В. Є. Колесник

Bio: В. Є. Колесник is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 10 citations.

Prevention of corruption the joint initiatives of the editorial board of "Law and modern States" journal and the Ministry of Education and Science of the Russian Federation

Abstract: Journal and Ulrich’s Periodical Directory International Database. The journal was published since 2008 as part of the periodical edition Nauchno-tekhnicheskie vedomosti SPbGPU

Миграция и Рынок Труда (Migration and Labor Market)

Abstract: Russian Abstract: В докладе рассматриваются процессы трудовой миграции в России и их влияние на общероссийский и региональные рынки труда. Основное внимание уделено двум видам трудовой миграции – внутренней, представленной россиянами, осуществляющими свою трудовую деятельность в регионах, отличных от их постоянного места жительства, и внешней, представленной иностранцами, работающими в России. В основе анализа – данные ФМС России, Росстата, в том числе регулярно проводимых обследований населения по проблемам занятости (ОНПЗ), экспертные оценки. Проведен краткий анализ политики в отношении трудовой миграции, осуществляемой в последние годы. Предложены меры в области содействия внутристрановой пространственной мобильности населения и в области внешней трудовой миграции.English Abstract: The paper analyses labor migration in Russia and its impact on national and regional labor markets. Emphasis is placed on two types of migration - internal, represented by Russians, engaged his career in regions other than their permanent residence, and external represented by foreigners working in Russia. The basis of the analysis - the data of the FMS of Russia, Rosstat, in the including regular surveys conducted on employment, expert evaluation. The brief analysis of policies regarding labor migration undertaken in recent years. The measures for the promotion of in-country spatial mobility of the population and in the field of external labor migration.

Neural Network Forecasting of Time Series

Abstract: 2019, vol. 19, no. 4, pp. 124–131 124 Introduction By neural networks are meant computational structures that simulate simple biological processes that are somehow related to a person's brain activity. They are parallel systems capable of adaptive learning by analyzing input effects. The elementary converter in these networks is the neuron. Artificial NN are built according to the principles of organization and functioning of their biological analogs. They are able to solve a wide range of problems of image recognition, identification, prediction, optimization, management of complex objects. Special attention should be paid to the use of neural network technologies (dynamic neural networks are the most relevant now) to improve the processing of radar information in difficult conditions, which require high computing power, when the dynamics of changing external conditions are very high and traditional approaches to the creation of processing systems can’t provide the required result [1]. In addition to the ability to solve a new class of problems, neural networks have a number of significant advantages. First, it is resistance to input noise, which allows the use of neural networks in highprecision communication systems. Such an opportunity for neural networks appears due to the so-called training. After training, they are able to ignore the inputs to which noise data is fed. Neural networks are able to function correctly, even if the input is noisy. Secondly, adaptation to change. This means that with small changes in the environment, the neural network is able to adapt to changes. Consider a neural network that predicts the growth / fall in prices on the exchange. However, gradually, day by day, the situation on the market is changing. If the network did not adapt to these changes, it would stop giving the right answers in a week. But artificial neural networks, learning from the data, each time adjust to the environment. Third, it is fault tolerance. They can give out correct results even at considerable damage of components making them. Fourth, ultra-high speed. The computer executes commands in sequence. However, in the head of a person, each neuron is a small processor (which receives a signal, converts it, and sends it to the output). And there are billions of such processors in our heads. We get a giant network of distributed computations. The signal is processed by neurons simultaneously. This property potentially manifests itself in artificial neural networks. If you have a multi-core computer, this property will be executed. For single-core computers, there will be no noticeable difference [2]. DOI: 10.14529/ctcr190412

Method of increasing the reliability of diagnostics of the state of the cutting tool in the automated processing of parts

Abstract: 76 Вісник НТУУ “КПІ”. Серія ПРИЛАДОБУДУВАННЯ. – 2018. – Вип. 55(1) же проблема контроля износа режущего инструмента и возможный путь ее решения. В основной части рассматривается метод диагностики состояния режущего инструмента в условиях автоматизированного производства, основанный на измерении сигнала акустической эмиссии и мощности резания. Основной задачей этого метода является сбор максимально точной информации о состоянии инструмента, чтобы исключить возможные непредвиденные ситуации, в которых система автоматизированного контроля может принять неверное решение и выполнить неправильное действие. Она позволяет контролировать интенсивность износа режущего инструмента и прогнозировать его работоспособность, что позволяет повысить точность, качество и эффективность механообработки. Ключевые слова: диагностика, процесс резания, автоматизация, режущий инструмент, износ, работоспособность режущего инструмента.