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  8. 41-5

Electronic modeling

Vol 41, No 5 (2019)

 

CONTENTS

Mathematical Modeling and Computation Methods

  MARUSENKOVA T.A.
Simulation Models for Synthesizing Noise of Mems Gyroscopes
3-16
  SAFONYK A.P., TARGONIY I.M.
Modeling and Automation of the Process of the Obtaining aCoagulant for Clarification and Discoloration of Industrial Wastewater
17-34

Informational Technologics

  KRAVTSOV G.A., LEVITIN V.V., KOSHEL‘ V.I., NIKITCHENKO V.V., PRIMUSHKO A.N.
Strong Artificial Intelligence: Background Precondition
35-58
  ZUBOK V.YU.
Features of the Model of the Offender at the Analysis of Attacks on Global Internet Routing
59-70

Computational Processes and systems

  PILKEVICH I.A., BOYCHENKO O.S., HUMENIUK I.V.
Method for the Synthesis of Wireless Information and Communication Network Based on Multi-Criteria Clustering
71-84

Application of Modeling Methods and Facilities

  IATSYSHYN A.V., KUTSAN YU. G., ARTEMCHUK V.O., KAMENEVA I.P., POPOV O.O., KOVACH V.O.
Means of Intellectual Analysis and Visualization Geospatial Atmospheric Air Monitoring Data
85-102
  KONASHEVYCH O.I.
Data Insertion in Blockchain For Legal Purposes. How to Sign Contracts Using Blockchain
103-120

SIMULATION MODELS FOR SYNTHESIZING NOISE OF MEMS GYROSCOPES

T.A. Marusenkova

Èlektron. model. 2019, 41(5):03-16
https://doi.org/10.15407/emodel.41.05.003

ABSTRACT

The work presents a solution to a problem of developing software for modeling noise of MEMSThe work presents a solution to a problem of developing software for modeling noise of MEMSgyroscopes. Such software is of great importance due to complexity of the algorithms forminimization of pose estimation errors by compensation for the transfer function drift based ondigital filtering. We have proposed two algorithms for synthesizing noise terms typical of MEMSgyroscopes. The first of these algorithms is based on integrating pseudorandom harmonic signals.The second one assumes frequency correction of an array of pseudorandom signals. The spectralcharacteristics of synthesized noise are analyzed using the Allan variance. We use our ownsoftware, IMU tester, based on M5Stack with SoC ESP32, to study noise parameters. The obtainedresults are of key importance for simulation of MEMS gyroscopes errors using the Monte-Carlomethod, optimization of the correctingKalman-based filters and firmware of integrated IMUsensors.

KEYWORDS

MEMS gyroscope, noise, model of noise synthesis, inertial measurement unit.

