The work that you helped run

[Eugene Lutsenko]

UDC 303.732.4
Physical-Mathematical sciences
AUTOMATED SYSTEMIC-COGNITIVE ANALYSIS OF CONTOURS OF IMAGES (generalization, abstraction, classification and identification)

Lutsenko Eugeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
SPIN-code: 9523-7101
prof.lutsenko@gmail.com

Kuban State Agrarian University, Krasnodar, Russia
Bandyk Dmitry Konstantinovich
SPIN-code: 4072-8442
bandyk_dd@mail.ru
artificial intelligence developer, Belarus

In the article the application of systemic-cognitive analysis, its mathematical model - the system theory of the information and its program toolkit - "Eidos" system for synthesis of the generalized images of classes, their abstraction, classification of the generalized images (clusters and constructs) comparisons of concrete images with the generalized images (identification) are examined. We suggest a new approach to the digitization of images, based on the use of the polar coordinate system, the center of gravity of the image and its contour. Before digitizing images we can use their changes to standardize the position of the picture-frames, their size and rotation. Therefore, if you specify this option, the results of digitization and image ASC-analysis can be invariant (independent) to their position, size and rotation. This means that in the model on the basis of a number of specific examples we will create one image of each class of images, independent of their specific implementations, i.e., the "Eidos" of these images (in the sense of Plato) - a prototype or archetype (in the Jungian sense) images. But the "Eidos" system provides not only the formation of prototype images, which quantitatively reflects the amount of information in the image elements of the prototype, but the removal of all irrelevant to identification (abstraction), and the comparison of specific images with generic (identification) and the generalized images of images together (classification). The article provides a detailed numerical example of ASC- analysis of images

Keywords: ASC-ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, INTELLIGENT SYSTEM "EIDOS", INPUT, DIGITIZATION OF IMAGES, SYNTHESIS OF GENERALIZED IMAGES, ABSTRACTION, CLASSIFICATION, COMPARISON SPECIFIC IMAGES WITH GENERIC (IDENTIFICATION)

Луценко Е.В. Автоматизированный системно-когнитивный анализ изображений по их внешним контурам (обобщение, абстрагирование, классификация и идентификация) / Е.В. Луценко, Д.К. Бандык // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2015. – №06(110). С. 138 – 167. – IDA [article ID]: 1101506009. – Режим доступа: http://ej.kubagro.ru/2015/06/pdf/09.pdf, 1,875 у.п.л.
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UDC 303.732. 4

Physical and mathematical sciences

SIMULATING AND PREDICTING GLOBAL CLIMATIC ANOMALIES SUCH AS EL NINO AND LA NINA
Cherednychenko Natalia Alekseevna
Vladivostok, Russia. nata426853@mail.ru

Alexander Trunev
Ph. D.
RISC SPIN code = 4945-6530
Director, A&E Trounev IT Consulting, Toronto, Canada

Lutsenko Evgeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
SPIN-code: 9523-7101
Kuban State Agrarian University, Krasnodar, Russia

The paper discusses the modeling and prediction of the climate of our planet with the use of artificial intelligence AIDOS-X. We have developed a number of semantic information models, demonstrating the presence of the elements of similarity between the motion of the lunar orbit and the displacement of the instantaneous pole of the Earth. It was found that the movement of the poles of the Earth leading to the variations in the magnetic field, seismic events, as well as violations of the global atmospheric circulation and water, and particular to the emergence of episodes such as El Niño and La Niña. Through semantic information models studied some equatorial regions of the Pacific Ocean, as well as spatial patterns of temperate latitudes, revealed their relative importance for the prediction of global climatic disturbances in the tropical and temperate latitudes. The reasons of occurrence of El Niño Modoki and their relationship with the movement of elements of the lunar orbit in the long-term cycles are established. Earlier, we had made a forecast of the occurrence of El Niño episode in 2015. Based on the analysis of semantic models concluded that the expected El Niño classical type. On the basis of the prediction block AIDOS-X calculated monthly evolution scenario of global climate anomalies. In this paper, the analysis of the actual implementation forecast of El Niño since its publication in January 2015 - before June 2015. It is shown that the predicted scenario of climatic anomalies actually realized. Calculations of future climate scenarios with system «Aidos-X» recognition module indicate that further possible abnormal excess temperature indicators of surface ocean waters in regions Nino 1,2 and Nino3,4 for 2015 may be comparable with similar abnormalities in the catastrophic El Niño of 1997-1998.

