
Main / Nadezhda Glebovna Yarushkina
Author: "Nadezhda Glebovna Yarushkina"
Pavel Vladimirovich Dudarin, Ulyanovsk State Technical University, Postgraduate Student at the Department of Information Systems of Ulyanovsk State Technical University (UlSTU); graduated from the Ulyanovsk State Technical University; an author of papers in the field of text clustering. [email: PDudarin@ibs.ru]P. Dudarin, Aleksandr Petrovich Pinkov, Ulyanovsk State Technical University, Candidate of Economics, graduated from Ulyanovsk branch of Kuibyshev Planning Institute; Acting Rector of Ulyanovsk State Technical University, an author of more than 50 papers, a monograph, and articles in the field of economics, planning, marketing, production engineering, higher education organization, and corporate training. [email: rector@ulstu.ru]A. Pinkov, Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; First ViceRector  ViceRector for Scientific Affairs of Ulyanovsk State Technical University; Head of the Department of Information Systems of UlSTU; graduated from Ulyanovsk Polytechnic Institute; an author of more than 300 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina


Methodology and the Algorithm for Clustering Economic Analytics Object
The purpose of the study, the results of which are described in the article, consist in developing new and modified methods and algorithms for solving the problem of clustering objects of economic analytics. The use of known algorithms for clustering formulations of economic indicators in order to determine the similarity of objects is complicated by the fact that the formulation of indicators are very short and the traditional indicators of terms occurrence (frequency) are inadequate. In addition, widespread occurrence of interviews and various forms of questionnaires in economic analysis implies the use of linguistic estimates. For example, "customer satisfaction level" indicator is difficult to quantify, so, instead of conventional points, fuzzy values such as “high”, “medium”, “low” are often used. As a result, it becomes feasible to use a fuzzy variant of the kmeans method  the method of fuzzy kmeans. Typically, the number of indicators in economic analysis is quite big, which makes it advisable to modify the algorithm on the basis of parallel execution. The study addresses the following issues: the kmeans method is modified, it is adapted to the characteristics of the economic analytics objects; the methodology of data preprocessing for clustering is developed; new versions of clustering objects of economic analytics are developed, and experimental research of the effectiveness of the developed methods for large volumes of data is carried out. Fcmалгоритм, clustering, method of kmeans, economic analysis, big data, fcmalgorithm, parallelization.



Sections: Artificial intelligence
Subjects: Artificial intelligence. 
Elena Iurevna Barabanova, Federal ResearchandProduction Center Joint Stock Company ‘ResearchandProduction Association ‘Mars’, Postgraduate Student at the Department of Information Systems of Ulyanovsk State Technical University; Industrial Engineer at the Department of Chief Industrial Engineer of Federal ResearchandProduction Center Joint Stock Company ‘ResearchandProduction Association ‘Mars’; an author of articles in the field of automation of control processes of operations planning. [email: mars@mv.ru]E. Barabanova, Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; graduated from the Faculty of Radioengineering at Ulyanovsk State Technical University; First ViceRector  ViceRector for Scientific Affairs of Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina


Estimation of Cameducation Effectiveness With the Use of the Modified Moving Approximation Transform
This article offers the new measured estimation of CAMprocessing teaching on the basis of the proposed efficiency coefficient. The authors describe the existing approaches to estimation of education effectiveness in general case. The necessity of designing the approach to efficiency estimation of CAMeducation with the use of methods for time series data mining is proven. The features of creation of the special education course for CAMspecialists with its estimation are investigated. The CAMprocessing metric applied either in education course development or the subsequent estimation of its effectiveness is defined. The proposed hypotheses were checked with the use of real data in the practical experiment on the developed model approbation. Moreover, the features of developing the efficient educational program for teaching mechanical engineering students were considered. The model of CAMeducation effectiveness estimation with the use of the modified Moving Approximation Transform was proposed. Effectiveness, cameducation, process model, control program.



