Scientific Journal of KubSAU

Polythematic online scientific journal
of Kuban State Agrarian University
ISSN 1990-4665
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Name

Lutsenko Yevgeniy Veniaminovich

Scholastic degree


Academic rank

professor

Honorary rank

Organization, job position

Kuban State Agrarian University
   

Web site url

lc.kubagro.ru

Email

prof.lutsenko@gmail.com


Articles count: 246

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224 kb

AN ALGORITHM AND A PROGRAM FOR CALCULATING THE NUMBER OF COMBINATIONS FOR LARGE NUMBERS WITHOUT CALCULATING THE INTERMEDIATE FACTORIALS BY THEIR DECOMPOSITION INTO PRIME FACTORS AND ABBREVIATIONS

abstract 1181604110 issue 118 pp. 1662 – 1671 29.04.2016 ru 362
Classical combinatorial formula to calculate the number of combinations from n on m: C(n,m)=n!/(m!(nm)!) involves the intermediate calculation of factorials, which is often impossible when n>170, due to limitations in the capacity of numbers that are used in programming languages and created through these systems. However, in some cases it is necessary to calculate the number of combinations for n and m much larger than this limit, such as when a value greater than 10000. In such cases, there is a definite problem, which manifests itself, for example in the fact that many on-line services meant to calculate the number of combinations with these parameters do not work properly. In this article, we present its solution in the form of an algorithm and software implementation. The essence of the approach is to first decompose the factorials into prime factors and reduce them, and then to produce multiplication. This approach differs from those cited in the Internet
2201 kb

AGGLOMERATIVE COGNITIVE CLUSTERING OF SYMPTOMS AND SYNDROMES IN VETERINARY MEDICINE

abstract 1391805033 issue 139 pp. 99 – 116 31.05.2018 ru 215
In the article, on a small numerical example, we consider the similarity and difference of symptoms and syndromes according to their diagnostic meaning, i.e. according to the information they contain about the belonging of conditionals of animals to different nosological images. This problem can be solved for veterinary with the use of a new method of agglomerative cognitive clustering, implemented in Automated System-Cognitive analysis (ASC-analysis). This method of clustering differs from the known traditional methods in: a) in this method, the parameters of the generalized image of the cluster are calculated not as averages from the original objects (symptoms) or their center of gravity, but are determined using the same basic cognitive operation of ASC-analysis, which is used to form generalized images of the classes based on examples of objects and which really correctly provides a generalization; b) the similarity criterion is not the Euclidean distance or its variants, but the integral criterion of non-metric nature: "the total amount of information", the application of which is theoretically correct and gives good results in unortonormated spaces, which are usually found in practice; c) cluster analysis is carried out not on the basis of initial variables, frequency matrices or matrix of similarity (differences), depending on the units of measurement on the axes (measurement scales), but in cognitive space, in which one unit of measurement is used for all axes: the amount of information, and therefore the results of clustering do not depend on the initial units of measurement of features of objects. All this allows us to get the results of clustering, understandable to specialists and amenable to meaningful interpretation, well-consistent with the experts ' assessments, their experience and intuitive expectations, which is often a problem for classical clustering methods
1070 kb

AGGLOMERATIVE COGNITIVE CLUSTERING OF NOSOLOGICAL IMAGES IN VETERINARY MEDICINE

abstract 1381804033 issue 138 pp. 122 – 139 30.04.2018 ru 151
The article deals with the similarity and difference of nosological images in veterinary medicine using a new method of agglomerative clustering implemented in Automated system-cognitive analysis (ASC-analysis) on a small numerical example. This method is called Agglomerative cognitive clustering. This method differs from the known traditional facts: a) parameters of a generalized image of the cluster are computed not as averages from the original objects (classes) or their center of gravity, and are defined using the same underlying cognitive operations of ASC-analysis, which is used for the formation of generalized images of the classes on the basis of examples of objects and which is really correct and provides a synthesis; b) as a criterion of similarity we do not use Euclidean distance or its variants, and the integral criterion of non-metric nature: "the total amount of information", the use of which is theoretically correct and gives good results in non-orthonormal spaces, which are usually found in practice; c) cluster analysis is not based on the original variables, matrices of frequency or a matrix of similarities (differences) dependent on the measurement units of the axes, and in the cognitive space in which all the axes (descriptive scales) use the same unit of measurement: the quantity of information, and therefore, the clustering results do not depend on the original units of measurement features. All this makes it possible to obtain clustering results that are understandable to specialists and can be interpreted in a meaningful way that is in line with experts' assessments, their experience and intuitive expectations, which is often a problem for classical clustering methods
4092 kb

