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- W2058367971 abstract "We present a data‐driven probabilistic framework to model the transient response of MOX sensors modulated with a sequence of voltage steps. Analytical models of MOX sensors are usually built based on the physico‐chemical properties of the sensing materials. Although building these models provides an insight into the sensor behavior, they also require a thorough understanding of the underlying operating principles. Here we propose a data‐driven approach to characterize the dynamical relationship between sensor inputs and outputs. Namely, we use dynamic Bayesian networks (DBNs), probabilistic models that represent temporal relations between a set of random variables. We identify a set of control variables that influence the sensor responses, create a graphical representation that captures the causal relations between these variables, and finally train the model with experimental data. We validated the approach on experimental data in terms of predictive accuracy and classification performance. Our results show that DBNs can accurately predict the dynamic response of MOX sensors, as well as capture the discriminatory information present in the sensor transients." @default.
- W2058367971 created "2016-06-24" @default.
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- W2058367971 date "2011-01-01" @default.
- W2058367971 modified "2023-10-18" @default.
- W2058367971 title "Data-driven Modeling of Metal-oxide Sensors with Dynamic Bayesian Networks" @default.
- W2058367971 doi "https://doi.org/10.1063/1.3626334" @default.
- W2058367971 hasPublicationYear "2011" @default.
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