{"id":{"repo_id":"calgary","oai_identifier":"oai:ucalgary.scholaris.ca:1880/123989"},"canonical_url":"https://search.dev.ndltd.org/etd/calgary/oai:ucalgary.scholaris.ca:1880/123989","repository":{"repo_id":"calgary","name":"University of Calgary","base_url":"https://ucalgary.scholaris.ca/server/oai/request"},"display":{"title":"A GAN-Augmented Machine Learning Framework for Predicting Raman Characteristics in Carbon Nanofiber Synthesis","abstract":"This thesis explores a hybrid data-driven framework for predicting the structural quality of Carbon Nanofibers (CNFs) synthesized via Chemical Vapor Deposition (CVD). Building upon prior work employing Conditional Tabular GAN (CTGAN) for data augmentation and XGBoost for quality prediction, this thesis expands the methodology by incorporating: (i) an end-to-end reactor data acquisition system (LabVIEW to cloud-based pipeline), and (ii) a new chemical process modeling approach covering hydrocarbon decomposition, catalyst activation, and CNF growth mechanisms. Contributions include physics-informed feature extraction in syn-thesis phases, synthetic data generation to address small data sets, and the integration of machine learning models to predict the intensity ratio of the D and G peaks in a Raman spectrum (𝐼𝐷 /𝐼𝐺 ), a key indicator of CNF quality. The augmented model achieved 𝑅2 ≃ 0.91, and root mean squared error of 0.065 for 𝐼𝐷 /𝐼𝐺 , outperforming unaugmented baselines (XGBoost 𝑅2 ≃ 0.76; support vector regression 𝑅2 ≃ 0.71). CTGAN preserved data characteristics (≃ 96–97 % marginal fidelity / relationship), allowing robust generalization despite the small-n regime.","abstract_html":"This thesis explores a hybrid data-driven framework for predicting the structural quality of Carbon Nanofibers (CNFs) synthesized via Chemical Vapor Deposition (CVD). Building upon prior work employing Conditional Tabular GAN (CTGAN) for data augmentation and XGBoost for quality prediction, this thesis expands the methodology by incorporating: (i) an end-to-end reactor data acquisition system (LabVIEW to cloud-based pipeline), and (ii) a new chemical process modeling approach covering hydrocarbon decomposition, catalyst activation, and CNF growth mechanisms. Contributions include physics-informed feature extraction in syn-thesis phases, synthetic data generation to address small data sets, and the integration of machine learning models to predict the intensity ratio of the D and G peaks in a Raman spectrum (𝐼𝐷 /𝐼𝐺 ), a key indicator of CNF quality. The augmented model achieved 𝑅2 ≃ 0.91, and root mean squared error of 0.065 for 𝐼𝐷 /𝐼𝐺 , outperforming unaugmented baselines (XGBoost 𝑅2 ≃ 0.76; support vector regression 𝑅2 ≃ 0.71). CTGAN preserved data characteristics (≃ 96–97 % marginal fidelity / relationship), allowing robust generalization despite the small-n regime.","abstract_has_math":false,"creators":["Foroughi, Amirhossein"],"institution":"Graduate Studies","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Engineering – Electrical & Computer","degree_department":null,"school":null,"contributors":[],"advisors":["Fapojuwo, Abraham"],"committee_chairs":[],"committee_members":["Santos, Ronnie de Souza","Song, Hua"],"year":2026,"date_issued":"2026-01-14","date_published":"2026-01-14","updated_at":"2026-07-24T01:30:40Z","subjects":["Carbon Nanofiber","XGBoost","CTGAN"],"languages":["en"],"rights":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://dx.doi.org/10.11575/PRISM/51023"],"render_values":[{"text":"https://dx.doi.org/10.11575/PRISM/51023","href":"https://dx.doi.org/10.11575/PRISM/51023","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1880/123989","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Fapojuwo, Abraham"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Santos, Ronnie de Souza","Song, Hua"]},{"key":"dc:creator","label":"Author","values":["Foroughi, Amirhossein"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-06"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-21T15:43:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-01-14"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Calgary"]},{"key":"dc:type","label":"Dc Type","values":["master thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering – Electrical & Computer"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Calgary"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Carbon Nanofiber","XGBoost","CTGAN"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["University of Calgary graduate students retain copyright ownership and moral rights for their thesis. 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Building upon prior work employing Conditional Tabular GAN (CTGAN) for data augmentation and XGBoost for quality prediction, this thesis expands the methodology by incorporating: (i) an end-to-end reactor data acquisition system (LabVIEW to cloud-based pipeline), and (ii) a new chemical process modeling approach covering hydrocarbon decomposition, catalyst activation, and CNF growth mechanisms. Contributions include physics-informed feature extraction in syn-thesis phases, synthetic data generation to address small data sets, and the integration of machine learning models to predict the intensity ratio of the D and G peaks in a Raman spectrum (𝐼𝐷 /𝐼𝐺 ), a key indicator of CNF quality. The augmented model achieved 𝑅2 ≃ 0.91, and root mean squared error of 0.065 for 𝐼𝐷 /𝐼𝐺 , outperforming unaugmented baselines (XGBoost 𝑅2 ≃ 0.76; support vector regression 𝑅2 ≃ 0.71). CTGAN preserved data characteristics (≃ 96–97 % marginal fidelity / relationship), allowing robust generalization despite the small-n regime."]},{"key":"dc:title","label":"Title","values":["A GAN-Augmented Machine Learning Framework for Predicting Raman Characteristics in Carbon Nanofiber Synthesis"]}]}],"canonical_facts":{"dc:contributor.advisor":["Fapojuwo, Abraham"],"dc:contributor.committeemember":["Santos, Ronnie de Souza","Song, Hua"],"dc:creator":["Foroughi, Amirhossein"],"dc:date":["2026-06"],"dc:date.accessioned":["2026-01-21T15:43:57Z"],"dc:date.issued":["2026-01-14"],"dc:description.abstract":["This thesis explores a hybrid data-driven framework for predicting the structural quality of Carbon Nanofibers (CNFs) synthesized via Chemical Vapor Deposition (CVD). 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