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Artykuły w czasopismach na temat "Jump-like deformation"
Kawabe, Takahiro. "Perceiving Animacy From Deformation and Translation". i-Perception 8, nr 3 (17.05.2017): 204166951770776. http://dx.doi.org/10.1177/2041669517707767.
Pełny tekst źródłaMüller, Toni, Jens-Uwe Sommer i Michael Lang. "Tendomers – force sensitive bis-rotaxanes with jump-like deformation behavior". Soft Matter 15, nr 18 (2019): 3671–79. http://dx.doi.org/10.1039/c9sm00292h.
Pełny tekst źródłaYasnii, P. V., Yu I. Pyndus, V. B. Hlad’o i I. V. Shul’han. "Computer modeling of the jump-like deformation of AMg6 alloy". Materials Science 44, nr 1 (styczeń 2008): 43–48. http://dx.doi.org/10.1007/s11003-008-9041-y.
Pełny tekst źródłaFedak, Serhii, Oleg Yasnii, Iryna Didych i Nadiya Kryva. "Characteristics of the deformation diagram of AMg6 alloy". Scientific journal of the Ternopil national technical university 110, nr 2 (2023): 33–39. http://dx.doi.org/10.33108/visnyk_tntu2023.02.033.
Pełny tekst źródłaLebedev, V. P., V. S. Krylovskiĭ, S. V. Lebedev i S. V. Savich. "Low-amplitude jump-like deformation of Pb–In alloys in the superconducting state". Low Temperature Physics 34, nr 3 (marzec 2008): 234–40. http://dx.doi.org/10.1063/1.2889412.
Pełny tekst źródłaYasniy, Oleh, Iryna Didych, Sergiy Fedak i Yuri Lapusta. "Modeling of AMg6 aluminum alloy jump-like deformation properties by machine learning methods". Procedia Structural Integrity 28 (2020): 1392–98. http://dx.doi.org/10.1016/j.prostr.2020.10.110.
Pełny tekst źródłaDolgin, A. M., i V. Z. Bengus. "Kinetics of high-velocity processes of low temperature jump-like deformation of niobium". physica status solidi (a) 94, nr 2 (16.04.1986): 529–35. http://dx.doi.org/10.1002/pssa.2210940212.
Pełny tekst źródłaKirichenko, G. I., V. D. Natsik, V. V. Pustovalov, V. P. Soldatov i S. E. Shumilin. "Jump-like deformation of single crystals of Sn–Cd alloys at temperatures ≲1 K". Low Temperature Physics 23, nr 9 (wrzesień 1997): 758–64. http://dx.doi.org/10.1063/1.593374.
Pełny tekst źródłaPustovalov, V. V. "Influence of superconducting transition on low temperature jump-like deformation of metals and alloys". Materials Science and Engineering: A 234-236 (sierpień 1997): 157–60. http://dx.doi.org/10.1016/s0921-5093(97)00151-2.
Pełny tekst źródłaMizutani, Yasushi, Susumu Tamai, Toshifumi Nakamura, Takehiko Takita i Shohei Omokawa. "Magnetic Resonance Imaging Evaluation of Acute Plastic Deformation of a Pediatric Radius". Journal of Hand Surgery (Asian-Pacific Volume) 26, nr 02 (11.01.2021): 280–83. http://dx.doi.org/10.1142/s2424835521720085.
Pełny tekst źródłaRozprawy doktorskie na temat "Jump-like deformation"
Didych, Iryna. "Estimation of structural integrity and lifetime of important structural elements". Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2021. http://www.theses.fr/2021UCFAC116.
Pełny tekst źródłaThis work has been performed under co-tutelle supervision between Ternopil IvanPuluj National Technical University in Ternopil (Ukraine) and UniversityClermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal in Clermont-Ferrand (France).This thesis solves the scientific task of responsible structural elements strength andlifetime evaluation. The aim of the thesis is to evaluate the strength and residuallifetime of structural elements by machine learning methods.Most parts of machines and structural elements while being in service are under theinfluence of loads of various nature. Such forces are applied either directly to theelement or transmitted through neighbor elements connected to it. For the normaloperation of the responsible structures parts, each element must have certain sizeand shape that will withstand the loads acting on it. In particular, it must haveappropriate strength properties, not deform significantly under the action ofstresses, be rigid, and preserve its original shape.The calculated residual lifetime of machines and structures can be predicted usingfatigue crack growth (FCG) diagrams. Often, the experimental data have a certainspread, which should be taken into account in their analysis. The experimentalmethod often takes a lot of time and human resources. Therefore, it is advisable tolearn how to calculate the residual lifetime using machine learning methods,particularly, neural networks, boosted trees, random forests, support-vectormachines and the method of k–nearest neighbors