References:
- Ostrik A.V., Nikolaev D.N., Cheprunov A.A. Explosive Technologies for Strength Tests of Thin-Walled Composite Constructions on Action of Side Non-Stationary Loadings Having Various Physical Nature. Konstruktsii iz kompozitsionnykh materialov = Composite Materials Constructions. 2019. № 3. pp. 55–63. (In Russ.).
- Storodubtseva T.N., Tomilin A.I. Composite structural materials, methods for studying their properties. Sovremennye naukoemkie tekhnologii = Modern high technologies. 2012. № 11. pp. 53–54. (In Russ.).
- Zershchikov K.Yu. Investigation of Stress Relaxation in Sealing Units with Polymer or Composite Sealings Used in Pipeline Valves. Konstruktsii iz kompozitsionnykh materialov = Composite Materials Constructions. 2018. № 1. pp. 50–55. (In Russ.).
- Kaledin V.O., Vyachkina E.A., Vyachkin E.S., Budadin O.N., Kozelskaya S.O. Applying Ultrasonic Thermotomography and Electric-Loading Thermography for Thermal Characterization of Small-Sized Defects in Complex-Shaped Spatial Composite Structures. Russian Journal of Nondestructive Testing. 2020. Vol. 56. № 1. pp. 58–69.
- Kozelskaya S.O. Computer System of Ultrasonic Thermotomography for Detecting Small-sized Defects in Products from Polymer Composite Materials. Promyshlennye ASU i kontrollery = Industrial Automatic Control Systems and Controllers. 2020. № 7. pp. 9–16. (In Russ.). DOI: 10.25791/asu.7.2020.1197
- Budadin O.N., Kulkov A.A., Kozelskaya S.O., Kaledin V.O., Vyachkin E.S. Method of Electric Power Thermography of Spatial Objects and Device for its Implementation. Patent RF № 2690033. Applied: September 14, 2018. Published: May 30, 2019. (In Russ.).
- Larin A.A., Fedotov M.Yu., Bukharov S.V., Reznichenko V.I. New Applications Systems of Fiber-Optical Sensors. Prikladnaya fotonika = Applied Photonics. 2017. № 4. pp. 311–324. (In Russ.).
- Nazirov M.F., Zhukov Yu.A., Yakovitskaya S.Yu. Strain Measurement of Carbon Fiber Reinforced Plastic (CFRP) Samples with Embedded Optical Fiber Sensors.
- Voprosy oboronnoy tekhniki. Seriya 16: Tekhnicheskie sredstva protivodeystviya terrorizmu = Military Enginery. Counter-terrorism technical devices. Issue 16. 2015. № 9–10 (87–88). pp. 95–101. (In Russ.).
- Morozova T.Yu., Bekarevich A.A., Budadin O.N. The New Approach to Identification of Product Defects. Kontrol. Diagnostika = Testing. Diagnostics. 2014. № 8. pp. 42–48. (In Russ.). DOI: 10.14489/td.2014.08.pp.042-048
- Bekarevich A.A., Budadin O.N., Morozova T.Yu., Toporov V.I. The method for adaptive forecast of the operation residual resource of the complex objects and the device for its implementation. Patent RF № 2533321. Applied: June 28, 2013. Published: November 20, 2014. Bulletin № 32. (In Russ.).
- Liu W., Wang Z., Liu X., Zeng N., Liu Y., Alsaadi F.E. A survey of deep neural network architectures and their applications. Neurocomputing. 2017. Vol. 234. pp. 11–26. DOI: 10.1016/j.neucom.2016.12.038
- Akimov D.A., Dyatchenkova A.Yu., Sachkov V.E. Self-Diagnostics of Technical Nodes of the Automobot in the Conditions of Incomplete Information is Based on Abduction. Sovremennaya nauka: aktualnye problemy teorii i praktiki. Seriya: Estestvennye i tekhnicheskie nauki = Modern Science: actual problems of theory and practic. Series «Natural & Technical Sciences». 2018. № 2. pp. 18–24. (In Russ.).
- Cox P.T., Pietrzykowski T. General diagnosis by abductive inference. Proc. IEEE Sympos. Logic Programming. San Francisco, 1987. pp. 183–189.
- Kotelnikov V.V., Akimov D.A. Training of convolutional neural networks for predicting and assessing the level of criticality of structures defects. Novshestva v oblasti tekhnicheskikh nauk: sb. nauch. tr. po itogam Mezhdunar. nauch.-prakt. konferentsii. (Innovations in the field of technical sciences: collection of research papers on the results of the International scientific-practical conference). Tyumen, 2016. pp. 73–77. (In Russ.).
- Sozykin A.V. An Overview of Methods for Deep Learning in Neural Networks. Vestnik Yuzhno-Uralskogo gosudarstvennogo universiteta. Seriya. Vychislitelnaya matematika i informatika = Bulletin of the South Ural State University. Series «Computational Mathematics and Software Engineering». 2017. Vol. 6. № 3. pp. 28–59. (In Russ.). DOI: 10.14529/cmse170303