Preview

Journal of «Almaz – Antey» Air and Space Defence Corporation

Advanced search

Forming an archive of electronic remedial maintenance files in the information system for service maintenance of military weaponry and special equipment

Abstract

TThe paper considers the process of archive formation for the case of documents stored as images in information support systems for service maintenance processes concerning military weaponry and special equipment. This operation makes it possible to eliminate data redundancy in documents depending on the purpose of the system on the whole and characteristic markers of the images restored.

For citation:


Kostrov B.V., Sumenkov N.A., Lukina N.V., Fokina N.S. Forming an archive of electronic remedial maintenance files in the information system for service maintenance of military weaponry and special equipment. Journal of «Almaz – Antey» Air and Space Defence Corporation. 2019;(4):67-72.

A concerted strategy of building up and running an organisation involved in service maintenance of military weaponry and special equipment (MWSE) is intended for maintaining their ser­viceable condition or good working order, with the specified performance indicators guaranteed, and providing the resources required for such maintenance. Lying at its base is a combination of functions performed by the operating organi­sation, repair agencies, and service centres. At the present stage this strategy is to be supported by:

  • current infrastructure for service mainte­nance of the existing MWSE fleet;
  • economic rationale of the expedience of performing factory repairs for particular items of special technical means (STM);
  • transition of enterprises and service centres to state-of-the-art electronic technologies providing technical state monitoring, maintenance, and first- line repairs of, in the first instance, the basic MWSE nomenclature in military units.

In accordance with the strategy of electronic technology application, the main element of the system of service maintenance information sup­port is an electronic remedial maintenance file. It is essentially a consolidated process document to be followed in the course of MWSE remedial main­tenance. It contains the aggregate of accounting records and reporting instruments characterising technical state of the object of maintenance, required scope and composition of works per­formed, work performers, human and material resources required for recovery of serviceable condition (good working order) of the object of maintenance. These documents are accumulated in an electronic database throughout the effective pe­riod of contracts for MWSE service maintenance. Because all the documents of an electronic remedial maintenance file are scan copies of real accounting records and reporting instruments, which defy effi­cient compression using standard cataloguing tools, a task arises to develop special archiving facilities for such documents.

To decrease the volume of stored data, it is proposed to reduce information redundancy of such documents, which are essentially binary images, based on the use of binary frequency analysis.

A binary spectral analysis can be based on Walsh binary functions, which create an orthogo­nal basis with some or other method of ordering. In our case we shall use ordering by the frequen­cy or the number of intersections of the values of zero axis basic function. Such basic functions can be written as follows:

Sw ={walw(i, j), i, j = 0, 1, ..., N-1},                   (1)

where walw (i, j) - Walsh function;

w - index denoting ordering by frequency, as opposed to Hadamard (h) or Paley p ordering;

i - defines function number;

j - function argument [1][2][3];

N = 2n, n = 1, 2, 3, ....

In discrete representation, Walsh ordering is determined by matrix [1], whose elements are as follows

ri(u); υi  - coefficients of binary representa­tion of the numbers of lines and columns.

Based on the representations given, a bina­ry spectral transformation, i. e. Walsh transform, can be built.

Walsh transform Hw for a certain block (n x n), where is defined by matrix equation

where Bx(n) - matrix of the coefficients of direct Walsh transform;

X(n) - initial image matrix.

A reverse Walsh transform is performed by the same matrix Hw( n) due to its orthogonal and symmetric property:

General structure of the process of com­pressing the scanned remedial maintenance file documents is given in Fig. 1.

 

Fig. 1. Image compressing process structure

When using each processing option, dif­ferent transformation options are obtained, and hence, different results as per the measured param­eters. For assessment of the considered images, the root mean square index of the initial and com­pressed image quality can be used. The mean squared error (MSE), applied for assessment of distortions of reconstructed images, is calculated by the formula [1][2][3][4]:

where N - image dimensions in pixels;

M0 - element of the initial image grey-level array;

M - element of the reconstructed image grey-level array.

If the images are identical, then MSE = 0. When applying data compression, upon orthog­onal Walsh transform completion, any insignifi­cant elements, whose loss does not cause changes visible to human eye as compared with the initial image, are deleted (or discarded). The number of discarded elements is determined proceeding from matrix representation of the transformation spectrum and is assigned by a threshold, with the elements below it set equal to zero. The degree of deleted elements is calculated by the formu­la [4][5]:

where C - total number of spectrum elements;

C0 - number of deleted elements.

This paper considers three technologies for reducing binary spectrum redundancy:

  1. deletion of insignificant elements;
  2. optimal quantisation of the remaining spectrum components into a fixed number of levels;
  3. optimal compression of equal value se­quences.

 

Fig. 2. Initial image (a) and its binary spectrum (b)

For the third action, standard archiver 7Zip was used in the experiments. An example of the initial image of document part and its binary spec­trum is given in Fig. 2.

Table 1 contains binary spectrum analy­sis results (see Fig. 2). A reconstructed image obtained after quantisation of spectrum mid-fre­quency components is shown in Fig. 3. Such op­eration allows to transit from the 8-bit coding of spectrum coefficients to the 4-bit one, which contains sign of the number and the number of the level which its value is assigned to. From the analysis of data given in Table 1 and Fig. 3, it can be concluded that transition to the 4-bit coding has practically no effect on the reconstructed im­age quality.

