Visualization method for multidimensional random processes

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dc.citation.epage10
dc.citation.issue1
dc.citation.journalTitleВимірювальна техніка та метрологія
dc.citation.spage5
dc.contributor.affiliationNational Automobile and Highway University
dc.contributor.affiliationState Biotechnological University
dc.contributor.authorPoliarus, Oleksandr
dc.contributor.authorLebedynskyi, Andriy
dc.contributor.authorChepusenko, Yevhenii
dc.contributor.authorLyubymova, Nina
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-03-11T08:26:31Z
dc.date.available2024-03-11T08:26:31Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractThe article proposes a method for visualizing multidimensional random process realizations using the example of the concentrations of harmful gases emitted into the atmosphere from a thermal power plant. The method is based on the transformation of gas concentration values in one point of multidimensional space at the same time into a two-dimensional curve, which is described by the sum of products of normalized concentrations by orthogonal Legendre functions of the corresponding order. The combination of such curves on a two-dimensional plane at discrete times creates a characteristic image that can be used to visually detect features of gas concentrations over time by a human operator.
dc.format.extent5-10
dc.format.pages6
dc.identifier.citationVisualization method for multidimensional random processes / Oleksandr Poliarus, Andriy Lebedynskyi, Yevhenii Chepusenko, Nina Lyubymova // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 84. — No 1. — P. 5–10.
dc.identifier.citationenVisualization method for multidimensional random processes / Oleksandr Poliarus, Andriy Lebedynskyi, Yevhenii Chepusenko, Nina Lyubymova // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 84. — No 1. — P. 5–10.
dc.identifier.doidoi.org/10.23939/istcmtm2023.01.005
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61415
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofВимірювальна техніка та метрологія, 1 (84), 2023
dc.relation.ispartofMeasuring Equipment and Metrology, 1 (84), 2023
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dc.relation.references[16] S. Koyamada, Y. Shikauchi, K. Nakae, M. Koyama, S. Ishii. Deep Learning of FMRI big data: a novel approach to subject-transfer decoding. – arXiv: 1502.00093v1 [stat ML] 31 January 2015 [Online]. Available: https://arxiv.org/pdf/1502.00093.pdf
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dc.relation.references[18] A. Genender-Feltheimer. Visualizing High Dimensional and Big Data. Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning, CAS, 2018, 5–7 Nov. 2018, Chicago, USA, pp. 112–121. DOI: 10.1016/j.procs.2018.10.308
dc.relation.references[19] K. Börner, C. Chen, K. Boyack. Visualizing knowledge domains. An. Rev. of Inf. Sc. & Techn., Vol. 37, 2003, Medford, NJ: Information Today, Inc./ Amer. Soc. for Inf. Sc. and Techn., Ch. 5, pp. 179–255. DOI: 10.1002/aris.1440370106
dc.relation.references[20] J. Emerson, W. Green, B. Schloerke, J. Crowley, D. Cook, H. Hofmann, H. Wickham. The Generalized Pairs Plot. Journ Comp. and Graph. Statistics, Vol. 22(1), 2013, pp. 79–91. DOI: 10.1080/10618600.2012.694762
dc.relation.references[21] J. Im, M. McGuffin, R. Leung. GPLOM: Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data, IEEE Trans. on Visualization and Comp. Graphics, 19 (12), 2013, pp. 2606–2614. DOI: 10.1109/TVCG.2013.160
dc.relation.references[22] J. vanWijk, R. van Liere. HyperSlice:Visualization of Scalar Functions of Many Variables, 1998. [Online]. Available: www.researchgate.net/publication/2660434_ HyperSlice
dc.relation.references[23] D. Andrews. Plots of high-dimensional data, Biometrics, Vol. 28, No. 1, 1972, pp. 69–97. DOI: 10.2307/2528964
dc.relation.references[24] O. Poliarus, Y. Poliakov, A. Lebedynskyi. Detection of landmarks by autonomous mobile robots using camerabased sensors in outdoor environments. IEEE Sensors Journal, Vol. 21, iss. 10, 2021, pp. 11443–11450, DOI: 10.1109/JSEN.2020.3010883
