name
Lutsenko Yevgeniy Veniaminovich
Scholastic degree
•
Academic rank
professor
Honorary rank
—
Organization, job position
• Kuban State Agrarian University
кафедра компьютерных технологий и систем
профессор
Research interests
Системно-когнитивный анализ, системы искусственного интеллекта, высшие формы сознания, перспективы человека, технологии и общества
Web site url
Current rating (overall rating of articles)
0
TOP5 co-authors
Articles count: 276
Сформировать список работ, опубликованных в Научном журнале КубГАУ
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01.00.00 Physical-mathematical sciences
DescriptionThe article discusses the application of automated system-cognitive analysis (ASC-analysis), its mathematical model which is system theory of information and its software tool, which is intellectual system called "Eidos" for solving problems related to identification of types and models of aircraft by their silhouettes on the ground, to be more precise, their external contours: 1) digitization of scanned images of aircraft and creation of their mathematical models; 2) formation of mathematical models of specific aircraft with the use of the information theory; 3) modeling of the generalized images of various aircraft types and models and their graphic visualization; 4) comparing an image of a particular plane with generalized images of various aircraft types and models, and quantifying the degree of similarities and differences between them, i.e., the identification of the type and model of airplane by its silhouette (contour) on the ground; 5) quantification of the similarities and differences of the generalized images of the planes with each other, i.e., clusterconstructive analysis of generalized images of various aircraft types and models. The article gives a new approach to digitizing images of aircraft, based on the use of the polar coordinate system, the center of gravity of the image and its external contour. Before digitizing images, we may use their transformation, standardizing the position of the images, their sizes (resolution, distance) and the angle of rotation (angle) in three dimensions. Therefore, the results of digitization and ASC-analysis of the images can be invariant (independent) relative to their position, dimensions and turns. The shape of the contour of a particular aircraft is considered as a noise information on the type and model of aircraft, including information about the true shape of the aircraft type and its model (clean signal) and noise, which distort the real shape, due to noise influences, both of the means of countering detection and identification, and environment. Software tool of ASC-analysis, i.e. Eidos intellectual system, provides identification of the type and the model of airplane by its silhouette, as it was shown in a simplified numerical example
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Description
It has been proved that theoretical scientific models created as a result of the learning process, reflect not the reality of "what it really is" and only the reality "what it is" in the process of interaction with tools of empirical knowledge, i.e. the organs of perception of a certain organism that supports a corresponding form of consciousness, experimental instruments and information-measuring systems of a certain functional level. Examples and consequences of the major mistakes that have been historically made by scientists for the substantial interpretation of theoretical scientific models: this error is unwarranted giving the model the ontological status ("hypostatizations") and its associated error model giving the status of universality. The history of the emergence and development of science was viewed as a process of sequential application of natural scientific method to the study of objects of knowledge, previously studied in the framework of philosophy. We have formulated a promising idea of solving problems of philosophy of natural science methods. In the framework of implementation of this idea, we have proposed a natural-scientific formulation and solution of the basic question of philosophy. This new scientific concept of "Relatively objective and Relatively subjective" and discusses the relationship of the content of these concepts from forms of consciousness. The article gives a natural-scientific definition of consciousness and offers periodic multi-criteria classification of forms of consciousness, including 49 forms of consciousness: the 7 types of 7 consciousness and cognition methods. It examines the dialectics of the changing ideological paradigms from antiquity to the present day and a place of scientific paradigms in the process. It also describes the law of denial-denial in the change of ideological paradigms and on the basis; it explores the hypothesis about the main features of the future ideological paradigm, formed in the present. We have formulated the correct principles of interpreting scientific models of natural-scientific method – scientific method of induction and the principles of open consciousness, i.e. the principles, opening the way for the formation of new, improved and more adequate models of reality than the existing ones which were considered the only true models
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Description
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
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Description
The article discusses the application of automated system-cognitive analysis (ASC-analysis), its mathematical model is a system of information theory and implements, its software tools – intellectual system called "Eidos" for solving one of the important tasks of ampelography: to quantify the similarities and differences of different clones of grapes using contours of the leaves. To solve this task we perform the following steps: 1) digitization of scanned images of the leaves and creation their mathematical models; 2) formation mathematical models of specific leaves with the application of information theory; 3) modeling the generalized images of leaves of different clones on the basis of specific leaves (multiparameter typing); 4) verification of the model by identifying specific leaf images with generic clones, i.e., classes (system identification); 5) quantification of the similarities and differences of the clones, i.e. cluster-constructive analysis of generalized images of leaves of various clones. The specific shape of the contour of the leaf is regarded as noise information on the clone to which it relates, including information about the true shape of a leaf of this clone (clean signal) and noise, which distort the real shape, due to the random influence of the environment. Software tools of ASA-analysis which is intellectual "Eidos" system provides the noise suppression and the detection of a signal about the true shape of a leaf of each clone on the basis of a number of noisy concrete examples of the leaves of this clone. This creates a single image of the shape of the leaf of each clone, independent of their specific implementations, i.e. "Eidos" of these images (in the sense of Plato) - the prototype or archetype (in the Jungian sense) of the images
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THE ASYMPTOTIC INFORMATION CRITERION OF NOISE QUALITY
