卷 11, 编号 3 (2024)

The article discusses the prospects and technical challenges of developing practical quantum computers. It is noted that quantum computers have a unique ability to perform multiple computations simultaneously, due to the use of quantum effects such as superposition and entanglement. This makes them extremely powerful in solving certain types of complex problems, including cryptography, optimization, quantum system modeling, and large database searches. However, the development of practical quantum computers faces serious technical challenges. A key issue is the extreme sensitivity of qubits (the fundamental elements of quantum computers) to external influences, which leads to the disruption of their quantum state. To address this problem, the possibility of using pulsed tunneling effect (PTE) is discussed. This may allow stabilizing the characteristics and quantum states of qubits and thus advance the development of practical quantum computers.

In this paper transients in the control system are investigated on the basis of experimental data. The construction of the transfer function of the control object using Simoyu method is realized by means of Python language. The model of the control system of the object, selection of the regulator and its settings are implemented using SimInTech modeling environment. Within the framework of the conducted research, methodological approaches to the formation of transfer functions of control objects represented in the form of polynomial expressions of various degrees of complexity, starting with polynomials of the first degree in the numerator and the second degree in the denominator, and ending with polynomials of the second degree in the denominator against the third degree in the numerator, have been developed and tested. A procedure for reading data in CSV format was used to build the Python program interface, which helped to simplify the integration of experimental results with analytical tools, providing a powerful platform for subsequent analysis, visualization, and interpretation of the resulting transfer functions. The procedures of debugging and optimization of the technique of visualization of results and estimation of calculation errors have been carried out, which allowed to provide a visual representation of data and high accuracy of the obtained transfer functions. In contrast to the known analytical studies in the field of differential equations describing transient processes, the use of numerical methods implemented by means of Python libraries and programming environments, in particular SimInTech, allows to simplify the analysis of transient processes of control objects.

Nowadays, convolutional neural networks have demonstrated significant performance gains over traditional machine learning methods for various real-world computational intelligence tasks such as digital image classification. However, to achieve the best accuracy, the network topology should be modeled using different architectures with different number of filters, kernel size, number of layers, etc., which actualizes the problem of developing and justifying appropriate selection methods. Taking into account the above mentioned, the aim of the paper is to justify an approach that will improve the topology of the neural network model for object segmentation in digital images based on convolutional neural networks. The research methods are system analysis, modeling, machine learning and fuzzy logic theory, and decision-making theory. As a result of the analysis, the paper proposes an algorithm to improve the topology of the neural network model based on differential evolution to optimize the accuracy of image segmentation and the training time of the network. Differential evolution is applied to determine the optimal number of layers in the network topology, which promotes faster convergence. Within the proposed algorithm, an encoding step was identified to represent the structure of each network using a fixed-length integer array, after which it is proposed to utilize differential evolution processes (mutation, recombination, and selection) to efficiently explore the search space. Prospects for further research are to develop methods and techniques to encode a candidate solution using different numbers of hidden blocks in each convolution.

The study is devoted to the problem of recognition of human activity recognition and the definition of normal and abnormal behavior (activity) depending on the action scene. Automated detection of abnormal activity using computer vision technologies and rapid response makes it possible to improve the work of rapid response services, thereby saving human lives or stopping offenses. The paper presents a comprehensive review of methods for recognizing human activity and detecting abnormal human activity based on deep learning. Various classifications of abnormal activity are investigated, and then deep learning methods and neural network architectures used to detect abnormal activity are discussed and analyzed. Based on the comparative analysis of various approaches, an algorithm for recognizing human activity has been proposed and a neural network has been developed that determines violent and nonviolent actions with an accuracy of 92,22% in 150 epochs.

In this study, we propose a model of an artificial neural network used as a specialized superstructure over a genetic algorithm, which allows influencing the process of finding solutions directly during the synthesis of solutions. Such a combination of methods will allow controlling the trajectory of the population in the solution space, which is especially important when working with big data processing technology, when stopping the solution search process due to the attenuation of the evolutionary procedure or finding the population in a local extremum requires stopping the genetic algorithm, performing additional adjustment of operators and restarting, the use of such an approach is ineffective, especially when working with big data and labor-intensive calculations. This article proposes a model of an artificial neural network that allows recognizing the state of the population of the genetic algorithm and making a decision to change the operating parameters of the genetic algorithm operators. The proposed model allows recognizing the processes of attenuation of the evolutionary procedure when solving the problem of structural and parametric synthesis of large discrete systems and determining measures of influence on the operating parameters of the genetic algorithm. This model recognizes the state of the population with an accuracy of more than 95%, which allows to significantly reduce the time for finding solutions in problems of applying a genetic algorithm to work with big data.

This article examines the use of fractals to estimate the probability of classical events controlled by quantum processes. A hypothesis explaining the opposite charges of the positron and electron is discussed, as well as the relationship with the main modern theories of quantum mechanics, such as quantum electrodynamics (QED), string theory, etc. The relationship with the tunnel effect and the pulsed tunnel effect is considered. Examples of practical application of fractals are given, for example, in photocatalysts. The concepts of the effective mass of a photon and the quantum nature of elementary particles, the idea of their internal structure and the formation of matter from the point of view of quantum mechanics are touched upon. Particular attention is paid to the fractal structure of the quantum field as a probability associated with the formation of a positron or electron, and the mathematical connection with the Dirac equation, QED and the Schrödinger equation.