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Methodological and Applied Contributions on Engineering Applications of Artificial Intelligence

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 10712

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Special Issue Editors

Dr. Pavel V. Matrenin

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Guest Editor

Department of Power Supply Systems, Novosibirsk State Technical University, 20 K. Marx Ave., 660073 Novosibirsk, Russia
Interests: machine learning; pattern recognition; optimization and control of power systems; stochastic optimization

Dr. Alexandra Khalyasmaa

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Guest Editor

Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia
Interests: machine learning; pattern recognition; cyber-physical systems; power engineering computing

Special Issue Information

Dear Colleagues,

The development of artificial intelligence methods is leading to increasingly broad use of artificial intelligence in technical systems. However, transport systems, mechanical engineering, power systems, chemical industry and the utilities sector require increased attention to ensure the safe and robust operation of artificial intelligence methods in engineering applications. Therefore, new challenges have arisen related to the collection and correct processing of data of various nature, application of robots and cyber-physical systems, the analysis of the performance indicators of intelligent control systems, and the minimization of risks of undesirable behavior of machine learning models.

The aim of this Special Issue is to collect scientific articles reflecting the latest advances in artificial intelligence applications in complex engineering problems, which includes, but is not limited to, data mining, feature engineering, stochastic optimization, computer vision, pattern recognition, time-series forecasting and machine learning.

High-quality research and reviews are welcome. Particular attention should be paid to the practical application of research results.

Dr. Pavel V. Matrenin
Dr. Alexandra Khalyasmaa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

complex engineering systems
identification of technical condition
modeling of control systems
robots and cyber-physical systems
artificial intelligence applications
safe and robust machine learning
feature engineering
machine learning
pattern recognition
computer vision
metaheuristic optimization

Published Papers (6 papers)

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Research

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18 pages, 5280 KiB

Open AccessArticle

Detection of Current Transformer Saturation Based on Machine Learning

by Ismoil Odinaev, Andrey Pazderin, Murodbek Safaraliev, Firuz Kamalov, Mihail Senyuk and Pavel Y. Gubin

Mathematics 2024, 12(3), 389; https://0-doi-org.brum.beds.ac.uk/10.3390/math12030389 - 25 Jan 2024

Viewed by 681

Abstract

One of the tasks in the operation of electric power systems is the correct functioning of the protection system and emergency automation algorithms. Instrument voltage and current transformers, operating in accordance with the laws of electromagnetism, are most often used for information support of the protection system and emergency automation algorithms. Magnetic core saturation of the specified current transformers can occur during faults. As a result, the correct functioning of the protection system and emergency automation algorithms is compromised. The consequences of current transformers saturation are mostly reflected in the main protections of network elements operating on a differential principle. This work aims to consider the analysis of current transformer saturation detection methods. The problem of identifying current transformer saturation is reduced to binary classification, and methods for solving the problem based on artificial neural networks, support vector machine, and decision tree algorithms are proposed. Computational experiments were performed, and their results were analyzed with imbalanced (dominance of the number of current transformer saturation modes over the number of modes with its normal operation) and balanced classes 0 (no current transformer saturation) and 1 (current transformer saturation). Full article

(This article belongs to the Special Issue Methodological and Applied Contributions on Engineering Applications of Artificial Intelligence)

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30 pages, 10628 KiB

Open AccessArticle

Comparing Neural Style Transfer and Gradient-Based Algorithms in Brushstroke Rendering Tasks

by Artur Karimov, Ekaterina Kopets, Tatiana Shpilevaya, Evgenii Katser, Sergey Leonov and Denis Butusov

Mathematics 2023, 11(10), 2255; https://0-doi-org.brum.beds.ac.uk/10.3390/math11102255 - 11 May 2023

Cited by 4 | Viewed by 1295

Abstract

Non-photorealistic rendering (NPR) with explicit brushstroke representation is essential for both high-grade imitating of artistic paintings and generating commands for artistically skilled robots. Some algorithms for this purpose have been recently developed based on simple heuristics, e.g., using an image gradient for driving brushstroke orientation. The notable drawback of such algorithms is the impossibility of automatic learning to reproduce an individual artist’s style. In contrast, popular neural style transfer (NST) algorithms are aimed at this goal by their design. The question arises: how good is the performance of neural style transfer methods in comparison with the heuristic approaches? To answer this question, we develop a novel method for experimentally quantifying brushstroke rendering algorithms. This method is based on correlation analysis applied to histograms of six brushstroke parameters: length, orientation, straightness, number of neighboring brushstrokes (NBS-NB), number of brushstrokes with similar orientations in the neighborhood (NBS-SO), and orientation standard deviation in the neighborhood (OSD-NB). This method numerically captures similarities and differences in the distributions of brushstroke parameters and allows comparison of two NPR algorithms. We perform an investigation of the brushstrokes generated by the heuristic algorithm and the NST algorithm. The results imply that while the neural style transfer and the heuristic algorithms give rather different parameter histograms, their capabilities of mimicking individual artistic manner are limited comparably. A direct comparison of NBS-NB histograms of brushstrokes generated by these algorithms and of brushstrokes extracted from a real painting confirms this finding. Full article

(This article belongs to the Special Issue Methodological and Applied Contributions on Engineering Applications of Artificial Intelligence)

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16 pages, 5110 KiB

Open AccessArticle

Preparation and Analysis of Experimental Findings on the Thermal and Mechanical Characteristics of Pulsating Gas Flows in the Intake System of a Piston Engine for Modelling and Machine Learning

by Leonid Plotnikov

Mathematics 2023, 11(8), 1967; https://0-doi-org.brum.beds.ac.uk/10.3390/math11081967 - 21 Apr 2023

