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Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

The article presents a brief review of the activities of the laboratory "Personal protection equipment for the personnel of hazardous production facilities" for creation of the regulatory-legal and regulatory-methodological support system for personal protection of the personnel of radiation and chemical hazardous facilities, of regular and non-staff emergency rescue teams of Rosatom State Corporation and of FMBA of Russia as well as of the population living in the influence area of the mentioned facilities. The issues of standardization and certification of personal protective equipment at NPPs and in the field of atomic energy use — both in the normal operation mode of dangerous objects, and in emergency situations of peace and war time are considered. The problems arising in the implementation of innovative personal protective equipment, primarily due to international obligations, are shown.