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  8. 42-5

Electronic modeling

Vol 42, No 5 (2020)

https://doi.org/10.15407/emodel.42.05

CONTENTS

Mathematical Modeling and Computation Methods

  O.E. Kovalenko
ONTOLOGY AND MODEL OF INFORMATION TRANSFORMATION IN SITUATIONAL AGENTS’ SYSTEMS
3-23

Informational Technologics

  O. Gordieiev
QUALITY MODELS AND ASSESSMENT OF SOFTWARE INTERFACE USABILITY FOR HUMAN-COMPUTER INTERACTION
24-37

Computational Processes and systems

  D.V. Efanov, V.V. Sapozhnikov, Vl.V. Sapozhnikov
Typical structure of a duplicate error correction scheme with code control with summation of weighted transitions
38-50

Application of Modeling Methods and Facilities

  N.І. Nedashkovskaya, S.O. Lupanenko
COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR FORECASTING COVID-19 SPREADING IN DIFFERENT COUNTRIES
51-65
  S.S. Shevchenko
Computational method for mechanical seal as a dynamic system
66-81
  S.I. Klipkov
FEATURES OF THE ANALYSIS OF PHYSICAL STABILITY OF STEADY-STATE MODES OF AC ELECTRICAL SYSTEMS
82-96
  A.V. Voloshko, R. Almabrok
REMOVING NOISE COMPONENTS OF INFORMATION SIGNALS BY USING ORTHOGONAL WAVELET TRANSFORM
97-110
  V.Yu. Zubok
NEW METRICS FOR ASSESSMENT THE RISKS OF THE INTERNET ROUTE HIJACK CYBERATTACS
111-119
  S. Gnatiuk, L. Sakovych, U. Miroshnychenko
MODELING OF THE ORDER OF CHECKING OF PARAMETERS IN TECHNICAL MAINTENANCE OF THE STATE OF RADIO ELECTRONIC MEANS		 120-129

ONTOLOGY AND MODEL OF INFORMATION TRANSFORMATION IN SITUATIONAL AGENTS’ SYSTEMS

O.E. Kovalenko

Èlektron. model. 2020, 42(5):03-23
https://doi.org/10.15407/emodel.42.05.003

ABSTRACT

A new methodological approach to information systematics is proposed. Classification features of definition of categories of information are formulated. According to this approach, a single root concept of information was introduced and its ontology was built. A model of information transformation based on the proposed ontology is developed. The application of the ontological model of information in the construction of the architecture of situational agents is presented. The universality of the proposed models is shown on the example of the BDI (beliefs, desires, intentions) agent model and the possibility of application in situational systems.

KEYWORDS

information, knowledge representation, ontology, situational management, BDI agent.

REFERENCES

  1. Singh, V.K., Gao, M. and Jain, R. (2012), “Situation recognition: an evolving problem for heterogeneous dynamic big multimedia data”, the Proceedings of the 20th ACM international conference on Multimedia, ACM, pp. 1209-1218.
    https://doi.org/10.1145/2393347.2396421
  2. Kovalenko, O.E. (2016), “Application of Ontologies in Situational Management Systems”, the Proceedings of the XV International Scientific Workshop on Modern Problems of Informatics in Management, Economics, Education, Kyiv, Svityaz, July 4-8, National Academy of Management, pp. 84-89.
  3. “Definition of information” (2018), Merriam-Webster dictionary, available at: https://www.merriam-webster.com/dictionary/information (accessed September 15, 2020)
  4. Floridi, L. (2015), “Semantic Conceptions of Information”, available at https://plato.stanford.edu/entries/information-semantic (accessed September 15, 2020)
  5. “ISO/IEC 2382-2015” (2015), Information technology, available at: https://www.iso.org/obp/ui/#iso:std:63598:en (accessed September 15, 2020)
  6. Ackoff, R.L. (1989), “From data to wisdom”, Journal of Applied Systems Analysis, Vol. 16, pp. 3-9.
  7. Adriaans, P. (2012), “Information”, The Stanford Encyclopedia of Philosophy, available at: https://plato.stanford.edu/archives/fall2013/ entries/information (accessed September 15, 2020).
  8. “Ontology Lookup Service” (2018), available at: https://www.ebi.ac.uk/ols/ search?q= information+ontology&groupField=iri&exact=on&start=0 (accessed September 15, 2020).
  9. “Information Artifact Ontology” (2017), available at: https://github.com/information-artifact-ontology/IAO (accessed September 15, 2020).
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  26. Kovalenko, O.Ye. (2019), “Principles of situational systems engineering”, Mathematical machines and systems, no. 4, pp. 65-78. 
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QUALITY MODELS AND ASSESSMENT OF SOFTWARE INTERFACE USABILITY FOR HUMAN-COMPUTER INTERACTION

