articles
| The article presents the developed packing algorithms that make it possible to solve the problem of optimized placement of a given set of flat objects taking into account additional geometric and technological constraints specified when arranging real objects in production. A description of the procedure for applying individual indents between object boundaries is given. To work with areas of placement of arbitrary geometry, restricted areas in the form of fixed objects of various geometries are introduced. An algorithm for uniform placement of a given set of objects throughout the given placement space is proposed. An algorithm for placing objects taking into account several placement start points is described, ensuring their placement as close as possible to one or two pre-marked points in the placement space. A speed-optimized algorithm for placing flat objects of arbitrary geometry, presented in the form of orthogonal polyhedra, is proposed, implementing fast layout of objects of complex geometry when arranging taking into account specified indents and placement start points. An algorithm for arranging flat objects is developed taking into account individual constraints on the minimum distance between special points of objects. A heuristic algorithm for selecting the best variant of orthogonal orientation of rectangular objects is proposed, minimizing the density of the formed layout. Examples of various layouts of objects obtained using the developed placement algorithms are given. Examples of solving some particular problems of arranging rectangular objects with various restrictions on the minimum distance specified between special points of objects are presented. The use of developed packing algorithms, taking into account various geometric and technological limitations, makes it possible to solve practical problems of arranging objects in real production conditions. Continue... | |
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№ 6(120)
30 december 2025 year
Rubric: Researching of processes and systems The author: Varnukhov A. |
In the context of rapid digitalization and the growing importance of online commerce, the task of forecasting sales volumes on marketplaces has become critically important for supporting managerial and strategic decision-making. Despite significant advances in neural network models, their practical application within digital platforms faces a number of limitations, including high demand volatility, data sparsity, the presence of numerous heterogeneous factors with varying dynamics, scalability challenges, as well as high requirements for computational resources and training data volumes. Furthermore, many neural network models operate as “black boxes”, which hinders their use in tasks requiring transparency and justification of forecasts, underscoring the relevance of developing specialized models that combine high predictive accuracy with interpretable results. The aim of this study is to develop and empirically validate a hybrid architecture of neural network model designed to overcome such limitations, taking into account the specific operational characteristics of marketplaces. The proposed model integrates a recurrent encoder for extracting temporal context, modified decoder blocks performing decomposition of the time series into a learnable basis of latent components, and a controlled fusion mechanism enabling adaptive incorporation of contextual information at each decoding level. The applied approach, which forms forecasts as an additive sum of specialized components, each trained to extract certain structural elements, provides an context-aware and structured representation of the time series, enables more accurate capture of long-term trends and periodic fluctuations, and enhances model robustness to noise and data sparsity. Experimental evaluation using data from the Wildberries marketplace demonstrated the model’s superiority in forecasting accuracy over classical and baseline models, confirming its applicability in environments typical of digital trading platforms. Continue... |
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№ 6(120)
30 december 2025 year
Rubric: Researching of processes and systems Authors: Utkin L., Ilin I., Konstantinov A., Verbova N. |
A wide variety of applied fields, including medicine, security, economics, and industry, are concerned with modeling the processes of various events occurring, such as a patient’s recovery, a company’s financial bankruptcy, industrial equipment failure, etc. Their modeling can be performed within the framework of survival analysis, a statistical method for analyzing time-to-event data whose distinctive feature, setting it apart from many other statistical and machine learning methods, is the presence of censored data. This occurs when an event is not observed and it is only known that it did not happen before a certain point in time. Censored data significantly complicates the modeling and prediction of critical events. Machine learning is an effective tool for survival analysis in the presence of censored data. In particular, modern transformer-based machine learning models demonstrate promising results in survival analysis due to their ability to account for complex dependencies. However, the standard attention mechanism in these models often ignores the fundamental structure of time-to-event data, namely, the distinction between censored and uncensored observations. To overcome this shortcoming, this paper proposes a new model and a new approach to implementing an attention mechanism that redefines attention weights by incorporating prior characteristics of survival analysis based on the Beran estimator or the Cox model. Instead of relying solely on distances between feature vector representations, as is done in current models, the proposed model computes attention weights as a weighted linear combination of components derived from key prior characteristics of survival analysis, such as distances between survival function estimates or time-to-event expectations for different training objects. The proposed approach enables a significant expansion of the class of transform models for survival analysis, achieving higher prediction accuracy. The algorithm implementing the proposed model is the basis for transformers. Experiments on real datasets confirm that the generalized model provides the best prediction among a number of known models. Continue... |
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№ 6(120)
30 december 2025 year
Rubric: Data protection Authors: Meleshko A., Desnitsky V., Kotenko I. |
