仿生嗅覺(jué)感知系統氣體識別和濃度估計模型
doi: 10.16383/j.aas.c220689
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山西大學(xué)自動(dòng)化與軟件學(xué)院 太原 030006 中國
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上海理工大學(xué)光電信息與計算機工程學(xué)院 上海 200093 中國
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上海理工大學(xué)理學(xué)院 上海 200093 中國
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南洋理工大學(xué)電氣與電子工程學(xué)院 新加坡 639798 新加坡
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北京大學(xué)計算機學(xué)院 北京 100871 中國
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同濟大學(xué)電子與信息工程學(xué)院 上海 201804 中國
Gas Recognition and Concentration Estimation Model for Bionic Olfactory Perception System
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School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
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School of Optical-electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China
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School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
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School of Computer Science, Peking University, Beijing 100871, China
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College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
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摘要: 常用氣體檢測模型需要使用氣體傳感器陣列響應信號的穩態(tài)值對氣體進(jìn)行種類(lèi)識別和濃度估計, 而在實(shí)際環(huán)境 中, 氣體一般處于動(dòng)態(tài)變化的狀態(tài), 氣體傳感器陣列響應信號難以達到穩態(tài)值或長(cháng)時(shí)間維持穩定狀態(tài). 針對上述問(wèn)題, 提出 一種由動(dòng)態(tài)小波殘差卷積神經(jīng)網(wǎng)絡(luò )(Dynamic wavelet residual convolutional neural network, DWRCNN)子模型和權重 信號自注意力(Weighted signal self-attention, WSSA)子模型組成的氣體檢測模型. 該模型可以直接使用氣體傳感器陣列 的原始動(dòng)態(tài)響應信號對動(dòng)態(tài)變化的氣體進(jìn)行成分識別, 并進(jìn)一步對每種成分氣體的濃度在線(xiàn)估計. 通過(guò)搭建的仿生嗅覺(jué)感 知系統對模型的性能進(jìn)行評估, 實(shí)驗結果表明, 與常用氣體識別模型相比, DWRCNN能獲得接近 100%氣體識別準確率, 且在線(xiàn)訓練時(shí)間短, 收斂速度快; 與常用氣體濃度估計模型相比, WSSA濃度估計模型能夠大幅提高氣體濃度估計精度, 并 能同時(shí)對不同氣體都保持較高氣體濃度估計精度, 解決了動(dòng)態(tài)環(huán)境中仿生嗅覺(jué)感知系統需要針對不同氣體選擇不同最優(yōu)氣 體濃度估計模型問(wèn)題.
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關(guān)鍵詞:
- 氣體識別 /
- 濃度估計 /
- 仿生嗅覺(jué)感知系統 /
- 注意力機制
Abstract: Conventional gas detection models require the use of steady-state values of the response signals of gas sensor arrays for gas recognition and gas concentration estimation, whereas in real environments, gases are typically in a state of dynamic change, making it difficult or time-consuming to achieve steady-state values for the response signals from gas sensor arrays. To address the aforementioned issues, a gas detection model consisting of dynamic wavelet residual convolutional neural networks (DWRCNN) sub-model and weighted signal self-attention (WSSA) sub-model is proposed. The model can directly identify the components of dynamically changing gases using the raw dynamic response signals from the gas sensor array, and it can also online estimate the concentration of each component gas. The performance of the model is evaluated by the self-built bionic olfactory perception system. The results demonstrate that DWRCNN achieves nearly 100% gas recognition accuracy in comparison to other prevalent gas recognition models, with a short online training time and a rapid convergence speed; The problem of selecting different optimal gas concentration estimation models for different gases in a dynamic environment for bionic olfactory perception systems is solved by the WSSA concentration estimation model, which can significantly improve the gas concentration estimation accuracy while simultaneously maintaining a very high gas concentration estimation accuracy for different gases. -