REFERENCES

1. Höflinger, F., Müller, J., Zhang, R., Reindl, L. and Burgard, W. (2013), “Wireless micro inertial measurement unit (IMU)”, IEEE Transactions on Instrumentation and Measurement, Vol. 62, Iss. 9, pp. 2583-2595.
https://doi.org/10.1109/TIM.2013.2255977
2. Blasch, E., Kostek, P., Paces, P. and Kramer, K. (2015), “Summary of avionics technologies”, IEEE Aerospace and Electronic Systems Magazine, Vol. 30, Iss. 9, pp. 6-11.
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3. Ahmed, H. and Tahir, M. (2017), “Accurate attitude estimation of a moving land vehicle using low-cost MEMS IMU sensors”, IEEE Transactions on Intelligent Transportation Systems, Vol. 18, Iss. 7, pp. 1723-1739.
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5. Nemec, D., Janota, A., Hrubos, M. and Simak, V. (2016), “Intelligent real-time MEMS sensor fusion and calibration”, IEEE Sensors Journal, Vol. 16, Iss. 19, pp. 7150-7160.
https://doi.org/10.1109/JSEN.2016.2597292
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8. (2017), The five motion senses: using MEMS inertial sensing to transform application, Analog Devices.
9. Shin, B., Kim, C., Kim, J., Lee, S., Kee, C., Kim, H. and Lee, T. (2016), “Motion recognitionbased 3D pedestrian navigation system using smartphone”, IEEE Sensors Journal, Vol. 16, Iss. 8, pp. 6977-6989.
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12. Li, Y., Georgy, J., Niu, X, Li, Q. and El-Sheimy, N. (2015), “Autonomous calibration of MEMS gyros in consumer portable devices”, IEEE Sensors Journal, Vol. 15, Iss. 7, pp. 4062-4072.
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https://doi.org/10.1109/TVT.2018.2887091
15. Shmaliy, Y., Zhao, S. and Ahn, C. (2019), “Optimal and unbiased filtering with colored process noise using state differencing”, IEEE Signal Processing Letters, Vol. 26, Iss. 4, pp. 548-551.
https://doi.org/10.1109/LSP.2019.2898770
16. Lin, X., Jiao, Y. and Zhao, D. (2018), “An improved Gaussian filter for dynamic positioning ships with colored noises and random measurements loss”, IEEE Access, Vol. 6, pp. 6620-6629.
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17. Allan, D. and Levine, J. (2016), “A historical perspective on the development of the Allan variances and their strengths and weaknesses”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Vol. 63, Iss. 4, pp. 513-519.
https://doi.org/10.1109/TUFFC.2016.2524687
18. Guerrier, S., Molinari, R. and Stebler, Y. (2016), “Theoretical limitations of Allan variance-based regression for time series model estimation”, IEEE Signal Processing Letters, Vol. 23, Iss. 5, pp. 597-601.
https://doi.org/10.1109/LSP.2016.2541867
19. Shao, T., Duan, Z., Ge, Q. and Liu, H. (2019), “Recursive performance ranking of Kalman filter withmismatched noise covariances”, IET Control Theory&Applications, Vol. 13, Iss. 4, pp. 459-466.
https://doi.org/10.1049/iet-cta.2018.5064
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MODELING AND AUTOMATION OF THE PROCESS OF THE OBTAINING A COAGULANT FOR CLARIFICATION AND DISCOLORATION OF INDUSTRIAL WASTEWATER

A.P. Safonyk, I.M. Targoniy

Èlektron. model. 2019, 41(5):17-34
https://doi.org/10.15407/emodel.41.05.017

ABSTRACT

The model of the electrocoagulator, which describes the processes taking place in the electrolyzer,The model of the electrocoagulator, which describes the processes taking place in the electrolyzer,was developed. The solution of the corresponding model problem was found. The effect ofcurrent strength on the concentration of divalent iron, water temperature was investigated. The algorithmof the two-circuit regulation of pollution concentration in sewage with feedback bonds ofcurrent between the plates of the coagulator and the concentration of pollution in water arriving inthe automated system of purification is developed. A functional scheme of automation with 5contours of regulation and a selected set of technical means of automation of leading manufacturersof companies was constructed. An automated control system of wastewater treatment was developedwith the implementation of a coagulant prediction algorithm in relation to the input pollutionconcentration. It was implemented to control the current strength in the electrolyzer withminimal electricity consumption. Assumed management system with the ability to change the entireperformance settings in real time using SCADA - system WinCC Flexible.

KEYWORDS

simulation model, electrocoagulation, coagulant, automation, regulation,simulation model, electrocoagulation, coagulant, automation, regulation,SCADA.