Table 1

Obtained spectrum analysis

Q-ty of elements in range

Thresholds

-85...-75 (inclusive) = 3

-77,5

-75...-65 (inclusive) = 11

-67,5

-65...-55 (inclusive) = 61

-57,5

-55...-45 (inclusive) = 174

-47,5

-45...-35 (inclusive) = 434

-37,5

-35...-25 (inclusive) = 1083

-27,5

-25...-15 = 3672

-17,5

-15...0 (inclusive) = 122 633

Not used

0...15 (inclusive) = 127 903

Not used

15...25 (inclusive) = 4106

17,5

25...35 (inclusive) = 1116

27,5

35...45 (inclusive) = 326

37,5

45...55 (inclusive) = 61

47,5

55...65 (inclusive) = 16

57,5

65...75 (inclusive) = 4

67,5

75...85 (inclusive) = 1

77,5

 

Fig. 3. Reconstructed image obtained after quantisation of spectrum mid-frequency components (MSE = 0.021476); high-frequency spectrum portion unchanged

Given in Fig. 4 are the results of deletion of insignificant spectrum elements according to threshold ±3 and the image reconstructed from that spectrum. Here, S = 56.43 %; MSE = 1.55. The number of discarded positive values is 73,960, negative - 73,971.

Fig. 4. The result of deletion of insignificant spectrum elements according to threshold±3 (а) and image reconstructed from this spectrum (S = 56.43 %; MSE = 1.55) (b)

Fig. 5 demonstrates the repeated experiment, as shown in Fig. 4, with threshold of ±6. Here, S = 80.24 %; СКО = 2.49. The number of discard­ed positive values is 105,369, negative - 103,122.

Fig. 5. The result of deletion of insignificant spectrum elements according to threshold±6 (а) and image reconstructed from this spectrum (S = 80.24 %; MSE = 2.49) (b)

From the data given it can be concluded that even under a high degree of image compression (over 80 %) the text readability remains satisfactory. Application of quantisation and spectrum compo­nents decimation allows to obtain the best result [6]. A comparison of the obtained documents by volume is given in Table 2.

Table 2

Comparative characteristics of the obtained documents by volume

Description

Format

Size, byte

before compression

in zip

1. Initial image

.bmp

263 222

106 320

Jpeg

57 436

55 536

2. Walsh transform spectrum (w/o compression and quantisation)

.bmp

263 222

81 039

Jpeg

165 590

165 605

3. Walsh transform spectrum (after quantisation and compression)

.bmp

263 222

15 738

JPeg

99 021

96 319

It should be pointed out that after spectral processing the documents can be efficiently com­pressed with standard archivers. The compres­sion degree at that can amount to approximately 30 times relative to the initial document.

Application of special document compres­sion techniques allows to create compact ar­chives of remedial maintenance files and promptly monitor the degree of execution MWSE service maintenance contracts.

Modelling in the MATLAB environment has proved good efficiency of the proposed methods and algorithms for compressing binary images that defy compression by conventional methods.

References

1. Ахмед Н., Рао К. Р. Ортогональные преобразования при обработке цифровых сигналов / под ред. И. Б. Фоменко. М.: Связь, 1980. 248 c.

2. Гонсалес Р., Вудс Р., Эддинс С. Цифровая обработка изображений в среде MATLAB. М.: Техносфера, 2006. 616 с.

3. Залманзон Л. А. Преобразования Фурье, Уолша, Хаара и их применение в управлении, связи и других областях. М.: Наука, 1989. 496 с.

4. Злобин В. К., Костров Б. В., Свирина А. Г. Спектральный анализ изображений в конечных базисах. М.: КУРС: ИНФРА, 2016. 172 с.

5. Костров Б. В., Бастрычкин А. С. Сжатие изображений на основе ортогональных преобразований // Известия Тульского государственного университета. Технические науки. 2016. Вып. 9. С. 113–118.

6. Костров Б. В., Соломенцева Н. И. Моделирование канала связи // Известия Тульского государственного университета. Технические науки. 2017. Вып. 2. С. 95–100.


About the Authors

B. V. Kostrov
Joint stock company “Ryazan Production and Teсhnological enterprise “Granit”
Russian Federation

Kostrov Boris Vasilevich – Doctor of Engineering Sciences, Professor, Deputy Head of Department of Automated Control Systems. Science research interests: image processing, artificial intelligence, information technology.

Ryazan



N. A. Sumenkov
Joint stock company “Ryazan Production and Teсhnological enterprise “Granit”
Russian Federation

Sumenkov Nikolay Aleksandrovich – Doctor of Engineering Sciences, Deputy General Director – Chief Engineer. Science research interests: operation of complex radioengineering systems.

Ryazan



N. V. Lukina
Ryazan State Radio Engineering University
Russian Federation

Lukina Natalya Vladimirovna – student. Science research interests: image processing, artificial intelligence, information technology.

Ryazan



N. S. Fokina
Joint stock company “Ryazan Production and Teсhnological enterprise “Granit”
Russian Federation

Fokina Natalya Sergeevna – Head of Administration of Production and Economics Support. Science research interests: using information technology and multimedia systems in image processing.

Ryazan



For citation:


Kostrov B.V., Sumenkov N.A., Lukina N.V., Fokina N.S. Forming an archive of electronic remedial maintenance files in the information system for service maintenance of military weaponry and special equipment. Journal of «Almaz – Antey» Air and Space Defence Corporation. 2019;(4):67-72.

Views: 100


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2542-0542 (Print)