Keywords: SEMANTIC INFORMATION
MODEL, COMPUTATIONAL EXPERIMENT, EL NINO, EL NINO MODOKI, POLAR MOTION

This article is written in English

Чередниченко Н.А. Моделирование и прогноз динамики глобальных климатических аномалий типа Эль-Ниньо и Ла-Нинья / Н.А. Чередниченко, А.П. Трунев, Е.В. Луценко // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2015. – №06(110). С. 1545 – 1577. – IDA [article ID]: 1101506102. – Режим доступа: http://ej.kubagro.ru/2015/06/pdf/102.pdf, 2,062 у.п.л.
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UDC 303.732.4
Physical-Mathematical sciences
AUTOMATED SYSTEMIC-COGNITIVE ANALYSIS OF IMAGES PIXELS (generalization, abstraction, classification and identification)

Lutsenko Eugeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
SPIN-code: 9523-7101
prof.lutsenko@gmail.com

Kuban State Agrarian University, Krasnodar, Russia

In the article the application of systemic-cognitive analysis and its mathematical model i.e. the system theory of the information and its program toolkit which is "Eidos" system for loading images from graphics files, synthesis of the generalized images of classes, their abstraction, classification of the generalized images (clusters and constructs) comparisons of concrete images with the generalized images (identification) are examined. We suggest using the theory of information for processing the data and its size for every pixel which indicates that the image is of a certain class. A numerical example is given in which on the basis of a number of specific examples of images belonging to different classes, forming generalized images of these classes, independent of their specific implementations, i.e., the "Eidoses" of these images (in the definition of Plato) – the prototypes or archetypes of images (in the definition of Jung). But the "Eidos" system provides not only the formation of prototype images, which quantitatively reflects the amount of information in the elements of specific images on their belonging to a particular proto-types, but a comparison of specific images with generic (identification) and the
generalization of pictures images with each other (classification)

Keywords: ASC-ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, INTELLIGENT SYSTEM "EIDOS", INPUT, DIGITIZATION OF IMAGES, SYNTHESIS OF GENERALIZED IMAGES, ABSTRACTION, CLASSIFICATION, COMPARISON SPECIFIC IMAGES WITH GENERIC (IDENTIFICATION)

Луценко Е.В. Автоматизированный системно-когнитивный анализ изображений по их пикселям (обобщение, абстрагирование, классификация и идентификация) / Е.В. Луценко // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2015. – №07(111). С. 334 – 362. – IDA [article ID]: 1111507019. – Режим доступа: http://ej.kubagro.ru/2015/07/pdf/19.pdf, 1,812 у.п.л.
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UDC 303.732.4
Physical-Mathematical sciences
THE SOLUTION OF PROBLEMS OF AMPELOGRAPHY BY USING ASC-ANALYSIS OF IMAGES OF LEAVES IN THEIR EXTERNAL CONTOURS (GENERALIZATION, ABSTRACTION, CLASSIFICATION AND IDENTIFICATION)

Lutsenko Eugeny Veniaminovich
Dr.Sci.Econ., Cand.Tech.Sci., professor
RSCI SPIN-code: 9523-7101
prof.lutsenko@gmail.com

Kuban State Agrarian University, Krasnodar, Russia
Bandyk Dmitry Konstantinovich
RSCI SPIN-code: 4072-8442
bandyk_dd@mail.ru
artificial intelligence developer, Belarus

Troshin Leonid Petrovich
Dr.Sci.Biol., professor
RSCI SPIN-код: 3386-2768
lptroshin@mail.ru
Kuban State Agrarian University, Krasnodar, Russia

