#### Name

Lutsenko Yevgeniy Veniaminovich

#### Scholastic degree

•

#### Academic rank

professor

#### Honorary rank

â€”

#### Organization, job position

Kuban State Agrarian University

#### Web site url

## Articles count: 276

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

#### HOW TO SOLVE THE TASK OF CLASSIFICATION OF TYPES OF RIFLE AMMUNITION USING THE METHOD OF ASCANALYSIS

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

#### HOW TO SOLVE THE TASK OF CLASSIFICATION OF TYPES OF RIFLE AMMUNITION USING THE METHOD OF ASCANALYSIS

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 â€“ 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 â€“ 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) â€“ 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 â€“ 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