doi: 10.17586/2226-1494-2017-17-6-1092-1099


T. M. Tatarnikova, M. A. Elizarov

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For citation: Tatarnikova T.M., Elizarov M.A. Variant of odor system for threats recognition. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 6, pp. 1092–1099 (in Russian). doi: 10.17586/2226-1494-2017-17-6-1092-1099

Subject of Research. The paper deals with a new design solution for the odor recognition system aimed at detecting threats. An algorithm for neural network training that solves the problem of recognizing dangerous substances by odor is developed. We present experiment results on the selection of neural network hyper parameters, its architecture and testing. Methods. The detection system is a comprehensive solution that gives the possibility to ensure the safety of life and human activity applying odor detection of threats. The solution complexity is realized by technology platform choice of the Internet of things and the neural network that solves recognition problem. Main Results. We propose a new approach to creation of odor system for detecting threats that makes it possible to improve technologies for ensuring the safety of life and people's activities. The working capacity of the proposed solution is demonstrated on the layout. It shows the possibility to use the technology of the Internet of things at the system implementation stage and to deploy their work on any territory, including hard-to-reach areas. The convergence of the results of the trained neural network with test sets of concentrations of hazardous substances in the air is shown. Practical Relevance. The odor system for threats detection can be useful as an element of an integrated solution to ensure the safety of people on any territory, depending on the tasks assigned. The odor detection system for dangerous substances has been brought to the layout, which makes it possible to detect such threats as the leakage of benzene, butane, methane, propane and the ignition at an early stage.

Keywords: human security, odor threats detection, detection system, Internet of things, system layout, neural network, supervised learning, threat recognition

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