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https://hdl.handle.net/10356/151454| Title: | Machine learning and silicon photonic sensor for complex chemical components determination | Authors: | Zhang, Hui Karim, Muhammad Faeyz Zheng, Shaonan Cai, Hong Gu, Yuandong Chen, Shoushun Yu, Hao Liu, Ai Qun |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2018 | Source: | 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 | Project: | NRF-CRP13-2014-01 | Conference: | 2018 Conference on Lasers and Electro-Optics (CLEO) | 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. | URI: | https://hdl.handle.net/10356/151454 | ISBN: | 978-1-943580-42-2 | DOI: | 10.1364/CLEO_AT.2018.JW2A.54 | Schools: | School of Electrical and Electronic Engineering | Organisations: | Institute of Microelectronics, A* STAR | Rights: | © 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). | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
| Appears in Collections: | EEE Conference Papers |
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| Machine learning and Silicon Photonic Sensor for Complex Chemical Components Determination.pdf | 368.41 kB | Adobe PDF |  View/Open |
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