Sections: Computeraided engineering
Subjects: Computeraided engineering. 
Vadim Sergeevich Moshkin, Ulyanovsk State Technical University, Assistant of the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of more than 40 papers in the field of intelligent systems. [email: postforvadim@yandex.ru]V. Moshkin, Aleksandr Nikolaevich Pirogov, JSC ‘AviastarSP’, Postgraduate Student of the Institute of Aviation Technologies and Managements of Ulyanovsk State Technical University, Head of the Department of Invest Projects at JSC ‘AviastarSP’. [email: anpirogov@icloud.com]A. Pirogov, Irina Aleksandrovna Timina, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University with a specialty of Applied Informatics (in Economics); an author of articles in the field of intelligent analysis of time series. [email: timina_i@mail.ru@ulstu.ru]I. Timina, Vadim Viktorinovich Shishkin, Institute of Aviation Technology and Managements of Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor; graduated from the Faculty of Radioengineering of Ulyanovsk Polytechnic Institute; Head of the Institute of Aviation Technology and Managements of Ulyanovsk State Technical University, an author of articles in the field of automated design of industrial products and Data Mining. [email: shvv@ulstu.ru]V. Shishkin, Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; graduated from the Faculty of Radioengineering at Ulyanovsk State Technical University; First ViceRector  ViceRector for Scientific Affairs of Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina


Intelligent Analysis of Project and Terminological Metrics in Project Management
This article describes the features of the interaction of participants in the project activity by the example of a large project organization. The model of activities on the basis of metrics version control systems is proposed. The direction of object OWL ontologies application in project activities is also considered. Moreover, an example of the use of ontological structures in solving problems of constructing the unified terminological environment of the project activity in the field of aircraft construction is given. The model of the object ontology of the aircraft flow assembly line (FAL) is proposed by the example of Il76MD90A assembly. The experiments of constructing the common terminological basis of the design process by terminology extraction from education materials on corresponding topics in the development of project documentation for the FAL automated management system (FAL AMS) of JSC ‘AviastarSP’ are presented. Project management, version control system, object ontology, terminological environment.



Sections: Artificial intelligence
Subjects: Artificial intelligence. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; graduated from the Faculty of Radioengineering at Ulyanovsk State Technical University; First ViceRector  ViceRector for Scientific Affairs of Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Evgenii Nilolaevich Egov, Ulyanovsk State Technical University, Postgraduate Student and Assistant at the Department of Information Systems of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [email: e.egov@ulstu.ru]E. Egov


The Algorithm for Identifying New Anomalies in Technical Time Series Diagnosis
The article discusses the ways to diagnose the time series in order to detect anomalies in them. The authors propose to determine the number of each point of the values of the two parameters. Also a set of situations related to changes in the values of these parameters between the points should be prepared. While analyzing series, the frequency of each situation occurrence should be determined. If the probability of situations occurrence is less than 0.01, then such situations may be attributed to an abnormal ones. On the basis of the previous situation choice, a template that allows identifying these anomalies in the future is created. As one of the pairs for situations identification, the entropy measures values obtained from fuzzy time series are proposed to use. The first measure of entropy is calculated by the value of the membership function point compared to the fuzzy label. The second measure of entropy is calculated on the basis of the deviation of the actual value trends from the forecasting one. Series analysis is performed on the basis of the second pair. This pair represent “fuzzy label  fuzzy trend” one. This pair was introduced to identify longterm stay in the areas of certain states, which can be attributed to the abnormal ones. It also describes the algorithm to identify previously unknown anomalies and search of anomalies patterns. The experiment was carried out in order to check the efficiency of the algorithm. Time series of physical quantities characterizing work of important units of helicopter engines in which it was necessary to reveal the presence of defects were investigated. The main interest of this paper is the anomaly detection algorithm based on the measure of the uncertainty of the time series. The article is intended for professionals diagnosing technical systems. Entropy measure, diagnosis, time series, anomalies.



