wozu-dichter / E-NOSE-AFOA

Smart electronic nose enabled by an all-feature olfactory algorithm (AFOA)

Introduction

This repository contains the code of the paper Smart electronic nose enabled by an all-feature olfactory algorithm (AFOA). Our method combines one-dimensional convolutional and recurrent neural networks with channel and temporal attention modules to fully utilize complementary global and dynamic information in an end-to-end manner. We further demonstrate that a novel data augmentation method can transform the raw data into a suitable representation for feature extraction. The experimental results show that the smart e-nose simply comprising of six semiconductor gas sensors achieves superior performances to state-of-the-art methods on real-world data.

Prerequisites

• python 3.6
• tensorflow 1.13
• keras 2.2
• numpy
• scikit-learn

How to Run

Preparation.

1. Modify the dataset path in main.py:

tfrecord_file='/home/xxxx/works/E-nose/AFOA/data/

2. Modify the pre-trained model path:

pretrained_model_path=/home/xxxx/works/COVID-19/PMP/pretrained_model_dataset1/

3. Copy datasets to your dataset path.

Training.

For example, train our model on the dataset

CUDA_VISIBLE_DEVICES=0 python main.py --batch-size 128 --lstm-hidden 64 --dataset e-nose --res-first-filters 16 --attention-mode cbam2 --epochs 100 --stage-num 3 --pool-size 8 --dropout 0.2 --r-dropout 0.1 --d-dropout 0.05 --sensor 0

It will save the models in ./saved_models/.

Testing.

For example, directly evaluate the model trained from dataset.

CUDA_VISIBLE_DEVICES=0 python eval.py --dataset e-nose --model-path your_model_name.h5 --sensor 0

Citation

If you use this code for your research, please cite our paper:

@article{fang2022smart,
  title={Smart Electronic Nose Enabled by an All-Feature Olfactory Algorithm},
  author={Fang, Cong and Li, Hua-Yao and Li, Long and Su, Hu-Yin and Tang, Jiang and Bai, Xiang and Liu, Huan},
  journal={Advanced Intelligent Systems},
  pages={2200074},
  year={2022},
  publisher={Wiley Online Library}
}