An approach to solving the problem of personality identification by gas-discharge visualization method

System analysis, control and data processing


Аuthors

Volkov S. S.

The Military Academy of Strategic Rocket Troops after Peter the Great, 8, Karbysheva str., Balashikha, Moscow region, 143900, Russia

e-mail: blockfm@yandex.ru

Abstract

The article considers the approach to solving the identification problem by gas-discharge visualization (GDV) method. This approach is planned to be applied in aerospace branch for the purpose of prevention of industrial espionage and penetration to the classified objects.

This work purpose consists in demonstrating the unique possibilities of the gas-discharge visualisation method.

The work novelty consists in the fact that the approach to solving the problem of identification with the GDV method was not applied anywhere else, and was not considered. The interest to the personality identification in an aerospace complex is caused by the fact that many modern identification methods have shortages and can be falsified. In this connection, a necessity for searching for the new identification methods, one of which is represeted by the suggested approach. The hypothesis that the GDV-images possess the «identification cells» was put forward, and the images with the filter obtained in the process of shooting are constant and arenot subjected to changing. Thus, based on the identification determination they will be the reference, with which the images without the filter will be compared. The images without a filter are made while an employee authentication

The article presents the example of application of the proposed identification method in aggregate with the automated assessment system of the operator psychophysiological state (PPhS). The given example is interesting to that it allows perform simultaneous monitoring of health and readiness for professional activity, and, at the same time, to carry out personality identification of the aerospace complex employee

In consequence of the study, it was confirmed that the above said approach really allows identifying the personality. The obtained data confirms the put forward hypothesis. However, it is necessary to perform at least ten iterations for the higher purity of the experiment.

The author recommends apply the approach to the personality identification with the GDV method combined with the automated system for phsycophysiological state estimation. This will allow both ensure claasified information integrity, and perform monitoring of physical and phsycological health of all employees of aviation, rocket and space branches.

Keywords:

automation process, automated system, personality identification

References

  1. Biever C. Vein camera keeps injections on target, 2004, New Scientist, available at: www.newscientist.com/article/dn6497-vein-camera-keeps-injections-on-target

  2. Senthilkumaran N.A., Rajesh R. Study on edge detection methods for image segmentation, Hroceeding of tye International Conference jn mathematics and computer science (ICMCS-2009), 2009, vol. 1, pp. 255 – 259.

  3. Vvedenskii B.A. Entsiklopedicheskii slovar’ (Encyclopedic dictionary), Moscow, Bol’shaya Sovetskaya entsiklopediya, 1953 −1955, vol. 1. – 719 p., vol. 2. – 719 p., vol. 3. – 744 p.

  4. Bui Tkhi Tkhu Chang, Spitsin V.G. Doklady TUSUR, 2010, no. 2-2(22), pp. 221 – 223.

  5. Bulgakov V.G. Vestnik Volgogradsgogo gosudarstvennogo universiteta. Yurisprudentsiya, 2005, no. 7, pp. 120 – 123.

  6. Vvedenie. Izobrazheniya v MATLAB i Image Processung Toolbox // Tsentr kompetentsii MathWorks, available at: http://matlab.exponenta.ru/imageprocess/book5/6_1.php

  7. Gimazetdinova A.R., Nurislamova A.I., Aminev F.G. Otechestvennaya yurisprudentsiya, 2018, no. 1 (26), pp. 49 – 52.

  8. Gonsales R. Vuds R. Tsifrovaya obrabotka izobrazhenii (Digital processing of images), Moscow, Tekhnosfera, 2005, 1070 p.

  9. Grishenkova N.P., Lavrov D.N. Matematicheskie struktury i modelirovanie, 2014, no.1 (29), pp. 43 – 64.

  10. Komarovskii Yu.A. Primenenie molekulyarno-geneticheskikh metodov v sudebno-meditsinskoi ekspertize (Application of molecular-genetic methods in forensic medical examination), Saint Petersburg, Sankt Peterburgskii yuridicheskii institut General’noi prokuratury RF, 1998, 16 p.

  11. Korotkov K.G. Osnovy GRV bioelektrografii (The GDV bioelectrograph basics), Saint Petersburg, ITMO, 2001, 356 p.

  12. Korotkov K.G. Printsipy analiza GRV bioelektrografii (Principles of GDV bioelectrography analysis), Saint Petersburg, Izd-vo Renome, 2007, 286 p.

  13. Krylov B.A., Grishentsev A.Yu., Velichko E.N. Metody registratsii, obrabotki i analiza izobrazhenii (Methods of images registration, processing and the analysis), Saint Petersburg, GU ITMO, 2010, 60 p.

  14. Mikhailov M.A. Uchenye zapiski Tavricheskogo natsional’nogo universiteta im. V.I. Vernadskogo, 2007, vol. 20 (59), no. 2, pp. 149 – 157.

  15. Moung Kh.O., Chzho Z.L., Prikhod’ko S.Yu. Trudy MAI, 2018, no. 99, available at: http://trudymai.ru/eng/published.php?ID=91920

  16. Revich Yu. Biometricheskaya utopiya, available at: http://www.biometrics.ru/news/article453

  17. Sokolova O.A., Lapteva A.O. Vestnik ekonomicheskoi bezopasnosti, 2018, no. 1, pp. 112 – 116.

  18. Sorokin V.N., V’yugin V.V., Tananykin A.A. Informatsionnye protsessy, 2012, vol. 12, no. 1, pp. 1 – 30.

  19. Fisenko V.T., Fisenko T.Yu. Komp’yuternaya obrabotka i raspoznavanie izobrazhenii (Computer processing and recognition of images), Saint-Petersburg, SPbGU ITMO, 192 p.

  20. Chernodarov A.V., Ivanov S.A. Trudy MAI, 2018, no. 99, available at: http://trudymai.ru/eng/published.php?ID=91962


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