The article discusses the use of automatic systemic-cognitive analysis (ASC-analysis), its mathematical model is a system of information theory and software tools – an intellectual system called "Eidos" for the solution of some problems of ampelography: 1) digitization of scanned images of the leaves and creation of their mathematical models; 2) the formation of mathematical models of specific leaves using the spreading of information theory; 3) the formation of models of generalized images of leaves of various sorts; 4) comparing an image of a specific leaf with a generalized image of the leaf of different varieties and finding a quantitative degree of similarity and differences between them, i.e. the identification of the varieties on the leaf; 5) quantification of the similarities and differences of the varieties, i.e. cluster-constructive analysis of generalized images of the leaves of different varieties. We propose a new approach to digitizing images of leaves, based on using the polar coordinate system, the center of gravity of the image and its external contour. Before scanning images we may use transformation to standardize the position of the still images, their sizes and rotation angle. Therefore, the results of digitization and ASC-analysis of the images might be invariant (independent) relatively to their position, size and rotation. The specific shape of the contour of the leaf is regarded as noise information on the variety, including information about the true shape of the leaf of the class (clean signal) and noise, which distort this true form, originating in a random environment. Software tools of ASC-analysis – intellectual "Eidos" system ensures noise reduction and the selection of the signal about the true shape of the leaf of each variety on the basis of a number of noisy concrete examples of the leaves of this variety. This creates a one way form of a leaf of each class, free from their concrete implementations, i.e., the "Eidos" of these images (in the sense of Plato) is a prototype or archetype (in the Jungian sense) of the images

Keywords: ASC-ANALYSIS, AUTOMATED SYSTEM-COGNITIVE ANALYSIS, "EIDOS" INTELLIGENT SYSTEM, INPUT, DIGITIZATION OF IMAGES, SYNTHESIS OF GENERALIZED IMAGES, ABSTRACTION, CLASSIFICATION, COMPARISON SPECIFIC IMAGES WITH GENERIC (IDENTIFICATION), AMPELOGRAPHY, LEAVES, VARIETY

Луценко Е.В. Решение задач ампелографии с применением АСК-анализа изображений листьев по их внешним контурам (обобщение, абстрагирование, классификация и идентификация) / Е.В. Луценко, Д.К. Бандык, Л.П. Трошин // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2015. – №08(112). С. 846 – 894. – IDA [article ID]: 1121508064. – Режим доступа: http://ej.kubagro.ru/2015/08/pdf/64.pdf, 3,062 у.п.л.
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The author is grateful to Roger Donnay, a professional software developer, developer of highly efficient development system programming eXPress++, is widely used when creating a system of "Eidos-X++" (Roger Donnay, Professional Developer, Developer eXPress++, Boise, Idaho USA, http://donnay-software.com), and all competent and responsive forum participants http://bb.donnay-software.com/donnay/index.php, was effective and disinterested aid in software development

[Eugene Lutsenko]

For the development of this direction, I would like to implement a Delaunay triangulation for Xbase++.

Delaunay Triangulation.

//Incoming data:

P – the array of points given. Each cell of the array is coordinates of the point.

Pcnt is an integer. //The number of points given.

//Initialization of variables:

G — an array of edges. Each cell of an array is two integers.//Each of these is the number of the point in the P array. These two points describe the line segment connecting them, which is an edge. The array length is greater than or equal to double the amount of points given.

Gcnt is an integer. //Current number of edges in the array. It is initialized by 0.

p1 is an integer.

p2 is an integer.

minP is an integer.

min – a floating point number.

i, j are integers.



//Search the first 2 points:

From the entire set of given points we are looking for the first two of these, that are subject to the condition that all other points are on one side from the straight line drawn through these two points.

In the array of edges G, to the cell G[0] we add the edge formed by these two points found. Gcnt variable assigned a value of 1.

//The actual triangulation:

i=0;

Until i<Gcnt we repeat cycle:

{

Variable p1 is assigned the first point of the edge G

The variable p2 is assigned the second point of edge G

min=1000000000;

minP=0;

for j=0 to Pcnt-1

{

if the condition is true (j is not equal p) and (j is not equal to p2):

{

Construct a circle passing through the points numbered p1, p2 and j. The radius of the circle is R.

If R<min then:

{

min=R;

minP=j;

}

}

}

If the array G has no edges defined by points p1 and minP, and minP and p2, until the cell G[Gcnt], then:

{

Assign G[Gcnt] the edge defined by the points p1 and minP

Assign G[Gcnt+1] an edge defined by the points p2 and minP

Increase Gcnt with 2

Add to output array of triangles a new triangle specified by the points with the numbers p1, p2, minP

}

Increase i with 1

}

Code: Select all

Для развития этого направления разработок мне бы очень хотелось реализовать триангуляцию Делоне.

