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ЦИФРОВАЯ ТРАНСФОРМАЦИЯ ТРАНСПОРТА / DIGITAL TRANSFORMATION OF TRANSPORT
УДК 519.254
DOI: 10.25559/SITITO.18.202201.98-106
Data Mining under the System of Managerial Skills Shipbuilding Sphere Application
S. P. Kirilchuk, D. S. Knyazeva*
V.I. Vernadsky Crimean Federal University, Simferopol, Russian Federation Address: 4 Academician Vernadsky Ave., Simferopol 295007, Russian Federation * darja.cnyazewa@yandex.ru
Abstract
The article discusses such modern methods and tools of artificial intelligence as applied to solving some problems of the Russian shipbuilding market. Researching cases present an example of an algorithm for experimental model of artificial intelligence and its mathematical formalization under environments of forecasting the dynamic development indicators of Russian shipbuilding industry. The article shows that the use of modern data mining methods and tools is in demand from the point of view of solving a wide class of applied problems, such as modeling the economic environment and business activity, trends and tendencies in various markets of developed and developing countries in the context of the cyclical nature of the world economy as a whole. In addition, such methods and tools are characterized by a high demand on the part of society in terms of developing (designing) experimental models for analyzing the evolutionary processes and functioning of complex socio-economic systems, which include high-tech sectors of the Russian industry, determining effective directions for the development of such systems. The article provides an example of an initiative research project that illustrates the formulation of a specific applied economic problem and its possible solution using methods and tools of artificial intelligence.
Keywords: data mining, artificial intelligence, neural networks, managerial skills, shipbuilding industry, applied computation, analysis
The authors declare no conflict of interest.
For citation: Kirilchuk S.P., Knyazeva D.S. Data Mining under the System of Managerial Skills: Shipbuilding Sphere Application. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2022; 18(1):98-106. doi: https://doi.org/10.25559/SITI-TO.18.202201.98-106
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Контент доступен под лицензией Creative Commons Attribution 4.0 License. The content is available under Creative Commons Attribution 4.0 License.
Современные информационные технологии и ИТ-образование
Том 18, № 1. 2022 ISSN 2411-1473 sitito.cs.msu.ru
Data mining в системе управленческих навыков (в приложении к сфере гражданского судостроения)
С. П. Кирильчук, Д. С. Князева*
ФГАОУ ВО «Крымский федеральный университет имени В. И. Вернадского», г. Симферополь, Российская Федерация
Адрес: 295007, Российская Федерация, Республика Крым, г. Симферополь, пр. Академика Вернадского, д. 4 * darja.cnyazewa@yandex.ru
Аннотация
Рассмотрены вопросы, связанные с применением методов искусственного интеллекта и когнитивных технологий в задачах моделирования управляемых динамических систем. Представлен обзор результатов решения некоторых задач когнитивного моделирования. Изучены аспекты построения и исследования когнитивных моделей с применением различных инструментов искусственного интеллекта. Охарактеризованы особенности применения нейронных сетей, эволюционных алгоритмов, объектно-ориентированных языков программирования, многоагентных архитектур в задачах когнитивного моделирования. Дано описание областей применения искусственного интеллекта и когнитивных технологий в задачах моделирования динамических систем. Рассмотрено методическое обеспечение для исследования траекторной динамики систем интеллектного управления. Предложен подход к построению динамических когнитивных карт для моделирования маятниковых систем с применением интеллектуальных технологий. Разработан обобщенный алгоритм стабилизации маятника с применением нечетких когнитивных карт. Рассмотренный подход позволяет синтезировать модели перевернутого маятника с учетом различных физических эффектов и решать ряд задач управления маятниковыми системами. Полученные результаты могут быть использованы при проектировании и совершенствовании управляемых технических систем.
Ключевые слова: дата майнинг, искусственный интеллект, нейронные сети, управленческие навыки, гражданское судостроение, прикладные вычисления и анализ
Авторы заявляют об отсутствии конфликта интересов.
Для цитирования: Кирильчук С. П., Князева Д. С. Data mining в системе управленческих навыков (в приложении к сфере гражданского судостроения) // Современные информационные технологии и ИТ-образование. 2022. Т. 18, № 1. С. 98-106. doi: https://doi.org/10.25559/SITI-TO.18.202201.98-106
Vol. 18, No. 1. 2022 ISSN 2411-1473 sitito.cs.msu.ru
Modern Information Technologies and IT-Education
ЦИФРОВАЯ ТРАНСФОРМАЦИЯ ТРАНСПОРТА с П КиРильчук,
Д. С. Князева
I. Introduction
Summarizing the results of modern research by authoritative scientists-economists shows that shipbuilding is a complex non-linear dynamic production and economic system, the development of which is a subject to the objective laws of economics as a field of scientific knowledge [1; 2].
