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Analytical equations (Maxwell) were used to extract frequency dependent electrical conductivity and permittivity of the cement paste (or the host matrix), interface, inclusion (iron) and voids to develop a generic electro-mechanical coupling model to for the strain sensing behavior. COMSOL Multiphysics 5.2a was used as finite element analysis software to develop the model. A MATLAB formulation was used to generate the microstructure with different volume fractions of inclusions. Material properties were assigned (the frequency dependent electrical parameters) and the coupled structural and electrical physics interface in COMSOL was used to model the strain sensing response. The experimental change in resistance matched well with the simulated values, indicating the applicability of the model to predict the strain sensing response of particulate composite systems.
The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.
My proposed project is an educational application that will seek to simplify the<br/>process of internalizing the chord symbols most commonly seen by those learning<br/>musical improvisation. The application will operate like a game, encouraging the<br/>user to identify chord tones within time limits and award points for successfully<br/>doing so.