## 4 Environmental Approach Using Air and Thermometric Sensors
### Proposed Sensor System and Recognition Method

Table 11: Major specifications of D6T-44L-06. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specifications** \\ \hline Target gas & CO\({}_{2}\) \\ Operating principle & NDIR \\ Measurement range & 0–5000 ppm \\ Accuracy & \(\pm\)30 ppm \(\pm\)3\% of reading at 101.3 kPa \\ Response time & 20 s diffusion time \\ Rate of measurement & 0.5 Hz \\ Operating temperature & 0–50 \({}^{\circ}\)C \\ Operating humidity & 0–95\% \\ Sensor board dimensions & L51 \(\times\) W58 \(\times\) H12 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Major specifications of K30. Based on our earlier study [219] and existing studies as shown in Table 2, we used LSTM embedded in VGG-16. Figure 7 portrays a typical LSTM network architecture of hidden layers. The internal LSTM structure comprises hidden layer units with memory cells called LSTM blocks, along with gates of three types: input gates, forgetting gates, and output gates. The vanishing gradient problem [220] is resolved using this mechanism. Input gates select enabled or disabled input feature signals. Forgetting gates select permission to reset the internal information stored in cells. Output gates determine the amount of information that is transmitted at the next phase. Based on recurrent neural network (RNN) algorithms, LSTM provides a one-step later prediction. The prediction is conducted from the input feature signals at the current time \(t\) and the feedback signals to the hidden layer at a previous time \(t-1\). The memory cells save internal information for a long period, which provides an important benefit compared to RNN for modeling temporally distant dependences. Letting \(x_{t}\) and \(c_{t}\), respectively, represent the input feature signal and output from memory cells, and letting \(I_{t}\), \(F_{t}\), and \(O_{t}\), respectively, stand for the outputs of the input, forgetting, and output gates, then LSTM output \(H_{t}\) is obtained as presented below. \[H_{t}=o_{t}\otimes\tanh(c_{t}), \tag{1}\] \[c_{t}=c_{t-1}\otimes f_{t}+i_{t}\otimes\tanh(W_{z}x_{t}+R_{z}h_ {t-1}+b_{z}),\] (2) \[ \begin{bmatrix}I_{t}\\ F_{t}\\ O_{t}\end{bmatrix} =\sigma_{1} \begin{bmatrix}W_{t}\\ W_{f}\\ W_{o}\end{bmatrix} x_{t}+\sigma_{2} \begin{bmatrix}R_{f}\\ R_{o}\\ R_{o}\end{bmatrix} h_{t-1}+\sigma_{3} \begin{bmatrix}b_{i}\\ b_{f}\\ b_{o}\end{bmatrix} . \tag{3}\]  
where \(W_{i,f,o,z}\), \(R_{i,f,o,z}\), and \(B_{i,f,o,z}\), respectively, stand for input weights, recurrent weights, and biases. Moreover, \(\sigma=\sigma_{1}=\sigma_{2}=\sigma_{3}\) and \(\otimes\), respectively, express the sigmoid function and the element-wise product. The dominant role of \(I_{t}\) is to update cell states. Propagation signals are controlled by \(F_{t}\), which refers to a previous cell state \(c_{t-1}\). Moreover, unnecessary signals are removed by \(F_{t}\), which prevents excessive information from the earlier cell output split by short \begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Target** & **Specifications** \\ \hline Measurement range & Air pressure & 300–1100 hPa \\  & Temperature & \(-\)40–85 \({}^{\circ}\)C \\  & Humidity & 0–100\% \\ Resolution & Air pressure & 0.18 hPa \\  & Temperature & 0.01 \({}^{\circ}\)C \\  & Humidity & 0.008\% \\ Accuracy tolerance & Air pressure & \(\pm\)1 hPa \\  & Temperature & \(\pm\)1\% \\  & Humidity & \(\pm\)3\% \\ Sampling intervals & & 1 s \\ Sensor dimensions & & L2.5 \(\times\) W2.5 \(\times\) H0.93 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Major specifications of BME280. Figure 7: LSTM network architecture of the hidden layer. Signals progress from left to right. term and long-term memories. The output gates control update values from hidden units. Similarly to the input gates, the output gates have a mechanism that is useful in avoiding inappropriate weight updates for redundant and undesired signals. In addition, the current cell state \(c_{t}\) and \(H_{t}\) are also used to calculate the subsequent input data at \(t+1\).