A Method for Recognition of Mixed Gas Composition Based on PCA and KNN

Xia, Wanyu; Song, Tingting; Yan, Zhanwei; Song, Kai; Chen, Deyun; Chen, Yinsheng

doi:10.1109/icocn53177.2021.9563663

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Testing on randomly selected gas datasets showed that the PCA-SVM model demonstrated significant advantages in classification performance, with an accuracy of 98.974% for a gas dataset containing 13 features and even reaching 100% accuracy for a dataset containing 27 features, thus improving classification performance. W. Xia et al [11] combined PCA and KNN algorithms to identify the components of mixed gases, using PCA to extract gas characteristics and KNN for gas type identification. Experimental results showed a significantly higher accuracy in gas identification in the reduced feature space compared to the unreduced scenario.…”

Section: Introductionmentioning

confidence: 99%

Mixed Gas Detection and Temperature Compensation Based on Photoacoustic Spectroscopy

Chao,

Runze,

Niansong

et al. 2024

IEEE Photonics J.

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In recent years, with the continuous progress of technology and the development of society, the demand for updating trace gas detection technology has been increasing. The ability to quickly and accurately detect the composition and concentration of gases has become a hot topic in current research. In response to address issues such as difficulties in judging data for classification and recognizing gas components with low accuracy, a KNN-SVM algorithm has been proposed. The algorithm primarily reclassifies ambiguous data that are close to the hyperplane but do not have a clear affiliation, capturing data characteristics more comprehensively. It determines the weight ratio of each algorithm through experiments to improve the accuracy of gas category discrimination. Experimental results show that, compared to the traditional SVM algorithm, the KNN-SVM algorithm performs better in gas classification prediction, with an accuracy rate of 99.167% and an AUC indicator of 99.375%, enhancing the accuracy of gas detection. In response to the impact of temperature on the system during the experimental process, a WOA-BP temperature compensation model was established to compensate for temperature in gas concentration detection. After comparing various optimized BP neural network models, the performance of the WOA-BP temperature compensation was the most outstanding, with an R2 of 97.89%, MAE of 1.4868, RMSE of 2.0416, and a convergence speed after 15 iterations, reducing detection errors and thus achieving precise detection of low-concentration mixed gases.

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Section: Introductionmentioning

confidence: 99%

Mixed Gas Detection and Temperature Compensation Based on Photoacoustic Spectroscopy

Chao,

Runze,

Niansong

et al. 2024

IEEE Photonics J.

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“…Deep learning based methods usually transform gas sample to be an matrix and then deals with the matrix with deep learning methods [7]- [9]. Conventional machine learning based method uses support vector machine, K-Nearest neighbor, etc [10], [11] to recognize gas. These methods achieved success to some extents.…”

Section: Introductionmentioning

confidence: 99%

MGRBA: Gas Recognition With Biclustering and Adaboost

Zhou,

Wang,

Huang

et al. 2024

IEEE Access

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Gas recognition has been widely used in many fields such as air quality monitoring in dangerous areas. However, existing recognition methods suffer from two limitations: first, the recognition accuracy is not high. Due to the stochastic nature of air turbulence, gas features are not steady. The global features are sensitive to feature variations. Existing methods are based on global similarity, ignoring local similarity. Samples may be dissimilar in respect of global similarity, but are similar in terms of local similarity; Second, most existing recognition methods are based on the closed-set assumption that the gases categories in the train and test set are same. However, in real world applications, the test set may have non-overlapping gas category with the train set. To address above limitations, biclustering is used to extract local similarity. However, original biclustering method is not suitable for extraction. Since original biclustering method is used to find all kinds of biclusters, here we just want to find column nearly constant bicluster Therefore, a modified biclustering method is proposed. The local similarity can be used to construct classifier to recognize gas with adaboost. However, original adaboost cannot be used for open-set recognition. Thus a modified adaboost that uses two thresholds is proposed to recognize the unknown gases. To assess the efficacy of the proposed method, it is tested on public dataset. Experiment results demonstrate that the proposed method outperforms several state-of-the-art methods in respect of several evaluation measures on both closed-set and open-set cases.

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