Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157499
Title: Artificial intelligence - algorithm development for laser spectroscopy studies
Authors: Png, Wei Xuan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Png, W. X. (2022). Artificial intelligence - algorithm development for laser spectroscopy studies. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157499
Project: A2237-211
Abstract: Principal component regression and partial least squares regression are some of the widely used machine learning algorithms in recent years. They are artificial intelligence models used for dimensional reduction and learning key features which are applicable for regressions. These two models were applied into the field of advanced chemometrics for quantitative analysis. The objective of this quantitative analysis was to predict the identity of gaseous compounds used within the mixture samples which had varying level of concentration. In this quantitative analysis, gaseous mixture of compounds C2H4 and NH3 were used to simulate gaseous mixture emitted from vehicle exhaust. Data collection was made possible through a home-made quantum cascade laser which captured complex spectroscopic gaseous information. These data were computed to generate dataset containing key absorption spectrums information used for the identification of the gaseous compounds. The generated data set were processed into machine language for the regression analysis to take place. The regression outputs were benchmarked through quantitative evaluation metrics which assured that the results were accurate and reliable.
URI: https://hdl.handle.net/10356/157499
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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