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• W4320913262 abstract "Based on the current concentration levels of gas components in the air, indoor air quality may be assessed. Gas information of mixed gas with various concentrations can be acquired and assessed using an electronic nose (E-nose). For mixed gas of varied concentrations, the E-nose's prediction accuracy using the conventional regression prediction algorithm is unsatisfactory. We propose a two-channel temporal convolutional network based on a model temporal convolutional network (TCN) suitable for time series processing that was utilized to increase the precision of the regression prediction of mixed gas concentration on the E-nose. In addition to improve the structure of the model, we also analyse its activation function. In the experiment, we use two types of gas mixture data with different concentrations, methane-ethylene and carbon monoxide(CO)-ethylene, in our experiments. It is compared with some baseline models including long short-term memory (LSTM), gated recurrent unit (GRU) and generic temporal convolutional network (TCN). In our experiments, we show that the two-channel TCN architecture is superior to both the models mentioned above and the generic TCN architecture." @default.
• W4320913262 created "2023-02-16" @default.
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• W4320913262 date "2023-05-01" @default.
• W4320913262 modified "2023-10-13" @default.
• W4320913262 title "A mixed gas concentration regression prediction method for electronic nose based on two-channel TCN" @default.
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• W4320913262 doi "https://doi.org/10.1016/j.snb.2023.133528" @default.
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