REFERENCES

1. Bomba, A.Ya. and Safonik, A.P. (2018), “Mathematical simulation of the process of aerobic treatment of wastewater under conditions of diffusion and mass transfer perturbations”, Journal of Engineering Physics and Thermophysics, Vol. 91, no. 2, pp. 318-323.
https://doi.org/10.1007/s10891-018-1751-x
2. Bomba, A., Klymiuk, Yu. and Prysiazhniuk, I. (2016), “Mathematical modeling of wastewater treatment from multicomponent pollution by using microporous particles”, Proceeding of the AIP Conference, 2016, 1773, 040003, pp. 1-11.
https://doi.org/10.1063/1.4964966
3. Kaur, R., Arora, A., Kaur, A., Singh, N. and Sharma, S. (2018), “Treatment of waste water through electrocoagulation”, Pollution Research, Vol 37, Iss. 2, pp. 394-403.
4. Nayak, B. (2018), “A review of electrocoagulation process for wastewater treatment”, International Journal of ChemTech Research, Vol. 11, no. 3, pp. 289-320.
5. Smoczynski, L., Munska, K.T. and Kosobucka, M. (2014), “Destabilization of model wastewater in the chemical coagulation process”, Ecological Chemistry and Engineering, Vol. 21, no. 2, pp. 269-279.
https://doi.org/10.2478/eces-2014-0021
6. Tkachev, R. (2012), “Study of electrocoagulation installation of wastewater treatment as an object of automation”, Vostochno-Evropeyskiy zhurnal peredovykh tekhnologiy, no. 6(60), pp 48-51.
7. Khandegar, V., Acharya, S. and Jain, A.K. (2018), “Data on treatment of sewage wastewater by electrocoagulation using punched aluminum electrode and characterization of generated sludge” , Data in Brief, Vol. 18, pp. 1229-1238.
https://doi.org/10.1016/j.dib.2018.04.020
8. Safonyk, A., Bomba, A. and Tarhonii, I. (2019), “Modeling and automation of the electrocoagulation process in water treatment”, Advances in Intelligent Systems and Computing, Vol. 871, pp. 451-463.
https://doi.org/10.1007/978-3-030-01069-0_32

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STRONG ARTIFICIAL INTELLIGENCE: BACKGROUND PRECONDITION

G.A. Kravtsov, V.V.Levitin,
V.I. Koshel‘, V.V. Nikitchenko, A.N. Primushko

Èlektron. model. 2019, 41(5):35-58
https://doi.org/10.15407/emodel.41.05.035

ABSTRACT

The article provides an overview of the fundamental foundations for building strong artificial intelligence.The article provides an overview of the fundamental foundations for building strong artificial intelligence.The validity of the hypotheses put proved by the method of field experiments isshown. It is argued that in order to construct a mathematical theory of strong artificial intelligence(AI) it is necessary to go over to the von Neumann-Bernays-Godel system of axioms and then thepossibility of using semantic structures represented by computer ontologies as algebraic structuresopens up. For the correct use of ontologies in artificial intelligence systems, it is necessarythat ontologies along the division planes are metric spaces.

KEYWORDS

artificial intelligence, axiom system, human brain, semantics, measure of difference,artificial intelligence, axiom system, human brain, semantics, measure of difference,internal model of the world, adaptability.

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FEATURES OF THE MODEL OF THE OFFENDER AT THE ANALYSIS OF ATTACKS ON GLOBAL INTERNET ROUTING

V.Yu. Zubok

Èlektron. model. 2019, 41(5):59-70
https://doi.org/10.15407/emodel.41.05.059

ABSTRACT

A model of information security breach is proposed. Through analysis of threats and methods ofA model of information security breach is proposed. Through analysis of threats and methods ofconducting attacks on global routing it is established that the source of such threats are externalviolators. The classification of such violators is given and an informal model of the security violatoris developed.

KEYWORDS

global routing, route hijack, threats model, information security intruder model,global routing, route hijack, threats model, information security intruder model,cybersecurity

REFERENCES

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https://doi.org/10.15407/emodel.40.05.067
3. Levy, M.J. (2019), “The deep-dive into how Verizon and a BGP Optimizer Knocked Large Parts of the Internet Offline Monday”, available at: https://blog.cloudflare.com/the-deep-diveinto-how-verizon-and-a-bgp-optimizer-knocked-large-parts-of-the-internet-offline-monday/(accessed Jul 27, 2019).
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