Триангуляция Делоне. 
	//Входящие данные:
	P – массив заданных точек. Каждая ячейка массива — координаты точки.
	Pcnt – целое число. //Количество заданных точек.
	//Инициализация переменных:
	G — массив рёбер. Каждая ячейка массива — два целых числа.//Каждое из этих двухчисел является номером точки в массиве P. Эти две точки описывают соединяющий их отрезок — ребро. Длина массива больше либо равна двойному количеству заданных точек
	Gcnt – целое число. //Текущее количество рёбер в массиве. Инициализируется значением 0.
	p1 – целое число. 
	p2 – целое число.
minP – целое число.
min – число с плавающей запятой.
	i, j – целые числа.

	//Поиск первых 2-х точек:
	Из всего множества заданных точек ищем первые две таких для которых выполняется условие — все остальные точки находятся по одну сторону от прямой проведенной через эти две точки. 
	В массив ребер G в ячейку G[0]заносим ребро образованное этими найденными двумя точками. Переменной Gcnt присваиваем значение 1.
	//Собственно триангуляция:
i=0;
До тех пор пока i<Gcnt повторяем цикл:
	{
Переменной p1 присваиваем первую точку ребра G[i]
	Переменной p2 присваиваем вторую точку ребра G[i]
	min=1000000000;
	minP=0;
	for j=0 to Pcnt-1
		{
		если выполняется условие (j не равно p1) и (j не равно p2) то:
			{
			Строим окружность проходящую через точки под номерами p1, p2 и j. 			Радиус этой окружности R.
			Если R<min то:
				{
				min=R;
				minP=j;
				}
			}
		}
Если в массиве G до ячейки G[Gcnt] нет ребра заданного точками p1 и minP и точками p2 и minP то: 	
		{
		Присваиваем G[Gcnt]  ребро заданное точками p1 и minP
		Присваиваем G[Gcnt+1]  ребро заданное точками p2 и minP
		Увеличиваем Gcnt на 2
		Добавляем в выходной массив треугольников новый треугольник заданный 		точками с номерами p1, p2,  minP
		}
	Увеличиваем i на 1
	}

[/size]

