title: ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° Ð¿Ð¾Ð´Ð´ÐµÑÐ¶ÐºÐ¸ Ð¿ÑÐ¸Ð½ÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ½ÑÑ ÑÐ¾ÑÑÐ°Ð²Ð¾Ð² Ð¼Ð¾ÑÐ¾ÑÐ½ÑÑ Ð¼Ð°ÑÐµÐ»
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Intelligent decision support system for the formation of engine oil formulations
Creators: name: ÐÐ¾ÑÐµÐ½ÑÐº, ÐÑÐ¸Ð³Ð¾ÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð¼Ð¾ÑÐ¾ÑÐ½ÑÐµ Ð¼Ð°ÑÐ»Ð°
deep sets
Ð²ÐµÐºÑÐ¾ÑÐ½ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ
few shot learning
Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
motor oil
embeddings
optimization

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â« ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð° Ð¿Ð¾Ð´Ð´ÐµÑÐ¶ÐºÐ¸ Ð¿ÑÐ¸Ð½ÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ½ÑÑ ÑÐ¾ÑÑÐ°Ð²Ð¾Ð² Ð¼Ð¾-ÑÐ¾ÑÐ½ÑÑ Ð¼Ð°ÑÐµÐ»Â».
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description: The subject of the graduate qualification work is Â«Intelligent decision support system for the formation of engine oil formulationsÂ».
This paper describes the process of developing an expert assistance system in the formation of component compositions of motor oils. The developed system al-lows us to solve the problem of predicting the properties of oil based on information about the component composition, as well as the task of synthesizing the mixture ac-cording to the specified requirements for the properties of the mixture, taking the cost into account.
Predictive models of two main types were studied â one based on the known empirical equations, and another based on neural networks. It is shown that in the problem of predicting oil volatility, the neural network model is significantly more accurate comparing to empirical model. In the problem of estimating oil viscosity, the accuracy of the neural model turned out to be close to the accuracy of the empirical model.
In the process of developing models, it was shown that the Deep Sets architec-ture is well suited for the task of predicting the properties of mixtures. In the case of the viscosity model, the possibility of constructing a model in the presence of rare components in the target data was also investigated. For this case, an approach based on the adaptation of rare components embeddings has shown its suitability.
The synthesis problem is formulated as the problem of maximizing the proba-bility of the properties of the mixture to fall within the ranges specified by the user and is solved using the constructed forecast models and well-known optimization methods.