SUSTAINABLE INNOVATIVE DEVELOPMENT: design and management

Contents

Korablev Igor Gennadievich
MODELING AND OPTIMIZATION OF HIERARCHICAL MANAGEMENT STRUCTURES

Panachev Anton Anatolievich
ORGANIZATIONALLY CLOSED ECONOMIC SYSTEMS AT MUNICIPALITY LEVEL: IDENTIFICATION AND TECHNOLOGY OF MANAGEMENT

Zaiter Murooj
ADAPTIVE CLOUD-BASED SECURITY ANALYTICS FOR INDUSTRIAL POWER SYSTEMS: A MULTI-CLASS DETECTION APPROACH

Dostovalov Andrey Andreevich, Kapustin Savely Andreevich
DEVELOPMENT OF A MARINE DEBRIS DETECTION MODEL

Vorobyov Yaroslav Vadimovich
OPERATING ALGORITHM OF THE AUTOMATIC AUDIO DESCRIPTION SYSTEM

Chekhomov Igor Vladislavovich
DEVELOPMENT OF A MODEL OF MANAGEMENT OF ORGANIZATIONAL SYSTEM OF INDUSTRIAL ENTERPRISE FOR OPTIMIZATION OF PRODUCTION OF CUSTOM PRODUCTS

Kibalnikov Sergey Vladimirovich
SKW-MATRIX AS A TRANSFORMATION OF THE TRANSCENDENTAL APPREHENSION OF KANT AND FICHTE

Korablev Igor Gennadievich, Corporate Architect at NLMK Information Technologies LLC

Abstract

In order to successfully compete within diverse ecosystems, organizations adopt hierarchical structures. In the military, business, animal prides, and packs, different participants establish connections and form hierarchies that enable an organization or group to compete, evolve, and survive more effectively. With the same set of performers, various configurations can render an organization or group more or less effective. This paper examines the modeling of performers’ characteristics and the dependency of a performer’s position within the hierarchy on those attributes. The rise of automation and digitization has led to the emergence of automated autonomous subsystems that can compete with humans, integrate into overall management structures, occupy specific levels in the management hierarchy, and function as “super-performers.” The study presents an experiment introducing performers into the hierarchy who significantly outperform others in terms of their characteristics and analyzes the impact of incorporating such performers on organizational effectiveness.

KEYWORDS: organization, management, hierarchy, structure, reengineering, automation, optimization, graph, tree, super-performer.

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Panachev Anton Anatolievich, academic degree applicant, Ural Federal University

Abstract

The article observed technologies of organizationally closed economic systems management, presented in standard cycle of management which includes methods of each step and examples of their using. There is also shows a current example of using technology of the particular task purpose – organizationally closed economic systems at municipal level managing. The example includes a description of transaction data mining, algorithms of systems identification, hybrid imitation model, current range of effective criteria and purposes of management. The technology allowed to identify 18 real existing organizationally closed economic systems at municipal territory.

KEYWORDS: network society, economic systems, banking transactions, technology of management.

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Zaiter Murooj, postgraduate, Dept. of Big Data Analytics and Video Analysis Methods, Ural Federal University

Abstract

The increasing digitalization of power systems in industrial companies has introduced new cybersecurity vulnerabilities that require sophisticated detection systems. This work suggests a cloud-based adaptive system to classify and detect various power system events, including cyber-attacks, natural faults, and normal operations. We design a multi-class detection system based on Phasor Measurement Units (PMUs) data and the system logs on a two-line, four-relay power system configuration. The proposed model can identify 37 different scenarios, including natural events, cyber-attacks, and normal operations, thanks to 128 features extracted from PMU measurements and system logs. Our approach achieves 97% overall accuracy in distinguishing between various power system events, with the best performance in identifying command injection attacks (with an average of 98% precision) and relay setting change attacks (95% precision). The model is demonstrated to be robust for different fault locations and attack scenarios, with high precision and recall rates even for complex multi-relay attacks. With feature importance analysis, we identify key measurements for attack detection, particularly phase magnitude measurements and voltage phase angles, for more efficient monitoring of power system security. Cloud deployment facilitates real-time processing of PMU data and quick detection of attacks, making it suitable for deployment at an industrial level. The model performs with 100% accuracy in identifying normal operations and high accuracy in detecting faults in various sections of the transmission line. The results confirm that our approach can distinctly classify natural faults and malicious attacks and can be used as a reliable security monitoring system for industrial power systems.

KEYWORDS: power system security, cloud computing, cyber-attack detection, machine learning, phasor measurement units, industrial control systems, multi-class classification, real-time monitoring, industrial enterprises, adaptive security analytics.

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Dostovalov Andrey Andreevich, master student, Dept. of Big Data Analytics and Video Analysis Methods, Ural Federal University

Kapustin Savely Andreevich, master student, Dept. of Big Data Analytics and Video Analysis Methods, Ural Federal University

Abstract

The study focuses on developing a machine learning model for the automatic detection of marine debris based on image analysis. A comparative analysis of deep learning models (Faster R-CNN, SSD, YOLO) was conducted, considering accuracy, processing speed, and computational resource requirements. The YOLOv8 model was identified as the most promising due to its high performance and stability. A unique dataset was developed, featuring annotated images of marine surfaces with debris objects. The model was trained, achieving significant metrics in accuracy (mAP50-95), recall, and object localization. A web service was created, enabling users to upload images, automatically detect marine debris, and export results in JSON format. This work contributes to the automation of environmental monitoring processes and serves as a foundation for further research in combating marine pollution. The results demonstrate the model’s potential for integration into real-time monitoring systems.

