Journal of Radio Electronics. eISSN 1684-1719. 2024. №5
Full text in Russian (pdf)
DOI: https://doi.org/10.30898/1684-1719.2024.5.1
MODELING OF INTERFERENCE IN AN ELECTRONIC DEVICE
UNDER INFLUENCE OF A PULSE MAGNETIC FIELD
USING AN ARTIFICIAL NEURAL NETWORK
Z.M. Gizatullin, R.M. Gizatullin, R.R. Mubarakov
Kazan National Research Technical University named after A.N. Tupolev-KAI,
420111, Russia, Kazan, К. Marx str., 10
The paper was received March 27, 2024
Abstract. Modern electronic means are quite sensitive to electromagnetic interference. At the same time, they must function reliably in the existing electromagnetic environment. Lightning discharges and industrial sources make a significant contribution to the formation of the electromagnetic environment around electronic equipment. In this case, pulsed magnetic fields with microsecond parameters most often arise. The most rational approach to ensuring electromagnetic compatibility of electronic means is the most complete accounting and protection from possible phenomena at the design stage. Different methods for modeling the consequences of exposure to electromagnetic interference have their own advantages and disadvantages. To develop a technique and modeling interference in electronic means based on an artificial neural network using the example of the influence of a pulsed magnetic field a the purpose of this work. A practical technique for calculation the magnitude of interference in electronic means using an artificial neural network the paper proposes. All stages of the technique are described: analysis of the main input parameters affecting the amount of interference in the electronic means; the use of a special experimental stand for measuring interference depending on significant input parameters; choosing the structure and parameters of an artificial neural network to modeling interference; choosing a training method for an artificial neural network; choosing a criterion for assessing the quality of training when solving a regression problem; normalization of training data; training an artificial neural network using measured data; modeling the amount of interference in the communication line of an electronic means when exposed to a pulsed magnetic field; assessment of consequences and selection of methods of protection against interference. As an example, we consider the problem of modeling the amount of interference in a communication line inside an electronic means when exposed to a pulsed magnetic field. The magnetic field has parameters recommended by the requirements of the regulatory document on electromagnetic compatibility of devices. In the problem under consideration, an acceptable discrepancy in results is achieved with an acceptable number of neural network training epochs.
Key words: electronic means, electromagnetic interference, pulsed magnetic field, lightning discharge, technique, modeling, artificial neural network.
Financing: The work was supported by the Kazan National Research Technical University named after A. N. Tupolev Strategic Academic Leadership Program (“PRIORITET–2030”).
Corresponding author: Gizatullin Zinnur Marselevich, zmgizatullin@kai.ru
References
1. Williams T. EMC for product designers. – Newnes, 2016.
2. Keller R.B. Design for Electromagnetic Compatibility--In a Nutshell: Theory and Practice. – Springer Nature, 2023. – P. 416.
3. Gizatullin Z.M. and etc. Celostnost' informacii v USB flesh-nakopitele pri vozdejstvii impul'snogo magnitnogo polya [Integrity of information in a USB flash drive when exposed to a pulsed magnetic field] //Zhurnal radioehlektroniki. – 2015. – № 8. – P. 8-8. (In Russian)
4. Uman M.A. The art and science of lightning protection //(No Title). – 2008. – P. 50.
5. Nuriev M.G. and etc. Analiz pomekhoustojchivosti vychislitel'noj tekhniki pri vozdejstvii razryada molnii na molniezashchitu zdaniya na osnove fizicheskogo modelirovaniya [Analysis of the noise immunity of computer equipment under the influence of a lightning discharge on the lightning protection of a building based on physical modeling] //Zhurnal radioehlektroniki. – 2019. – № 6. – P. 14-14. (In Russian)
6. Gizatullin Z.M. Elektromagnitnaya sovmestimost' elektronnyh sredstv ob"ektov elektroenergetiki pri vneshnih elektromagnitnyh vozdejstviyah po seti pitaniya [Electromagnetic compatibility of electronic means of electric power facilities under external electromagnetic influences through the power supply network] // Izvestiya vysshikh uchebnykh zavedenii. Problemy ehnergetiki. – 2007. – № 9-10. – P.37-45. (In Russian)
7. Safina R.M., Shkinderov M.S., Mubarakov R.R. Pomekhoustojchivost' sistem kontrolya i upravleniya dostupom v zdaniya pri vozdejstvii elektromagnitnyh pomekh po seti elektropitaniya [Noise immunity of control and management systems for access to buildings under the influence of electromagnetic interference through the power supply network] //Zhurnal radioehlektroniki. – 2021. – № 6. (In Russian)
8. Nuriev M.G., Gizatullin R.M., Gizatullin Z.M. Fizicheskoe modelirovanie elektromagnitnyh pomekh v bespilotnom letatel'nom apparate pri vozdejstvii kontaktnoj seti elektrotransporta [Physical modeling of electromagnetic interference in an unmanned aerial vehicle when exposed to the contact network of electric transport] //Izvestiya vysshih uchebnyh zavedenij. Aviacionnaya tekhnika. – 2018. – № 2. – P.137-141. (In Russian)
9. Gizatullin Z.M., Gizatullin R.M., Nuriev M.G. Prediction of noise immunity of computing equipment under the influence of electromagnetic interference through the metal structures of building by physical modeling //2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). – IEEE, 2020. – P. 120-123.