Under the digital transformation environments due to high-tech industries, as well as large-scale digitalization of industrial enterprises, the question of the need to use new methods and tools in economic research in development of Russian shipbuilding is on the agenda [3; 4]. Artificial intelligence (AI) as an interdisciplinary area of research, along with structuring and in-depth analysis of metadata (data mining, big data), can act as a significant help under the process of solving applied economic problems for all industries and complexes [5; 6].
It seems that modern methods and tools developed within the framework of these areas of scientific research are of interest from the point of view due to solving a wide class of applied problems, such as modeling the economic situation and business activity, trends and tendencies on various markets of developed and developing countries under the context of the cyclical global the economy as a whole.
In addition, modern methods and tools developed within the framework of the above areas of scientific research are characterized by high demand from society in terms of developing (designing) experimental models for analyzing the processes of evolution and functioning due to complex socio-economic systems, which include high-tech sphere of Russian industry, including the determination of effective directions for the development of such systems. For the purposes of the study, we will give an example of an initiative research project that illustrates the formulation of a specific applied economic problem and its possible solution using artificial intelligence methods and tools.
II. Brief annotation of an initiative research project for the application of data mining under the civil shipbuilding environments
1. Research topic: "Development of scientific ideas about civil shipbuilding as a non-linear dynamic production and economic system".
2. The purpose for research is to develop the theory and methodology of economic and mathematical modeling of processes occurring in civil shipbuilding, as well as to develop (design) an experimental model that allows the management of high-tech shipyards to make a better assessment due to dynamics of economic activity indicators in conjunction with market conditions and forecast.
3. Tasks to be solved in case of research:
to generalize the existing research approaches for displaying socio-economic processes and systems in the form of mathematical models based on the fundamental provisions of nonlinear dynamics as an interdisciplinary field of scientific knowledge; to analyze modern applied economic and mathematical models that formalize the approaches of nonlinear dynamics to the display of socio-economic processes occurring in sectors and complexes of the economies of developed and developing countries;
to formulate and to substantiate a number of theoretical and methodological provisions that allow developing existing scientific approaches to the construction of economic and mathematical models used in the economic practice of shipyards to improve the process of predicting the dynamics of key indicators in conjunction with market conditions and forecast;
to develop a simulation economic and mathematical model that provides better forecasting of the dynamics of key indicators of the development of shipyards (an artificial intelligence model that constructs a trainable artificial neural network to solve the applied economic problem - the formation of more accurate forecasts for key indicators of the development due to shipyards); to testt the proposed simulation economic and mathematical model in the conditions of actually functioning shipyards and give an analytical interpretation of the results obtained.
4. The uniqueness (novelty) of the ongoing research project lies in the reconstruction (construction) of a trainable neural network of artificial intelligence correlated with its biological counterparts (the human cerebral cortex, containing about neurons, each of which is connected on average with others neurons, generating about interconnections) to solve an important applied economic problem - more accurate forecasting of key indicators of the development of shipyards.
The degree of complexity of the algorithms for the functioning of a neural network is so high that the implementation of calculations can be provided exclusively by high-performance computing and highly efficient methods of organizing computer calculations (parallel computing and/or other methods adequate to solve the problem posed, used under the applied theory of algorithms). Successful implementation of research work within the framework of the project requires the use of a unique infrastructure for high-performance computing - the supercomputer complex of Lomonosov Moscow State University, namely the supercomputers "Lomonosov" and "Chebyshev" as unique systems of the highest performance range in Russia and over the world.
5. Expected results of research
The research involves the formulation and solution of an applied problem - the design of a more advanced simulation economic and mathematical model in comparison with existing analogues for predicting key indicators of the development of shipyards - an artificial intelligence model.
It is assumed that the construction of the original model will be based on the synthesis of a new neural network configuration, based on the known types of neural networks of artificial intelligence by increasing the number of network neurons, the density of connections between neurons and the number of layers of neurons in the network, as well as introducing several types of synapses (connections between neurons) for the purpose of increasing the efficiency due to the neural network.
The constructed artificial intelligence model will be in demand under the modern economic practice of civil shipbuilding, as it will allow to obtain more accurate forecasts for dynamics of key indicators due to development of shipyards, thanks to such characteristics as multifactorial, complex geometry, multivariance, and a high degree to accuracy of calculations.
