
Keyword: "genetic algorithm"
/table> Igor Borisovich Saenko, Doctor of Sciences in Engineering, Professor; graduated from the Marshal Budjonny Military Academy of Signal Corps; Leading Researcher of the Laboratory of Computer Security Problems of St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS); an author of articles, monographs, and patents in the field of computer security problems, artificial intelligence methods, information and telecommunication systems. email: ibsaen@comsec.spb.ruI. B. Saenko Igor Vitalevich Kotenko, Doctor of Sciences in Engineering, Professor; graduated from the A.F. Mozhaisky Military Space Academy and the Marshal Budjonny Military Academy of Signal Corps; Head of the Laboratory of Computer Security Problems of SPIIRAS; an author of articles, monographs, and patents in the field of computer security problems, artificial intelligence methods, information and telecommunication systems. email: ivkote@ comsec.spb.ruI. V. Kotenko 
 Using a rolebased approach and genetic optimization for vlan design in large critical infrastructures The article considers a new approach to the formation of access control schemes in the design of virtual local area networks (VLANs) in large critical infrastructures, based on the use of an improved genetic algorithm, the introduction of multiple roles for network nodes and the accounting of mapping between a set of network nodes and a set of roles. The problem statement is justified as the optimization of access control schemes in VLAN according to the criterion of minimum number of virtual subnets taking into account the peculiarities of role approach. The problem being solved is shown to belong to the Boolean matrix factorization problem class, in which the original matrix is decomposed into four ones forming two independent pairs of straight and transposed matrices. Improvements of the genetic algorithm involve multichromosome representation of individuals, a new kind of fitness function, and a twodimensional kind of crossing operation. The experimental evaluation showed a benefit in the speed of the role genetic algorithm at a large task dimension of up to 5 times at a sufficiently high accuracy of finding the optimal solution. VLAN, access control, genetic algorithm, critical infrastructure. 
 
Sections: Computeraided engineering
Subjects: Computeraided engineering. 
Amir Muratovich Akhmetvaleev, Kazan National Research Technical University named after A.N. TupolevKAI, Postgraduate Student of Kazan National Research Technical University named after A.N. TupolevKAI; graduated from the Faculty of Technical Cybernetics and Informatics of the Kazan State Technical University named after A.N. TupolevKAI; an author of scientific works in the field of mathematical modelling, data analysis and machine learning methods. [email: amir.akhmetvaleev@gmail.com]A. Akhmetvaleev,
Aleksei Sergeevich Katasev, Kazan National Research Technical University named after A.N. TupolevKAI, Candidate of Engineering; graduated from the Faculty of Physics and Mathematics of Elabuga State Pedagogical Institute; Associate Professor of the Information Security Systems Department of Kazan National Research Technical University named after A.N. TupolevKAI; an author of scientific works in the field of mathematical modelling, data analysis and development of intelligent decision support systems. [email: Kat_726@mail.ru]A. Katasev


Instrumental Software Complex for Automation to Determine the Functional State of Intoxication of a Person
This article actualizes the need to determine the functional state of a person. Its decision is based on the application of the pupillometry method, which allows to determine the state of a person by his pupillary reaction to light changes. In order to automate the processes of determining the functional state of a person, an instrumental program complex is developed, based on the use of the neural network model. Its structure, composition and characteristics of components are described. The functioning of the program complex is considered on the example of the modules of neural network model construction, estimation of its accuracy on the basis of bootstrapping method, structural optimization of the model, and determining the functional state of a person. A number of researches and experiments were conducted on the basis of the program complex. The results of the influence of the number of stages of bootstrapping on the accuracy of the neural network model, the results of reduction of neural networks, the comparison of the accuracy of the model with the accuracy of other classification methods are presented. The results of the research showed the effectiveness of the neural network model and the possibility of its practical use to determine the functional state of the person in various subject areas. Human functional state, papillomometry, neural network model, genetic algorithm, bootstrapping.



