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Кулик, Сергей Дмитриевич

Организационные подразделения

Организационная единица

Институт интеллектуальных кибернетических систем

Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.

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Intelligent Information System for Telemedicine
(2020) Kondakov, A.; Kulik, S.; Кулик, Сергей Дмитриевич
© 2020 The Authors. Published by Elsevier B.V.The problem of telemedicine systems was very important and relevant in Russian Federation for many years. Communications and technologies in most regions allow to introduce such systems, but it became possible only in 2018 when federal law (No. 242-FZ On amendments to certain legislative acts of the Russian Federation to clarify the procedure of personal data processing in information and telecommunication networks [1]) about telemedicine came into effect. Since this moment many IT companies begin developing remote systems for healthcare. There are many different types of telemedicine systems and services, but all of them have the same concept. It is fast and easy way to consult patient, make diagnosis and prescribe treatment. It especially important for patients who live far enough from hospitals, people with disabilities or for people who live in regions where hospitals have no modern and quality equipment. Also some regions have problems with qualified personnel. This article presents a new intelligent information system for the treatment of psoriasis. This system was developed for Russian clinic which has a big experience in psoriasis treatment and now it became possible to make a big network of clinics that can located in different parts of the country (also in most remote) and still have all the benefits of central clinic.
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Computational model for forensic biological hair analysis
(2020) Nikonets, D.; Suchkova, E.; Kulik, S.; Кулик, Сергей Дмитриевич
© 2020 The Authors. Published by Elsevier B.V.Human hair is one of the subjects, which is dealt with in researches of the forensic biological examination. To increase the efficiency of evaluating the results of a forensic microscopical analysis of hair from a person's head, in the course of this work, a mathematical model was developed for obtaining a probabilistic-statistical evaluation of the set of matching features, characterizing human hair (the random match probability). Such probability has been estimated for all hair samples from control dataset. As a result of the work, we can conclude that in most cases the estimation of the probability of appearance of the hair features set doesn't give us the precise results which allow to make an unequivocal positive conclusion in the identification of a person by hair. This made it possible to increase the efficiency of forensic biological experts.
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Educational Intelligent System Using Genetic Algorithm
(2020) Protopopova, J.; Kulik, S.; Кулик, Сергей Дмитриевич
© 2020 The Authors. Published by Elsevier B.V.This paper presents an Intelligent Information System for education. The system was created for teaching students to use genetic algorithm in application to optimization tasks. The system allows to quickly encode a solution of the problem and pick up most suitable configuration of genetic algorithm. The paper also demonstrates a specific example of usage of educational system to solve an optimization task. The paper contains a description of the educational system and its features, a description of its capabilities of working with genetic algorithm and its graphical interface.
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Effective scientific personnel training in the field of modern computer technologies for the implementation of advanced research projects of the Megascience class
(2020) Shtanko, A. N.; Kulik, S. D.; Kondakov, A. A.; Кулик, Сергей Дмитриевич
© Published under licence by IOP Publishing Ltd.Successful research projects of the Megascience class usually require a well-trained team of scientists from various fields of knowledge. These scientists must be high-skilled experts. Each member of a team like that must have the necessary, specialized cross-industry skills, for example, in such areas as artificial intelligence, convolutional neural networks, specialized intelligent search engines, and full-text analysis. One of the key aspects of effective personnel training for successful implementation of Megascience projects into reality is the acquisition by students professional skills, abilities, and knowledge to use tools of modern scientific technologies, containing, for example, libraries of programs (functions). In particular, convolutional neural networks and intelligent search systems can be applied in various research projects in the field of physics, chemistry, biology, and medicine, for example, in telemedicine, for effective decision-making in diagnosing a patient. Therefore, understanding the principles of neural networks and intelligent search systems is a necessary competence of researchers working in the framework of Megascience projects. Classic search engines are based on indexing the textual information of the database that is being searched. Intelligent search engines can improve the search experience through intelligent data processing, including using convolutional neural networks. This report examines practical examples and areas of the successful application of convolutional neural networks and information systems in practice.
