In this paper, a new switched-capacitor (SC) interface circuit for humidity sensor application is proposed. The sensing principle makes use of the difference between the thermal conductivity of air and water vapor at elevated temperatures. It causes a voltage difference between two isolated diodes. The reference diode is sealed and has a fixed thermal conductance, while the sensing diode is exposed to the environment having a humidity-dependent thermal conductance. The exposed diode is connected to the proposed SC circuit, which can be easily monolithically integrated using GlobalFoundries 0.18-µm CMOS process. The simulation results show that the humidity sensitivity of the sensor is 6.84, 12.44, 22.51mV/%RH for 20ºC, 30ºC and 40ºC respectively. The key advantage is that the circuit operates across the full range of humidity levels for environment temperatures from 20ºC to 40ºC. The humidity sensing interface operates under a supply voltage of 3V and dissipates power as low as 400µW including the biasing circuits.
The air environment (e.g., high concentration of carbon dioxide) in a pig house will affect the health conditions and growth performance of the pigs, and the quality of pork as well. In order to reduce the cumulative concentration of carbon dioxide in the pig house, the prediction model was established by the deep learning method to predict the changes of the carbon dioxide cumulative concentration in a pig house. This model will also be used for the real-time monitoring and adjustment of the concentration of carbon dioxide of the pig house. The experiment was designed to collect environmental parameters (e.g., temperature, humidity, wind speed, and carbon dioxide concentration) data in the pig house for several months. The ensemble empirical mode decomposition–gated recurrent unit (EEMD–GRU) prediction model was established in the prediction of carbon dioxide concentration in the pig house. The results show that compared with the other models, the prediction accuracy of the EEMD–GRU model is the highest, and the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and r-squared (R2) of carbon dioxide concentration in autumn and winter are 123.2 ppm, 88.3 ppm, 3.2%, and 0.99, respectively. The RMSE, MAE, MAPE, and R2 for carbon dioxide concentration are 129.1 ppm, 93.2 ppm, 5.9%, and 0.76 in spring and summer. The prediction model proposed in this paper can effectively predict the concentration of carbon dioxide in the pig house and provide effective help for the precise control of the pig house environment.
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