Scientific Journal of KubSAU

Intuitively 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

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

In 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

In 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

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

One 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

Insects are a major component of natural biocenoses and agrocenoses. One of the largest and most numerous families are ground beetles (Carabidae); their number, according to various estimates, is more than 30,000 species. For Carabidae beetles it is common to have different ways of eating, a place of habitation, occupied layers, seasonal and daily activity. They live both on the surface and in the soil, more rarely on bushes and trees. The types of the family of ground beetles – active beetles with long, thin antennae of uniform thickness, long elytra and long legs, adapted to running. Their sizes vary from a few millimeters to 10 cm. As active predators, ground beetles play a huge practical importance, destroying pests before reaching the last threshold, thereby providing a natural regulation. Based on the fact, that the number of beetles is large, and their sizes are sometimes only a few millimeters, there is a problem of determining the species of these insects (or their identification), therefore it took a special tool, which, on the one hand, facilitate obtaining data about these insects, and on the other hand, would increase their accuracy. This article proposes a new (to this subject area) approach to identify different species of ground beetles along their outer contour with the use of software tools for automated system-cognitive analysis (ASC-analysis) – the universal cognitive analytical system called "Eidos," which is well-proven in the study of other objects. The reason why it was decided to use this system is that normal (standard) identification of ground beetles, have certain disadvantages: the human factor (manifest error in the determination); quite time consuming; the inability to increase the number of criteria to improve the reliability of the model comparison. This article aims to overcome these drawbacks, by the use of universal cognitive analytical system "Eidos", the automated system-cognitive analysis (ASC-analysis). A numerical example is given

The work discusses various examples of physical systems which state is determined by the logarithmic law - quantum and classical statistical systems and relativistic motion in multidimensional spaces. It was established that the Fermi-Dirac statistics and BoseEinstein-Maxwell-Boltzmann distribution could be described by a single equation, which follows from Einstein's equations for systems with central symmetry. We have built the rate of emergence of classical and quantum systems. The interrelation between statistical and dynamic parameters in supergravity theory in spaces of arbitrary dimension was established. It is shown that the description of the motion of a large number of particles can be reduced to the problem of motion on a hypersphere. Radial motion in this model is reduced to the known distributions of quantum and classical statistics. The model of angular movement is reduced to a system of nonlinear equations describing the interaction of a test particle with sources logarithmic type. The HamiltonJacobi equation was integrated under the most general assumptions in the case of centrally-symmetric metric. The dependence of actions on the system parameters and metrics was found out. It is shown that in the case of fermions the action reaches extremum in fourdimensional space. In the case of bosons there is a local extremum of action in spaces of any dimension

The authors have developed and manufactured a large number of different designs of relative helical drums for mixing animal feed. We have conducted 749 field experiments with the drums of the 10 different designs with different parameters modes of operation. In all experiments, we measured the quality of the feed mixture. However, directly based on empirical data, rational choice of design features and parameters of the operation modes of the reels is not possible. For this, you must first develop a model reflecting these empirical data. The construction of meaningful analytical models of different types of drums is a difficult and demanding scientific task, the complexity of which is due to the large variety and complexity of forms of drums and their mode of usage, a large number of diverse physical factors affecting the processes in the drum. As a consequence, the development of analytical models associated with a large number of simplifying assumptions that reduce their versatility and reliability. Therefore, it is important to search of a mathematical method and software tools provide a quick and simple for the user to identify and influence the design of the drum and the parameters of the operating modes on the quality of the feed mixture directly on the basis of empirical (experimental) data. The work proposes a solution to this problem with the use of a new universal innovative method of artificial intelligence: automated system-cognitive analysis (ASC-analysis) and its programmatic Toolkit – universal cognitive analytical system called "Eidos". In the system of "Eidos" we have implemented a software interface that provides direct input into the system large amounts of empirical data from Excel file. Created on their basis in the system of "Eidos" system-cognitive model allows the visual form to reflect the effect of the structure of the drum and the parameters of the operating modes on the quality of the resulting feed mixture and to develop on this basis the science-based and appropriate recommendations for the rational choice of design features and parameters of the modes relative to the screw drums. We have also given a numerical example

In the USSR higher attestation Commission from 1975 to the collapse of the USSR was subordinated not to the Ministry of education and science, but to the Council of Ministers of the USSR directly. However, since then there is a steady trend of gradual reduction of the status of the Commission. Today it is not just included in the Ministry of education, it is just one of the units of one of its structures: the Rosobrnadzor. Reduced status of the HAC inevitably leads to a decline in the status and in the adequacy of scientific degrees assigned as well as scientific ranks. This process of devaluation of traditional academic degrees and titles assigned to the HAC, has reached the point when a few years ago there were abolished salary increments for them. Now, instead of that, every university and research institutes have developed their local, i.e. non-comparable with each other scientometric methods of evaluation of the results of scientific and teaching activities. Despite the diversity of these techniques, there is a common thing among all of them, which is the disproportionate role of the h-index. The value of the Hirsch index starts to play an important role in the protection, when considering competitive cases for positions, as well as in determining the monthly rewards for the results of scientific and teaching activities. By itself, this index is well founded, theoretically. However, in connection with the practice of its application in our conditions, in the collective consciousness of the scientific community there was a kind of mania, which the authors call the "Hirschmania". This mania is characterized by elevated unhealthy interest to the value of the Hirsch index, as well as incorrect manipulation of its value, i.e. inadequate artificial exaggeration of this value, as well as a number of negative consequences of that interest. In this study we have made an attempt to construct a quantitative measure for assessing the extent of improper manipulation of the value of the Hirsch index, and offered a science-based modification of the h-index, insensitive (resistant) to the manipulation. The article presents a technique for all the numerical calculations, which is simple enough for any author to use