Code for the paper "Modeling Electrical Motor Dynamics using Encoder-Decoder with Recurrent Skip Connection" (Accepted in AAAI 2020)

This is the github repository containing the code for the paper "Modeling Electrical Motor Dynamics using Encoder-Decoder with Recurrent Skip Connection" by Sagar Verma, Nicolas Henwood, Marc Castella, Francois Malrait, and Jean-Christophe Pesquet.

Project page

**Note: All the tables in the paper are for models trained for predicting individual quantities from three input quantities. It is possible to use the model for any input-ouput combinations by passing appropriate arguments. Also MAE is first computed on normalized output and then aggregated and normalized. Ignore # the reported MAE, RMSLE, and RMSE.

Requirements

The code has been tested on:

• 2xNvidia V100 GPU
• Ubuntu 18.04 LTS on 48 vCPUs and 186 GB of RAM
• Python 3.6.10
• Pytorch v1.4.0

Dataset

Motor Data

Run

Installation

git clone https://github.com/INRIA-OPIS/MotorNN.git
git checkout AAAI2020_release
pip install -r requirements.txt
pip install -e .

Download and extract dataset. Create weights and logs path.

To train a model use following

cd MotorNN
python motor_dynamics/summoner.py --gpu=0 --task=train --train_sim_dir={DATA_PATH}/train_sim/ --val_sim_dir={DATA_PATH}/val_sim/ --weights_dir={WEIGHTS_PATH} --logs_dir={LOGS_PATH} --model=deep_cnn --epochs=100 --batch_size=512 --lr=0.1 --inp_quants='voltage_d,voltage_q,speed' --out_quants='current_d' --stride=1 --window=100 --act=relu --loss=mse

Contact

For any queries, please contact

Sagar Verma: sagar15056@iiitd.ac.in