LED efficiency consists in comparing two cooling curves of the LED crystal obtained after exposure to short current pulses of positive and negative polarities. The measurement results are adversely affected by noise in the electrical measuring circuit. The widely used instrumental noise suppression filters, as well as classical digital infinite impulse response (IIR), finite impulse response (FIR) filters, and adaptive filters fail to yield satisfactory results. Unlike adaptive filters, blind methods do not require a special reference signal, which makes them more promising for removing noise and reconstructing the waveform when measuring the efficiency of LEDs. The article suggests a method for sequential blind signal extraction based on a cascading neural network. Statistical analysis of signal and noise values has revealed that the signal and the noise have different forms of the probability density function (PDF). Therefore, it is preferable to use high-order statistical moments characterizing the shape of the PDF for signal extraction. Generalized statistical moments were used as an objective function for optimization of neural network parameters, namely, generalized skewness and generalized kurtosis. The order of the generalized moments was chosen according to the criterion of the maximum Mahalanobis distance. The proposed method has made it possible to implement a multi-temporal comparison of the crystal cooling curves for measuring LED efficiency. |
| first_indexed | 2024-03-10T12:08:18Z |
| format | Article |
| id | doaj.art-1769678a0e4d456ba17469ec934438c1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:08:18Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1769678a0e4d456ba17469ec934438c12023-11-21T16:24:11ZengMDPI AGSensors1424-82202021-04-01218289110.3390/s21082891Intelligent LED Certification System in Mass ProductionGalina Malykhina0Dmitry Tarkhov1Viacheslav Shkodyrev2Tatiana Lazovskaya3High School of Cyber-Physical Systems and Control, Peter the Great St. Petersburg State Polytechnic University, 195251 Saint Petersburg, RussiaScientific and Technological Centre (STC) “Mathematical Modelling and Intelligent Control Systems”, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, RussiaHigh School of Cyber-Physical Systems and Control, Peter the Great St. Petersburg State Polytechnic University, 195251 Saint Petersburg, RussiaScientific and Technological Centre (STC) “Mathematical Modelling and Intelligent Control Systems”, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, RussiaIt is impossible to effectively use light-emitting diodes (LEDs) in medicine and telecommunication systems without knowing their main characteristics, the most important of them being efficiency. Reliable measurement of LED efficiency holds particular significance for mass production automation. The method for measuring LED efficiency consists in comparing two cooling curves of the LED crystal obtained after exposure to short current pulses of positive and negative polarities. The measurement results are adversely affected by noise in the electrical measuring circuit. The widely used instrumental noise suppression filters, as well as classical digital infinite impulse response (IIR), finite impulse response (FIR) filters, and adaptive filters fail to yield satisfactory results. Unlike adaptive filters, blind methods do not require a special reference signal, which makes them more promising for removing noise and reconstructing the waveform when measuring the efficiency of LEDs. The article suggests a method for sequential blind signal extraction based on a cascading neural network. Statistical analysis of signal and noise values has revealed that the signal and the noise have different forms of the probability density function (PDF). Therefore, it is preferable to use high-order statistical moments characterizing the shape of the PDF for signal extraction. Generalized statistical moments were used as an objective function for optimization of neural network parameters, namely, generalized skewness and generalized kurtosis. The order of the generalized moments was chosen according to the criterion of the maximum Mahalanobis distance. The proposed method has made it possible to implement a multi-temporal comparison of the crystal cooling curves for measuring LED efficiency.https://www.mdpi.com/1424-8220/21/8/2891efficiencyLEDmulti-temporal comparisoncascade neural networkgeneralized statistical moments |
| spellingShingle | Galina Malykhina Dmitry Tarkhov Viacheslav Shkodyrev Tatiana Lazovskaya Intelligent LED Certification System in Mass Production Sensors efficiency LED multi-temporal comparison cascade neural network generalized statistical moments |
| title | Intelligent LED Certification System in Mass Production |
| title_full | Intelligent LED Certification System in Mass Production |
| title_fullStr | Intelligent LED Certification System in Mass Production |
| title_full_unstemmed | Intelligent LED Certification System in Mass Production |
| title_short | Intelligent LED Certification System in Mass Production |
| title_sort | intelligent led certification system in mass production |
| topic | efficiency LED multi-temporal comparison cascade neural network generalized statistical moments |
| url | https://www.mdpi.com/1424-8220/21/8/2891 |
| work_keys_str_mv | AT galinamalykhina intelligentledcertificationsysteminmassproduction AT dmitrytarkhov intelligentledcertificationsysteminmassproduction AT viacheslavshkodyrev intelligentledcertificationsysteminmassproduction AT tatianalazovskaya intelligentledcertificationsysteminmassproduction |