Artificial intelligence - algorithm development for laser spectroscopy studies
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 ap...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157499 |
| _version_ | 1811681725063888896 |
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| author | Png, Wei Xuan |
| author2 | Wang Qijie |
| author_facet | Wang Qijie Png, Wei Xuan |
| author_sort | Png, Wei Xuan |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:45:30Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157499 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:45:30Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1574992023-07-07T19:16:44Z Artificial intelligence - algorithm development for laser spectroscopy studies Png, Wei Xuan Wang Qijie School of Electrical and Electronic Engineering qjwang@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics 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. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-18T13:20:05Z 2022-05-18T13:20:05Z 2022 Final Year Project (FYP) 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 https://hdl.handle.net/10356/157499 en A2237-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics Png, Wei Xuan Artificial intelligence - algorithm development for laser spectroscopy studies |
| title | Artificial intelligence - algorithm development for laser spectroscopy studies |
| title_full | Artificial intelligence - algorithm development for laser spectroscopy studies |
| title_fullStr | Artificial intelligence - algorithm development for laser spectroscopy studies |
| title_full_unstemmed | Artificial intelligence - algorithm development for laser spectroscopy studies |
| title_short | Artificial intelligence - algorithm development for laser spectroscopy studies |
| title_sort | artificial intelligence algorithm development for laser spectroscopy studies |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Engineering::Electrical and electronic engineering::Optics, optoelectronics, photonics |
| url | https://hdl.handle.net/10356/157499 |
| work_keys_str_mv | AT pngweixuan artificialintelligencealgorithmdevelopmentforlaserspectroscopystudies |