Models, methods and means of ontology development of cyclic signal processing
DOI:
https://doi.org/10.31471/2311-1399-2021-1(15)-8-17Keywords:
ontology, modeling, processing methods, cyclic signals.Abstract
The paper presents the construction of conceptual and formal models of ontology in the subject area "Modeling and processing of cyclic signals based on the theory of cyclic functional relations". There is implemented the ontology of mathematical modelling of cyclic signals, namely, the ontology of cyclic functional relations in the OWL DL language in Protégé. The proposed ontology has allowed to present the theory of cyclic functional relations in machine-interpretive form, which allows to serve as a basis for the development of onto-oriented information systems for modeling, generation, processing (analysis, forecasting, decision making) of cyclic signals.
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Lytvynenko, IV, Lupenko, SA Dem’yanchuk, NR & Horkunenko, AB 2011, ‘Simulation modeling of interconnected economic processes on the basis of a vector of cyclically rhythmically connected random processes’,Electronics and control systems, vol. 28, no. 2, pp. 133–141 [in Ukrainian].
Gorkunenko, AB & Lupenko, SA 2012, ‘Substantiation of diagnostic and prognostic features in information systems of analysis and forecasting of cyclical economic processes’, Scientific Bulletin of NLTU of Ukraine: collection of scientific and technical works, no. 22.9, pp. 347−352 [in Ukrainian].
Onyskiv, P, Lupenko, S, Lytvynenko, I & Zozulia, A/ 2020, ‘Mathematical modeling and processing of high
resolution rhythmocardio signal based on a vector of stationary and stationary related random sequences.
IDDM’2020’, 3rd International Conference on Informatics & Data-Driven Medicine, November 19–21, Växjö, Sweden. CEUR Workshop Proceedings, 2753, pp. 149–155. http://ceur-ws.org/Vol-2753/short8.pdf
Lupenko, S, Lytvynenko, I & Stadnyk, N 2020, ‘Method for reducing the computational complexity of processing discrete cyclic random processes in digital data analysis systems’, Scientific Journal of Ternopil National Technical University, vol. 97, no. 1, pp. 110–121. https://doi.org/10.33108/visnyk_tntu
Lupenko, S, Lytvynenko, I, Stadnyk, N & Zozulia, A 2020, ‘Mathematical model of rhythmocardiosignal in vector view of stationary and stationary-related case sequences’,Advanced Information Systems, vol. 4, no. 2, pp. 42–46.
doi:10.20998/2522-9052.2020.2.08
Hutsaylyuk, V, Lytvynenko, I, Maruschak, P & Schnell, G 2020, ‘A new method for modeling the cyclic structure of the surface microrelief of titanium alloy ti6al4v after processing with femtosecond pulses’,Materials, 13(21), pp. 1–8, 4983. doi: 10.3390/ma13214983
Marushak, PO, Lytvynenko, IO, Lupenko, SA & Popovych, PV 2016, ‘Modeling of the Ordered Surface
Topography of Statically Deformed Aluminum Alloy’,Materials Science, vol. 52, no. 1, pp. 113–122. DOI:10.1007/s11003-016-9933-1
Lytvynenko, IV & Marushchak, PO 2015, ‘Analysis of the State of the Modified Nanotitanium Surface with the
Use of the Mathematical Model of a Cyclic Random Process’,Optoelectronics, Instrumentation and Data rocessing,
vol. 51, no. 3, pp. 254–263. DOI:10.3103/S8756699015030073
Lupenko, S, Lytvynenko, I, Stadnyk, N & Zozulia, A2020, ‘Model of signals with double stochasticity in the form
of a conditional cyclic random process’, ICT&ES-2020: Information-Communication Technologies & Embedded Systems, November 12, 2020, Mykolaiv, Ukraine. CEUR Workshop Proceedings, 2762, pp. 201–208. http://ceurws.org/Vol-2762/short1.pdf
Gardner, WA, Napolitano, A & Paura, L 2015, ‘Cyclostationarity: Half a century of research’, Signal Processing, no. 86, pp. 639–697. https://doi.org/10.1016/j.sigpro.2005.06.016
Hurd, HL, Periodically Correlated Random Sequences: Spectral Theory and Practice, The University of North Carolina at Chapel Hill Hampton University.
Kochel, P 1980, ‘Periodically stationary Markovian decision models’, Elektron. Informationsverarb. Kybernet, no. 16, pp. 553–567 [in German].
Nematollahi, AR & Soltani, AR 2000, ‘Discrete time periodically correlated Markov processes’, Probability
and Mathematical Statistics, no. 20 (1), pp. 127–140.
Ghysels, E, McCulloch, RE & Tsay, RS 1993, ‘Bayesian Inference for a General Class of Periodic Markov
Switching Models’.
Ghysels, E 1992, ‘On the Periodic Structure of the Business Cycle’, Cowles Foundation, Yale University, no. 1028. https://doi.org/10.2307/1392085
Bittanti, S, Lorito, F & Strada, S 1991, ‘Markovian representations of cyclostationary processes’, Topics in
Stochastic Systems: Modelling, Estimation and Adaptive Control, Springer, Berlin, Germany, vol. 161, pp. 31–46.
Israa Shaker Tawfic, Sema Koc Kayhan 2017, ‘Improving recovery of ECG signal with deterministic guarantees using split signal for multiple supports of matching pursuit (SSMSMP) algorithm’, Computer Methods and Programs in Biomedicine, vol. 139, pp. 39–50. doi.org/10.1016/j.cmpb.2016.10.014
Fumagalli, F, Silver, AE, Tan, Q, Zaidi, N & Ristagno, G 2018, ‘Cardiac rhythm analysis during ongoing cardiopulmonary resuscitation using the Analysis During Compressions with Fast Reconfirmation technology’, Heart Rhythm, 15(2), pp. 248–255. doi: 10.1016/j.hrthm.2017.09.003.
Napoli, NJ, Demas, MW, Mendu, S, Stephens, CL, Kennedy, KD, Harrivel, AR, Bailey, RE & Barnes, LE 2018, ‘Uncertainty in heart rate complexity metrics caused by Rpeak perturbations’, Computers in Biology and edicine, 103, pp. 198–207. doi: 10.1016/j.compbiomed.2018.10.009.
Napolitano, A 2016, ‘Cyclostationarity: Limits and generalizations’, Signal Processing, vol. 120, March 2016, pp. 323–347. DOI: 10.1016/j.sigpro.2015.09.013
Karyotis, V & Khouzani, MHR 2016, ‘Malware Diffusion Models for Modern Complex Networks’, Theory and Applications, p. 324. ISBN 978-0-12-802714-1.
Sericola, B 2013, Markov Chains: Theory and Applications, London, 416 p. ISBN: 978-1-848-21493-4.
Shaffer, F & Ginsberg, JP 2017, ‘An Overview of Heart Rate Variability Metrics and Norms’, Frontiers in Public Health, vol. 5, Article 258, pp. 1–17, doi: 10.3389/fpubh.2017.00258.
Gorshkov, S 2016, Introduction to Ontological Modeling. TriniData, Yekaterinburg, Russia. [in Russian]
OWL Web Ontology Language Guide. W3C Recommendation. [Online]. Available: http://www.w3.org/TR/owl-guide/. Accessed on: Dec.13, 2019.
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