Software for innovative information technologies
Reference:
Alpatov A.N., Bogatireva A.A.
Data storage format for analytical systems based on metadata and dependency graphs between CSV and JSON
// Software systems and computational methods.
2024. № 2.
P. 1-14.
DOI: 10.7256/2454-0714.2024.2.70229 EDN: TVEPRE URL: https://en.nbpublish.com/library_read_article.php?id=70229
Abstract:
In the modern information society, the volume of data is constantly growing, and its effective processing is becoming key for enterprises. The transmission and storage of this data also plays a critical role. Big data used in analytics systems is most often transmitted in one of two popular formats: CSV for structured data and JSON for unstructured data. However, existing file formats may not be effective or flexible enough for certain data analysis tasks. For example, they may not support complex data structures or provide sufficient control over metadata. Alternatively, analytical tasks may require additional information about the data, such as metadata, data schema, etc. Based on the above, the subject of this study is a data format based on the combined use of CSV and JSON for processing and analyzing large amounts of information. The option of sharing the designated data types for the implementation of a new data format is proposed. For this purpose, designations have been introduced for the data structure, which includes CSV files, JSON files, metadata and a dependency graph. Various types of functions are described, such as aggregating, transforming, filtering, etc. Examples of the application of these functions to data are given. The proposed approach is a technique that can significantly facilitate the processes of information analysis and processing. It is based on a formalized approach that allows you to establish clear rules and procedures for working with data, which contributes to their more efficient processing. Another aspect of the proposed approach is to determine the criteria for choosing the most appropriate data storage format. This criterion is based on the mathematical principles of information theory and entropy. The introduction of a criterion for choosing a data format based on entropy makes it possible to evaluate the information content and compactness of the data. This approach is based on the calculation of entropy for selected formats and weights reflecting the importance of each data value. By comparing entropies, you can determine the required data transmission format. This approach takes into account not only the compactness of the data, but also the context of their use, as well as the possibility of including additional meta-information in the files themselves and supporting data ready for analysis.
Keywords:
Apache Parquet, Integration of data formats, Data analysis, Analysis Ready Data, Metadata, Data processing, CSV, JSON, Data storage formats, Big Data
Forms and methods of information security administration
Reference:
Sharipov R.R., Yusupov B.Z., Martynov A.M., Zaripova R.S.
Developing the Methodology for the Effective Placement of Security and Fire Alarm Systems
// Software systems and computational methods.
2024. № 2.
P. 15-29.
DOI: 10.7256/2454-0714.2024.2.41036 EDN: ZNJPJH URL: https://en.nbpublish.com/library_read_article.php?id=41036
Abstract:
The article focuses on security and fire alarm systems (SFAS) as means of ensuring the safety of facilities, viewing them as integrated complexes for promptly detecting potential threats. The main emphasis is on detectors, including their classification and role within the system. The article examines various configurations of SFAS and ways of connecting and processing signals from detectors, allowing for an evaluation of how these factors affect the system's effectiveness. The lifecycle of SFAS is described, highlighting the importance of each stage from design to operation. The article provides an overview of regulatory documents, emphasizing the importance of compliance with standards and requirements when implementing SFAS. Recommended for security professionals and individuals interested in delving into this topic. Additionally, the article addresses issues related to the placement of SFAS and their impact on system effectiveness. It analyzes vulnerabilities arising from irrational placement of components and presents a methodology for optimizing placement to enhance security. The methodology is described step by step, considering input and output processes at each stage. The authors conduct practical testing of the methodology in an educational laboratory with an installed SFAS, identifying placement errors and formulating recommendations for correction. The article is beneficial for professionals in the design and installation of SFAS, as well as for those seeking to improve the level of protection of facilities, accentuating the critical importance of proper component placement.
Keywords:
Protection, Installation, Components, Optimization, Effectiveness, Methodology, Placement, Fire alarm, Security systems, Design
Knowledge Base, Intelligent Systems, Expert Systems, Decision Support Systems
Reference:
Zubov D.V., Lebedev D.A.
