Informatics and Automation

Currently, existing mathematical models and algorithms provide optimization of schedules for the execution of single tasks or fixed task packages on devices of conveyor systems containing buffers of limited sizes. These models and algorithms do not allow searching for optimal solutions for grouping the same type of tasks into packages and by sequence of packages to implement operations with them on devices of conveyor systems. Increasing the efficiency of using the resources of conveyor systems is achieved by optimizing solutions for grouping the same type of tasks into packages and by sequences of packages for performing operations with them. The solution to this problem is carried out in the work by using an approach that implements two-level optimization, which allows you to form a hierarchy of subtasks for finding effective solutions. The involvement of the mentioned approach involves the development of mathematical models of hierarchical games that allow identifying effective solutions of the type under consideration. Two mathematical models of hierarchical games have been constructed, the use of which makes it possible to optimize package compositions at the upper level by the leading player and optimize package execution schedules in pipeline systems at the lower level by the slave player. The method of determining the optimal solutions for each of the players provides for the order of moves set in the game and the exchange of solutions between them during the game. The first mathematical model of the hierarchical game implements the definition of effective solutions when taking into account the downtime of processing devices in the process of implementing operations with packages. The second mathematical model of the game implements the definition of effective solutions, taking into account the total waiting time for buffers to place tasks in them, with which operations on previous devices were completed. To do this, expressions have been formed that allow you to determine buffer downtime while waiting for tasks from packages to be ready for placement based on the time characteristics of the processes of performing operations with them on the devices of the systems under consideration. The algorithm for determining optimal solutions according to the order of operations with packages at the lower level in each of the hierarchical games is based on a developed mathematical model of the processes of implementing actions with packages in these systems and the corresponding modeling algorithm. The implementation of the optimization approach under consideration allowed us to obtain results that showed that the use of buffers can significantly increase the efficiency of the processes of performing operations with packets on the devices of the systems under consideration; increasing the size of intermediate buffers allows us to increase the efficiency of these processes to a greater extent with significant heterogeneities in the values of time parameters characterizing them; using the first model of a hierarchical game allows us to achieve a greater increase in the efficiency of processes in comparison with the second model.

The rational placement of load-bearing walls remains a complex and poorly studied problem, despite the existence of numerous algorithms and models for solving the similar problem of column placement. The main complexity factors are the large number of alternative solutions, the significant time required to calculate deformations for a given wall placement, and the multi-objective nature of the problem. In addition to the nonlinear criterion for estimating deformations, it is necessary to minimize the length of load-bearing walls and the number of their unique lengths. A model for the rational placement of load-bearing walls is proposed, which divides the walls into functional parts with a specific step and considers all the required target criteria. Adjacent wall parts with the same functionality are combined into segments. The combinatorial formulation applied in the model of the problem allows the use of genetic algorithms as a solution tool. Therefore, a new approach to multi-objective genetic algorithm is proposed, containing metrics for calculating population diversity at the phenotype and genotype levels. Modifications of crossover, mutation, and selection operators, considering the segmental structure of the wall's genotype, are presented. A comparative analysis of the developed algorithm with other known multi-objective genetic algorithms showed that the developed algorithm finds, on average, three times more non-dominated solutions, particularly more plans with a lower deformation estimates, despite the twice-longer execution time. The proposed model differs significantly from previous models in terms of handling deformations in slab-support systems, comparing placement plans with each other rather than calculating precise reinforcement estimates, which is often unnecessary at the early stages. The proposed genetic algorithm scheme increases the number of found nondominated solutions without losing their diversity, and can be used to solve other multi-objective problems, taking into account the specified features. The developed algorithm was easily integrated into the CAD-based decision support software and can be used in practice by building designers.

An approach to verification of functional and structural specifications implemented in custom integrated circuits based on invasive research methods is presented. The relevance of this research is determined by the necessity of verification of functional-structural specifications supplied by third-party implementers of hardware implementations of information security algorithms, the difficulty of detecting modifications of these algorithms and undocumented capabilities implemented at the hardware level, and the lack of uniform, universal or standardized methods for solving this problem. The mathematical formulation of the research problem is specified; its essence is to verify the equality of the values of the declared specification parameters and their values restored by the reverse engineering method. The results of the application of the verification technique of functional and structural specifications are presented using examples of its adaptation to the study of hardware-implemented DES and AES encryption algorithms. The restored functional and structural blocks of the algorithms (in particular, the substitution block) were successfully verified.

