A Mixed Gas Composition Identification Method Based on Sample Augmentation

Chen, Yinsheng; Xia, Wanyu; Chen, Deyun; Zhang, Tianyu; Song, Kai

doi:10.1109/i2mtc48687.2022.9806499

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many types of kernel functions in support vector regression models, and choosing different kernel functions will lead to huge differences in model prediction accuracy. Commonly used kernel functions [10] are linear kernel function, polynomial kernel function, radial basis kernel function, and sigmoid kernel function.…”

Section: Support Vector Machinementioning

confidence: 99%

Greenhouse gas prediction method based on particle swarm optimized SVR

Wang

Mingxue

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

View full text Add to dashboard Cite

To solve the problems of low accuracy of greenhouse gas detection and difficult selection of model parameters, this paper proposes a Support Vector Machine Regression (SVR), algorithm based on Particle Swarm Optimization (PSO). By comparing the performance of four common kernel functions of SVR on the test set, the kernel function with the best performance is selected as the kernel function of SVR. On this basis, comparing the Grid Search method (GridSearchCV) with the PSO, the optimal combination of super parameters C and gamma are selected. The results show that the PSO can efficiently select the optimal combination of super parameters and greatly improve the modeling efficiency. Finally, the greenhouse gas concentration of the SVR optimized based on the two algorithms is estimated through experiments. The accuracy of the optimized SVR algorithm reaches 94.42%, which improves the model's prediction accuracy.

show abstract

Section: Support Vector Machinementioning

confidence: 99%

Greenhouse gas prediction method based on particle swarm optimized SVR

Wang

Mingxue

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

View full text Add to dashboard Cite

show abstract

Identification of Gas Mixtures with Few Labels Using Graph Convolutional Networks

Fan,

Schaffernicht,

Lilienthal

2024

2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN)

View full text Add to dashboard Cite

An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms

Zeng,

Shahzeb,

Cheng

et al. 2024

Micromachines

View full text Add to dashboard Cite

This study addresses the challenge of multi-dimensional and small gas sensor data classification using a gelatin–carbon black (CB-GE) composite film sensor, achieving 91.7% accuracy in differentiating gas types (ethanol, acetone, and air). Key techniques include Principal Component Analysis (PCA) for dimensionality reduction, the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation, and the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms for classification. PCA improved KNN and SVM classification, boosting the Area Under the Curve (AUC) scores by 15.7% and 25.2%, respectively. SMOTE increased KNN’s accuracy by 2.1%, preserving data structure better than polynomial fitting. The results demonstrate a scalable approach to enhancing classification accuracy under data constraints. This approach shows promise for expanding gas sensor applicability in fields where data limitations previously restricted reliability and effectiveness.

show abstract

A Mixed Gas Composition Identification Method Based on Sample Augmentation

Cited by 3 publications

References 10 publications

Greenhouse gas prediction method based on particle swarm optimized SVR

Greenhouse gas prediction method based on particle swarm optimized SVR

Identification of Gas Mixtures with Few Labels Using Graph Convolutional Networks

An Enhanced Gas Sensor Data Classification Method Using Principal Component Analysis and Synthetic Minority Over-Sampling Technique Algorithms

Contact Info

Product

Resources

About