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14 pages, 1400 KiB

Open AccessArticle

Approaches to Numerical Solution of Optimal Control Problem Using Evolutionary Computations

by Askhat Diveev, Elena Sofronova and Sergey Konstantinov

Appl. Sci. 2021, 11(15), 7096; https://doi.org/10.3390/app11157096 - 31 Jul 2021

Cited by 9 | Viewed by 2676

Two approaches to the numerical solution of the optimal control problem are studied. The direct approach is based on the reduction of the optimal control problem to a nonlinear programming problem. Another approach is so-called synthesized optimal control, and it includes the solution [...] Read more.

Two approaches to the numerical solution of the optimal control problem are studied. The direct approach is based on the reduction of the optimal control problem to a nonlinear programming problem. Another approach is so-called synthesized optimal control, and it includes the solution of the control synthesis problem and stabilization at some point in the state space, followed by the search of stabilization points and movement of the control object along these points. The comparison of these two approaches was carried out as the solution of the optimal control problem as a time function cannot be directly used in the control system, although the obtained discretized control can be embedded. The control object was a group of interacting mobile robots. Dynamic and static constraints were included in the quality criterion. Implemented methods were evolutionary algorithms and a random parameter search of piecewise linear approximation and coordinates of stabilization points, along with a multilayer network operator for control synthesis. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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20 pages, 3661 KiB

Open AccessArticle

Intelligent Control Systems in Urban Planning Conflicts: Social Media Users’ Perception

by Nailia Gabdrakhmanova and Maria Pilgun

Appl. Sci. 2021, 11(14), 6579; https://doi.org/10.3390/app11146579 - 17 Jul 2021

Cited by 3 | Viewed by 2080

Abstract

The relevance of this study is determined by the need to develop technologies for effective urban systems management and resolution of urban planning conflicts. The paper presents an algorithm for analyzing urban planning conflicts. The material for the study was data from social [...] Read more.

The relevance of this study is determined by the need to develop technologies for effective urban systems management and resolution of urban planning conflicts. The paper presents an algorithm for analyzing urban planning conflicts. The material for the study was data from social networks, microblogging, blogs, instant messaging, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the construction of the North-Eastern Chord (NEC) in Moscow (RF). To analyze the content of social media, a multimodal approach was used. The paper presents the results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users’ perceptions based on their digital footprints. Artificial neural networks, differential equations, and mathematical statistics were involved in building the models. Differential equations of dynamic systems were based on observations enabled by machine learning. Mathematical models were developed to quickly detect, prevent, and address conflicts in urban planning in order to manage urban systems efficiently. In combination with mathematical and neural network model the developed approaches, made it possible to draw a conclusion about the tense situation around the construction of the NEC, identify complaints of residents to constructors and city authorities, and propose recommendations to resolve and prevent conflicts. Research data could be of use in solving similar problems in sociology, ecology, and economics. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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10 pages, 257 KiB

Open AccessArticle

Hierarchical Population Game Models of Coevolution in Multi-Criteria Optimization Problems under Uncertainty

by Vladimir A. Serov

Appl. Sci. 2021, 11(14), 6563; https://doi.org/10.3390/app11146563 - 16 Jul 2021

Cited by 3 | Viewed by 1535

Abstract

The article develops hierarchical population game models of co-evolutionary algorithms for solving the problem of multi-criteria optimization under uncertainty. The principles of vector minimax and vector minimax risk are used as the basic principles of optimality for the problem of multi-criteria optimization under [...] Read more.

The article develops hierarchical population game models of co-evolutionary algorithms for solving the problem of multi-criteria optimization under uncertainty. The principles of vector minimax and vector minimax risk are used as the basic principles of optimality for the problem of multi-criteria optimization under uncertainty. The concept of equilibrium of a hierarchical population game with the right of the first move is defined. The necessary conditions are formulated under which the equilibrium solution of a hierarchical population game is a discrete approximation of the set of optimal solutions to the multi-criteria optimization problem under uncertainty. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

19 pages, 3197 KiB

Open AccessArticle

Method of Multilevel Adaptive Synthesis of Monitoring Object Knowledge Graphs

by Kirill Krinkin, Alexander Vodyaho, Igor Kulikov and Nataly Zhukova

Appl. Sci. 2021, 11(14), 6251; https://doi.org/10.3390/app11146251 - 6 Jul 2021

Cited by 2 | Viewed by 1652

Abstract

The paper introduces a method for adaptive deductive synthesis of state models, of complex objects, with multilevel variable structures. The method makes it possible to predict the state of objects using the data coming from them. The data from the objects are collected [...] Read more.

