The researchers have used advanced neural networks and network sensors to quickly identify the source of harmful gases such as nitrogen dioxide.
The device relies on nano-islands of metal catalysts embedded on graphene surfaces which reacts to targeted gas molecules.
Nitrogen dioxide molecules bind to graphene, changing the conductivity of the sensor, allowing the system to detect the smallest of leaks.
Kyusang Lee, associate professor of electrical and computer engineering and materials science engineering, and one of the lead researchers on the project, explains, "By integrating AI with state-of-the-art gas sensors, we're able to pinpoint gas leaks with unprecedented accuracy, even in large or complex environments. The artificial olfactory receptors are able to detect tiny changes in gas concentrations and communicate that data to a near-sensor computing system, which uses machine learning algorithms to predict the source of the leak."
The system contains a "trust-region Bayesian optimization algorithm," a machine learning technique that breaks down complex problems into smaller regions to find the most efficient sensor positions.
This means fewer resources are used whilst providing faster and accurate gas leak detection.
