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.

Saved in:

Bibliographic Details
Main Authors:	Zhang, Hui, Karim, Muhammad Faeyz, Zheng, Shaonan, Cai, Hong, Gu, Yuandong, Chen, Shoushun, Yu, Hao, Liu, Ai Qun
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2021
Subjects:	Engineering::Electrical and electronic engineering Sensors Optical Resonators
Online Access:	https://hdl.handle.net/10356/151454
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-151454
record_format	dspace
spelling	sg-ntu-dr.10356-1514542021-07-02T07:20:19Z Machine learning and silicon photonic sensor for complex chemical components determination Zhang, Hui Karim, Muhammad Faeyz Zheng, Shaonan Cai, Hong Gu, Yuandong Chen, Shoushun Yu, Hao Liu, Ai Qun School of Electrical and Electronic Engineering 2018 Conference on Lasers and Electro-Optics (CLEO) Institute of Microelectronics, A* STAR 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. National Research Foundation (NRF) Accepted version This work was supported by Singapore National Research Foundation under the Competitive Research Program (NRF-CRP13-2014-01). 2021-07-02T07:02:19Z 2021-07-02T07:02:19Z 2018 Conference Paper Zhang, H., Karim, M. F., Zheng, S., Cai, H., Gu, Y., Chen, S., Yu, H. & Liu, A. Q. (2018). Machine learning and silicon photonic sensor for complex chemical components determination. 2018 Conference on Lasers and Electro-Optics (CLEO), 1-2. https://dx.doi.org/10.1364/CLEO_AT.2018.JW2A.54 978-1-943580-42-2 https://hdl.handle.net/10356/151454 10.1364/CLEO_AT.2018.JW2A.54 1 2 en NRF-CRP13-2014-01 © 2018 The Author(s). All rights reserved. This paper was published in Proceedings of 2018 Conference on Lasers and Electro-Optics (CLEO) and is made available with permission of The Author(s). application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering Sensors Optical Resonators
spellingShingle	Engineering::Electrical and electronic engineering Sensors Optical Resonators Zhang, Hui Karim, Muhammad Faeyz Zheng, Shaonan Cai, Hong Gu, Yuandong Chen, Shoushun Yu, Hao 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, Hui Karim, Muhammad Faeyz Zheng, Shaonan Cai, Hong Gu, Yuandong Chen, Shoushun Yu, Hao Liu, Ai Qun
format	Conference or Workshop Item
author	Zhang, Hui Karim, Muhammad Faeyz Zheng, Shaonan Cai, Hong Gu, Yuandong Chen, Shoushun Yu, Hao Liu, Ai Qun
author_sort	Zhang, Hui
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	2021
url	https://hdl.handle.net/10356/151454
_version_	1705151285092679680

Machine learning and silicon photonic sensor for complex chemical components determination

Similar Items