圖 3 當CO濃度為140 ppm時(shí), CO傳感器陣列的動(dòng)態(tài)響應信號曲線(xiàn)
Fig. 3 Dynamic response signal curve of the sensor array for 140 ppm CO
圖 4 傳感器陣列動(dòng)態(tài)響應信號小波分解過(guò)程
Fig. 4 Wavelet decomposition process of dynamic response signal of sensor array
圖 5 TGS2610在140 ppm CO下的動(dòng)態(tài)響應信號曲線(xiàn)和相應的5層低頻小波系數曲線(xiàn)
Fig. 5 Dynamic response signal curve and corresponding 5-layer low-frequency wavelet coefficient curve at 140 ppm CO for TGS2610
圖 6 傳感器陣列動(dòng)態(tài)響應信號轉換為小波系數圖像過(guò)程
Fig. 6 Process of converting the dynamic response signal of the sensor array into a wavelet coefficient map
圖 10 不同模型的氣體識別結果混淆矩陣
Fig. 10 Confusion matrix of gas recognition results with different models
圖 11 不同模型氣體識別的準確率曲線(xiàn)和損失函數曲線(xiàn)
Fig. 11 Accuracy curve and loss function curve of gas recognition with different models
圖 13 不同模型的氣體濃度估計誤差箱式圖
Fig. 13 Error box plots of gas concentration estimation with different models
圖 14 SA模型和WSSA模型的氣體濃度估計散點(diǎn)圖
Fig. 14 Scatter plots of gas concentration estimation for SA model and WSSA model
圖 15 氣腔進(jìn)氣口位置和傳感器陣列高度示意圖
Fig. 15 Diagram of gas cavity inlet position and sensor array height
表 1 氣體傳感器陣列詳細信息
Table 1 Gas sensor array details
通道編號 傳感器型號 公司名稱(chēng) 敏感的主要氣體種類(lèi) 通道0 MQ135 Winsen NH3、H2S、C6H6 通道1 TGS813 FIGARO CH4、CH3CH2CH3 通道2 TGS2611 FIGARO CH4 通道3 TGS2610 FIGARO CH3CH2CH3、C4H10 通道4 TGS2620 FIGARO C2H6O、有機溶劑 通道5 TGS2600 FIGARO H2、C2H6O 通道6 TGS2602 FIGARO VOC、NH3、H2S、CH2O 通道7 MP503 Winsen C2H6O、C4H10、CH2O 
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表 2 不同模型的氣體識別準確率 (%)
Table 2 Gas recognition accuracy of different models (%)
方法 KNN SVM RF NB 準確率 95.74 96.45 95.74 92.91 方法 BPNN CNN CapsNet DWRCNN 準確率 97.87 99.29 100.00 100.00
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表 3 CO濃度估計指標
Table 3 Metrics of CO concentration estimation
方法 MAE RMSE EV ${\rm{R}}^2$ BR 6.552 8.094 0.943 0.942 SVM 4.258 7.015 0.963 0.957 DT 5.472 8.039 0.949 0.943 KNN 5.033 7.075 0.958 0.956 RF 4.713 7.074 0.959 0.956 Adaboost 5.477 7.643 0.950 0.949 GBDT 4.817 7.019 0.960 0.957 Bagging 4.760 7.061 0.959 0.956 XGBoost 4.672 7.035 0.961 0.960 OSA 3.630 4.229 0.987 0.986 LSTM 2.934 3.845 0.988 0.988 WS-LSTM 2.350 3.035 0.993 0.993 SA 2.916 3.756 0.989 0.989 WSSA 2.090 2.646 0.995 0.994
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表 4 H2濃度估計指標
Table 4 Metrics of H2 concentration estimation
方法 MAE RMSE EV ${\rm{R}}^2$ BR 16.097 18.284 0.683 0.638 SVM 5.034 6.976 0.955 0.947 DT 5.206 8.987 0.921 0.913 KNN 6.312 8.865 0.931 0.915 RF 5.073 7.157 0.951 0.945 Adaboost 5.441 7.209 0.952 0.944 GBDT 5.687 8.444 0.931 0.923 Bagging 5.346 7.667 0.940 0.936 XGBoost 5.512 8.724 0.937 0.935 OSA 4.155 5.343 0.977 0.977 LSTM 4.264 5.305 0.974 0.973 WS-LSTM 3.781 4.457 0.985 0.984 SA 4.156 5.560 0.975 0.975 WSSA 2.360 3.028 0.993 0.992
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表 5 混合氣體中CO濃度估計指標
Table 5 Metrics of CO concentration estimation in the gas mixture
方法 MAE RMSE EV R2 BR 20.134 24.487 0.530 0.526 SVM 20.009 24.657 0.519 0.519 DT 20.537 25.746 0.515 0.476 KNN 21.236 26.701 0.443 0.436 RF 18.529 23.614 0.581 0.559 Adaboost 19.950 25.019 0.526 0.505 GBDT 20.830 26.193 0.481 0.457 Bagging 19.608 25.288 0.531 0.494 XGBoost 15.931 20.101 0.619 0.592 OSA 10.909 13.589 0.860 0.859 LSTM 10.439 14.050 0.856 0.849 WS-LSTM 7.958 11.188 0.911 0.904 SA 9.209 12.958 0.872 0.872 WSSA 6.014 7.616 0.956 0.956
