Evidence map›Paper›PMID 33924279›Full record

ArticleSensors (Basel, Switzerland)2021

Intelligent LED Certification System in Mass Production.

Galina Malykhina, Dmitry Tarkhov, Viacheslav Shkodyrev, Tatiana Lazovskaya

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Galina MalykhinaHigh School of Cyber-Physical Systems and Control, Peter the Great St. Petersburg State Polytechnic University, 195251 Saint Petersburg, Russia.
Dmitry TarkhovScientific and Technological Centre (STC) "Mathematical Modelling and Intelligent Control Systems", Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.
Viacheslav ShkodyrevHigh School of Cyber-Physical Systems and Control, Peter the Great St. Petersburg State Polytechnic University, 195251 Saint Petersburg, Russia.
Tatiana LazovskayaScientific and Technological Centre (STC) "Mathematical Modelling and Intelligent Control Systems", Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.ORCID 0000-0002-3324-6213

Funding

Ministry of Science and Higher Education of the Russian Federation 075-15-2020-934
6 · The paper itself

Abstract

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.

Indexed as

cascade neural networkefficiencygeneralized statistical momentsLEDmulti-temporal comparison

Identifiers

PMID33924279
PMCPMC8074899

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.