The paper introduces a method for adaptive deductive synthesis of state models, of complex objects, with multilevel variable structures. The method makes it possible to predict the state of objects using the data coming from them. The data from the objects are collected with sensors installed on them. Multilevel knowledge graphs (KG) are used to describe the observed objects. The new adaptive synthesis method develops previously proposed inductive and deductive synthesis methods, allowing the context to be taken into account when predicting the states of the monitored objects based on the data obtained from them. The article proposes the algorithm for the suggested method and presents its computational complexity analysis. The software system, based on the proposed method, and the algorithm for multilevel adaptive synthesis of the object models developed, are described in the article. The effectiveness of the proposed method is shown in the results from modeling the states of telecommunication networks of cable television operators. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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25 pages, 1578 KiB

Open AccessArticle

Changes in the Intelligence Levels and Structure in Russia: An ANOVA Method Based on Discretization and Grouping of Factors

by Tatiana Avdeenko, Anastasiia Timofeeva, Marina Murtazina and Olga Razumnikova

Appl. Sci. 2021, 11(13), 5864; https://doi.org/10.3390/app11135864 - 24 Jun 2021

Cited by 2 | Viewed by 2801

Abstract

In the present paper, we investigate how the general intelligence quotient (IQ) and its subtests changed for students from Russian University from 1991 to 2013. This study of the effect of such factors as gender, department, and year on the IQ response is [...] Read more.

In the present paper, we investigate how the general intelligence quotient (IQ) and its subtests changed for students from Russian University from 1991 to 2013. This study of the effect of such factors as gender, department, and year on the IQ response is carried out using the ANOVA model. Given the unevenness of the initial sample by years and departments, and consequently, heterogeneity of variances when divided by the original natural categories, we decided to aggregate the values of explanatory variables to build an adequate model. The paper proposes and investigates an algorithm for joint discretization and grouping, which uses the procedure of partial screening of solutions. It is an intermediate option between the greedy algorithm and exhaustive search. As a goodness function (an optimality criterion), we investigate 26 intermediate options between the AIC and BIC criteria. The BIC turned out to be the most informative and the most acceptable criterion for interpretation, which penalizes the complexity of the model, due to some decrease in accuracy. The resulting partition of the explanatory variables values into categories is used to interpret the modeling results and to arrive at the final conclusions of the data analysis. As a result, it is revealed that the observed features of the IQ dynamics are caused by changes in the education system and the socio-economic status of the family that occurred in Russia during the period of restructuring the society and intensive development of information technologies. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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10 pages, 409 KiB

Open AccessArticle

The Admissible Control Correction Method in a Nonlinear Terminal Perturbed Problem

by Yuliya Belinskaya, Mikhail Dmitriev and Dmitry Makarov

Appl. Sci. 2021, 11(12), 5560; https://doi.org/10.3390/app11125560 - 16 Jun 2021

Viewed by 1549

Abstract

A solution of a nonlinear perturbed unconstrained point-to-point control problem, in which the unperturbed system is differentially flat, is considered in the paper. An admissible open-loop control in it is constructed using the covering method. The main part of the obtained admissible control [...] Read more.

A solution of a nonlinear perturbed unconstrained point-to-point control problem, in which the unperturbed system is differentially flat, is considered in the paper. An admissible open-loop control in it is constructed using the covering method. The main part of the obtained admissible control correction in the limit problem is found by expanding the perturbed problem solution in series by the perturbation parameter. The first term of the expansion is determined by A.N. Tikhonov’s regularization of the Fredholm integral equation of the first kind. As shown by numerical experiments, the found structure of an admissible control allows one to find the final form of high precision point-to-point control based on the solution of an auxiliary variational problem in its neighborhood. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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16 pages, 2792 KiB

Open AccessArticle

Application of Genetic Algorithms for the Selection of Neural Network Architecture in the Monitoring System for Patients with Parkinson’s Disease

by Yulia Shichkina, Yulia Irishina, Elizaveta Stanevich and Armando de Jesus Plasencia Salgueiro

Appl. Sci. 2021, 11(12), 5470; https://doi.org/10.3390/app11125470 - 12 Jun 2021

Cited by 6 | Viewed by 2432

Abstract

This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient [...] Read more.

This article describes an approach for collecting and pre-processing phone owner data, including their voice, in order to classify their condition using data mining methods. The most important research results presented in this article are the developed approaches for the processing of patient voices and the use of genetic algorithms to select the architecture of the neural network in the monitoring system for patients with Parkinson’s disease. The process used to pre-process a person’s voice is described in order to determine the main parameters that can be used in assessing a person’s condition. It is shown that the efficiency of using genetic algorithms for constructing neural networks depends on the composition of the data. As a result, the best result in the accuracy of assessing the patient’s condition can be obtained by a hybrid approach, where a part of the neural network architecture is selected analytically manually, while the other part is built automatically. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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15 pages, 530 KiB

Open AccessArticle

Control Synthesis as Machine Learning Control by Symbolic Regression Methods

by Elizaveta Shmalko and Askhat Diveev

Appl. Sci. 2021, 11(12), 5468; https://doi.org/10.3390/app11125468 - 12 Jun 2021

Cited by 10 | Viewed by 2615

Abstract

The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented [...] Read more.