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表 6 混合氣體中H2濃度估計指標
Table 6 Metrics of H2 concentration estimation in the gas mixture
方法 MAE RMSE EV R2 BR 9.956 12.378 0.897 0.897 SVM 8.008 10.106 0.931 0.931 DT 11.326 15.503 0.842 0.838 KNN 7.297 9.641 0.937 0.937 RF 7.852 10.823 0.922 0.921 Adaboost 9.120 11.582 0.915 0.909 GBDT 7.763 10.622 0.924 0.924 Bagging 8.019 11.394 0.915 0.912 XGBoost 7.840 10.089 0.932 0.931 OSA 8.886 11.720 0.912 0.910 LSTM 7.783 8.848 0.949 0.949 WS-LSTM 5.095 6.878 0.969 0.969 SA 5.906 7.776 0.960 0.960 WSSA 4.318 6.362 0.974 0.973
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表 7 氣體識別準確率 (%)
Table 7 Gas recognition accuracy (%)
氣腔進(jìn)氣口位置 A B C 準確率 100 100 100 傳感器陣列擺放高度 E F G 準確率 100 100 100
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表 8 氣腔進(jìn)氣口位置不同時(shí)單一氣體濃度估計的指標
Table 8 Metrics for concentration estimation of single gas with different gas cavity inlet positions
進(jìn)氣口位置 氣體種類(lèi) MAE RMSE EV R2 A CO 2.090 2.646 0.995 0.994 H2 2.360 3.028 0.993 0.992 B CO 2.326 3.017 0.994 0.994 H2 2.287 2.898 0.994 0.994 C CO 2.185 2.812 0.995 0.994 H2 2.419 3.177 0.992 0.992
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表 9 氣腔進(jìn)氣口位置不同時(shí)混合氣體濃度估計的指標
Table 9 Metrics for concentration estimation of mixed gases with different gas cavity inlet positions
進(jìn)氣口位置 氣體種類(lèi) MAE RMSE EV R2 A CO 6.014 7.616 0.956 0.956 H2 4.318 6.362 0.974 0.973 B CO 5.679 6.899 0.963 0.962 H2 4.562 6.713 0.973 0.973 C CO 5.878 7.256 0.961 0.961 H2 4.785 6.896 0.972 0.972
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表 10 傳感器陣列擺放高度不同時(shí)單一氣體濃度估計指標
Table 10 Metrics for concentration estimation of single gas with different sensor array placement heights
高度 氣體種類(lèi) MAE RMSE EV R2 E CO 2.090 2.646 0.995 0.994 H2 2.360 3.028 0.993 0.992 F CO 2.283 2.878 0.994 0.994 H2 2.226 2.873 0.994 0.994 G CO 2.375 3.122 0.993 0.993 H2 2.451 3.163 0.992 0.992
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表 11 傳感器陣列擺放高度不同時(shí)混合氣體濃度估計指標
Table 11 Metrics for concentration estimation of mixed gases with different sensor array placement heights
高度 氣體種類(lèi) MAE RMSE EV R2 E CO 6.014 7.616 0.956 0.956 H2 4.318 6.362 0.974 0.973 F CO 6.323 8.012 0.955 0.955 H2 4.619 6.992 0.972 0.972 G CO 6.225 7.896 0.956 0.955 H2 4.673 7.105 0.972 0.972
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表 12 本文模型的氣體識別準確率 (%)
Table 12 Gas recognition accuracy of our model (%)
信號采集 第1次 第2次 第3次 準確率 100.00 100.00 99.29
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表 13 單一氣體濃度估計指標
Table 13 Metrics for concentration estimation of single gas
信號采集 氣體種類(lèi) MAE RMSE EV R2 第1次 CO 2.090 2.646 0.995 0.994 H2 2.360 3.028 0.993 0.992 第2次 CO 2.512 3.283 0.992 0.992 H2 2.814 3.684 0.991 0.991 第3次 CO 2.985 3.872 0.989 0.989 H2 3.350 4.115 0.988 0.987
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表 14 混合氣體濃度估計指標
Table 14 Metrics for concentration estimation of mixed gases
信號采集 氣體種類(lèi) MAE RMSE EV R2 第1次 CO 6.014 7.616 0.956 0.956 H2 4.318 6.362 0.974 0.973 第2次 CO 6.711 8.813 0.941 0.940 H2 5.157 6.972 0.967 0.967 第3次 CO 7.016 9.437 0.934 0.934 H2 5.815 7.654 0.962 0.962
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