Add Literature Review #80
Reference in New Issue
Block a user
Delete Branch "latex/literature-review"
Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?
This pull request introduces significant updates to the literature review and theoretical foundation sections of a thesis, focusing on structural health monitoring (SHM) methodologies, including vibration-based techniques, machine learning classifiers, and the Short-Time Fourier Transform (STFT). The changes enhance the theoretical framework and provide detailed discussions on classical and modern approaches, as well as their applications to SHM.
Literature Review Enhancements:
latex/chapters/en/02_literature_review/literature_review/abdeljaber2017.tex,latex/chapters/id/02_literature_review/literature_review/abdeljaber2017.tex, [1] [2]latex/chapters/id/02_literature_review/literature_review/index.tex, latex/chapters/id/02_literature_review/literature_review/index.texR1-R13)Theoretical Foundation Updates:
latex/chapters/en/02_literature_review/theoritical_foundation/stft.tex, latex/chapters/en/02_literature_review/theoritical_foundation/stft.texR1-R11)latex/chapters/en/02_literature_review/theoritical_foundation/machine_learning.tex, latex/chapters/en/02_literature_review/theoritical_foundation/machine_learning.texR1-R33)Structural Organization:
latex/chapters/en/02_literature_review/index.tex, latex/chapters/en/02_literature_review/index.texR1-R10)Pull Request Overview
This PR significantly enhances the thesis' literature review and theoretical foundation sections related to Structural Health Monitoring by incorporating detailed discussions on a CNN‐based approach, vibration-based techniques, STFT methodology, and classical machine learning algorithms.
Reviewed Changes
Copilot reviewed 6 out of 6 changed files in this pull request and generated 2 comments.
Show a summary per file
@@ -0,0 +1,6 @@Traditional structural health monitoring methods often rely on hand-crafted features and manually tuned classifiers, which pose challenges in terms of generalization, reliability, and computational efficiency. As highlighted by [Author(s), Year], these approaches frequently require a trial-and-error process for feature and classifier selection, which not only reduces their robustness across structures but also hinders their deployment in real-time applications due to the computational load of the feature extraction phase.The placeholder '[Author(s), Year]' is used in this file; consider replacing it with the actual citation details to improve clarity and accuracy of the reference.
@@ -0,0 +1,11 @@\subsection{Short-Time Fourier Transform (STFT)}The folder name 'theoritical_foundation' appears to be misspelled; consider renaming it to 'theoretical_foundation' for consistency and clarity.
@@ -0,0 +1,6 @@Traditional structural health monitoring methods often rely on hand-crafted features and manually tuned classifiers, which pose challenges in terms of generalization, reliability, and computational efficiency. As highlighted by [Author(s), Year], these approaches frequently require a trial-and-error process for feature and classifier selection, which not only reduces their robustness across structures but also hinders their deployment in real-time applications due to the computational load of the feature extraction phase.abdeljaber2017.texwasnt being inputted. The right changes are made underliterature_review/index.tex