Machine learning for material recognition with a triboelectric nanogenerator (TENG)

This project was written for my bachelor's thesis.

It was written to classify TENG voltage output from pressing it against different materials. Contents:

• Data preparation/plotting/loading utilites
• (Bi)LSTM + fully connected + softmax model for name classifiying TENG output
• Progress tracking utilities to easily find the best parameters

Model training

Adjust the parameters in main.py and run it. All models and the settings they were trained with are automatically serialized with pickle and stored in a subfolder of the <model_dir> that was set in main.py.

Model evaluation

Run find_best_model.py <model_dir> with the <model_dir> specified in main.py during training.