O. Gordieiev

Èlektron. model. 2020, 42(5):24-37
https://doi.org/10.15407/emodel.42.05.024

ABSTRACT

The article proposes models of interactive quality and evaluating the interactive quality of the usability of the software interface for human-computer interaction. Such models are interconnected due to a single nomenclature of subcharacteristics. The model for assessing the interactive quality of the usability of the software interface for human-computer interaction consists of two parts and includes many metrics that correspond to the specified sub-characteristics. A feature of this model is that all the primitives for calculating the indicated metrics of the interactive quality of the usability of the software interface for human-computer interaction can be obtained only with the help of the software and hardware complex for the eye-tracker.

KEYWORDS

interactive quality, software usability, human-computer interaction, interactive usability metrics.

REFERENCES

  1. International standard ISO/IEC 25010:2011. (2011), “Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – System and software quality models”, International Organization for Standardization, International Electrotechnical Commission, Institute of Electrical and Electronics Engineers.
  2. International standard ISO/IEC 9126-1:2001. (2001), “Software engineering – Product quality. Part 1: Quality model”, International Organization for Standardization, International Electrotechnical Commission. Institute of Electrical and Electronics Engineers.
  3. International standard ISO 9241-11:2018. (2018), “Ergonomics of human-system interaction – Part 11: Usability: Definitions and concepts”, International Organization for Stan­dar­dization, International Electrotechnical Commission, Institute of Electrical and Elect­ronics Engineers.
  4. International standard ISO/IEC 14764:2006. (2006), “Software Engineering – Software Life Cycle Processes – Maintenance”, International Organization for Standardization, International Electrotechnical Commission, Institute of Electrical and Electronics Engineers.
  5. Seffah, A., Donyaee, M., Kline, R. B. and Padda, H. K. (2006), “Usability measurement and metrics: A consolidated model”, Software Quality Journal, V 14, no. 2, pp. 159-178. 
    https://doi.org/10.1007/s11219-006-7600-8
  6. Rawashdeh, A. and Bassem, M. A. (2006), “New Software Quality Model for Evaluating COTS Components”, Journal of Computer Science, V 2, no. 4, pp. 373-381.
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  7. Upadhyay, N., Despande, B. and Agrawal, V. (2011), “Towards a Software Component Quality Model”, Proceedings of the 1st International Conference on Computer Science and Information Technology, Penang, Malaysia, February 22-24, 2011, pp. 398-412.
    https://doi.org/10.1007/978-3-642-17857-3_40
  8. Shneiderman, B. and Plaisant, C. (2004), Designing the User Interface: Strategies for Effective Human-Computer Interaction, Pearson, 4th Edition.
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  10. Georgiadoui, E. (2003), “GEQUAMO-A Generic, Multilayered, Customizable Software Quality model”, Software Quality Control Journal, Vol.11, no. 4, pp. 313-323.
    https://doi.org/10.1023/A:1025817312035
  11. International standard ISO/IEC 25022:2016. (2016), “Systems and software engineering – Systems and software Quality requirements and evaluation (SQuaRE) – Measurement of quality in use”, International Organization for Standardization, International Electrotechnical Commission, Institute of Electrical and Electronics Engineers.
  12. International standard ISO/IEC 25023:2016. (2016), “Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – Measurement of system and software product quality”, International Organization for Standardization, International Electrotechnical Commission, Institute of Electrical and Electronics Engineers.
  13. Gordieiev, O., Kharchenko, V. and Leontiiev, K. (2018), “Usability, security and safety interaction: profile and metrics based analysis”, Proceedings of the 13 International Conference on Dependability and Complex Systems (DepCoS-RELCOMEX), Brunow, Poland, July 2-6, pp. 238-247. 
    https://doi.org/10.1007/978-3-319-91446-6_23
  14. Godieiev, O., Kharchenko, V. and Vereshchak, K. (2017), “Usable Security Versus Secure Usability: an Assessment of Attributes Interaction”, Proceedings of the 13th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer (ICTERI), Kyiv, Ukraine, May 15-18, pp. 727-740.
  15.  Gordieiev, O., Kharchenko, V. and Fusani, M. (2015), “Evolution of software quality models: usability, security and greenness issues”, Proceedings of the 19-th International Conference on Computers (part of CSCC 15), Zakynthos Island, Greece, July 16-20, pp. 519-523.

