ISSN 1991-2927
 

ACP № 4 (58) 2019

Author: "Aleksandr Petrovich Pinkov"

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. [e-mail: rector@ulstu.ru]A. Pinkov,

Aleksandr Nikolaevich Afanasev, Ulyanovsk State Technical University, Doctor of Engineering, Professor; graduated from the Radioengineering Faculty of Ulyanovsk Polytechnic Institute; First Vice-Rector for Distance and Extended Education at Ulyanovsk State Technical University; an author of more than 200 articles in the field of CAD, interested in automated training systems, computational process and computer structure organization, intelligent system design, CAD, composite workflow control, graphic language diagrammatics. [e-mail: a.afanasev@ulstu.ru]A. Afanasev,

Nikolai Nikolaevich Voit, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor at the Department of Computer Science of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of more than 114 articles in the field of intelligent CAD systems, CASE and CALS technologies; interested in intelligent systems for development of complex computer-aided systems, automated training systems, graphical languages and grammatics. [e-mail: n.voit@ulstu.ru]N. Voit,

Dmitrii Sergeevich Kanev, Ulyanovsk State Technical University, graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Chief Staff Scientist at the Department of Computer Science of Ulyanovsk State Technical University; an author of more than 20 articles in the field of CAD; interested in development and distribution of firmware systems intended for maintenance, intensification, and increase of trainee involvement in educational process with the use of information technologies. [e-mail: dima.kanev@gmail.com]D. Kanev

Development of Methods and Means of Computer Systems for Machine Objects Cad Training 000_11.pdf

Currently, the most promising areas in the field of computer-based training are adaptive and multimedia technologies. The main objective of adaptive learning systems (ALS) is the implementation of learning management taking into account the individual characteristics of users. Adaptive methods can reduce the time and improve the efficiency of the learning process at the expense of the users held in an optimal training zone, changing the sequence of presentation material and tasks, content, learning pace and load. The article provides the analysis of the existing methods for construction of adaptive automated training systems, their advantages and disadvantages are determined. ALS with the author's method of synthesis of a learning path, the method for correcting the student's profile through automatic analysis of operations carried out in CAD software packages (by the example of COMPASS CAD system).

Automated training systems, domain model, student model, adaptation methods, recommendations system.

2017_ 1

Sections: Information systems

Subjects: Information systems.


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. [e-mail: 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. [e-mail: rector@ulstu.ru]A. Pinkov,

Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; First Vice-Rector - Vice-Rector 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. [e-mail: jng@ulstu.ru]N. Yarushkina

Methodology and the Algorithm for Clustering Economic Analytics Object 000_12.pdf

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 k-means method - the method of fuzzy k-means. 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 k-means 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.

Fcm-алгоритм, clustering, method of k-means, economic analysis, big data, fcm-algorithm, parallelization.

2017_ 1

Sections: Artificial intelligence

Subjects: Artificial intelligence.


© FRPC JSC 'RPA 'Mars', 2009-2018 The web-site runs on Joomla!