Современные информационные технологии и ИТ-образование
Том 18, № 1. 2022 ISSN 2411-1473 sitito.cs.msu.ru
S. P. Kirilchuk, D. S. Knyazeva
DIGITAL TRANSFORMATION OF TRANSPORT
101
III. Mathematical formali zationof methods and algorithms for coostrucOmg tin artificial intelligence model due to solving th e op-plied problem under the civil shipbuilding environments
Mathematical formalization of methods and algorithms for constructing an artificial intelligence model duito solvingthe applied problem is presented below.
A. Choice of architecture (type) of artificitl ipteliiognce neural network
The choice of the architecture of theniuptl network in atioidanti with the features and complexity of the set prtnticgl ttongmic tas a with the degree of accuracy (error) speciflnd gy the expert The choice is made on the basis ot alheadygxisting nnural netwhtk architectures, the effectiveness of which has bren proven ah thn theoretical level (mathematically), as vtaU as practitsUy (undnr the real economic practice of economioanPltiisl:riuiitiayerperctplrool Hamming network, Word network, Hoxfleld -lofword, lPollOí-da g^l^s work, cognitron, neocognitron [7-13].
If the task set cannot be reduced to any ofpheknawntxstppcxriifi-cial intelligence neural networks, it is nectnsary tocarry out a set of works to synthesize a new neural netwoth ionfigurtf:ion. UnOeotre process of designing a new neura 1 nehwork archirecgoit, flit fnlltov-ing basic rules should be followe dtde-lki:
network capabilities increase with an increase in the number of network neurons, the density of connections between them and din number of layers;
the introduction of feedbacks (synapses) between neurons, along with an increase in network capabilities, raises the question of the dynamic stability of the network (for the successful operation of such a network, dynamic stability cnndrtions munt bo metf otheri wise the network may not convarne iothn carrn ok solugipn, o o,hav-ing reached the correct value of the outpuisignnl ^1:s(^mcitir^tiiix, after several iterations, get away fromst!! ^^IopOi the complexity of the algoriphms iotihe functionmt of iha digwork, the introduction of several types of synapses (connections between neurons) enhances the power of the neural network and pt the same time significantly increases the fequirements for efficienf hardware implementation of calculations (the Met for high-p e rf formance computing and the choice of effective motyods foe organizing computer calculationi,euch asparaOel nom]ti^li^no, or htktc methods adequate to the solution of the problem, which are used under the applied theory of algorithms.
B. B. Artificial Intelligence Neural Network Training
An artificial neuron is an integralpart 0f the simulated neural network. The structure of an artificialnouron (^ontinti ofhisrip lykeg of elements: multipliers (synapses), an adder, and a nonlinear converter. Synapses communicagiketwpcn atiffirigl peuropSi multiply the input signal by a number characterizing the strength of the connection (the weight of the synapse).
The adder performs the addition of signals coming through syn-aptic connections from other artificial neurons and external input signals. The non-linear converter implements a non-linear function of one argument - the output of the adder. This function is the activation function or transfer function of an artificial neuron. An ar-
tificial neuron as a whole implements a scalar function of a vector argument.
Ogthematical model of a neuron: s =Tl=1wlXl + b , (1)
y = f(s).
(2)
where - weight of synapse; i = 1,..., n; b - offset value (bias), s - summation result; x. - input vector component (input signal); y - neuron output; n - number of neuron inputs; f - non-linear transformation (activationfunction).
The computational element, formalized by the calculation formulas (1) and (2),isconsideredas a simplifiedmathematical model of bi-ologicalneurons[17-19].
The neural network is trained by adjusting the weights of synapses, which formalize the connections between artificial neurons. For a neural network with a complex structure, the number of weights is large and the learning process is a complex, lengthy and time-consuming computational process. For various types of structures of artificial intelligence neural networks, specially developed learning algorithms are used. For the purposes of solving the formulated applied problem, it is supposed to use the Error Back Propagation Algorithm [20].
The ErrorBackPropagationAlgorithm is an iterative gradient learning algorithm used to minimize the standard deviation of the current from the desired outputs of multilayer neural networks with serial connections. According to the least squares method, the ob-jectivefunctionof theneural network error to beminimized is the value:
>2 (3)
wlietg y°ii - tcel pupoui since pi Pha neurony py tga outpul layen oh idee nik-sl nttwiicn wdenthe lut0 image °s fist to ltn inpnts; dy^ p renoire!- 01110:1101 itate tCthe given ntutr-n.
Tldi onmmatian in fatcied nuo ofiop p11 morons iof flce ou-piit ieyc ir gild ninor hi imagro jfrngesfed by Che mCwo^. Giahicni d^^tesilt m!nim^atign aajnstsihcwaigkr cfiefi:ieienti asfoUowg
= -p JL, W
wliete Wiy - weight coefficient of the synaptic connection connecting the i-th neuron of the layer (q-1) with y-th neuron of the layer q; fi - leatnirgi nag- -oitot — (0 <C /t <t -).