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Using convolutional neural networks for recognition of objects varied in appearance in computer vision for intellectual robots
(2020) Kulik, S.; Shtanko, A.; Кулик, Сергей Дмитриевич
© 2020 The Authors. Published by Elsevier B.V.The paper describes an effort to train a convolutional neural network capable of reliably recognizing complex objects that are highly varied in their shapes and appearances in images. Neural networks show very good results on objects that have constant appearances but may have trouble recognizing abstract objects that appear in different shapes, art-styles and lack solid structure, for example, national flags. In an image, a flag may appear waving on a pole, as an element of clothes, in a form of stickers, etc. Due to these differences in appearance computer vision systems may show unsatisfactory results on these types of objects. However, detecting such objects is a necessary task in computer vision, especially for intelligent robots in order to understand the environment. The aim of the research is to apply convolutional neural networks for the detection of flags. In this research, we prepared training and testing sets of objects, trained a neural network for detection task, conducted testing experiments and measured the neural net's performance. These results can be applied in cognitive and robotics technologies as well as general computer vision tasks.
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Scientific personnel training in convolutional neural networks for the implementation of research projects of the MegaScience class
(2019) Shtanko, A. N.; Kulik, S. D.; Кулик, Сергей Дмитриевич
© Published under licence by IOP Publishing Ltd.Megascience projects require an all-inclusive interdisciplinary approach. Because of that scientific personnel engaged in projects of this type must possess relevant required interdisciplinary skills. Artificial intelligence in particular convolutional neural networks has a wide range of applications, and it could be used to solve complicated problems in all kinds of various fields of science. Thus, the understanding of the principles of neural networks' working is a necessary skill for scientific personnel. In this paper, we're considering practical examples and fields of applications of neural networks in the real world.
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Increasing the effectiveness of intelligent module by enlarging training dataset from real data
(2021) Shtanko, A.; Kulik, S.; Кулик, Сергей Дмитриевич
© 2020 Elsevier B.V.. All rights reserved.This paper concentrates on the design of the intelligent module and neural networks in the area of intelligent data processing. It raises the following issues: what real problems are faced in training datasets for neural networks, how they can be solved, and how an intelligent module can be useful in this area. During the development of a neural network system, it is important to improve the network performance after the system's release. Additional training data need to be taken from real-life data, combined with existing training data to produce a better version of the neural network's weights. Since the number of these samples can be large, it's useful to filter out samples similar to those already included in training data. We designed a module architecture and an algorithm that employ cascading filters in order to find the best samples from real data for the training dataset. The key feature of the intelligent module is that it does not generate a completely new dataset, but uses saved data from the real-life samples and samples from the existing database. Weights are then updated during the training of the neural network and then filtered using a special algorithm.
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АДАПТИВНЫЙ ТРЕНАЖЕР ДЛЯ ПОДГОТОВКИ ПСИХОЛОГОВ (#AT-PSY)
(Федеральное государственное бюджетное образовательное учреждение высшего образования «Московский государственный психолого-педагогический университет», 2023) Катышев, Д. А.; Куравский, Л. С.; Ермаков, С. С.; Кулик, С. Д.; Савенков, Е. А.; Кулик, Сергей Дмитриевич
Программа предназначена для тренировки использования элементов консультативной работы для профориентационных задач. Областью применения является адаптивное обучение для подготовки психологов. Программа предъявляет пользователю задания, которые становятся на очередном этапе все более сложными. При успешном ответе пользователя программа выполняет переход на более высокий уровень сложности заданий. При неуспешном ответе программа предоставляет облегченные задания, выдает подсказки и, в случае верного ответа пользователь возвращается на тот же уровень трудности, на котором был дан неверный ответ. Необходимые текущие результаты фиксируются. В случае успешного ответа на последнее задание тест завершается. Программа имеет возможность добавления и редактирования новых заданий и тестов.