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https://hdl.handle.net/10356/138831| Title: | Machine learning and silicon photonic sensor for complex chemical components determination | Authors: | Zhang, Haochi Muhammad Faeyz Karim Zheng, Shaonan Cai, H. Gu, Y. D. Chen, Shou Shun Yu, H. Liu, Ai Qun |
Keywords: | Engineering::Electrical and electronic engineering | Issue Date: | 2018 | Source: | 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 | metadata.dc.contributor.conference: | 2018 Conference on Lasers and Electro-Optics (CLEO): Applications and Technology | 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/10356/138831 | ISBN: | 9781943580422 | DOI: | 10.1364/CLEO_AT.2018.JW2A.54 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 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). | 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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