Sections: Information systems
Subjects: Information systems. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First ViceRector  ViceRector for Science of Ulyanovsk State Technical University (UlSTU); graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Valeriia Vadimovna Voronina, Ulyanovsk State Technical University, Candidate of Engineering; graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; Associate Professor at the Department of Information Systems at Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [email: vvsh85@mail.ru]V. Voronina, Irina Aleksandrovna Timina, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University with a specialty of Applied Informatics (in Economics); an author of articles in the field of intellectual analysis of time series. [email: timina_i@mail.ru@ulstu.ru]I. Timina, Evgenii Nikolaevich Egov, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [email: e.egov@ulstu.ru]E. Egov


Forecasting Technical System State With the Application of Entropy Measure for Fuzzy Time Series Diagnosis
This article discusses the ways to forecast time series of technical systems on the basis of the hypothesis of trends conservation, the hypothesis of trends stability and the hypothesis of forecasting for a specified period as well as forecasting with the use of the measure of entropy for fuzzy time series. The method of calculating the measure of entropy for fuzzy time series has been described in the previous issue of the journal. The software system of diagnosing and forecasting fuzzy time series based on the measure of entropy is also considered in the article. The system is divided into several modules, with the opportunity to use some of them in the other systems of time series prediction. The main interest of this paper is the prediction algorithm that was designed on the basis of time series measure of entropy and the comparison of the two approaches to forecasting fuzzy time series. The comparison was made on the basis of the values of MAPE, MSE, RMSE errors obtained from values of 10 rows predicted by two programs. The first program is based on the selection of one of the hypotheses, the second one described in this article is based on the prediction with the use of measure of entropy. This article is intended for professionals diagnosing technical systems. Measure of entropy, prediction, time series.



Sections: Mathematical modeling
Subjects: Mathematical modeling, Automated control systems. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First ViceRector  ViceRector for Science of Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University ; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Irina Aleksandrovna Timina, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of UlSTU with a specialty of Applied Informatics (in Economics); an author of articles in the field of data mining. [email: timina_i@mail.ru@ulstu.ru]I. Timina


Automated System Model and Control Tools on the Base of Program Code Metrics History
The article discusses the issue of project management associated with the development of software products through using automated version control system (VCS) and the analysis of program code metrics. This problem is solved through studying VCS functioning with the further use of the data analysis component of the project management based on the application of the time series (TS) model, the construction of fuzzy TS trends, clustering for dominant fuzzy trends separation, extracting time series predicate, the similarity measure of time series, their correlation, prediction and correction of the forecast. Time series of the number of errors in the total number of changes, the number of improvements in the same number of changes, the number of new functions were used as program code metrics. The hypothesis of the trend permanency was chosen for prediction. The given approach was examined on the examples. Version control system, time series, fuzzy trend, forecasting, forecast adjustment.



Sections: Computeraided engineering
Subjects: Computeraided engineering, Automated control systems. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First ViceRector  ViceRector for Science of Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University ; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Valeriia Vadimovna Voronina, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series [email: vvsh85@mail.ru]V. Voronina, Evgenii Nilolaevich Egov, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intelligent information systems. [email: e.egov@ulstu.ru]E. Egov


Entropy Application to the Diagnosis of Technical Time Series
The article deals with the method for time series diagnosis based on the measure of the time series uncertainty. The formula for finding the measure of entropy for fuzzy time series is determined. The algorithm for finding the measure of entropy for fuzzy time series is of particular interest. A model of expert diagnostic rules for aircraft accessories is developed. The models of the behavior of objects such as the main gearbox and power plant engine helicopter are offered. Interpretation of natural experiment for the purpose of diagnosis of helicopter units held by analyzing the quality of the built models. A set of programs for mathematical modeling and predicting the behavior of aircraft accessories based on fuzzy measure of the uncertainty of the time series is developed. The model showed high accuracy in determining the characteristics of the time series and the identification of dangerous areas while experimenting. The developed algorithm can be successfully applied for the diagnosis and prediction of time series. This article is intended for specialists diagnosing technical systems. Measure of entropy, diagnosis, time series.