Code: Select all

Триангуляции Делоне на Visual Basic Application for MS Excel.
http://mykaralw.narod.ru/projects/xlmatrix/delaunaylist.html
Public Function TriangulationDelaunay(point As Variant) As Variant
Attribute TriangulationDelaunay.VB_Description = "Построение системы треугольников по набору точек point=mmatrix(XY)."
Attribute TriangulationDelaunay.VB_ProcData.VB_Invoke_Func = "\n28"
Dim n As Long
Dim nt As Long
Dim h As Long
Dim i As Long
Dim j As Long
Dim k As Long
Dim l As Long
Dim m As Long
Dim hn As Long
Dim hf As Boolean
Dim kt As Long
Dim kr As Double
Dim counta As Long
Dim counttr As Long
Dim minX As Double
Dim maxK As Double
Dim minlen As Double
Dim x1 As Double
Dim x2 As Double
Dim x3 As Double
Dim y1 As Double
Dim y2 As Double
Dim y3 As Double
Dim ax As Double
Dim ay As Double
Dim bx As Double
Dim by As Double
Dim xa As Double
Dim ya As Double
Dim sa As Double
Dim sb As Double
Dim sc As Double
Dim x As Double
Dim y As Double
Dim x0 As Double
Dim y0 As Double
Dim k1 As Double
Dim k2 As Double
Dim xx As Double
Dim yy As Double
Dim len1 As Double
Dim t1 As Double
Dim t2 As Double
Dim t3 As Double
Dim xc As Double
Dim yc As Double
Dim r2c As Double
Dim r2 As Double
Dim alive() As Long
Dim tri() As Long
Dim res() As Long
  n = UBound(point)
  nt = n * 10
  counta = 0
ReDim alive(1 To nt, 1 To 4)
ReDim tri(1 To nt, 1 To 3)
  i = 1
  minX = point(1, 1)
  For h = 2 To n
    If point(h, 1) < minX Then
      minX = point(h, 1)
      i = h
    End If
  Next h
  alive(1, 1) = i
  maxK = 0
  For h = 1 To n
    If h <> i Then
      If point(h, 1) = point(i, 1) Then
          j = h
          h = n
      Else
        kr = Abs((point(h, 2) - point(i, 2)) / (point(h, 1) - point(i, 1)))
        If kr > maxK Then
          maxK = kr
          j = h
        End If
      End If
    End If
  Next h
  alive(1, 2) = j
  alive(1, 3) = -1
  counta = 1
  counttr = 0
  h = 1
  kt = counta
  Do While (counta > 0 And kt < nt - 2)
    m = 0
    hf = False
    hn = 0
    For h = 1 To kt
      If (alive(h, 3) <> 0) And alive(h, 4) = 0 Then
        m = h
        h = kt
      End If
    Next h
    If m > 0 Then
      counta = counta - 1
      i = alive(m, 1)
      j = alive(m, 2)
      k = alive(m, 3)
      x1 = point(i, 1)
      y1 = point(i, 2)
      x2 = point(j, 1)
      y2 = point(j, 2)
      hn = 0
      For h = 1 To n
        hf = False
        If (h <> i) And (h <> j) And (h <> k) Then
          x3 = point(h, 1)
          y3 = point(h, 2)
          sc = x1 * (y2 - y3) + x2 * (y3 - y1) + x3 * (y1 - y2)
          If sc <> 0 Then
            t1 = x1 ^ 2 + y1 ^ 2
            t2 = x2 ^ 2 + y2 ^ 2
            t3 = x3 ^ 2 + y3 ^ 2
            sa = t1 * (y2 - y3) + t2 * (y3 - y1) + t3 * (y1 - y2)
            sb = t1 * (x2 - x3) + t2 * (x3 - x1) + t3 * (x1 - x2)
            xc = 0.5 * sa / sc
            yc = -0.5 * sb / sc
            r2c = (x1 - xc) ^ 2 + (y1 - yc) ^ 2
            For l = 1 To n
              If (l <> i) And (l <> j) And (l <> h) Then
                hf = True
                x = point(l, 1)
                y = point(l, 2)
                r2 = (x - xc) ^ 2 + (y - yc) ^ 2
                If r2 < r2c Then
                  hf = False
                  hn = 0
                  l = n
                Else
                  hf = True
                End If
              End If
            Next l
          End If
        End If
        If hf Then
          hn = h
          h = n
        End If
      Next h
      If hf Then
        alive(m, 4) = hn
        k = 0
        For h = 1 To kt
          If (alive(h, 1) = i And alive(h, 2) = hn) Or (alive(h, 1) = hn And alive(h, 2) = i) Then
            If alive(h, 3) <> 0 Then k = h
            h = kt
          End If
        Next h
        If k = 0 Then
          kt = kt + 1
          alive(kt, 1) = i
          alive(kt, 2) = hn
          alive(kt, 3) = j
          counta = counta + 1
        ElseIf k > 0 Then
          alive(k, 4) = j
          counta = counta - 1
        End If
        k = 0
        For h = 1 To kt
          If (alive(h, 1) = j And alive(h, 2) = hn) Or (alive(h, 1) = hn And alive(h, 2) = j) Then
            If alive(h, 3) <> 0 Then k = h
            h = kt
          End If
        Next h
        If k = 0 Then
          kt = kt + 1
          alive(kt, 1) = j
          alive(kt, 2) = hn
          alive(kt, 3) = i
          counta = counta + 1
        ElseIf k > 0 Then
          alive(k, 4) = i
          counta = counta - 1
        End If
        counttr = counttr + 1
        tri(counttr, 1) = i
        tri(counttr, 2) = j
        tri(counttr, 3) = hn
      Else
        alive(m, 4) = -1
      End If
    End If
  Loop
ReDim res(1 To counttr, 1 To 3)
  For h = 1 To counttr
    For m = 1 To 3
      res(h, m) = tri(h, m)
    Next m
  Next h
  TriangulationDelaunay = res
End Function

[/size]

[Eugene Lutsenko]

At the input of the program table: X, Y, Z points.
Output table: X1,Y1, Z1, X2, Y2, Z2, X3, Y3, Z3 triangles