dc.relation.referencesen[1] G. Phillips-Wren. Intelligent Systems to Support Human Decision Making. In book: Artificial Intelligence, 2017, pp. 3023–3036. DOI: 10.4018/978-1-5225-1759-7.ch125
dc.relation.referencesen[2] S.Mansmann, T. Neumuth, M. H. Scholl,Multidimensional Data Modeling for Business Process Analysis, 26th Int. Conf. on Conceptual Modeling, Nov. 5–9, 2007, Auckland, New Zealand. DOI: 10.1007/978-3-540-75563-0_4
dc.relation.referencesen[3] J. Starck, F. Murtagh, Handbook of Astronomical Data Analysis. Elsevier, 2002. [Online] Available: https://www.academia.edu/2608657/Handbook_of_Astronomical_Data_ Analysis
dc.relation.referencesen[4] Pak Chung Wong and R. Daniel Bergeron. 30 Years of Multidimensional Multivariate Visualization. In Sc. Visualization, Overviews, Methodologies and Techniques. IEEE Computer Society Press, pp. 3–33, 1994. Available: https://www.cs.unc.edu/xcms/courses/comp715-s10/papers/Wong97_30_years_of_multidimensional_multivariate_visualization.pdf
dc.relation.referencesen[5] H. C. Purchase, N. Andrienko, T. J. Jankun-Kelly, M. Ward. Theoretical Foundations of Information Visualization. In: Inf. Visualization: Human-Centered Issues and Perspectives, 1970, pp. 46–64. DOI: 10.1007/978-3-540-70956-5_3.
dc.relation.referencesen[6] W. Weaver, C. Shannon. The mathematical theory of communication. Physics, 2009. DOI:10.1098/rspa.2009.0063
dc.relation.referencesen[7] D. Asimov, "The grand tour: A tool for viewing multidimensional data", SIAM Journ. on Sc. & Stat. Comp., pp. 128–143, 1985. DOI: 10.1137/0906011
dc.relation.referencesen[8] R. Bergeron, W. Cody, W. Hibbard, D. Kao, K. Miceli, L. Treinish, S. Walther. Database Issues for Data Visualization: Data Model Development. In IEEE Visualization '93 Workshop, San Jose, California, USA, October 26, 1993, pp. 3–15. In Proc. Lecture Notes in Comp. Sc. 871, Springer 1993. [Online] Available: https://link.springer.com/book/10.1007/BFb0021138
dc.relation.referencesen[9] I. Romanova, "Modern Methods of Multidimensional Data Visualization: Analysis, Classification, Implementation and Applications in Technical Systems, Science and Education of the Bauman MSTU", Vol. 3, 2016, pp. 133–167. DOI:10.7463/0316.0834876
dc.relation.referencesen[10] Zongben Xu, Yong Shi. Exploring Big Data Analysis: Fundamental Scientific Problems, Ann. data sci., 2 (4), 2015, pp. 363–372. DOI: 10.1007/s 40745-015-00637
dc.relation.referencesen[11] Yau Nathan. Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics s. Indianapolis, In: Wiley Publishing, 2011. [E-book] Available: https://www.perlego.com/book/1011299/visualize-this-theflowingdata-guide-to-design-visualization-and-statistics-pdf
dc.relation.referencesen[12] H. Chernoff. The Use of Faces to Represent Points in KDimensional Space Graphically. Journ. Am. Stat. Ass., Vol. 68, No. 342, pp. 361–368, 1973. DOI: 10.2307/2284077
dc.relation.referencesen[13] J. Heer, M. Bostock, V. Ogievetsky, A Tour through the Visualization Zoo. A survey of powerful visualization techniques, from the obvious to the obscure. Communications of the ACM. Stanford University. 2010. Vol. 53, Iss. 6, pp. 59–67. DOI: 10.1145/1743546.1743567
dc.relation.referencesen[14] V. Ogievetsky, J. Heer. D3: Data Driven Documents, IEEE Trans. Visualization & Comp. Graphics, 2011 [Online]. Available: http://vis.stanford.edu/files/2011-D3-InfoVis.pdf
dc.relation.referencesen[15] M. Marjani, F. Nasaruddin, A. Gani, A. Karim, I. Abaker, T Hashen, A Siddiqa, I. Yaqoob. Big Data Analytics: Architecture, Opportunities, and Open Res. Challenges. IEEE Access, 2017, Vol. 5, pp. 5247–5261. DOI: 10.1109/ACCESS.2017.2689040
dc.relation.referencesen[16] S. Koyamada, Y. Shikauchi, K. Nakae, M. Koyama, S. Ishii. Deep Learning of FMRI big data: a novel approach to subject-transfer decoding, arXiv: 1502.00093v1 [stat ML] 31 January 2015 [Online]. Available: https://arxiv.org/pdf/1502.00093.pdf
dc.relation.referencesen[17] L. van der Maaten, G. Hinton. Visualizing Data using t-SNE. Journ. Mach. Learn. Res., Vol. 9, 2008, pp. 2579–2605 [Online]. Available: https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf.
