SYSTEM ANALYSIS OF TECHNOLOGICAL PROCESSES

The article discusses ways to solve engineering problems in the study of technological processes using methods of system analysis. The essence of this method is to study the technology as a cybernetic system with an assessment of the" reactions” of this system to external influences formed during an active experiment. At the same time, optimization problems are solved analytically. Analytical optimization is based on two main principles. The regression equations obtained as a result of processing experimental data and testing statistical hypotheses are models that adequately describe real processes. Each of these equations is an algebraic function of several variables, to which methods of mathematical analysis are applicable, including the study of extremums of functions in partial derivatives. The next step is to develop a process algorithm and develop computer programs that allow you to select the composition and predict the properties of the product. As an engineering interpretation, it is possible to construct optimized nomograms that allow solving both direct and inverse problems; that is, predicting the result or selecting technological factors. The research methods described in the article are implemented in the study of technologies of cellular concrete, foam concrete, cement-polymer concrete and products made of mineral wool and foam glass. As an example, the article considers the optimization of the selection of the composition of fine-grained concrete reinforced with chopped glass fiber. The implementation of the developed method allowed us to determine the optimal value of the determining parameters, including the consumption of fiber and plasticizer, as well as to form a method for studying the properties of products.

Developing interactive interaction of dual buffering systems and conversion class systems with continuous supply of technological products

Many modern industrial production facilities consist of sequentially operating systems with a continuous supply of technological product. The task of stabilizing the qualitative and quantitative parameters of output products at all stages of such production is a very difficult task and often leads to additional time and money costs. Therefore, improving the efficiency of these processes is a relevant issue. A review of analogous solutions to this type of problem revealed the variability of their authors’ approaches. However, all of them are aimed at optimizing existing control trajectories, rather than creating a new, more accurate trajectory. Earlier, as part of the description of the basic principles of structural and parametric optimization of the management of production processes of this type, only the improved work of technological subsystems was reported. This paper describes the principles of control over the proposed dual buffering system and its interactive interaction with other technological subsystems. The introduction of buffering systems makes sequential technological subsystems more independent of each other. That makes it possible to increase the degree of freedom for each control subsystem and thereby improve the efficiency of finding the optimal mode of operation of the entire cybernetic system. A conceptual model of the dual buffering system was built, the stabilization of the quantitative parameter at the output of the buffering system was substantiated through the development of an adaptation mechanism, and simulation modeling of the synthesized system was carried out. The study shows that the use of buffering systems could improve the quality of energy utilization and reduce the wear of technological mechanisms by 14 % in general

The design of self-organizing human–swarm intelligence

Human–swarm interaction is a frontier in the realms of swarm robotics and human-factors engineering. However, no holistic theory has been explicitly formulated that can inform how humans and robot swarms should interact through an interface while considering real-world demands, the relative capabilities of the components, as well as the desired joint-system behaviours. In this article, we apply a holistic perspective that we refer to as joint human–swarm loops, that is, a cybernetic system made of human, swarm and interface. We argue that a solution for human–swarm interaction should make the joint human–swarm loop an intelligent system that balances between centralized and decentralized control. The swarm-amplified human is suggested as a possible design that combines perspectives from swarm robotics, human-factors engineering and theoretical neuroscience to produce such a joint human–swarm loop. Essentially, it states that the robot swarm should be integrated into the human’s low-level nervous system function. This requires modelling both the robot swarm and the biological nervous system as self-organizing systems. We discuss multiple design implications that follow from the swarm-amplified human, including a computational experiment that shows how the robot swarm itself can be a self-organizing interface based on minimal computational logic.

Human as the cybernetic system

Предложены авторские трактовки понятий «человек» (как высокоразвитая кибернетическая система (биоробот), которая функционирует на основании определенных программ (врожденных и сформированных в процессе жизни), «кибернетическая антропология» (как наука, рассматривающая человека как компьютеризированную систему управления, которая функционирует на основании определенных программ), «психопрограммистика» (как отрасль кибернетической антропологии, изучающая врожденные программы человека, которые определяют мышление и поведение последнего). Раскрывается структура человека как биоробота, основными элементами которой являются: 1) Органическая машина (объект управления); 2) Управляющий орган (субъект управления); 3) Устройство «прямой связи»; 4) Устройство «обратной связи». Выявляются основные элементы Управляющего органа человека как биоробота: 1) Совершенный компьютер; 2) Персональный компьютер; 3) Сенсор (Душа). Предложены авторские трактовки понятий «чувства» и «эмоции» с позиции кибернетической антропологии. Раскрываются основные элементы программ, которые лежат в основе безусловных и условных рефлексов. The author's interpretations of the concepts of “human” are proposed as a highly developed cybernetic system (biorobot), which functions on the basis of certain programs (innate and formed in the process of life); “Cybernetic anthropology” as a science that considers a person as a computerized control system that functions on the basis of certain programs, as well as “psychoprogramming” as a branch of cybernetic anthropology, which studies the innate programs of a person that determine the thinking and behavior of the latter. The structure of a person as a biorobot is revealed, the main elements of which are: 1) Organic machine (control object); 2) Managing body (subject of management); 3) "Direct communication" device; 4) Device "feedback". The main elements of the Managing body of a person as a biorobot are revealed: 1) Perfect computer; 2) Personal computer; 3) Sensor (Soul). The author's interpretations of the concepts of "feelings" and "emotions" from the standpoint of cybernetic anthropology are proposed. The main elements of programs underlying unconditioned and conditioned reflexes are revealed.

The Incorporation of Big Data in Mathematical Training for the Fourth Industrial Revolution

The context of Industry 4.0 is changing the training of mathematical students, new and old generations, in such a way that educational institutions implement strategies and actions to adapt study plans according to the requirements of the new industrial revolution. On the other hand, big data is a cybernetic system that functions as a tool that incorporates mathematical training and implementation and that has recently been included in the educational sphere in order to collaborate with the development of specific competencies based on information technologies and communication, with the purpose of interacting in the intelligent environments proposed by Industry 4.0.

Artificial Intellect as a Result of Organization Management Technology Evolution

The article studies the process of analogue organization changing at the expense of introducing artificial intellect technologies. In the development of organization management technologies we can distinguish three stages of their evolution: automation, informatization and digitalization. Automation was used in the period of functional management thanks to systems of stock and production capacity planning. Informatization is typical of the process approach to management, for example, on the basis of systems of ERP, CRM, MRPII, etc. class. The current stage of management technology evolution is characterized by introduction of digital platforms, digital doubles, Internet of things, technologies of big data analysis and key technologies, which gives an opportunity to use artificial intellect and transform drastically the system of organization management. The article provides examples of applying technologies of artificial intellect in different spheres of organization work, such as production of autos, trains, electronic components and others, studies advantages and results of their application. The author investigates two contours of digitalization: interaction between the object and subject of management in reality and interaction between digital models of the object and subject of management. It is shown that the key element in contours is synchronization of real and digital models providing homeostasis of the organization as a cybernetic system.