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+MLforSHM,
+by SIDDHARTH S, SHAH VISHWA VIPULKUMAR and THUKRAL SHAURYA MANISH
+
+The objectives of this project align with the objectives of the paper [1]. We plan to use Machine Learning for Structural Health Monitoring. 
+The ultimate aim of this project is to take noisy accelerometer data from accelerometers placed on two adjacent floors and be able to accurately predict
+the status of the building. The status of the building can be one of 3 classes, Immediate Occupancy (IO), Life Safety (LS) or Collapse Prevention (CP) 
+as explained in the paper summary of [1]. 
+
+This project contains 4 folders - Code, Documents, Input, Output
+
+Code folder contains:
+finalc.py
+Link to code on Colab.txt
+datasetgeneration.m
+testsetgeneration.m
+
+Documents folder contains:
+MLforSHMreport.pdf
+REFERENCES.pdf
+dataset creation.png
+Denoising approach.png
+Direct approach.png
+Getting final classification.png
+
+Input folder contains:
+noisy.xlsx
+test_noisy.xlsx
+
+Output folder contains:
+pure.xlsx
+test_pure.xlsx
+
+The code flowcharts given in the documents folder has been coded in python and the code explaination is given in the code itself through comments.
+
+There is more info about the datasets, the source code and the procedure to execute code given below...
+
+A.Datasets - MATLAB
+These datasets have been created using MATLAB code datasets.m, we have made use of sin and awgn functions
+for randomizing and creating the representative accelerometer data
+1.Input Data Set for Training: pure.xlsx, noisy.xlsx
+2.Input Data Set for Testing: test_pure.xlsx, test_noisy.xlsx
+
+
+B. Source Code - Python
+These datasets have been fed to the code finalc.ipynb, This code performs the following high level operations.
+1. Randomization and Reshaping the datasets to fit the Deep Neural Networks
+2. CNN and ANN models for denoising noisy data which have been finalized after various iterations and accuracy on the test set.
+3. Testing the model on the testing datasets and checking the extent of denoising
+4. Labeling the pure, noisy and denoised dataset into IO, LS and CP using IDR calculations as mentioned in [1]
+5. Testing the accuracy of classification for noisy and denoised datasets against pure dataset as reference.
+
+
+C. Procedure to Execute the Code
+We have implemented and tested this code on Google Colab and the steps to execute the code is given here.
+We strongly recommend running the code using the first method as the second method is very likely to give issues during the installation of
+TensorFlow and has not been extensively tested
+
+There are two ways to execute the code:
+a)On google colab
+b)On the terminal using finalc.py
+
+a)Steps to run the code on google colab
+
+1.Open the below link
+
+https://colab.research.google.com/drive/1wVfQ8GnsTKe9uhZHovUatj72gWzqNBdJ?usp=sharing
+
+2.connect to a runtime
+3.upload the dataset files pure.xlsx, noisy.xlsx, test_pure.xlsx, test_noisy.xlsx to the colab session
+4.Change the runtime type to GPU
+5.run the first cell which installs TensorFlow
+6.Run the next cell which contains all the code
+
+b)Steps to run the code in the terminal
+
+1.install the latest version of python
+2.Open the terminal and execute the following pip install commands
+
+pip install numpy
+pip install -q tensorflow tensorflow-datasets
+pip install matplotlib
+pip install pandas
+pip install scipy
+
+3.include pure.xlsx, noisy.xlsx, test_pure.xlsx, test_noisy.xlsx as well as finalc.py in the same directory
+4.run finalc.py on the terminal
+
+
+References
+[1] A. Ibrahim, A. Eltawil, Y. Na and S. El-Tawil, "A Machine Learning Approach for
+Structural Health Monitoring Using Noisy Data Sets," in IEEE Transactions on Automation Science and Engineering, vol. 17, no. 2, pp. 900-908, April 2020, doi:
+10.1109/TASE.2019.2950958.