The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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16 pages, 3033 KiB

Open AccessArticle

Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

by Nikita Andriyanov

Appl. Sci. 2021, 11(11), 5235; https://doi.org/10.3390/app11115235 - 4 Jun 2021

Cited by 18 | Viewed by 3166

Abstract

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one [...] Read more.

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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17 pages, 4732 KiB

Open AccessArticle

Hybridization of Intelligent Solutions Architecture for Text Understanding and Text Generation

by Anton Ivaschenko, Arkadiy Krivosheev, Anastasia Stolbova and Oleg Golovnin

Appl. Sci. 2021, 11(11), 5179; https://doi.org/10.3390/app11115179 - 2 Jun 2021

Cited by 5 | Viewed by 2580

Abstract

This study proposes a new logical model for intelligent software architecture devoted to improving the efficiency of automated text understanding and text generation in industrial applications. The presented approach introduces a few patterns that provide a possibility to build adaptable and extensible solutions [...] Read more.

This study proposes a new logical model for intelligent software architecture devoted to improving the efficiency of automated text understanding and text generation in industrial applications. The presented approach introduces a few patterns that provide a possibility to build adaptable and extensible solutions using machine learning technologies. The main idea is formalized by the concept of expounder hybridization. It summarizes an experience of document analysis and generation solutions development and social media analysis based on artificial neural networks’ practical use. The results of solving the task by the best expounder were improved using the method of aggregating multiple expounders. The quality of expounders’ combination can be further improved by introducing the pro-active competition between them on the basis of, e.g., auctioning algorithm, using several parameters including precision, solution performance and score. Analysis of the proposed approach was carried out using a dataset of legal documents including joint-stock company decision record sheets and protocols. The solution is implemented in an enterprise content management system and illustrated by an example of processing of legal documentation. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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12 pages, 2302 KiB

Open AccessArticle

Regular Two-Dimensional Packing of Congruent Objects: Cognitive Analysis of Honeycomb Constructions

by Nikolay N. Klevanskiy, Sergey I. Tkachev, Ludmila A. Voloshchuk, Rouslan B. Nourgaziev and Vladimir S. Mavzovin

Appl. Sci. 2021, 11(11), 5128; https://doi.org/10.3390/app11115128 - 31 May 2021

Cited by 2 | Viewed by 1776

Abstract

A new approach to investigate the two-dimensional, regular packing of arbitrary geometric objects (GOs), using cognitive visualization, is presented. GOs correspond to congruent non-convex polygons with their associated coordinate system. The origins of these coordinate systems are accepted by object poles. The approach [...] Read more.

A new approach to investigate the two-dimensional, regular packing of arbitrary geometric objects (GOs), using cognitive visualization, is presented. GOs correspond to congruent non-convex polygons with their associated coordinate system. The origins of these coordinate systems are accepted by object poles. The approach considered is based on cognitive processes that are forms of heuristic judgments. According to the first heuristic judgment, regular packing of congruent GOs on the plane have a honeycomb structure, that is, each GO contacts six neighboring GO, the poles of which are vertices of the pole hexagon in the honeycomb construction of packing. Based on the visualization of the honeycomb constructions a second heuristic judgment is obtained, according to which inside the hexagon of the poles, there are fragments of three GOs. The consequence is a third heuristic judgment on the plane covering density with regular packings of congruent GOs. With the help of cognitive visualization, it is established that inside the hexagon of poles there are fragments of exactly three objects. The fourth heuristic judgment is related to the proposal of a triple lattice packing for regular packing of congruent GOs. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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19 pages, 485 KiB

Open AccessFeature PaperArticle

Universal Approach to Solution of Optimization Problems by Symbolic Regression

by Elena Sofronova and Askhat Diveev

Appl. Sci. 2021, 11(11), 5081; https://doi.org/10.3390/app11115081 - 30 May 2021

Cited by 14 | Viewed by 2265

Abstract

Optimization problems and their solution by symbolic regression methods are considered. The search is performed on non-Euclidean space. In such spaces it is impossible to determine a distance between two potential solutions and, therefore, algorithms using arithmetic operations of multiplication and addition are [...] Read more.