Sections: Information systems
Subjects: Information systems. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First ViceRector  ViceRector for Science of Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University ; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Tatiana Vasilevna Afanaseva, Ulyanovsk State Technical University, Doctor of Engineering; Associate Professor, Deputy Head of Information Systems Department at Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering at Ulyanovsk State Technical University; an author of articles and monographs in the field of time series data mining. [email: tv.afanasjeva@gmail.com]T. Afanaseva, Aleksei Mikhailovich Namestnikov, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor, Associate Professor at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University; an author of articles and a monograph in the field of intelligent systems for storage and processing of information. [email: nam@ulstu.ru]A. Namestnikov, Gleb Iurevich Guskov, Ulyanovsk State Technical University, PostGraduate Student of the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; an author of articles in the field of time series data mining. [email: guskovgleb@gmail.com]G. Guskov


Integration of Fuzzy Granular and Ontological Methods for Time Series Analysis
The theoretical and methodological foundations of fuzzy granular modeling were developed to solve the problem of fuzzy trend analysis of time series. Their practical implementation as a program complex allows to get the solution of some applied problems. Fuzzy granular presentation of time series includes five levels: from numeric value granulation to the main trend granulation. The authors propose to use fuzzy ontologies of problem area to interpret the results of time series analysis. he problem area fuzzy ontologies were offered to interpret the results of time series analysis. The ontology basis is based on the RDF model that defines classes, instances, ontological relationships, and contingencies. The logical interference of recommendation is realized on the basis of interaction between fuzzy OWL ontology and ruleoriented SWRL rules system. The article deals with the possibilities of integration of several methods of time series forecasting and corresponding aggregates. The integration of techniques for time series analysis and ontological analysis demonstrates the competitiveness of fuzzy trend models based on the mutual reinforcement of ontological and fuzzy granular methods. Fuzzy time series, fuzzy tendencies, ontology, fuzzy ontology.



Sections: Information systems
Subjects: Information systems, Artificial intelligence. 
Ilya Alekseevich Andreev, Ulyanovsk State Technical University, a student at the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; an author of several articles in the field of information extraction from text. [email: aresilya@yandex.ru]I. Andreev, Vitaliy Aleksandrovich Bashaev, Ulyanovsk State University, a postgraduate student; graduated from the Faculty of Linguistics and International Cooperation of Ulyanovsk State University; an author of several articles in the field of information extraction from text. [email: perevod73@yandex.ru]V. Bashaev, Victor Victorovich Klein, Ulyanovsk State Technical University, a student at the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of several articles in the field of information extraction from text. [email: vikklein93@gmail.com]V. Klein, Vadim Sergeevich Moshkin, Ulyanovsk State Technical University, a postgraduate student at the Department of Information Systems of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; an author of articles in the field of data analysis intelligence systems. [email: postforvadim@yandex.ru]V. Moshkin, Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, Head of the Department of Information Systems at Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina


A Semantic Metric of the Termhood Based on the Subject Area Ontology
The article describes a semantic metric for a retrieval of a list of terms from the texts of a specific knowledge domain, based on the analysis of its ontology. A formal model of the used OWLontology, as well as models and algorithms for the degree evaluation of a word termhood or word combinations of text arrays are presented. In addition, the evaluation metrics of the presented semantic algorithms performance are given. The implementation of the formal models of a domain knowledge representation in an ontological form and the algorithms developed in the software system for the terminology extraction from the text is considered. In conclusion, the results of the computational experiments performed for the extraction of terms based on the ontology of the NC turningmilling machine operation from the texts of an appropriate knowledge domain are provided. A conducted research is summarized. The most efficient algorithms for the degree evaluation of a word termhood or word combinations are revealed, and a perspective for further scientific research in this area is examined. Term extraction, semantic metric, ontology.