dc.relation.referencesen[18] A. Genender-Feltheimer. Visualizing High Dimensional and Big Data. Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning, CAS, 2018, 5–7 Nov. 2018, Chicago, USA, pp. 112–121. DOI: 10.1016/j.procs.2018.10.308
dc.relation.referencesen[19] K. Börner, C. Chen, K. Boyack. Visualizing knowledge domains. An. Rev. of Inf. Sc. & Techn., Vol. 37, 2003, Medford, NJ: Information Today, Inc./ Amer. Soc. for Inf. Sc. and Techn., Ch. 5, pp. 179–255. DOI: 10.1002/aris.1440370106
dc.relation.referencesen[20] J. Emerson, W. Green, B. Schloerke, J. Crowley, D. Cook, H. Hofmann, H. Wickham. The Generalized Pairs Plot. Journ Comp. and Graph. Statistics, Vol. 22(1), 2013, pp. 79–91. DOI: 10.1080/10618600.2012.694762
dc.relation.referencesen[21] J. Im, M. McGuffin, R. Leung. GPLOM: Generalized Plot Matrix for Visualizing Multidimensional Multivariate Data, IEEE Trans. on Visualization and Comp. Graphics, 19 (12), 2013, pp. 2606–2614. DOI: 10.1109/TVCG.2013.160
dc.relation.referencesen[22] J. vanWijk, R. van Liere. HyperSlice:Visualization of Scalar Functions of Many Variables, 1998. [Online]. Available: www.researchgate.net/publication/2660434_ HyperSlice
dc.relation.referencesen[23] D. Andrews. Plots of high-dimensional data, Biometrics, Vol. 28, No. 1, 1972, pp. 69–97. DOI: 10.2307/2528964
dc.relation.referencesen[24] O. Poliarus, Y. Poliakov, A. Lebedynskyi. Detection of landmarks by autonomous mobile robots using camerabased sensors in outdoor environments. IEEE Sensors Journal, Vol. 21, iss. 10, 2021, pp. 11443–11450, DOI: 10.1109/JSEN.2020.3010883
dc.relation.urihttps://www.academia.edu/2608657/Handbook_of_Astronomical_Data_
dc.relation.urihttps://www.cs.unc.edu/xcms/courses/comp715-s10/papers/Wong97_30_years_of_multidimensional_multivariate_visualization.pdf
dc.relation.urihttps://link.springer.com/book/10.1007/BFb0021138
dc.relation.urihttps://www.perlego.com/book/1011299/visualize-this-theflowingdata-guide-to-design-visualization-and-statistics-pdf
dc.relation.urihttp://vis.stanford.edu/files/2011-D3-InfoVis.pdf
dc.relation.urihttps://arxiv.org/pdf/1502.00093.pdf
dc.relation.urihttps://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectVisualization
dc.subjectMultidimensional random process
dc.subjectVisualization method
dc.titleVisualization method for multidimensional random processes
dc.typeArticle

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Вимірювальна техніка та метрологія. – 2023. – Випуск 84, №1