Optimization problems and their solution by symbolic regression methods are considered. The search is performed on non-Euclidean space. In such spaces it is impossible to determine a distance between two potential solutions and, therefore, algorithms using arithmetic operations of multiplication and addition are not used there. The search of optimal solution is performed on the space of codes. It is proposed that the principle of small variations of basic solution be applied as a universal approach to create search algorithms. Small variations cause a neighborhood of a potential solution, and the solution is searched for within this neighborhood. The concept of inheritance property is introduced. It is shown that for non-Euclidean search space, the application of evolution and small variations of possible solutions is effective. Examples of using the principle of small variation of basic solution for different symbolic regression methods are presented. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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19 pages, 46651 KiB

Open AccessArticle

Determination of Workspaces and Intersections of Robot Links in a Multi-Robotic System for Trajectory Planning

by Laxmidhar Behera, Larisa Rybak, Dmitry Malyshev and Elena Gaponenko

Appl. Sci. 2021, 11(11), 4961; https://doi.org/10.3390/app11114961 - 28 May 2021

Cited by 10 | Viewed by 3413

Abstract

One of the problems in the development of multi-robotic systems is the safe navigation of a group of robots. To solve it, the restrictions imposed by the structural elements of its agents are determined. The article presents a multi-robotic system consisting of parallel [...] Read more.

One of the problems in the development of multi-robotic systems is the safe navigation of a group of robots. To solve it, the restrictions imposed by the structural elements of its agents are determined. The article presents a multi-robotic system consisting of parallel and serial robots installed on mobile platforms. The parallel robot is made based on a tripod with the ability to rotate the robot’s base relative to the horizontal axis. The analysis of its working and technological area is carried out, taking into account singularity zones. The developed algorithms for determining the workspaces are based on deterministic methods for approximating the set of solutions to systems of nonlinear inequalities. In this case, restrictions in spaces of different coordinates are presented in the form of n-dimensional boxes. Approaches to solving two problems are proposed to determine the possible intersection of links for the collaborative performance of tasks by a multi-robotic system. The first task is to determine the intersection of the links for the given positions and the relative position of the manipulators. The second is in determining the minimum distance between the technological areas of manipulators, which consist of the workspace and all possible positions of the intermediate links. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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14 pages, 2165 KiB

Open AccessArticle

Bit Streaming Processing Algorithms for Intelligent Hardware Converters

by Olga Bureneva, Mikhail Kupriyanov and Nikolay Safyannikov

Appl. Sci. 2021, 11(11), 4899; https://doi.org/10.3390/app11114899 - 26 May 2021

Cited by 2 | Viewed by 3721

Abstract

The need to transfer the primary data conversions close to the sensors, to the endpoints of monitoring systems, as well as in IoT terminal devices makes the development of new approaches to computing and the design of appropriate algorithms relevant. The article shows [...] Read more.

The need to transfer the primary data conversions close to the sensors, to the endpoints of monitoring systems, as well as in IoT terminal devices makes the development of new approaches to computing and the design of appropriate algorithms relevant. The article shows stream processing algorithms that provide functional transformations of signals presented in bit stream form (single pulse streams, PWM signal streams) and binary codes at the same time. In such algorithms, the computational process is based on discretization, pulse frequency sweep and pulse-width sweep of codes as well as organization of parallel-serial processing. The suggested principles of algorithm organization are based on the fact that the computation is considered not as an event associated with calculation but as a continuous process of a result formation. The transition to algorithmic representations proposed by the authors makes it possible to obtain universal behavioral descriptions, independently of the specific hardware on which their implementation is performed. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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17 pages, 1367 KiB

Open AccessArticle

Automated Test Data Generation Based on a Genetic Algorithm with Maximum Code Coverage and Population Diversity

by Tatiana Avdeenko and Konstantin Serdyukov

Appl. Sci. 2021, 11(10), 4673; https://doi.org/10.3390/app11104673 - 20 May 2021

Cited by 7 | Viewed by 3112

Abstract

In the present paper, we investigate an approach to intelligent support of the software white-box testing process based on an evolutionary paradigm. As a part of this approach, we solve the urgent problem of automated generation of the optimal set of test data [...] Read more.

In the present paper, we investigate an approach to intelligent support of the software white-box testing process based on an evolutionary paradigm. As a part of this approach, we solve the urgent problem of automated generation of the optimal set of test data that provides maximum statement coverage of the code when it is used in the testing process. We propose the formulation of a fitness function containing two terms, and, accordingly, two versions for implementing genetic algorithms (GA). The first term of the fitness function is responsible for the complexity of the code statements executed on the path generated by the current individual test case (current set of statements). The second term formulates the maximum possible difference between the current set of statements and the set of statements covered by the remaining test cases in the population. Using only the first term does not make it possible to obtain 100 percent statement coverage by generated test cases in one population, and therefore implies repeated launch of the GA with changed weights of the code statements which requires recompiling the code under the test. By using both terms of the proposed fitness function, we obtain maximum statement coverage and population diversity in one launch of the GA. Optimal relation between the two terms of fitness function was obtained for two very different programs under testing. Full article

(This article belongs to the Special Issue 14th International Conference on Intelligent Systems (INTELS’20))

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