10. Zhechev E.S. and etc. Eksperimental'nye issledovaniya zerkal'no-simmetrichnogo modal'nogo fil'tra vo vremennoj i chastotnoj oblastyah [Experimental studies of a mirror-symmetric modal filter in the time and frequency domains] //Sistemy upravleniya, svyazi i bezopasnosti. – 2019. – № 2. – P.162-179. (In Russian)
11. Gizatullin Z.M., Gizatullin R.M. Modelirovanie elektromagnitnoj obstanovki na osnove teorii masshtabnogo eksperimenta dlya zadach elektromagnitnoj sovmestimosti i zashchity informacii [Modeling of the electromagnetic environment based on the theory of large-scale experiment for problems of electromagnetic compatibility and information security] //Informatsionnye tekhnologii. – 2013. – № 4. – P.19-22. (In Russian)
12. Gizatullin Z.M., Shleimovich M.P. Analysis of Noise Immunity of the UAV Onboard Control System Based on Physical Modeling of Induced Interference //Russian Aeronautics. – 2021. – Т. 64. – P. 554-561.
13. Takahashi T., Schibuya N. EMC Simulation and Modeling //IEEJ Transactions on Electronics, Information and Systems. – 2003. – Т. 123. – №. 7. – P. 1192-1195.
14. Alkhadzh Kh. A. and etc. Verifikaciya modelirovaniya provodnyh antenn metodom momentov [Verification of simulation of wired antennas by the method of moments] //Zhurnal radioehlektroniki. – 2021. – № 11. (In Russian)
15. Luo M., Huang K.M. Prediction of the electromagnetic field in metallic enclosures using artificial neural networks //Progress In Electromagnetics Research. – 2011. – Т. 116. – P. 171-184.
16. Gizatullin Z., Gizatullin R., Drozdikov V. Research of noise immunity of computer equipment of control systems under action of pulsed magnetic field //2019 International Russian Automation Conference (RusAutoCon). – IEEE, 2019. – P. 1-5.
17. Evdokimova T.S., Andreyanov N.V., Fatkullina L.F. Metody rasshireniya naborov dannyh na osnove obucheniya s podkrepleniem [Methods for expanding data sets based on reinforcement learning] //Nauchno-tekhnicheskii vestnik Povolzh'ya. – 2023. – № 11. – P.59-62. (In Russian)
18. De Marchi L., Mitchell L. Hands-On Neural Networks: Learn how to build and train your first neural network model using Python. – Packt Publishing Ltd, 2019.
19. Andreyanov N.V. and etc. Analiz stenda bortovoj sistemy dlya metodov obnaruzheniya osnovannyh na glubokih nejronnyh setyah [Analysis of an on-board system test bed for detection methods based on deep neural networks] //Nauchno-tekhnicheskii vestnik Povolzh'ya. – 2022. – № 5. – P.13-16. (In Russian)
20. Gizatullin Z.M. and etc. Issledovanie algoritma analiza izobrazhenij raduzhnoj obolochki glaza na osnove svertochnoj nejronnoj seti [Study of an algorithm for analyzing iris images based on a convolutional neural network] // Nauchno-tekhnicheskii vestnik Povolzh'ya. – 2023. – № 6. – P.55-57. (In Russian)
21. Cherny S.N., Gibadullin R.F. The recognition of handwritten digits using neural network technology //2022 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). – IEEE, 2022. – P. 965-970.
22. Khadse C.B., Chaudhari M.A., Borghate V.B. Electromagnetic compatibility estimator using scaled conjugate gradient backpropagation based artificial neural network //IEEE Transactions on Industrial Informatics. – 2016. – Т. 13. – №. 3. – P. 1036-1045.
23. Kirillov V.Yu., Zhukov P.A., Torlupa A.A. Primenenie radiopogloshchayushchih materialov dlya oslableniya vysokochastotnyh pomekh v elektricheskih cepyah elektrotekhnicheskih kompleksov letatel'nyh apparatov [The use of radio absorbing materials to reduce high-frequency interference in electrical circuits of electrical systems of aircraft] //Ehlektrichestvo. – 2022. – № 4. – P.66-71. (In Russian)
24. Gibadullin R.F., Vershinin I.S., Glebov E.E. Razrabotka prilozheniya dlya associativnoj zashchity fajlov [Development of an application for associative file protection] //Inzhenernyj vestnik Dona. – 2023. – № 6(102). – P.118-142. (In Russian)
25. Gibadullin R.F., Vershinin I.S. Associativnaya zashchita chislovyh svedenij v tekstovyh dokumentah s primeneniem biblioteki Parallel Framework platformy .NET [Associative protection of numeric information in text documents using the Parallel Framework library of the .NET platform] //Computational Nanotechnology. – 2023. – № 3. – P.121-129. (In Russian)
26. Gizatullin Z.M. and etc. Snizhenie elektromagnitnyh pomekh i zashchita informacii v vychislitel'noj tekhnike s pomoshch'yu ekraniruyushchih stekol [Reducing electromagnetic interference and protecting information in computing using shielding glasses] //Vestnik Kazanskogo gosudarstvennogo ehnergeticheskogo universiteta. – 2017. – № 3(35). – P.46-57. (In Russian)
27. Gazizov T.R. and etc. Puti resheniya aktual'nyh problem proektirovaniya radioelektronnyh sredstv s uchetom elektromagnitnoj sovmestimosti [Ways of solving actual problems of designing radio-electronic facilities with regard to electromagnetic compatibility] //Tekhnika radiosvyazi. – 2014. – № 2(22). – P.11-22. (In Russian)
For citation:
Gizatullin Z.M., Gizatullin R.M., Mubarakov R.R. Modeling of interference in an electronic device under influence of a pulse magnetic field using an artificial neural network. //Journal of Radio Electronics. – 2024. – № 5. https://doi.org/10.30898/1684-1719.2024.5.1 (In Russian)