Sections: Artificial intelligence
Subjects: Artificial intelligence, Information systems. 
Amir Muratovich Akhmetvaleev, Kazan National Research Technical University named after A.N. Tupolev, Akhmetvaleev, graduated from the Faculty of Technical Cybernetics and Informatics of Kazan National Research Technical University named after A.N. Tupolev; Postgraduate student of Kazan National Research Technical University named after A.N. Tupolev; an author of scientific works in the field of mathematical modelling, data analysis and machine learning methods. [email: amir.akhmetvaleev@gmail.com]A. Akhmetvaleev, Aleksei Sergeevich Katasev, Kazan National Research Technical University named after A.N. Tupolev, Candidate of Engineering; graduated from the Faculty of Physics and Mathematics of Elabuga State Pedagogical Institute; Associate Professor at the Department of Information Security Systems of Kazan National Research Technical University named after A.N. Tupolev; an author of scientific works in the field of mathematical modelling, data analysis and development of intelligent decision support systems. [email: Kat_726@mail.ru]A. Katasev


Neuro Network Model and Software Package for Human Functional State Determining
The article considers the problem of determining the functional state of intoxication of a person. For its solution, the authors propose a method based on the analysis of data pupillograms  time series characterizing the dynamics of changing the size of the pupils of the person on lightpulse exposure. As a tool for data mining and model building in order to determine the functional state of the person the authors propose to use the mathematical apparatus of artificial neural network  single layer perceptron. The original neural network model is proposed, and its adequacy is evaluated. In order to improve the efficiency of its practical use, a method of reduction of a neural network structure based on genetic algorithm is being developed. The proposed method is a twostage genetic optimization that allows to determine the significant input features for a neural network on a given input feature space in order to optimize the structure of neurons in the hidden layer. The results of the experiments on the basis of the developed program complex have shown high efficiency of determination of the person functional state based on the reduced neural network model. The model can be effectively used in intelligent surveillance systems in public safety systems, as well as in medical diagnostics as a tool for contactless determination of the functional state of intoxication of a person. Neural network model, determination of the functional state, genetic algorithm, pupillometry, evaluation of data quality, data cleaning.



Sections: Artificial intelligence
Subjects: Artificial intelligence, Information systems. 
Ekaterina Aleksandrovna Zentsova, Ulyanovsk State Technical University, graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; Postgraduate Student at the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University; an author of scientific papers in the field of statistical process control. [email: e_zentsova@mail.ru]E. Zentsova


Comparison of parameters optimization approaches of hotteling’s control chart
Quality of a product produced in a multivariate process is determined by multiple quality characteristics. If each characteristic of a set of correlated characteristics is controlled by an univariate control chart independently, it should lead to wrong conclusions. Unnecessary process stoppage and adjustment as well as incorrect detection of an outofcontrol process state are possible. Therefore, the statistical control of a process with correlated quality characteristics should be carried out using multivariate control charts. The most common multivariate statistical tool is a Hotelling’s control chart. It is used for technological process stability monitoring and allows detection of large shifts in the multivariate process settings level. In order to improve detection of small shifts, the implementation of adaptive control schemes with different sets of variable parameters is suggested. The use of these schemes implies tightening control of the process when a sample point on a plot exceeds the warning limit. That contributes to earlier detection of the process mean shift. The set of conditions that must be satisfied to perform a meaningful and unbiased comparison of these schemes is specified. On their basis an optimization problem is formulated. The period between an assignable cause occurrence and detection is used as an objective function. For solving the optimization problem a genetic algorithm is suggested. This study presents a design of six adaptive control schemes. Comparative analysis of their sensitivity for detecting different magnitude of the process shift is performed. Adaptive control scheme, hotelling’s control chart, markov chain, genetic algorithm.



Sections: Mathematical modeling
Subjects: Mathematical modeling. 
Aleksei Mikhailovich Namestnikov, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University; Associate Professor at the Chair 'Information Systems' of the Faculty of Information Systems and Technology; author of articles and a monograph in the field of intelligent systems for storage and processing of information [email: nam@ulstu.ru]A. Namestnikov


Conceptual Indexing of Design Documentson Basisof Genetic Optimization
The article contains a formal description for a process of finding of a dominant concept in a text fragment of a design document. It also presents an adaptation for standard genetic algorithm in order to solve the task of optimum segmentation of design document into text fragments, ontology concepts dominated, and considers implementations of crossingover and mutation statements. Intellectual system, indexing, genetic algorithm, ontology.



Sections: Computeraided design systems
Subjects: Artificial intelligence, Computeraided engineering. 

Clustering of Information Resources on Basis of Genetic Algorithm
The article offers a solution method for the task of clustering of electronic information resources on basis of genetic algorithm. All the documents from project repository are described by frequency distributions of met terms. Input data are presented by geneticalgorithm structures. Informational resource, clustering, indexing, genetic algorithm, crossover, suitability function.



Sections: Artificialintelligence systems
Subjects: Artificial intelligence. 