Diagnostics of failures of technological equipment of chemical industries using artificial intelligence
// Software systems and computational methods.
2024. № 2.
P. 30-40.
DOI: 10.7256/2454-0714.2024.2.70729 EDN: XBIJYK URL: https://en.nbpublish.com/library_read_article.php?id=70729
Abstract:
The paper considers the problem of automated recognition of single emergencies in chemical and oil refining industries. Modern chemical and technological production facilities are maintained and managed by a small number of personnel, which increases the burden on each operator. To reduce the number of operator errors, their training is regularly conducted on simulators equipped with a set of both standard situations (routine start-up, shutdown, normal process management, switching from one mode to another) and emergency scenarios (column depressurization, pump failure, failure of the power supply system). Nevertheless, it is impossible to foresee all possible failures during operator training, and even a trained operator may not notice the first signs of an accident, and therefore it is necessary to create a decision support system that helps the operator to recognize failures of technological equipment in a timely manner. To recognize failures, it is proposed to use a neural network trained on an array of simulated accident data. An industrial simulator based on the RTsim platform was used to simulate typical accidents. The novelty of the research lies in the use of artificial intelligence methods to diagnose the property of the technological process according to the SCADA system and the use of data for training a neural network not from a real object (which will always be insufficient), but from a model that exactly corresponds to a specific technological site. The number of simulated scenarios used to train a neural network can be quite large, which reduces the proportion of erroneous system responses. The developed system confidently copes with the recognition of individual equipment failures. The results obtained can be used to help process operators and to improve emergency protection systems. The analysis of the time required by the system to recognize an emergency situation can be used to design new production facilities, modify the control and management system.
Keywords:
industrial safety, decision-support system, simulation modeling, Digital Twin, RTsim, computer trainer, artificial intelligence, accident, failure, oil refining
Mathematical models and computer simulation experiment
Reference:
Filippova K.A., Ayusheev T.V., Damdinova T.T., Tsidipov T.T.
Investigation of the stress–strain state of a composite blade in ANSYS WorkBench
// Software systems and computational methods.
2024. № 2.
P. 41-52.
DOI: 10.7256/2454-0714.2024.2.70712 EDN: XDTLCG URL: https://en.nbpublish.com/library_read_article.php?id=70712
Abstract:
In this paper, the static strength of a UAV blade made of composite material was calculated. Composite materials have an advantage over traditional materials (metals and alloys) in the field of aviation - gain in weight, low sensitivity to damage, high rigidity, high mechanical characteristics. At the same time, the identification of vulnerabilities in a layered structure is a difficult task and in practice is solved with the help of destructive control. Composite materials available in the ANSYS materials library were used in the modeling: Epoxy Carbon Woven (230 Gpa) Prepreg woven carbon fiber in the form of a semi–finished prepreg impregnated with epoxy resin carbon fiber with Young's modulus E=230 GPa and Epoxy Carbon (230 Gpa) Prepreg is a unidirectional carbon fiber prepreg impregnated with epoxy resin with a Young's modulus E=230 GPa. Modern software products, such as ANSYS WorkBench, allow comprehensive investigation of the layered structure. Several variants of blade designs with different fillers as the median material were investigated. The forward and reverse destruction criteria based on the Tsai-Hill theory were used. The influence of gravity was not taken into account. It is shown that the developed blade design meets the requirements. Balsa wood, pine, aspen and polyurethane foam were chosen as the middle material of the blade. Pine and aspen wood were selected according to the criteria of their availability and having the lowest density. The materials library of the ANSYS WorkBench software package used does not have characteristics for all of them, so the characteristics of the selected materials (pines and aspens) were added manually. For modeling and calculations in the ANSYS WorkBench program, such characteristics as density, axial elastic modulus, Poisson's coefficients, shear modulus and tensile and compressive strength limits are required.
Keywords:
middle fillers, fiberglass, carbon fabrics, Tsai-Hill theory, failure criterion, stress, ANSYS WorkBench, static strength, blade, composite material