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Typical structure of a duplicate error correction scheme with code control with summation of weighted transitions

D.V. Efanov, Dr Sc. (Tech.)
Peter the Great St. Petersburg Polytechnic University
Russian Federation, 195251, St. Petersburg, Polytechnic St., 29,
contact phone number (+7) 911 7092164, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
V.V. Sapozhnikov, Dr Sc. (Tech.), Vl.V. Sapozhnikov, Dr Sc. (Tech.),
Emperor Alexander I St. Petersburg State Transport University,
Russian Federation, 190031, St. Petersburg, Moskovsky ave., 9,
contact phone number (+7) (812) 4578579, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2020, 42(5):38-50
https://doi.org/10.15407/emodel.42.05.038

ABSTRACT

Error correction circuit typical structures are described — majority and duplication structure with control by parity. A new structure of the correction circuit based on duplication with weighted-transitions sum code control is proposed. The code is constructed by weighting the transitions between the adjacent bits in data vectors, numbers from sequentially increasing powers of the number «two», starting from the zero degree. The specified code detects any errors in data vectors, except for errors associated with distortions of all data bits at the same time. The weighted sum code features allow it to be used in the synthesis of error detection circuits. An example of the correction circuit synthesis is given. The experiments results using control combinational circuits MCNC Benchmarks showed that the duplication structure with weighted-transitions sum code control in many cases allows one to obtain lower complexity indicators values of the correction circuits technical implementation than the known structure of majority correction.

KEYWORDS

combinational automation devices, systems with fault detection, systems with error correction in calculations, fault-tolerant systems, duplication, triplication.

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COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR FORECASTING COVID-19 SPREADING IN DIFFERENT COUNTRIES

N.І. Nedashkovskaya, S.O. Lupanenko

Èlektron. model. 2020, 42(5):51-65
https://doi.org/10.15407/emodel.42.05.051

ABSTRACT

In this work, mathematical models of the spread of the coronavirus COVID-19 in various countries are built, and a comparative analysis of these models for the United States, Mexico, Russia, Belgium and Ukraine was performed. Baseline data on the number of infections obtained from the daily reports of the World Health Organization and the the Center for Systems Science and Engineering at Johns Hopkins University. To simulate the spread of coronavirus, two powerful classes of machine learning methods have been selected that allow predicting nonlinear time series: support vector machines and feedforward multilayer neural networks. The advantages and disadvantages of these methods are revealed, and the issues of regularization are considered. The construction and training of time series models to describe the spread of COVID-19 in different countries, the choice of the best model, the construction of forecast and the visualization of results were performed in an implemented software module in the python environment using modern scikit-learn, pandas and matplotlib libraries. Using the grid search method with cross-validation, the best parameters of neural network and support vector models which describe the spread of COVID-19 in the USA, Mexico, Russia, Belgium and Ukraine were selected. Based on the constructed models, the growth of COVID-19 diseases in these countries was predicted.

KEYWORDS

support vector machines, multilayer feedforward neural networks, regularization, COVID-19, forecasting of epidemic spreading.

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