The article deals with the concept of building computer training complexes as one of the effective aids for ensuring the safety of potentially dangerous technological processes which include the storage and transportation of liquefied hydrocarbon gases. The authors have developed and described simulator model of one of the key technological devices for the storage of liquefied hydrocarbons which underlies the proposed software platform for the synthesis of training simulator systems.
Approaches to information description of various types of valves configuration are considered when modeling process flows in training models of computer simulator complexes.
The paper presents the approaches to the use of system analysis methods in modelling of hand-arm vibration syndrome (HAVS) in workers exposed to local vibration. The practice of using regression, information-entropy models artificial deep neural networks based on deep learning taking into account the dose of local vibration as an exposure factor is shown. The construction of HAVS occurrence model in the form of a multi-parameter regression approximated using a multilayer neural network is considered. A binary classifier that allows a set of attributes to attribute a person to a group of healthy people or to a group of people with HAVS is built. After training, the HAVS dynamics model made it possible to obtain a qualitative picture of the dependence of changes in the endocrine system on the individual experience dose of vibration and to determine the period of increased risk of pathology. The method applying to the occupational diseases differential diagnosis support system integration is discussed. Further study directions are also outlined.
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