The paper presents a study of the issues of detecting attacks on modification of digital models of products (details) intended for 3D printing in modern intelligent additive manufacturing systems. In general, such systems are networks that include multiple 3D printers (i. e. 3D farms) operating in parallel, capable of printing series of products at user requests, for instance elements of physical structures of robots and vehicles, blades of unmanned aerial vehicles and other parts made of plastic, metal and other materials. Existing examples of such 3D installations are vulnerable to the actions of attackers who try to make a hidden unauthorized modification by influencing digital models. After such an attack, end products may have a design defect with visual characteristics that are almost indistinguishable from the original sample of such a product. For instance, by influencing a defective element of the UAV body, an attacker may reduce its controllability and even lead to its crash. The paper considers an experimental substantiation of the hypothesis on the possibility of detecting modification attacks on digital models of products based on processing and analysis of the program code of such models. The features of defects in 3D product models presented in the G-code language and selected from open 3D model databases are analyzed. A data set consisting of original and modified product models is compiled. An approach to modification detection using embedding to transform data into numerical vectors and train classifiers on them using supervised learning methods is proposed. Experiments on test data samples demonstrated the feasibility of the proposed approach to modification detection and the prospects for its further development and application in practice. Continue... |
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№ 1(121)
27 february 2026 year
Rubric: Models and methods Authors: Meshalkin V. P., Bobkov V., Bulygina O. V., Vorotilova M. |
A promising way to ensure the competitiveness of industrial products is to improve the energy efficiency of technological processes by reducing fuel and energy consumption. For energy-intensive production such as a chemical-engineering system for processing ore beneficiation waste, this task boils down to optimizing the operating modes of conveyor roasting machines that thermally prepare the raw material for remelting. The high computational complexity of the problem, caused by the extensive set of controlled process line parameters and the polyfractional nature of the ore raw material, limits the applicability of analytical methods. It is proposed to use a nature-inspired method based on the principles of social behavior in cockroach colonies. Its specific has led to the following modifications to the basic Cockroach Swarm Optimization. Firstly, a multicolony approach, which involves considering a multi-layered arrangement of dispersed ore components of different sizes within a single colony, is used to narrow the search space. Secondly, to improve the algorithm’s convergence rate, it is proposed to introduce elements of fuzzy set theory into it to determine the “free” parameters (the “perception” distance, which defines the maximum distance from the best solutions; the step size of agent movement within the search space toward the best solutions). The developed fuzzy multicolony method was implemented as a computer program using Python. The results of computational experiments demonstrated the convergence of the proposed method for optimizing the operating modes of a conveyor roaster, and a comparison with its standard operating mode demonstrated its potential for use in controlling the chemical-engineering process of thermally preparing polyfractional ore raw materials for remelting. Continue... |
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№ 1(121)
27 february 2026 year
Rubric: Algorithmic efficiency Authors: Mukharlyamov R., Kaspirovich I. |
The research is devoted to the development of an algorithm for applying the quaternion formalism to the modeling of the dynamics of exoskeletons and similar anthropomorphic mechanisms, such as robots, space suits, simulators, and related systems. The fundamental problem of developing exoskeletons, anthropomorphic mechanisms, and robotic systems is being investigated, which requires accelerating the process of analytical construction of mathematical models described by differential equations. To tackle this problem, the authors propose the use of hypercomplex number algebra, specifically quaternions. The application of quaternion algebra to the study of locomotion in anthropoid robotic devices such as exoskeletons with active propulsion systems that control the relative positions of the joints during movement should improve the construction of the mathematical model. These arguments determine the relevance of the research topic and the scientific novelty of the study. The development of high-speed methods for writing differential equations of motion based on quaternion algebra to describe the locomotion of spatial mechanical anthropoid systems determines the practical significance of the research results. The work presents a method for constructing an algorithm to model the dynamics of the shank of an exoskeleton, represented as a link connected by a spherical joint allowing rotation with respect to a fixed reference frame. The proposed mechanical model has been implemented as a program within the universal computer algebra system Wolfram Mathematica. The program is designed for simulating the dynamics of the exoskeleton link. Since the system does not provide built-in functions for working with analytically defined quaternions, the authors developed the required routines themselves. The program consists of several modules: a module for quaternion operations; a module for transformation matrices (used for validation and debugging of the quaternion module); a module for the automated formulation of the Lagrange equations of the second kind; a module for specifying the programmed motion of the model and computing the control torques in the joints; a module for numerically solving the Cauchy problem; and a module for animation and visualization of the model’s motion as well as for exporting the graphical results of the numerical simulations. The program’s results allow for the analysis of the dynamics of a mathematical model of a system based on the solution of the direct and inverse dynamics problem, and can be recommended for the design of exoskeletons, anthropomorphic robots, and manipulators with a programmable operating mode. Continue... |
