/NN-VMD

Neural Networks based Variational Mode Decomposition

Primary LanguagePython

NN-VMD

Prerequisite

  • Python 3.7+
  • Julia 1.7+
  • If you use docker gpu, you should install nvidia-cuda-toolkit and nvidia-container-toolkit
sudo apt install -y nvidia-cuda-toolkit # nvidia-cuda-toolkit installation

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit # nvidia-container-toolkit installation

sudo /etc/init.d/docker restart

How to use (Windows)

More...

  1. ECG5000 Data download from timeseriesclassification Execute ./data.bat

  2. Install library using pip install -r requirements.txt

  3. Execute julia requirements.jl (install lib)

  4. Execute python julia_setting.py

  5. Execute python file python train.py

  6. You can modify VMD setting or AI model via

./utils/util.py and ./utils/preprocessing.jl and ./models/model.py

How to use (Linux)

More...

  1. ECG5000 Data download from timeseriesclassification Execute sh data.sh

  2. Install library using pip install -r requirements.txt

  3. Execute julia requirements.jl (install lib)

  4. Execute python julia_setting.py

  5. Execute python file python train.py

  6. You can modify VMD setting or AI model via

./utils/util.py and ./utils/preprocessing.jl and ./models/model.py

How to use (Docker)

More...

  1. ECG5000 Data download from timeseriesclassification Execute sh data.sh

  2. if Docker turn off, Execute sudo service docker start

  3. Execute docker build -t nn-vmd .

  4. Execute GPU version docker run -it --gpus all --name nn-vmd nn-vmd:latest bash train.sh(default : MTL)

    Execute CPU version docker run -it --name nn-vmd nn-vmd:latest bash train.sh

  5. Option Execute

docker start nn-vmd (required)
docker exec -it nn-vmd bash train.sh cnn
docker exec -it nn-vmd bash train.sh vae

Plan

  • VAE (Variational Auto Encoder)
  • Graph neural nets + Shallow neural nets
  • Multi-task learning (e.g. decomposition and classification task)

Citation

@inproceedings{han2022ai,
  title={AI model based variational mode decomposition using signal data},
  author={Han, Seungwoo},
  journal={proceedings of symposium of the korean institute of communications and information sciences},
  pages={1362--1363},
  year={2022}
}