ADAPTIVE SYNTHESIS OF INTELLIGENT MEASUREMENT SYSTEMS WITH THE USE OF ASC-ANALYSIS AND "EIDOS" SYSTEM. SYSTEM IDENTIFICATION IN ECONOMETRICS, BIOMETRICS, ECOLOGY, PEDAGOGY, PSYCHOLOGY AND MEDICINE

abstract 1161602001 issue 116 pp. 1 – 60 29.02.2016 ru 923
The article proposes using the automated system-cognitive analysis (ASC-analysis) and its software tool, which is the system called "Eidos" for synthesis and application of adaptive intelligent measuring systems to measure values of parameters of objects, and for system state identification of complex multivariable nonlinear dynamic systems. The article briefly describes the mathematical method of ASC-analysis, implemented in the software tool – universal cognitive analytical system named "Eidos-X++". The mathematical method of ASC-analysis is based on system theory of information (STI) which was created in the conditions of implementation of program ideas of generalizations of all the concepts of mathematics, in particularly, the information theory based on the set theory, through a total replacement of the concept of “many” with the more general concept of system and detailed tracking of all the consequences of this replacement. Due to the mathematical method, which is the basis of ASC-analysis, this method is nonparametric and allows you to process comparably tens and hundreds of thousands of gradations of factors and future conditions of the control object (class) in incomplete (fragmented), noisy data numeric and non-numeric nature which are measured in different units of measurement. We provide a detailed numerical example of the application of ASC-analysis and the system of "Eidos-X++" as a synthesis of systemic-cognitive model, providing a multiparameter typization of the states of complex systems, and system identification of their states, as well as for making decisions about managing the impact of changing the composition of the control object to get its quality (level of consistency) maximally increased at minimum cost. For a numerical example of a complex system we have selected the team of the company, and its component – employees and applicants (staff). However, it must be noted that this example should be considered even wider, because the ASC-analysis and the "Eidos" system were developed and implemented in a very generalized statement, not dependent on the subject area, and can successfully be applied in other areas
12261 kb

A SCIENTOMETRIC INTELLIGENT MEASURING SYSTEM OF RSCI DATA BASED UPON THE ASK ANALYSIS AND EIDOS SYSTEM

abstract 1221608014 issue 122 pp. 157 – 212 31.10.2016 ru 314
Adequate and effective assessment of the efficiency, effectiveness and the quality of scientific activities of specific scientists and research teams is crucial for any information society and a society based on knowledge. The solution to this problem is the subject of scientometrics and its purpose. The current stage of development scientometrics differs greatly from his previous appearance in the open as well as paid on-line access to huge amount of detailed data on a large number of indicators on individual authors and on scientific organizations and universities. The world has well-known bibliographic databases: Web of Science, Scopus, Astrophysics Data System, PubMed, MathSciNet, zbMATH, Chemical Abstracts, Springer, Agris, or GeoRef. In Russia, it is primarily the Russian scientific citing index (RSCI). RSCI is a national information-analytical system, accumulating more than 9 million publications of Russian scientists, as well as the information about citation of these publications from more than 6,000 Russian journals. There is too much information; it is so-called "Big data". But the problem is how to make sense of these large data, more precisely, to identify the meaning of scientometric indicators) and thus to convert them into great information ("great information"), and then apply this information to achieve the objective of scientometrics, i.e. to transform it into a lot of knowledge ("great knowledge") about the specific scientists and research teams. The solution to this problem is creating a "Scientific smart metering system" based on the use of the automated system-cognitive analysis and its software tools – an intellectual system called "Eidos". The article provides a numerical example of the creation and application of Scientometric intelligent measurement system based on a small amount of real scientific data that are publicly available using free on-line access to the RSCI
1731 kb

30 YEARS OF EIDOS SYSTEM - ONE OF THE OLDEST DOMESTIC UNIVERSAL SYSTEMS OF ARTIFICIAL INTELLECT WIDELY APPLIED AND DEVELOPING NOWADAYS

abstract 0540910004 issue 54 pp. 48 – 77 21.12.2009 ru 3072
In the article the current version and some prospects of development of Universal cognitive analytical Eidos system - one of the oldest really working domestic universal systems of an artificial intellect widely applied and developing nowadays is shortly described
4335 kb

"EIDOS" SYSTEM AS A GEOCOGNITIVE SYSTEM (GCS) FOR RECOVERING UNKNOWN VALUES OF SPATIALLY DISTRIBUTED FUNCTIONS BASED ON DESCRIPTIVE INFORMATION FROM CARTOGRAPHIC DATABASES

abstract 1171603001 issue 117 pp. 1 – 51 31.03.2016 ru 317
The article proposes to use the automated systemcognitive analysis (ASC-analysis) and its software tool which is "Eidos" system to solving multiparameter typing, system identification and cartographic visualization of spatially-distributed natural, environmental and socio-economic systems. Imagine, that we have an original point cloud with coordinates (X,Y,Z), each with known values of gradation descriptive scales of nominal, ordinal, or numeric type S(s1,s2,...,sn). Then the "Eidos" system provides: 1) building a model that contains generalized knowledge about the strength and the direction of the influence of descriptive gradations of scales at Z=M(S); 2) estimation of the values of Z for points (X,Y) described in the same descriptive scales S(s1,s2,...,sn), but not a part of the original point cloud; 3) a cartographic visualization of the spatial distribution of values of the function Z=M(S) for points outside the initial cloud, using Delaunay triangulation. Basically, this means that the "Eidos" system ensures recovery of the unknown function values on the grounds of the argument and implements it in a generic setting, independent of subject area. We propose a new scientific concept called "Geo-cognition system", which is defined as a software system that provides conversion of source data into information, and knowledge in visualization and mapping of this knowledge, resulting in the cognitive map becomes graphics. This feature can be used to quantify the degree of suitability of the watersheds for cultivation of certain crops, the evaluation of the ecological situation on particular territories on the structure and intensity of anthropogenic load, visualization of results of forecasting of earthquakes and other unwanted risks or emergencies, as well as for solving many other similar mathematical essence of tasks in a variety of subject areas. We have also shown a simple numerical example
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