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Combustion Regime Monitoring by Flame Imaging and Machine Learning. / Abdurakipov, S. S.; Gobyzov, O. A.; Tokarev, M. P. et al.

In: Optoelectronics, Instrumentation and Data Processing, Vol. 54, No. 5, 01.09.2018, p. 513-519.

Research output: Contribution to journal › Article › peer-review

Harvard

Abdurakipov, SS, Gobyzov, OA, Tokarev, MP & Dulin, VM 2018, 'Combustion Regime Monitoring by Flame Imaging and Machine Learning', Optoelectronics, Instrumentation and Data Processing, vol. 54, no. 5, pp. 513-519. https://doi.org/10.3103/S875669901805014X

APA

Abdurakipov, S. S., Gobyzov, O. A., Tokarev, M. P., & Dulin, V. M. (2018). Combustion Regime Monitoring by Flame Imaging and Machine Learning. Optoelectronics, Instrumentation and Data Processing, 54(5), 513-519. https://doi.org/10.3103/S875669901805014X

Vancouver

Abdurakipov SS, Gobyzov OA, Tokarev MP, Dulin VM. Combustion Regime Monitoring by Flame Imaging and Machine Learning. Optoelectronics, Instrumentation and Data Processing. 2018 Sept 1;54(5):513-519. doi: 10.3103/S875669901805014X

Author

Abdurakipov, S. S. ; Gobyzov, O. A. ; Tokarev, M. P. et al. / Combustion Regime Monitoring by Flame Imaging and Machine Learning. In: Optoelectronics, Instrumentation and Data Processing. 2018 ; Vol. 54, No. 5. pp. 513-519.

BibTeX

@article{bf106fa7487f42fcb5b721a084142403,
title = "Combustion Regime Monitoring by Flame Imaging and Machine Learning",
abstract = "A method for automatic determination of combustion regimes using flame images on the basis of a convolutional neural network on labeled data is under consideration. It is shown that the accuracy of regime classification reaches 98% on the flame images of a gas burner. The results of the operation of the convolutional neural network and classification using different linear models are compared.",
keywords = "computer training, convolutional neural network, flame, image classification, monitoring",
author = "Abdurakipov, {S. S.} and Gobyzov, {O. A.} and Tokarev, {M. P.} and Dulin, {V. M.}",
year = "2018",
month = sep,
day = "1",
doi = "10.3103/S875669901805014X",
language = "English",
volume = "54",
pages = "513--519",
journal = "Optoelectronics, Instrumentation and Data Processing",
issn = "8756-6990",
publisher = "Allerton Press Inc.",
number = "5",

}

RIS

TY - JOUR

T1 - Combustion Regime Monitoring by Flame Imaging and Machine Learning

AU - Abdurakipov, S. S.

AU - Gobyzov, O. A.

AU - Tokarev, M. P.

AU - Dulin, V. M.

PY - 2018/9/1

Y1 - 2018/9/1

N2 - A method for automatic determination of combustion regimes using flame images on the basis of a convolutional neural network on labeled data is under consideration. It is shown that the accuracy of regime classification reaches 98% on the flame images of a gas burner. The results of the operation of the convolutional neural network and classification using different linear models are compared.

AB - A method for automatic determination of combustion regimes using flame images on the basis of a convolutional neural network on labeled data is under consideration. It is shown that the accuracy of regime classification reaches 98% on the flame images of a gas burner. The results of the operation of the convolutional neural network and classification using different linear models are compared.

KW - computer training

KW - convolutional neural network

KW - flame

KW - image classification

KW - monitoring

UR - http://www.scopus.com/inward/record.url?scp=85057571967&partnerID=8YFLogxK

U2 - 10.3103/S875669901805014X

DO - 10.3103/S875669901805014X

M3 - Article

AN - SCOPUS:85057571967

VL - 54

SP - 513

EP - 519

JO - Optoelectronics, Instrumentation and Data Processing

JF - Optoelectronics, Instrumentation and Data Processing

SN - 8756-6990

IS - 5

ER -

ID: 17670060