855114d633
- Removed unnecessary imports (os, pandas, numpy) from the STFT notebook. - Adjusted the number of damage cases in the multiprocessing pool to correctly reflect the range. - Updated model training code for Sensor B to ensure consistent naming and structure. - Cleaned up commented-out code for clarity and maintainability.
Summary
This repository contains the work related to my thesis, which focuses on damage localization prediction. The research explores the application of machine learning techniques to structural health monitoring.
Note: This repository does not contain the secondary data used in the analysis. The code is designed to work with data from the QUGS (Qatar University Grandstand Simulator) dataset, which is not included here.
The repository is private and access is restricted only to those who have been given explicit permission by the owner. Access is provided solely for the purpose of brief review or seeking technical guidance.
Restrictions
- No Derivative Works or Cloning: Any form of copying, cloning, or creating derivative works based on this repository is strictly prohibited.
- Limited Access: Use beyond brief review or collaboration is not allowed without prior permission from the owner.
All contents of this repository, including the thesis idea, code, and associated data, are copyrighted © 2024 by Rifqi Panuluh. Unauthorized use or duplication is prohibited.
How to Run stft.ipynb
- run
pip install -e .in root project first - run the notebook