Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination

H. Zhang; M. F. Karim; S. N. Zheng; H. Cai; Y. D. Gu; S. S. Chen; H. Yu; A. Q. Liu

Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination

H. Zhang, M. F. Karim, S. N. Zheng, H. Cai, Y. D. Gu, S. S. Chen, H. Yu, A. Q. Liu

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

We propose an integrated microring resonator sensing system based on Backward-Propagation Neural Networks (BPNN)-Adaboost algorithm to predict component fraction in binary liquid mixtures. A minimum absolute error of 0.0023 and mean squared error of 0.000345 is achieved by this training model.

Original language	English
Title of host publication	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)	9781943580422
Publication status	Published - 6 Aug 2018
Externally published	Yes
Event	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - San Jose, United States Duration: 13 May 2018 → 18 May 2018

Publication series

Name	2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

Conference

Conference	2018 Conference on Lasers and Electro-Optics, CLEO 2018
Country/Territory	United States
City	San Jose
Period	13/05/18 → 18/05/18

ASJC Scopus subject areas

Instrumentation
Atomic and Molecular Physics, and Optics

Cite this

Zhang, H., Karim, M. F., Zheng, S. N., Cai, H., Gu, Y. D., Chen, S. S., Yu, H., & Liu, A. Q. (2018). Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination. In 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings Article 8427311 (2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc..

@inproceedings{707373dfb2cd47489a08570e8d8a3db7,

title = "Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination",

abstract = "We propose an integrated microring resonator sensing system based on Backward-Propagation Neural Networks (BPNN)-Adaboost algorithm to predict component fraction in binary liquid mixtures. A minimum absolute error of 0.0023 and mean squared error of 0.000345 is achieved by this training model.",

author = "H. Zhang and Karim, {M. F.} and Zheng, {S. N.} and H. Cai and Gu, {Y. D.} and Chen, {S. S.} and H. Yu and Liu, {A. Q.}",

note = "Publisher Copyright: {\textcopyright} 2018 OSA.; 2018 Conference on Lasers and Electro-Optics, CLEO 2018 ; Conference date: 13-05-2018 Through 18-05-2018",

year = "2018",

month = aug,

day = "6",

language = "English",

isbn = "9781943580422",

series = "2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings",

}

Zhang, H, Karim, MF, Zheng, SN, Cai, H, Gu, YD, Chen, SS, Yu, H & Liu, AQ 2018, Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination. in 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings., 8427311, 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 Conference on Lasers and Electro-Optics, CLEO 2018, San Jose, United States, 13/05/18.

Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination. / Zhang, H.; Karim, M. F.; Zheng, S. N. et al.
2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8427311 (2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination

AU - Zhang, H.

AU - Karim, M. F.

AU - Zheng, S. N.

AU - Cai, H.

AU - Gu, Y. D.

AU - Chen, S. S.

AU - Yu, H.

AU - Liu, A. Q.

PY - 2018/8/6

Y1 - 2018/8/6

N2 - We propose an integrated microring resonator sensing system based on Backward-Propagation Neural Networks (BPNN)-Adaboost algorithm to predict component fraction in binary liquid mixtures. A minimum absolute error of 0.0023 and mean squared error of 0.000345 is achieved by this training model.

AB - We propose an integrated microring resonator sensing system based on Backward-Propagation Neural Networks (BPNN)-Adaboost algorithm to predict component fraction in binary liquid mixtures. A minimum absolute error of 0.0023 and mean squared error of 0.000345 is achieved by this training model.

UR - http://www.scopus.com/inward/record.url?scp=85052601725&partnerID=8YFLogxK

M3 - Conference article published in proceeding or book

AN - SCOPUS:85052601725

SN - 9781943580422

T3 - 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

BT - 2018 Conference on Lasers and Electro-Optics, CLEO 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 Conference on Lasers and Electro-Optics, CLEO 2018

Y2 - 13 May 2018 through 18 May 2018

ER -

Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination

Abstract

Publication series

Conference

ASJC Scopus subject areas

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