People re-identification (ReID) plays a pivotal role in modern surveillance, enabling continuous tracking of individuals across various CCTV cameras and enhancing the effectiveness of public security systems. However, ReID in real-world CCTV footage presents challenges, including changes in camera angles, variations in lighting, partial occlusions, and similar appearances among individuals. In this paper, we propose a robust deep learning framework that leverages convolutional neural networks (CNNs) with a customized triplet loss function to overcome these obstacles and improve re-identification accuracy. The framework is designed to generate unique feature embeddings for individuals, allowing precise differentiation even under complex environmental conditions. To validate our approach, we perform extensive evaluations on benchmark ReID datasets, achieving state-of-the-art results in terms of both accuracy and processing speed. Our model's performance is assessed using key metrics, including Cumulative Matching Characteristic (CMC) and mean Average Precision (mAP), demonstrating its robustness in diverse surveillance scenarios. Compared to existing methods, our approach consistently outperforms in both accuracy and scalability, making it suitable for integration into large-scale CCTV systems. Furthermore, we discuss practical considerations for deploying AI-based ReID models in surveillance infrastructure, including system scalability, real-time capabilities, and privacy concerns. By advancing techniques for re-identifying people, this work not only contributes to the field of intelligent surveillance but also provides a framework for enhancing public safety in real-world applications through automated and reliable tracking capabilities.

One of the most important aspects of contemporary educational systems is student engagement detection, which involves determining how involved, attentive, and active students are in class activities. For educators, this approach is essential as it provides insights into students' learning experiences, enabling tailored interventions and instructional enhancements. Traditional techniques for evaluating student engagement are often time-consuming and subjective. This study proposes a novel real-time detection framework that leverages Transformer-enhanced Feature Pyramid Networks (FPN) with Channel-Spatial Attention (CSA), referred to as BiusFPN_CSA. The proposed approach automatically analyses student engagement patterns, such as body posture, eye contact, and head position, from visual data streams by integrating cutting-edge deep learning and computer vision techniques. By integrating the attention mechanism of CSA with the hierarchical feature representation capabilities of FPN, the model can accurately detect student engagement levels by capturing contextual and spatial information in the input data. Additionally, by incorporating the Transformer architecture, the model achieves better overall performance by effectively capturing long-range dependencies and semantic relationships within the input sequences. Evaluation using the WACV dataset demonstrates that the proposed model outperforms baseline techniques in terms of accuracy. Specifically, in terms of accuracy, the FPN_CSA_Trans_EH variant of the proposed model outperforms FPN_CSA by 3.28% and 4.98%, respectively. These findings underscore the efficacy of the BiusFPN_CSA framework in real-time student engagement detection, offering educators a valuable tool for enhancing instructional quality, fostering active learning environments, and ultimately improving student outcomes.

Modern organizational management technologies involve collecting and processing large amounts of data to calculate the parameters of the functioning of the objects and processes under study. Since the main feature of the collected parameters is their binding to territories, on the one hand, and attribution to time periods, on the other hand, the use of geographic information systems and technologies is required. Despite the development of modern geographic information technologies, the issues of their practical application to support decision-making, taking into account the combined influence of spatial and temporal factors, have not been fully resolved. The article proposes an assembled model of geoinformation multilayer space-time, which is a graph whose vertices are the parameter values ordered by layers with the placement of time marks in the time layer, and the arcs describe the relations between them that are divided into three types: topological, semantic and chronological. Conjugation and ordering of parameters, according to the proposed model, allows you to correctly pose and solve the optimization problem, and, consequently, eliminate the problem of the practical use of accumulated analytics in the processes of supporting management decision-making. The proposed model is used in the digital platform of integral monitoring for the digital transformation of the processes of collecting, analyzing and visualizing utility resource data. The general management task is considered, and a specific example is given for one of the urgent tasks of regional management, i.e. social gasification, in which the optimization of the process of processing applications for connecting residential buildings to the gas supply system within the boundaries of the selected region is carried out. The assembled model of geoinformation multilayer space-time allows formulating universal statements of decision support problems for various geoinformatics applications in logistics, transport resource management, as well as in situational centers for enterprise and regional management, business analytics systems and organizational systems management.