Año
Autores / Publicación
Peña Morales, D., Chernykh, A., Dorronsoro, B., & Ruiz, P. (2022). A novel multi-objective optimization approach to guarantee equality of service and energy efficiency in a heterogeneous bus fleet system. ENGINEERING OPTIMIZATION. doi: 10.1080/0305215X.2022.2055007. (ID: 28213)
Chernykh, A., Babenko, M., Shiriaev, E., Pulido Gaytan, L. B., Cortés Mendoza, J. M., Avetisyan, A., Drozdov, A. Y., & Kuchukov, V. (2022). An Efficient Method for Comparing Numbers and Determining the Sign of a Number in RNS for Even Ranges. Computation, 10(2), 17. doi: 10.3390/computation10020017. (ID: 27459)
Canosa Reyes, R. M., Chernykh, A., Cortés Mendoza, J. M., Pulido Gaytan, L. B., Rivera Rodríguez, R., Lozano Rizk, J. E., Concepción Morales, E., Castro Barrera, H. E., Barrios Hernandez, C. B., Medrano Jaimes, H. F., Avetisyan, A., Babenko, M., & Drozdov, A. Y. (2022). Dynamic performance¿Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds. PLoS ONE, 17(1), e0261856. doi: 10.1371/journal.pone.0261856. (ID: 27456)
Babenko, M., Chernykh, A., & Kuchukov, V. (2022). Improved Modular Division Implementation with the Akushsky Core Function. Computation, 10(1), 9. doi: 10.3390/computation10010009. (ID: 27457)
Babenko, M., Nazarov, A., deryabin, m., Kucherov, n., Chernykh, A., Viet Hung, N., Avetisyan, A., & Toporkov, V. (2022). Multiple Error Correction in Redundant Residue Number Systems: A Modified Modular Projection Method with Maximum Likelihood Decoding. Applied Sciences, 12(1), 463. doi: 10.3390/app12010463. (ID: 27453)
Garate, B., Diaz, S., Iturriaga, S., Nesmachnow , S., Shepelev , V., & Chernykh, A. (2021). Autonomous Swarm of Low-Cost Commercial Unmanned Aerial Vehicles for Surveillance. PROGRAMMING AND COMPUTER SOFTWARE, 47(1), 558-577. doi: 10.1134/S0361768821080120. (ID: 27470)
Babenko, M., Nazarov, A., Chernykh, A., Pulido Gaytan, L. B., Cortés Mendoza, J. M., & Vashchenko, i. (2021). Algorithm for Constructing Modular Projections for Correcting Multiple Errors Based on a Redundant Residue Number System Using Maximum Likelihood Decoding. PROGRAMMING AND COMPUTER SOFTWARE, 47(1), 839-848. doi: 10.1134/S0361768821080089. (ID: 27479)
Alaasam, A., Radchenko, G., & Chernykh, A. (2021). Refactoring the Monolith Workflow into Independent Micro-Workflows to Support Stream Processing. PROGRAMMING AND COMPUTER SOFTWARE, 47(1), 591-600. doi: 10.1134/S0361768821080077. (ID: 27475)
Vershkov, N., Babenko, M., Chernykh, A., Pulido Gaytan, L. B., Cortés Mendoza, J. M., Kuchukov, V., & Kuchukova, N. N. (2021). Optimization of Neural Network Training for Image Recognition Based on Trigonometric Polynomial Approximation. PROGRAMMING AND COMPUTER SOFTWARE, 47(1), 830-838. doi: 10.1134/S0361768821080272. (ID: 27474)
Volkov, I., Radchenko, G., & Chernykh, A. (2021). Digital Twins, Internet of Things and Mobile Medicine: A Review of Current Platforms to Support Smart Healthcare. PROGRAMMING AND COMPUTER SOFTWARE, 47(1), 578-590. doi: 10.1134/S0361768821080284. (ID: 27472)
Feoktistov, A., Kostromin, R., Gorsky, S., Bychkov, I., Chernykh, A., & Basharina, O. (2021). Algorithms for Planning on Computational Model with Redundancy and Uncertainty. PROGRAMMING AND COMPUTER SOFTWARE, 47(1), 601-614. doi: 10.1134/S0361768821080119. (ID: 27471)
Chernykh, A., Babenko, M., Avetisyan, A., & Drozdov, A. Y. (2021). En-AR-PRNS: Entropy-Based Reliability for Configurable and Scalable Distributed Storage Systems. Mathematics, 10(1), 84. doi: 10.3390/math10010084. (ID: 27451)
Kirsanova, A., Radchenko, G., & Chernykh, A. (2021). Fog Computing State of the Art: Concept and Classification of Platforms to Support Distributed Computing System. Supercomputing Frontiers and Innovations, 8(3), 17-50. doi: 10.14529/jsfi210302. (ID: 27466)
Alaasam, A., Radchenko, G., & Chernykh, A. (2021). Micro-Workflows Data Stream Processing Model for Industrial Internet of Things. Supercomputing Frontiers and Innovations, 8(1), 82-98. doi: 10.14529/jsfi210106. (ID: 27469)
Pulido Gaytan, L. B., Chernykh, A., Cortés Mendoza, J. M., Babenko, M., Radchenko, G., Avetisyan, A., & Drozdov, A. Y. (2021). Privacy-preserving neural networks with Homomorphic encryption: Challenges and opportunities. Peer-to-Peer Networking and Applications, 14(1), 1666-1691. doi: 10.1007/s12083-021-01076-8. (ID: 27463)