SUMMARYGas-source localization systems are required to quickly detect gas concentrations at various locations while moving. It is therefore necessary to design the system by taking account of the delay in the response and recovery of the gas sensor in the presence of variations in gas concentration. Also, in order to model the dynamic characteristics of the gas sensor in open air, it is essential to understand the delay in response and recovery of the gas sensor. To carry out a transient response analysis of such a gas sensor, it is necessary to know the gas concentration (changing in time) at the gas sensor. In the present study, in order to analyze the transient response of a QCM (quartz crystal microbalance) gas sensor, the gas concentration is visualized. A photodiode is placed close to the gas sensor and the intensity of scattered laser light is measured optically in order to know the concentration of the gas passing over the sensor. The sensor response and the intensity of scattered light are measured simultaneously and the sensor response is estimated from the visualized gas concentration by means of the models of a second-order system and a neural network. When the measured results and the estimated results are compared, the model based on the neural network is found to estimate more accurately.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.