#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Aug 31 14:09:35 2020 @author: milena """ Simulation of acceleration signal produced when a car is traversing a road with cracks, potholes and patches. Car parameters: Golden car model Road length: 100m Defect geometries: patch: dmin = 0.005; dmax = 0.05; dincr = 0.005; % minimum, maximum and increment of severity wmin = 0.250; wmax = 5.00; wincr = 0.0500; % minimum, maximum and increment of length pothole: dmin = -0.010; dmax = -0.25; dincr = -0.005; % minimum, maximum and increment of severity wmin = 0.050; wmax = 0.50; wincr = 0.0250; % minimum, maximum and increment of length crack; dmin = -0.020; dmax = -0.30; dincr = -0.005; % minimum, maximum and increment of severity wmin = 0.004; wmax = 0.04; wincr = 0.0020; % minimum, maximum and increment of length Speed: 0-120 km/h with step=2km/h Sampling frequency: around 300Hz (can differ by few Hzs between examples) Cases with too narrow defects to be recorded with this sampling frequency, are removed during preprocessing. Directory contents: - Matlab-files: output of simulation module - each .m file corresponds to one defect type, geometry and speed. - train-val-test: all matlab files are converted to numpy arrays/floats/string and organized into a pandas and dataframe which is saved as a pickle file ('full_simulation.pkl'). Each row in dataframe corresponds to one matlab file. The full file is split into 60%/20%/20% train/valid/test files. The too narrow defects without any point are removed. - train-val-test-normalized: the train file from train-val-test is scaled to 0-1 range. The scaler is saved and applied to valid and test files. - train-val-test-normalized-split-into-windows: A sliding window of size=2m and step=1 point is applied on train/valid/test files from train-val-test-normalized. The results are saved in this directory. Those are the final results for analysis. - scaler.pkl: train scaler file.
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