IV. Data mining and its application under the civil shipbuilding sphere
The use of data mining and neural network models allows solving a wide class of applied industry problems in Russian civil shipbuild-ing(Figurel).
Data mining of prices for materials and equipment, credit load, volatility of exchange rates, labor productivity and the level of depreciation due to fixed assets allows shipyards to form more accurate forecasts of such key indicators of economic activity as output, cash flows, financial performance, internal rate of return for investment projects, providing their production and economic development under the medium and long term planning horizons.
Vol. 18, No. 1. 2022 ISSN 2411-1473 sitito.cs.msu.ru
Modern Information Technologies and IT-Education
С. П. Кирильчук, Д. С. Князева
INPUTS l
FORECAST
input layer
output layer
hidden layer
1 - prices for materials & equipment ;
2 - credit load;
3 - depreciation rate for fixed assets;
4 - labor productivity;
5 - exchange rate volatility;
F i g. 1. Applied use of neuralnetworksunder Russiancivilshipbuilding environmentsSource:compiledbytheauthorsbasedon a generalization [21-33]
6 - output;
7 - profit (losses);
8 - cash flows;
9 - internal rate of return
V. Conclusion
The use of methods and tools of artificial intelligence, along with hardware implementation of calculations, a formalized infrastructure for high-performance computing (Lomonosov Moscow State
University supercomputer complex), allows to significantly improve management skills and provide a qualitatively new level of formation due to quantitative estimates for the prospective development of Russian shipyards as complex of nonlinear dynamic production and economic systems.
6
2
7
3
8
4
9
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Современные информационные технологии и ИТ-образование
Том 18, № 1. 2022 ISSN 2411-1473 sitito.cs.msu.ru
S. P Kirilchuk, DIGITAL TRANSFORMATION OF TRANSPORT
D. S. Knyazeva
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Vol. 18, No. 1. 2022 ISSN 2411-1473 sitito.cs.msu.ru
Modern Information Technologies and IT-Education
ЦИФРОВАЯ ТРАНСФОРМАЦИЯ ТРАНСПОРТА с П КиРильчук,
Д. С. Князева
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Submitted 27.02.2022; approved after reviewing 14.03.2022; accepted for publication 21.03.2022.
About the authors:
Svetlana P. Kirilchuk, Head of Enterprise Economics Department, Institute of Economics and Management, V.I. Vernadsky Crimean Federal University (4 Academician Vernadsky Ave., Simferopol 295007, Russian Federation), Dr.Sci. (Economy), Professor, ORCID: https:// orcid.org/0000-0001-6888-1981, economika307@yandex.ru
Daria S. Knyazeva, Master degree student of Enterprise Economics Department, Institute of Economics and Management, V.I. Vernadsky Crimean Federal University (4 Academician Vernadsky Ave., Simferopol 295007, Russian Federation), ORCID: https://orcid.org/0000-0003-2058-9468, darja.cnyazewa@yandex.ru
All authors have read and approved the final manuscript.
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Современные информационные технологии и ИТ-образование
Том 18, № 1. 2022 ISSN 2411-1473 sitito.cs.msu.ru
S. P. Kirilchuk, D. S. Knyazeva
DIGITAL TRANSFORMATION OF TRANSPORT 105
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Поступила 27.02.2022; одобрена после рецензирования 14.03.2022; принята к публикации 21.03.2022.
Vol. 18, No. 1. 2022 ISSN 2411-1473 sitito.cs.msu.ru
Modern Information Technologies and IT-Education
С. П. Кирильчук, Д. С. Князева
Кирильчук Светлана Петровна, заведующий кафедрой экономики предприятия, Институт экономики и управления, ФГАОУ ВО «Крымский федеральный университет имени В. И. Вернадского» (295007, Российская Федерация, Республика Крым, г. Симферополь, пр. Академика Вернадского, д. 4), доктор экономических наук, профессор, ORCID: https://orcid.org/0000-0001-6888-1981, economika307@yandex.ru
Князева Дарья Сергеевна, магистрант кафедры экономики предприятия, Институт экономики и управления, ФГАОУ ВО «Крымский федеральный университет имени В. И. Вернадского» (295007, Российская Федерация, Республика Крым, г. Симферополь, пр. Академика Вернадского, д. 4), ORCID: https://orcid.org/0000-0003-2058-9468, darja.cnyazewa@yandex.ru
Все авторы прочитали и одобрили окончательный вариант рукописи.
Современные информационные технологии и ИТ-образование
Том 18, № 1. 2022 ^ 2411-1473 sitito.cs.msu.ru