Sections: Artificial intelligence
Subjects: Artificial intelligence. 
Vadim Sergeevich Moshkin, Ulyanovsk State Technical University, Postgraduate Student at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technology of Ulyanovsk State Technical University; an author of articles in the field of intelligent systems. [email: postforvadim@yandex.ru]V. Moshkin, Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, Head of the Department of Information Systems at Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina


Ontological Timeseries Analysis System
This article describes a semantic approach to analyzing the time series as an example of local area network (LAN) status parameters using the ontology of problem area. We represent a formal model of the OWLontology for the considered subject domain, an ontological view model for a set of production rules. An inference algorithm for LAN architecture modification during its status estimation while artificially increasing traffic is proposed.We solved the aggregation problem of the different approaches to expert knowledge representation through the product knowledge integration into the ontological model using SWRL rules. In addition, the implementation of this algorithm in the timeseries analysis software TSAnalyzer is considered.The results of computational experiments on LANstatus simulation while artificially increasing traffic as an example of the LAN of the Center for Development of Electronic Media Technologies at Ulyanovsk State Technical University are represented. We summarized the research results conducted and evaluated further research findings expectation in this domain. Ontology, time series, data mining, semantics.



Sections: Artificial intelligence
Subjects: Artificial intelligence, Automated control systems. 
Ilia Alekseevich Andreev, Ulyanovsk State Technical University, a student of the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; an author of several articles in the field of information extraction from a text. [email: aresilya@yandex.ru]I. Andreev, Vitalii Aleksandrovich Bashaev, Ulyanovsk State Technical University, a postgraduate student, graduated from the Faculty of Linguistics and International Cooperation at Ulyanovsk State University; an author of articles in the field of information extraction from a text. [email: perevod73@yandex.ru]V. Bashaev, Victor Victorovich Klein, Ulyanovsk State Technical University, a student of the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; an author of several articles in the field of information extraction from a text. [email: vikklein93@gmail.com]V. Klein, Nadezhda Glebovna Iarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, Head of the Department of Information Systems at Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computation, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Iarushkina


Combining Statistic and Linguistic Methods to Exstract Twoword Terms From a Text
This article contains the experiment results on the combination of linguistic and statistical methods for extraction of twoword terms from a text on "CnC Machines" discipline. Along with the experiment and the results, a special attention is paid to the description of the developed software architecture. Term extraction, linguistic method, statistic method, bigrams frequency.



Sections: Artificial intelligence system
Subjects: Artificial intelligence, Information systems. 
Tatiana Vasilyevna Afanasyeva, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor at the Chair 'Applied Mathematics and Information Science' of Ulyanovsk State Technical University; author of articles, a monograph, a textbook in the field of intellectual analysis of time series. [email: tv.afanaseva@mail.ru]T. Afanasyeva, Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; ProRector for Science; head of the Chair 'Information Systems' at Ulyanovsk State Technical University; author of articles and monographs in the field of intellectual analysis of data. [email: jng@ulstu.ru]N. Yarushkina


Efficiency Analysis of Fuzzy Trend Model for Forecasting of Time Series
The article describes a new model for the analysis of time series for the forecast of small time series. The base of the new model is formalization and identification of a new object of time series  a fuzzy trend. The suggested model does not have assumptions used in stochastic simulation, and is easy for the implementation and developed for linguistic interpretation of results. The experiment studies of the accuracy figures of the suggested model reveal its adequacy for forecasting of small time series and competitiveness in comparison with its analogues. Forecasting, time series, fuzzy trend, accuracy figures.



