Machine learning and silicon photonic sensor for complex chemical components determination

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.

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Bibliographic Details
Main Authors:	Zhang, Haochi, Muhammad Faeyz Karim, Zheng, Shaonan, Cai, H., Gu, Y. D., Chen, Shou Shun, Yu, H., Liu, Ai Qun
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2020
Subjects:	Engineering::Electrical and electronic engineering Sensors Optical Resonators
Online Access:	https://hdl.handle.net/10356/138831
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-138831
record_format	dspace
spelling	sg-ntu-dr.10356-1388312020-05-13T04:50:23Z Machine learning and silicon photonic sensor for complex chemical components determination Zhang, Haochi Muhammad Faeyz Karim Zheng, Shaonan Cai, H. Gu, Y. D. Chen, Shou Shun Yu, H. Liu, Ai Qun School of Electrical and Electronic Engineering 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology Engineering::Electrical and electronic engineering Sensors Optical Resonators 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. NRF (Natl Research Foundation, S’pore) Accepted version 2020-05-13T04:50:22Z 2020-05-13T04:50:22Z 2018 Conference Paper Zhang, H., Muhammad Faeyz Karim, Zheng, S. N., Cai, H., Gu, Y. D., Chen, S. S., . . . Liu, A. Q. (2018). Machine learning and silicon photonic sensor for complex chemical components determination. 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology, JW2A-54-. doi:10.1364/CLEO_AT.2018.JW2A.54 9781943580422 https://hdl.handle.net/10356/138831 10.1364/CLEO_AT.2018.JW2A.54 2-s2.0-85049132649 en © 2018 The Author(s). All rights reserved. This paper was published by Optical Society of America (OSA) in 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology and is made available with permission of the author(s). application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering Sensors Optical Resonators
spellingShingle	Engineering::Electrical and electronic engineering Sensors Optical Resonators Zhang, Haochi Muhammad Faeyz Karim Zheng, Shaonan Cai, H. Gu, Y. D. Chen, Shou Shun Yu, H. Liu, Ai Qun Machine learning and silicon photonic sensor for complex chemical components determination
description	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.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Zhang, Haochi Muhammad Faeyz Karim Zheng, Shaonan Cai, H. Gu, Y. D. Chen, Shou Shun Yu, H. Liu, Ai Qun
format	Conference or Workshop Item
author	Zhang, Haochi Muhammad Faeyz Karim Zheng, Shaonan Cai, H. Gu, Y. D. Chen, Shou Shun Yu, H. Liu, Ai Qun
author_sort	Zhang, Haochi
title	Machine learning and silicon photonic sensor for complex chemical components determination
title_short	Machine learning and silicon photonic sensor for complex chemical components determination
title_full	Machine learning and silicon photonic sensor for complex chemical components determination
title_fullStr	Machine learning and silicon photonic sensor for complex chemical components determination
title_full_unstemmed	Machine learning and silicon photonic sensor for complex chemical components determination
title_sort	machine learning and silicon photonic sensor for complex chemical components determination
publishDate	2020
url	https://hdl.handle.net/10356/138831
_version_	1681059364945788928

Machine learning and silicon photonic sensor for complex chemical components determination

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