Projection of Real Mass Deformation Scenes – Intelligent Support
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RIS BIB ENDNOTEProjection of Real Mass Deformation Scenes – Intelligent Support
Publication date: 30.12.2021
Geoinformatica Polonica, 2021, Vol. 20 (2021), pp. 117-136
https://doi.org/10.4467/21995923GP.21.010.14981Authors
Projection of Real Mass Deformation Scenes – Intelligent Support
The paper analyses the process of post-mining displacements generated by underground mining. Innovative mathematical structures for the modeling of hazard field emission were developed as strong solutions to partial differential equations in R3+1. Moreover, a stochastic equation in L2(Ω) (probabilistic space) was defined and applied as a model that takes into account the randomness of the process. Monitoring of a mining area based on solutions in the GNSS technology and classical geodesy supports the analysis of topological transformations of a given subspace. The data was archived and stored in digital form and then analyzed in many ways. The quality of the representation (measurements and modeling) was estimated with the use of incremental statistics. Thus, obtained distributions of density function are not ranked as normal distribution. The performed analyses make it possible to predict the optimal scenarios for post-mining environmental hazards.
Aliev R.A., Alizadeh A.V., Huseynov O.H., Jabbarova K.I.: Z-Number-Based Linear Programming. “International Journal of Intelligent Systems”, Vol. 30, No. 5, 2015.
Brockwell P. J., Davis R. A.: Introduction to time series and forecasting. Springer –Verlag, New York 1996.
Bruna J., Mallat S.: Invariant scattering convolution networks. IEEE transsections on pattern analysis and machine intelligence 35(8) 1872 –1886. 2013.
Evans C,: Równania różniczkowe cząstkowe. PWN. Warszawa 2002.
Knothe S. : Równanie profilu ostatecznie wykształconej niecki osiadania. Archiwum Górnictwa i Hutnictwa, t. I, z. 1, Warszawa 1953.
Knothe S.: Wpływ czasu na kształtowanie się niecki osiadania. Archiwum Górnictwa i Hutnictwa, t. I, z. 1, Warszawa 1953.
La Valle S. M.: Planning Algorithms. Cambridge University Press, 2006.
Luszniewicz A., Słaby T.: Statystyka z pakietem komputerowym STATISTICA TM PL. Teoria i zastosowania. Wydawnictwo C.H. Beck, Warszawa 2001.
Oksendal B.: Stochastic Differential Equations. An Introduction with Applications. Springer-Verlag, Berlin Heidelberg 2000.
Piwowarski W.: Estimation of the missing results of the surveys concerning a non-stationary post- mining dislocations field. Geodezja i Kartografia t. LII, z. 3, Wydawnictwo PAN, Warszawa 2003.
Piwowarski W., Juzwa J., Kuciara I., Siciński K.: Analyses of parabolic processes to assess mapping stability of mining area ground dislocations in the INGEO system. Mining–Informatics, Automation and Electrical Engineering, 04(528), pp 15-25, Katowice 2016.
Shamir O.: Distribution –specific hardness of learning neural networks. The Journal of Machine Learning Research 19: 1135 –1163. 2018.
R.Sztencel, J. Jakubowski: Wstęp do teorii prawdopodobieństwa. Wydawnictwo SCRIPT, Warszawa 1980.
Tadeusiewicz R.: Sieci neuronowe. Akademicka Oficyna Wydawnicza RM Warszawa 1993.
Zastawniak T., Brzeźniak Z.: Basic Stochastic Processess. A Course Through Exercises, Springer-Verlag, Londyn 1999.
Information: Geoinformatica Polonica, 2021, Vol. 20 (2021), pp. 117-136
Article type: Original scientific article
Titles:
Projection of Real Mass Deformation Scenes – Intelligent Support
Odwzorowanie scen rzeczywistych destrukcji górotworu – inteligentne wspomaganie
Published at: 30.12.2021
Article status: Open
Licence: CC BY-NC-ND 3.0 PL
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