DescriptionIntuitively everyone understands that noise is a signal in which there no information is, or which in practice fails to reveal the information. More precisely, it is clear that a certain sequence of elements (the number) the more is the noise, the less information is contained in the values of some elements on the values of others. It is even stranger, that noone has suggested the way, but even the idea of measuring the amount of information in some fragments of signal of other fragments and its use as a criterion for assessing the degree of closeness of the signal to the noise. The authors propose the asymptotic information criterion of the quality of noise, and the method, technology and methodology of its application in practice. As a method of application of the asymptotic information criterion of noise quality, we offer, in practice, the automated systemcognitive analysis (ASC-analysis), and as a technology and software tools of ASC-analysis we offer the universal cognitive analytical system called "Eidos". As a method, we propose a technique of creating applications in the system, as well as their use for solving problems of identification, prediction, decision making and research the subject area by examining its model. We present an illustrative numerical example showing the ideas presented and demonstrating the efficiency of the proposed asymptotic information criterion of the quality of the noise, and the method, technology and methodology of its application in practice
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Description
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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HOW TO SOLVE THE TASK OF CLASSIFICATION OF TYPES OF RIFLE AMMUNITION USING THE METHOD OF ASCANALYSIS
DescriptionIn forensics there is an urgent need to determine the type of rifle (automatic, rifle, large caliber pistol) depending on its used ammunition found at the scene of the use of weapons. We offer a solution to this problem with the use of new innovative method of artificial intelligence: automated system-cognitive analysis (ASC-analysis) and its program toolkitwhich is a universal cognitive analytical system called "Eidos". In the "Eidos" system we have implemented the software interface that allows posting of images and identifying their outer contours. By multivariable typing, the system creates a systemic-cognitive model, the use of which, if the model is sufficiently accurate, may be helpful in solving problems of system identification, prediction, classification, decision support and research of the modeled object by studying its model. For this task the following stages: 1) input images of ammunitions into the "Eidos" system and creation of their mathematical models; 2) the synthesis and verification of the models of generalized images of ammunition for types of weapons based on the contour images of specific munitions (multiparameter typing); 3) improving the quality of the model by separating classes for typical and atypical parts; 4) quantification of the similarities-the differences between specific types of munitions with generic images of different types of ammunition of the weapon (system identification); 5) quantification of the similarity-differences between types of ammunition, i.e. cluster-constructive analysis of generalized images of ammunition. A numerical example is given. We also possess a successful experience of solving similar problems in other subject areas
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HOW TO SOLVE THE TASK OF CLASSIFICATION OF TYPES OF RIFLE AMMUNITION USING THE METHOD OF ASCANALYSIS
DescriptionIn criminology, there are actual problems of determining the type (machine gun, rifle, large caliber, pistol) and a particular model of small rifle for its ammunition, in particular, discovered in the use of weapons. The article proposes a solution to this problem with the use of a new innovative method of artificial intelligence: automated system-cognitive analysis (ASCanalysis) and its programmatic toolkit – a universal cognitive analytical system called "Eidos". In the system of "Eidos", we have implemented a software interface that provides input to the system images, and the identification of their external contours on the basis of luminance and color contrast. Typing by multiparameter contour images of specific ammunition, we create and verify the system-cognitive model, with the use of which (if the model is sufficiently reliable), we can solve problems of system identification, classification, study of the simulated object by studying its model and others. For these tasks we perform the following steps: 1) enter the images of ammunitions into the system of "Eidos" and create mathematical models of their contours; 2) synthesis and verification of models of the generalized images of ammunition for types of weapons based on the contour images of specific munitions (multivariate typology); 3) quantification of the similarities-differences of the specific ammunition with generalized images of ammunition of various types and models of small rifle (system identification); 4) quantification of the similarities-differences of the types of munitions, i.e. cluster-constructive analysis
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01.00.00 Physical-mathematical sciences
DescriptionClassical 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
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METHODS OF REDUCING SPACE DIMENSION OF STATISTICAL DATA
01.00.00 Physical-mathematical sciences
DescriptionOne of the "points of growth" of applied statistics is methods of reducing the dimension of statistical data. They are increasingly used in the analysis of data in specific applied research, such as sociology. We investigate the most promising methods to reduce the dimensionality. The principal components are one of the most commonly used methods to reduce the dimensionality. For visual analysis of data are often used the projections of original vectors on the plane of the first two principal components. Usually the data structure is clearly visible, highlighted compact clusters of objects and separately allocated vectors. The principal components are one method of factor analysis. The new idea of factor analysis in comparison with the method of principal components is that, based on loads, the factors breaks up into groups. In one group of factors, new factor is combined with a similar impact on the elements of the new basis. Then each group is recommended to leave one representative. Sometimes, instead of the choice of representative by calculation, a new factor that is central to the group in question. Reduced dimension occurs during the transition to the system factors, which are representatives of groups. Other factors are discarded. On the use of distance (proximity measures, indicators of differences) between features and extensive class are based methods of multidimensional scaling. The basic idea of this class of methods is to present each object as point of the geometric space (usually of dimension 1, 2, or 3) whose coordinates are the values of the hidden (latent) factors which combine to adequately describe the object. As an example of the application of probabilistic and statistical modeling and the results of statistics of non-numeric data, we justify the consistency of estimators of the dimension of the data in multidimensional scaling, which are proposed previously by Kruskal from heuristic considerations. We have considered a number of consistent estimations of dimension of models (in regression analysis and in theory of classification). We also give some information about the algorithms for reduce the dimensionality in the automated system-cognitive analysis