Cited by 4 | Viewed by 1208

Abstract

Today, reciprocating internal combustion engines are used in many branches of the economy (power engineering, machine engineering, transportation, and others). In order for piston engines to meet stringent environmental and economic regulations, it is necessary to develop complex and accurate control systems for the physical processes in engine elements based on digital twins, machine learning, and artificial intelligence algorithms. This article is aimed at preparing and analysing experimental data on the gas dynamics and heat transfer of pulsating air flows in a piston engine’s intake system for modelling and machine learning. The key studies were carried out on a full-scale model of a single-cylinder piston engine under dynamic conditions. Some experimental findings on the gas-dynamic and heat-exchange characteristics of the flows were obtained with the thermal anemometry method and a corresponding measuring system. The effects of the inlet channel diameter on the air flow, the intensity of turbulence, and the heat transfer coefficient of pulsating air flows in a piston engine’s inlet system are shown. A mathematical description of the dependences of the turbulence intensity, heat transfer coefficient, and Nusselt number on operation factors (crankshaft speed, air flow velocity, Reynolds number) and the inlet channel’s geometric dimensions are proposed. Based on the mathematical modelling of the thermodynamic cycle, the operational and environmental performance of a piston engine with intake systems containing channels with different diameters were assessed. The presented data could be useful for refining engineering calculations and mathematical models, as well as for developing digital twins and engine control systems. Full article

(This article belongs to the Special Issue Methodological and Applied Contributions on Engineering Applications of Artificial Intelligence)

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18 pages, 2207 KiB

Open AccessArticle

Choice of Solutions in the Design of Complex Energy Systems Based on the Analysis of Variants with Interval Weights

by Stanislav A. Eroshenko, Alexander A. Pastushkov, Mikhail P. Romanov and Alexey M. Romanov

Mathematics 2023, 11(7), 1672; https://0-doi-org.brum.beds.ac.uk/10.3390/math11071672 - 30 Mar 2023

Viewed by 896

Abstract

Ensuring high-quality and uninterrupted power supply to consumers is one of the main problems of creating reliable power systems of a new generation. It is associated with the implementation of an integral assessment of the technical state of equipment of the power stations and substations, based on technical diagnostics data. Integral assessment involves the choice of ranges of the set of parameters of the technical state for groups of constituent elements of equipment, as well as the determination of their weight coefficients. Currently, the problem is solved with the help of expert assessments, arbitrarily in each specific case, which may lead to an incorrect integral assessment of the state of the equipment. The principle of decomposition makes it possible to determine the individual performance characteristics of each of them. At the same time, their subsequent aggregation ensures that the emergent properties of the system are taken into account. Such an approach was used in this work to evaluate individual types of equipment and their constituent elements. The algorithm for constructing a tree with a minimum random weight, proposed in this paper, makes it possible to increase the validity of decisions. They are made at various stages of designing complex technical systems and include tasks with an integral assessment of the technical state of equipment of power plants and substations. In the proposed algorithm, as a result of using the tree of variants, a matroid is formed, on which, using the “greedy” algorithm, the optimal solution can be determined. Full article

(This article belongs to the Special Issue Methodological and Applied Contributions on Engineering Applications of Artificial Intelligence)

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25 pages, 7375 KiB

Open AccessArticle

Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics

by Alexandra I. Khalyasmaa, Pavel V. Matrenin, Stanislav A. Eroshenko, Vadim Z. Manusov, Andrey M. Bramm and Alexey M. Romanov

Mathematics 2022, 10(14), 2486; https://0-doi-org.brum.beds.ac.uk/10.3390/math10142486 - 17 Jul 2022

Cited by 8 | Viewed by 1615

Abstract

This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects’ observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers’ technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F₁-score, was estimated to be 83%. Full article

(This article belongs to the Special Issue Methodological and Applied Contributions on Engineering Applications of Artificial Intelligence)

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Review

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23 pages, 1946 KiB

Open AccessReview

Review of the Digital Twin Technology Applications for Electrical Equipment Lifecycle Management

by Alexandra I. Khalyasmaa, Alina I. Stepanova, Stanislav A. Eroshenko and Pavel V. Matrenin

Mathematics 2023, 11(6), 1315; https://0-doi-org.brum.beds.ac.uk/10.3390/math11061315 - 08 Mar 2023

Cited by 14 | Viewed by 3996

Abstract

Digital twin is one of the emerging technologies for the digital transformation of the power industry. Many existing studies claim that the widespread application of digital twins will shift the industry to a principally new level of development. This article provides an extensive overview of the industrial application experience of digital twin technologies for solving the problems of modern power systems with a particular focus on the task of high-voltage power equipment lifecycle management. The latter task contours one of the most promising areas for the application of the digital twins in the power industry since it requires deep analysis of the technological processes dynamics and the development of physical, mathematical and computer models that cover all the potential benefits of the digital twin technology. At the moment, there is a lack of reliable data on the problems of assessing and predicting the technical state of high-voltage power equipment. The use of digital twin technology in modern power systems will allow for aggregating data from a variety of real objects and will allow the automatization of collecting and processing of big data by implementing artificial intelligence methods, which will ultimately make it possible to manage the life cycle of the power equipment. The article puts to scrutiny the industrial experience of digital twins creation, considering the technical solutions suggested by the largest manufacturers of electrical equipment. A classification of digital twins, examples and main features of their application in the power industry, including the problem of managing the life cycle of high-voltage electrical equipment, are considered and discussed. Full article

(This article belongs to the Special Issue Methodological and Applied Contributions on Engineering Applications of Artificial Intelligence)

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