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首页> 外文期刊>IEEE sensors journal >A Novel Gas Recognition and Concentration Estimation Model for an Artificial Olfactory System With a Gas Sensor Array
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A Novel Gas Recognition and Concentration Estimation Model for an Artificial Olfactory System With a Gas Sensor Array

机译：气体传感器阵列的人工嗅觉系统的新型气体识别与浓度估计模型

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Traditional algorithms cannot readily address the fact that artificial olfaction in a dynamic ambient environment requires continuous selection and execution of the optimal algorithm to detect different gases. This paper presents a deep learning WCCNN-BiLSTM-many-to-many GRU (wavelet coefficient convolutional neural network-bidirectional long short-term memory-many-to-many-gated recurrent unit) model for qualitative and quantitative artificial olfaction of gas based on the automatic extraction of time-frequency domain dynamic features and time domain steady-state features. The model consists of two submodels. One submodel recognizes a gas by the WCCNN-BiLSTM model, and the experiments based on actual data from our fabricated artificial olfactory system demonstrate that the gas recognition accuracy is nearly 100%. The other submodel quantifies the gas by the many-to-many GRU model with less labeled data; this submodel is comparable to conventional algorithms such as DT (decision tree), SVMs (support vector machines), KNN (k-nearest neighbor), RF (random forest), AdaBoost, GBDT (gradient-boosting decision tree), bagging, and ET (extra tree) according to PCA (principal component analysis) dimensionality reduction. The experimental results of 10-fold cross-validations show that the proposed many-to-many GRU outperforms the aforementioned conventional algorithms with remarkable metrics and can maintain higher concentration estimation accuracy for different unknown gases with less labeled data.
机译：传统算法不能容易地解决动态环境环境中的人工嗅觉需要连续选择和执行最佳算法来检测不同的气体。本文介绍了深度学习WCCNN-Bilstm-多对多GRU（小波系数卷积神经网络 - 双向短期内记忆长期记忆 - 多对多通用的经常性单元）模型，用于气体的定性和定量人工嗅觉关于时频域动态特征的自动提取和时域稳态特征。该模型由两个子模型组成。一个子模型通过WCCNN-Bilstm模型识别出气体，并且基于来自我们制造的人造嗅觉系统的实际数据的实验表明，气体识别精度近于100％。另一个子模型通过多对多GRU模型量化气体，具有较少标记的数据;该子模型与诸如DT（决定树），SVMS（支持向量机），KNN（K最近邻居），RF（随机林），ADABOOST，GBDT（梯度升压决策树），袋装和的常规算法等常规算法根据PCA（主成分分析）维度减少等（额外的树）。 10倍交叉验证的实验结果表明，提出的多对多GRU优于上述传统算法，具有显着的指标，并且可以对不同的未知气体保持更高的浓度估计精度，具有较少标记的数据。

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