KEYWORDS: marine debris, detection, pollution monitoring, image analysis, on-board cameras, satellite monitoring, automation, ecosystems, plastic waste, environmental impact, surveillance technologies, detection techniques.

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Vorobyov Yaroslav Vadimovich, postgraduate, Dept. of Big Data Analytics and Video Analysis Methods, Ural Federal University

Abstract

Any information system operates in accordance with its internal algorithms. In the case of a system based on artificial intelligence, the development of a detailed algorithm becomes an even more important task: it is necessary to reduce the number of system errors in advance by calculating possible scenarios for its operation. So, for an automatic audio description system, it was decided to design an algorithm that will subsequently be used directly in the development of such a system. The article presents a detailed algorithm and description of the tables that this system accesses when going through the steps of the algorithm. It is assumed that by the last point of the algorithm, all the text descriptions necessary for dubbing the video sequence and the corresponding time intervals will be collected in the database.

KEYWORDS: algorithm, automatic audio description, visual impairment, database visualization.

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Chekhomov Igor Vladislavovich, postgraduate, Dept. of Big Data Analytics and Video Analysis Methods, Ural Federal University

Abstract

In the context of mass production and Industry 4.0, custom manufacturing becomes important to improve the competitiveness of enterprises. However, managing this type of production is fraught with challenges: longer production times, complexity of inventory and human resource management, as well as difficulties in balancing workloads and optimizing production schedules. Unlike mass production, where processes are standardized and predictable, custom manufacturing requires a more flexible and adaptive approach to planning and resource allocation. The high variability of orders leads to frequent adjustments in production workflows, increasing the risk of inefficiencies and bottlenecks. Additionally, ensuring the optimal use of human resources becomes a critical issue, as different tasks require varying levels of expertise and specialization. The paper analyzes models and methods of management of industrial enterprises with custom or non-batch production and proposes an audit of a real factory. The study examines existing approaches to production planning and explores their limitations when applied to enterprises with high product variability. As a result of the analysis, the author sets a management problem, considering the experience of other researchers and real problems identified during the audit, and proposes a universal management model for enterprises with non-batch production, aiming to improve operational efficiency, resource utilization, and production adaptability.

KEYWORDS: mathematical modelling, production management, data-driven management, custom production.

Download article DEVELOPMENT OF A MODEL OF MANAGEMENT OF ORGANIZATIONAL SYSTEM OF INDUSTRIAL ENTERPRISE FOR OPTIMIZATION OF PRODUCTION OF CUSTOM PRODUCTS

Kibalnikov Sergey Vladimirovich, Doctor of Technical Sciences, Laureate of the WIPO Gold Medal, Academician of RANS, Leading Research Associate at the Faculty of Geography of Lomonosov Moscow State University, Professor at Dubna State University

Abstract

The world is driven by the symbiosis of the objective and subjective. Between them there is always a boundary. Our research is an attempt to see and mark this Frontier. At the Border (membrane), the density of occurrence of innovations is orders of magnitude higher than the density in a continuous medium. A special role on this Frontier is given to the human brain. The brain is the gateway between the ideal and the material. Idealists and materialists are united by the brain. The availability of computed tomography made it possible to conduct a non-invasive study of the processes of thought [1,2,3], which show that modern science is close to an invariant description of the general picture of the world (GPW), which suits both “physicists” and “lyricists”.

KEYWORDS: object, subject, boundary, continuous medium, apperception, potential, flow, predicate, resistance, efficiency, NBIC, Gaia, autopoiesis, SKW-thinking, SKW-matrix.

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Contents

Khoroshunov Alexey Alexeevich
MODERN TRENDS IN THE GROWTH OF THE LIGHT INDUSTRY MARKET OF THE RUSSIAN FEDERATION IN THE CONTEXT OF E-COMMERCE

Afanasyeva Oksana Nikolaevna, Siris Angelina Aleksandrovna
INFLUENCE OF INFLATION AND DEFLATION ON INTEREST AND CREDIT RISKS OF COMMERCIAL BANKS

Tabarov Safarali Fayzalievich
IMPROVEMENT OF THE RUSSIAN SYSTEM OF ENVIRONMENTAL STANDARDIZATION IN LIGHT OF INTERNATIONAL TRENDS

Papulovskaya Nataliya Vladimirovna, Kharisov Azamat Robertovich, Sharaviev Danil Olegovich
MICROCLIMATE MANAGEMENT IN GREENHOUSES USING SOUND WAVES

Khoroshunov Alexey Alexeevich, PhD student, The Plekhanov Russian University of Economics

Abstract

This article examines the current state of the domestic light industry and considers e-commerce markets as a growth driver for domestic textiles. Particular attention is paid to the emerging problems in the light industry and serious barriers for domestic manufacturers under the influence of cheap imports of textile products. An analysis of key domestic clothing manufacturers is conducted, comparative characteristics are determined by which a growing enterprise should be distinguished from a stagnating one. Features of the growth of domestic consumption of light industry goods are noted.

KEYWORDS: e-commerce, electronic sales, light industry, marketplaces, enterprise growth indicators, fashion trends, stagnant enterprises.

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