Sections: Artificialintelligence systems
Subjects: Artificial intelligence, Automated control systems, Mathematical modeling. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; ProRector for Science; head of the Chair 'Information Systems' at Ulyanovsk State Technical University; author of articles and monographs in the field of intellectual analysis of data. [email: jng@ulstu.ru]N. Yarushkina, Valeria Vadimovna Voronina, Ulyanovsk State Technical University, Postgraduate student at the Chair 'Information Systems' of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technology of Ulyanovsk State Technical University; author of articles in the field of intellectual systems for storage and processing of data. [email: vvsh85@mail.ru]V. Voronina, Tatiana Vasilyevna Afanasyeva, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor at the Chair 'Applied Mathematics and Information Science' of Ulyanovsk State Technical University; author of articles, a monograph in the field of intellectual analysis of data. [email: tv.afanaseva@mail.ru]T. Afanasyeva


Diagnostics of Helicopter Nodes on Basis of a Model of Grained Time Series
In the present paper the authors consider a solution to the problem of diagnostics of helicopter nodes. The diagnostics is carried out by analyzing time series of key physical quantities, based on an expert rulebase containing statements on the significance of trends of change of these variables. In the paper, some expert rules for the helicopter nodes such as a helicopter propulsion engine and main gearbox, are also folmulated. Diagnostics, time series, helicopters, expert rulebase.



Sections: Artificialintelligence systems
Subjects: Artificial intelligence, Information systems. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, graduated from the Faculty of RadioEngineering at the Ulyanovsk Polytechnic Institute; ProRector for Science; holds the Chair Information Systems at the Ulyanovsk State Technical University; author of articles and monographs in the field of intellectual analysis of data. [email: jng@ulstu.ru]N. Yarushkina


Intellectualization of Cad for Complex Technical Systems Under Uncertain Conditions
This article deals with basic steps and development of intellectualization of CADsystems for complex technical systems though basic paradigms. The specific feature of the article consists in the following: the manifestation of basic paradigms is shown on basis of problemoriented systems developed by the Ulyanovsk State Technical University in cooperation with FRPC OJSC RPA Mars during the last 25 years. The cooperation, unique from the point of view of its duration, allows seeing how the embedded intelligence of particular developments grew, and is reasonably reflected in the chronological order of the article content. Intellectual cadsystem, knowledge engineering, soft computing, computational intelligence, soft expertsystem, fuzzy tendency of time series.



Sections: Computeraided design systems
Subjects: Artificial intelligence, Automated control systems, 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. 

Integral Method of Fuzzy Modeling and Analysis of Fuzzy Tendencies
The article deals with a new method of modeling of time series, which integrates intellectual methods of
task solution concerning knowledge extraction from time series not only in numerical form but also in the
form of linguistic description of levels and elementary tendencies. Fuzzy model, time series, fuzzy tendency, knowledge extraction, forecast.



Sections: Theoretical issues of automation of command and control processes
Subjects: Artificial intelligence. 
Nadezhda Glebovna Yarushkina, [email: mars@mv.ru]N. Yaroushkina,
Irina Grigorievna Perfilyeva, [email: mars@mv.ru] I. Perfilyeva,
Andrey Gennadievich Igonin, [email: mars@mv.ru] A. Igonin,
Anton Alexeevich Romanov, [email: mars@mv.ru] A. Romanov,
Tagir Ragatovich Younusov, [email: mars@mv.ru] T. Yunusov,
Valeriia Vadimovna Shishkina, [email: mars@mv.ru] V. Shishkina


Development of Internetservice Integrating Fuzzy Modeling and Analysis of Fuzzy Tendencies of Time Series
The article presents an implementation of a new serviceoriented architecture of a new fuzzymodeling
method. The novelty of the got software in the form of Internetservice consists in the implementation
of a new integral method of fuzzy modeling and analysis of fuzzy tendencies of time series in order to
increase managementdecision efficiency as well as in the accounting of new management requirements
for reduction of costs concerning maintenance, operation and update/upgrade of software and hardware. The integralmethod analysis results in the fact that the error of shortterm forecast precision does not
exceed 20%, the errors of shortterm forecast of fuzzytendency types are equal to 0. Intellectual system, decisionmaking, internetservice, fuzzy modeling, forecast of time series.



Sections: Theoretical issues of automation of command and control processes
Subjects: Artificial intelligence. 
