Minglv Jiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Minglv Jiang has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Haoran Sheng
School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
1 shared publication - Haoyu Li
School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
1 shared publication - Kaiyan Peng
School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
1 shared publication - Mingyong Li
CSSC AlphaPec Instrument (Hubei) Co., Ltd., Yichang 443005, China
1 shared publication - Na Li
Northwest Survey & Planning Institute of National Forestry and Grassland Administration, Xi’an 710048, China
1 shared publication - Qiang Li
Key Laboratory of Physical Electronics and Devices for Ministry of Education and Shaanxi Provincial Key Laboratory of Photonics & Information Technology, Xi’an Jiaotong University, Xi’an 710049, China
1 shared publication
Latest Publications
- Open Access
E-Nose: Time–Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction
In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time–frequency attention convolutional neural network (TFA-CNN). A time–frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model’s robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time–frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.
Open AccessE-Nose: Time–Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction
In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time–frequency attention convolutional neural network (TFA-CNN). A time–frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model’s robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time–frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.